Proefschrift Drukker by Nicole Nijhuis

Gene signature for risk stratification and treatment of breast cancer

Gene signature for risk stratification Gene signature for risk stratification and treatment of breast cancer and treatment of breast cancer Incorporating tumor biology in clinical decision-making Incorporating tumor biology in clinical decision-making

UITNODIGING voor het bijwonen van de openbare verdediging van het proefschrift

Gene signature for risk stratification and treatment of breast cancer Incorporating tumor biology in clinical decision-making

Gene signature for risk stratification and treatment door Caroline Drukker

Incorporating tumor biology in clinical decision-maki Vrijdag 28 maart 2014 om 12.00 uur Agnietenkapel van de Universiteit van Amsterdam Oudezijds Voorburgwal 231 te Amsterdam Aansluitend bent u uitgenodigd voor een receptie ter plaatse. Paranimfen: Emelie Kooij emeliekooy@hotmail.com Eveline Trietsch evelinetrietsch@hotmail.com

Gene signature for risk stratification and treatment of breast cancer Incorporating tumor biology in clinical decision-making

Caroline Drukker Tweede Sweelinckstraat 1-II 1073 EG Amsterdam 06-26480020 carolinedrukker@gmail.com

Caroline Drukker

Gene signature for risk stratification and treatment of breast cancer Incorporating tumor biology in clinical decision-making

Caroline Drukker

The work described in this thesis was performed at the Netherlands Cancer Institute, Amsterdam, the Netherlands. In cooperation with the European Organization for Research and Treatment of Cancer (EORTC), Brussels, Belgium and the Dutch breast cancer screening facilities (BOB), Utrecht, the Netherlands. The research was funded by grants from BBMRI-NL and the EORTC. Unrestricted financial support for publication of this thesis was provided by: Netherlands Cancer Institute, Academic Medical Center, Roche, Sanofi-Aventis, Agendia NV, Chipsoft, and Amgen. Layout:

Gildeprint - Enschede

Printed by:

Gildeprint - Enschede

ISBN: 978-94-6108-609-9 Online: http://dare.uva.nl/ ÂŠ 2014 Caroline Drukker, Amsterdam, the Netherlands

Gene signature for risk stratification and treatment of breast cancer Incorporating tumor biology in clinical decision-making

ACADEMISCH PROEFSCHRIFT

ter verkrijging van de graad van doctor aan de Universiteit van Amsterdam op gezag van de Rector Magnificus prof. dr. D.C. van den Boom ten overstaan van een door het college voor promoties ingestelde commissie, in het openbaar te verdedigen in de Agnietenkapel op vrijdag 28 maart 2014, te 12.00 uur

door Caroline Anne-Marie Drukker geboren te Amsterdam

Promotiecommissie Promotores:

Prof. dr. E.J.T. Rutgers

Prof. dr. L.J. van â&#x20AC;&#x2122;t Veer

Co-promotores:

Prof. dr. S.C. Linn

Dr. M.K. Schmidt

Overige leden:

Prof. dr. S. Rodenhuis

Prof. dr. ir. F.E. van Leeuwen

Prof. dr. G.J. den Heeten

Prof. dr. ir. J.J.M. van der Hoeven

Prof. dr. R. Versteeg

Prof. dr. J.H.G. Klinkenbijl

Faculteit der Geneeskunde

Table of contents Chapter 1

Introduction and outline

Chapter 2

Voorspelling van de prognose van patiĂŤnten met vroeg stadium

borstkanker: de bijdrage van een genexpressie-profiel.

Chapter 3

A prospective evaluation of a breast cancer prognosis signature

in the observational RASTER study.

Chapter 4

Optimized outcome prediction in breast cancer by combining

the 70-gene signature with clinical risk prediction algorithms.

Chapter 5

Risk estimations and treatment decisions in

early stage breast cancer: agreement among oncologists

and the impact of the 70-gene signature.

Chapter 6

Long-term impact of the 70-gene signature

on breast cancer outcome.

Chapter 7

Mammographic screening detects low risk

tumor biology breast cancers.

7 19

Chapter 8

Gene-expression profiling to predict

the risk of locoregional recurrence in breast cancer.

Chapter 9

General discussion and future prospects

Chapter 10

Summary 155

Samenvatting 161

PhD portfolio

167

Acknowledgements (Dankwoord)

177

Curriculum Vitae

185

119

139

Gene signature for risk stratification and treatment of brea Incorporating tumor biology in clinical decision-making

Chapter 1

of breast cancer

king

Introduction and outline

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39 8 | Chapter 1

Breast cancer Breast cancer is the most frequently diagnosed cancer among women worldwide.1 Over the past two decades, the incidence rate in the Netherlands increased from 56 per 100.000 women diagnosed with invasive breast cancer in 1991 to 83 per 100.000 in 2011.2 This increasing incidence may (partly) be explained by the introduction of population-based screening programs in 1990, which resulted in an increase in the detection of early stage breast cancer after full coverage was achieved in 1997.3 Another important observation is a decrease in breast cancer mortality-rates, from 45 per 100.000 women in 1991 to 38 per 100.000 women in 20112, which may be explained by early detection due to the implementation of screening programs as well as the improvement and more extensive use of adjuvant systemic treatment.3,4 Adjuvant systemic therapy After primary treatment consisting of surgery with or without radiotherapy to achieve locoregional control most breast cancer patients are nowadays systemically treated in the adjuvant or neoadjuvant setting. Adjuvant systemic therapy (AST), including endocrine therapy, chemotherapy and/or trastuzumab, is used to control micrometastatic disease and improve long-term outcome.5 Data from the Early Breast Cancer Trialistsâ&#x20AC;&#x2122; Collaborative Group (EBCTCG) confirmed the survival benefit of AST by showing a significant better disease-free and overall survival for patients treated with chemotherapy and/or endocrine therapy in different subgroups.4 Guidelines in breast cancer treatment The selection of those patients at a high risk of recurrence who are most likely to benefit from AST has traditionally been based on clinicopathological factors such as age, tumor size, grade, estrogen-receptor status (ER), progesterone-receptor status (PR), Human Epidermal growth factor Receptor-2 (HER2) and the status of the axillairy lymph nodes.6 There are multiple breast cancer guidelines and clinical tools that use these clinicopathological factors to estimate the risk of recurrence and provide a related recommendation for AST.7-9 The components used in these guidelines are all very similar, but show slight differences in their definitions of high and low risk. Most guidelines only identify a small group of patients who are at a low risk of recurrence and for whom AST is of limited value. Consequently, a majority of patients are classified as high risk and therefore become eligible for AST. This will result in a substantial number of patients being treated with AST while they are unlikely to derive significant benefit from it. The differences in the definitions of low risk used by established guidelines create a non-overlapping group of patients at a low or high risk, indicating a suboptimal predictive accuracy for the individual patient.7,10,11 For example, the online decision-making tool Adjuvant! Online uses age, tumor size, grade, ER and nodal status to estimate the 10-year survival probability of a given patient and the possible survival-benefit that can be derived from adjuvant endocrine therapy and chemotherapy, while the Nottingham Prognostic Index (NPI) only provides a high or low risk estimation based on a

Introduction and outline | 9

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39

score which is calculated using only tumor size, grade and nodal status. Detailed information on prognostic factors used by established clinicopathological guidelines is summarized in Table 1. Even when using extensively validated clinicopathological factors, predicting the risk of recurrence for the individual patient remains challenging. Already for a long time, pathologists, clinicians and researchers are aware that breast cancer is a heterogeneous disease. Morphology, receptor expression and molecular subtypes all contribute to the clinical course of breast cancer in the individual patient. Variations in clinical behaviour and outcome have been described for several decades.12 Guidelines and clinical tools have improved over the past years and are now including clinicopathological factors such as HER2 status and Ki67.13,14 Nevertheless, HER2 and Ki67 only account for a small part of this heterogeneity and still most guidelines do not adjust for the heterogeneity entirely. Therefore, clinicopathological guidelines have only limited ability to predict individual patient outcomes.15 Insight in the biology of breast cancer: introducing gene signatures Over the past decades, researchers identified many single genes involved in the proliferation and metastatic capacity of breast cancer. Breast cancer progression is a result however of multiple genetic aberrations, and thus one gene will never be responsible for the entire cancer process.16,17 Therefore, researchers were looking for methods to evaluate the relationships among and within different cellular pathways. The introduction of micro-array analyses provided a way to evaluate multiple genes in multiple pathways at once in a more robust manner. Micro-array technology is used to develop gene signatures that are related to the metastatic potential of an individual breast cancer. These signatures can refine risk estimations based on standard clinicopathological guidelines.18 One of these signatures is the 70-gene signature (MammaPrintÂŽ), which was developed by van â&#x20AC;&#x2DC;t Veer and colleagues at the Netherlands Cancer Institute (NKI) in Amsterdam, the Netherlands. The 70-gene signature measures the level of expression of a set of genes by semi-quantitatively determining the level of messenger RNA (mRNA) transcripts.19 The intensity of the nucleic acids that hybridize to the individual gene probes are commonly shown in a two-color array.19 Green reflects low expression and red reflects high expression of that gene in the tumor (Figure 1). After hybridization the slides are scanned with a dual laser scanner (Agilent Technologies) and the data is processed using a specific algorithm providing an index-score which originally ranges from 0 to 1.20 The 70-gene signature was developed using frozen tumor samples from 78 patients who were diagnosed at the NKI with lymph node-negative breast cancer and who were up to 55 years of age at the time of diagnosis. 44 of these 78 patients remained free of distant metastases for at least 5 years. These patients were defined as good prognosis or low risk.

10 | Chapter 1

<35 or ≥ 35 Yes

Yes

NABON 2012

PREDICT plus Yes

ER/PR

Yes

Method of detection, CT regimen

Yes, more than 3+ Biological nodes is high risk subtype

Not specified. Suggested: <3% survival benefit in 10-years no chemotherapy; 3-5% chemotherapy discussed as possible option

10-years survival probability ≥85%. N0, <35, grade I tumor ≤ 1 cm OR ≥35 yrs, grade I tumor ≤ 2 cm.

[0.2 x Size] + Number of nodes + Grade; low risk = score < 3.4

Luminal A; ER/PR +, HER2 -, low Ki67

Other factors Low risk is defined as Co-morbidities, Not specified. CT regimen

AOL = Adjuvant! Online; NPI=Nottingham Prognostic Index; NABON=Nationaal Borstkanker Overleg Nederland; ER=estrogen receptor; PR=progesterone receptor; HER2=Human Epidermal Growth factor Receptor 2; CT=chemotherapy.

Yes

NPI

Size Grade Hist. type ER/PR HER2 Ki67 Nodal status Yes Yes Ductal, in case of other ER Yes hist. type, information is available online Yes

Age Yes

St. Gallen Pre- or post expert panel menopausal 2011

Guideline AOL

Table 1. Clinicopathological factors used by breast cancer guidelines to estimate the risk of recurrence

Introduction and outline | 11

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39

Figure 1. Micro-array technology used for the 70-gene signature

The remaining 34 patients developed distant metastases within 5 years after diagnosis and were defined as poor prognosis or high risk.12 Tumors with an index-score >0.4 are classified as 70gene signature low risk and tumors with an index-score <0.4 as 70-gene signature high risk. The signature was validated by van de Vijver et al. in a consecutive series of 151 lymph node-negative and 144 lymph node-positive breast cancer patients, diagnosed at the NKI, aged up to 53 years at the time of diagnosis.21 Buyse et al. performed an independent validation in 302 lymph nodenegative patients from 5 European hospitals, aged up to 60 years at the time of diagnosis.22 The prognostic value of the 70-gene signature has also been retrospectively confirmed in several patient subgroups, such as postmenopausal patients, patients with positive axillary lymph nodes and in case of HER2-positive disease.23-28 Aside from the 70-gene signature, a few other gene signatures have found their way to the clinic. The characteristics of these tests, including PAM 50, Oncotype Dx, EndoPredict, Breast Cancer Index and MapQuant Dx, are described in Table 2. The analyses presented in this thesis focus on the 70-gene signature. Using the 70-gene signature in the daily clinical practice To prospectively evaluate the feasibility of implementation of the 70-gene signature in the community-based setting, the MicroarRAy PrognoSTics in Breast CancER (RASTER) study was conducted.29 Between 2004 and 2006 427 eligible patients were included in 16 hospitals in the Netherlands. Implementation of the 70-gene signature appeared feasible, even though the test could only be performed on fresh frozen tumor samples at the time. Recently, also formalin fixed paraffin embedded (FFPE) tumor samples can be used to perform the 70-gene signature.30 12 | Chapter 1

Micro-array Agendia (Amsterdam, NL)

MammaPrint 70-gene signature

Frozen or FFPE 189 pt, ER+/-, HER2 +/-, T1-2N0-1

qRT-PCR ARUP Laboratories (Salt Lake City, USA)

Pam50 55-gene signature

ER+ and patients with up to 3 pos. axillary lymph nodes Postmenopausal ER+ pt, treated with aromatase-inhibitors

Continuous variable divided in 2 groups; High and Low risk (EP score) Prognosis prediction of ER+, HER2- pt treated with tamoxifen EP clin score by combining EP score with clinicopathological factors ER+ tumor in postmenopausal women

FFPE=formalin fixed parafin embedded; pt=patient; ER=estrogen receptor; HER2=Human Epidermal Growth Factor receptor 2

Prognostic Patients with up to 3 xx value in other pos. axillary lymph nodes, subgroups patients who are 55-70 yrs, and for HER2+ disease

Breast Cancer Index 2-gene ratio HOXB13 and IL17R and molecular grade index (MGI) qRT-PCR Biotheranostics (San Diego, USA)

Micro-array/ qRT-PCR Ipsogen (Marseille, FR)

MapQuant Dx 97-gene (micro-array) or 8-gene (qRT-PCR) signature

Molecular grading of ER+, grade 2 tumors

Low grade GGI or high grade GGI

Possibly also prognostic ER+ pt, treated with for late recurrences aromatase-inhibitors

Continuous variable divided in 3 groups; High, Intermediate, Low risk Prognosis prediction of ER+, N0 pt treated with tamoxifen xx

FFPE Frozen or FFPE 64 pt, ER+ 588 pt, ER+, N0, treated with tamoxifen for 2-gene ratio. 410 pt ER+, N0 for MGI 668 pt, ER+ from 1702 pt, ER+, HER2-, 265 pt, ER+, N0, 597 pt, ER+ NSABP B-14 study treated with tamoxifen treated with tamoxifen (treated with tamoxifen) (2 series)

qRT-PCR Sividon Diagnostics (Keulen, DU)

EndoPredict 11-gene signature

Frozen or FFPE FFPE 964 pt, ER+, HER2447 pt, ER+ from NSABP B-20 study (tamoxifen-treated arm)

qRT-PCR Genomic Health (Redwood City, USA)

Oncotype Dx 21-gene recurrence score

Continuous variable divided in 3 groups; High, Intermediate, Low risk Initially Prognosis prediction of T1- Prognosis prediction Prognosis prediction of developed for 2N0 pt, ER+/-,<61 yrs of N0 pt, ER+, treated ER+, N0 pt treated with with endocrine therapy tamoxifen Additional mRNA levels ER, PR and xx mRNA levels ER, PR and information HER2 (TargetPrint), intrinsic HER2 subtypes (BluePrint)

Validation set 295 pt, T1-2N0-1, <53 yrs, 761 pt for prognosis ER +/133 pt for prediction ER+/-, HER2 +/-, T12N0-1 Output High and Low risk Continuous variable

Tissue sample Frozen or FFPE Training set 78 pt, T1-2N0, <55 yrs ER +/-

Technique Provided by

Assay

Table 2. Characteristics of gene signatures currently available

Introduction and outline | 13

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39

Shortly after confirmation of its feasibility in the RASTER study, the 70-gene signature was subjected to an international, multicenter, randomized-controlled trial called Microarray in Node-negative and 1-3 lymph node positive Disease may Avoid Chemotherapy (MINDACT). Patients enrolled in the MINDACT trial had their risk of recurrence assessed by the known online decision making tool Adjuvant! Online and the 70-gene signature. In case of a concordant low risk estimation patients would only receive endocrine therapy, while in case of a concordant high risk estimation patients would receive adjuvant chemotherapy with or without endocrine therapy. If the clinicopathological risk estimation was discordant with the 70-gene signature risk estimation patients were randomized between treatment according to the risk estimation by the 70-gene signature or treatment according to the clinicopathological risk estimation. On July 1st 2011, the required 6673 patients were successfully enrolled in the trial. The MINDACT trial will evaluate whether adjuvant chemotherapy can safely be omitted in patients with a tumor that is low risk according to the 70-gene signature, while clinical guidelines (in this case Adjuvant! Online) assessed this tumor as high risk. Meanwhile, better prognostication is desired in routine clinical practice and for this reason the 70-gene signature is increasingly applied when there is uncertainty regarding the indication of AST. Several studies from the Netherlands Cancer Institute have shown the impact of the introduction of the 70-gene signature on the quality of life of patients and the cost-effectiveness of genomic testing was confirmed multiple times.31-33 On the other hand, the effect on clinical decision-making had not systematically been studied. Rationale and outline of this thesis The aim of this thesis is to evaluate outcome prediction and clinical relevance of the 70-gene signature for locoregional and distant recurrence, its influence on risk assessment and AST recommendations, and its additional value to established clinical guidelines used in breast cancer treatment. In addition, we used the 70-gene signature to gain better insight in the biological background of tumors detected in a population-based screening program. The first part of this thesis focuses on the current applicability of the 70-gene signature in daily clinical practice and the impact of the 70-gene signature on clinical decision-making. Chapter 2 of this thesis provides a current overview of the prognostic value of the 70-gene signature in different subgroups of patients as described in recently published, retrospective studies. Chapter 3 provides the first prospective evidence of the prognostic value of the 70-gene signature. The 5-year follow-up data of the RASTER study shows the outcome of patients for whom the 70gene signature was used to decide whether or not an individual patient should receive adjuvant systemic treatment. In this chapter the 70-gene signature is compared to Adjuvant! Online. Because Adjuvant! Online is the most commonly used, but not the only guideline in breast cancer, we also compared the additional value of the 70-gene signature to other established guidelines in chapter 4. As described earlier, clinicopathological guidelines vary in their risk estimations. At

14 | Chapter 1

this point in time, there is no data on the agreement among oncologists using clinicopathological factors for risk estimations and the impact of the 70-gene signature on clinical decision-making. Therefore, agreement among oncologists before and after providing the 70-gene signature result was evaluated in chapter 5. Also, we aimed to evaluate long-term outcome of patients for whom a 70-gene signature result was available. Therefore we updated the original consecutive series as published by van de Vijver et al. in 2002 (chapter 6). The second part of this thesis focuses on new areas where the 70-gene signature may improve the biological understanding of breast cancer. Method of detection has proven to be an independent prognostic factor in breast cancer. Patients with a screen-detected cancer have more favorable outcome, independent of known clinicopathological factors such as age, size and ER-status. To investigate whether this observation is supported by a more favorable tumor biology in screen-detected cancers, we described the proportions of high, low and ultralow risk according to the 70-gene signature among screen-detected and interval cancers in the Dutch MINDACT cohort in chapter 7. Since a transition from film-screen mammography (FSM) to full field digital mammography (FFDM) took place at the same time as the MINDACT trial was conducted in the Netherlands we were also able to evaluate the impact of this transition on the biological background of the tumors detected in the nation-wide screening program. The 70-gene signature was developed to predict the risk of distant recurrence in breast cancer. Because of the correlation between distant and locoregional recurrence, we hypothesized that the 70-gene signature would also be able to predict the risk of locoregional recurrence after both breast conserving surgery and mastectomy. The results of analyzing this hypothesis in a pooled dataset of all patients included in one of the 70-gene signature validation studies, who were diagnosed and treated at the Netherlands Cancer Institute, is described in chapter 8.* This thesis ends with a general discussion and future prospects in chapter 9 and a summary of all results is presented in chapter 10.

*All studies described in this thesis are performed in accordance with the FEDERA codes of conduct.34

Introduction and outline | 15

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39

References 1

Ferlay J, Shin HR, Bray F, Forman D, Mathers C, Parkin DM. Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008. Int J Cancer 2010; 127:2893-917.

Nederlandse Kankerregistratie beheerd door IKNL

Esserman LJ, Shieh Y, Rutgers EJ, Knauer M, Retel VP, Mook S et al. Impact of mammographic screening on the detection of good and poor prognosis breast cancers. Breast Cancer Res Treat 2011; 130:725-34.

Early Breast Cancer Trialists’ Collaborative Group (EBCTCG). Effects of chemotherapy and hormonal therapy for early breast cancer on recurrence and 15-year survival: an overview of the randomised trials. Lancet 2005; 365:1687-717.

Sotiriou C, Piccart MJ. Taking gene-expression profiling to the clinic: when will molecular signatures become relevant to patient care? Nat Rev Cancer 2007; 7:545-53.

Cleator S, Ashworth A. Molecular profiling of breast cancer: clinical implications. Br J Cancer 2004; 90:1120-4.

D’Eredita’ G, Giardina C, Martellotta M, Natale T, Ferrarese F. Prognostic factors in breast cancer: the predictive value of the Nottingham Prognostic Index in patients with a long-term follow-up that were treated in a single institution. Eur J Cancer 2001; 37:591-6.

Goldhirsch A, Glick JH, Gelber RD, Coates AS, Senn HJ. Meeting highlights: International Consensus Panel on the Treatment of Primary Breast Cancer. Seventh International Conference on Adjuvant Therapy of Primary Breast Cancer. J Clin Oncol 2001; 19:3817-27.

Ravdin PM, Siminoff LA, Davis GJ, Mercer MB, Hewlett J, Gerson N et al. Computer program to assist in making decisions about adjuvant therapy for women with early breast cancer. J Clin Oncol 2001; 19:980-91.

Goldhirsch A, Wood WC, Gelber RD, Coates AS, Thurlimann B, Senn HJ. Meeting highlights: updated international expert consensus on the primary therapy of early breast cancer. J Clin Oncol 2003; 21:3357-65.

Olivotto IA, Bajdik CD, Ravdin PM, Speers CH, Coldman AJ, Norris BD et al. Population-based validation of the prognostic model ADJUVANT! for early breast cancer. J Clin Oncol 2005; 23:271625.

van ‘t Veer LJ, Dai H, van de Vijver MJ, He YD, Hart AA, Mao M et al. Gene expression profiling predicts clinical outcome of breast cancer. Nature 2002; 415:530-6.

Integraal Kankercentrum Nederland. NABON richtlijn mammacarcinoom 2012.

Wishart GC, Bajdik CD, Dicks E, Provenzano E, Schmidt MK, Sherman M et al. PREDICT Plus: development and validation of a prognostic model for early breast cancer that includes HER2. Br J Cancer 2012; 107:800-7.

Cardoso F. Microarray technology and its effect on breast cancer (re)classification and prediction of outcome. Breast Cancer Res 2003; 5:303-4.

Hanahan D, Weinberg RA. The hallmarks of cancer. Cell 2000; 100:57-70.

Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell 2011; 144:646-74.

Ross JS, Hatzis C, Symmans WF, Pusztai L, Hortobagyi GN. Commercialized multigene predictors of clinical outcome for breast cancer. Oncologist 2008; 13:477-93.

Harris JR, Lippman ME, Osborne CK, Morrow M. Diseases of the Breast. Fourth ed. Lippincott Williams & Wilkins, 2009.

Glas AM, Floore A, Delahaye LJ, Witteveen AT, Pover RC, Bakx N et al. Converting a breast cancer microarray signature into a high-throughput diagnostic test. BMC Genomics 2006; 7:278.

16 | Chapter 1

van de Vijver MJ, He YD, van ‘t Veer LJ, Dai H, Hart AA, Voskuil DW et al. A gene-expression signature as a predictor of survival in breast cancer. N Engl J Med 2002; 347:1999-2009.

Buyse M, Loi S, van ‘t Veer L, Viale G, Delorenzi M, Glas AM et al. Validation and clinical utility of a 70-gene prognostic signature for women with node-negative breast cancer. J Natl Cancer Inst 2006; 98:1183-92.

Bueno-de-Mesquita JM, Linn SC, Keijzer R, Wesseling J, Nuyten DS, van Krimpen C et al. Validation of 70-gene prognosis signature in node-negative breast cancer. Breast Cancer Res Treat 2009; 117:48395.

Knauer M, Cardoso F, Wesseling J, Bedard PL, Linn SC, Rutgers EJ et al. Identification of a low-risk subgroup of HER-2-positive breast cancer by the 70-gene prognosis signature. Br J Cancer 2010; 103:1788-93.

Mook S, Schmidt MK, Viale G, Pruneri G, Eekhout I, Floore A et al. The 70-gene prognosis-signature predicts disease outcome in breast cancer patients with 1-3 positive lymph nodes in an independent validation study. Breast Cancer Res Treat 2009; 116:295-302.

Mook S, Knauer M, Bueno-de-Mesquita JM, Retel VP, Wesseling J, Linn SC et al. Metastatic potential of T1 breast cancer can be predicted by the 70-gene MammaPrint signature. Ann Surg Oncol 2010; 17:1406-13.

Mook S, Schmidt MK, Weigelt B, Kreike B, Eekhout I, van de Vijver MJ et al. The 70-gene prognosis signature predicts early metastasis in breast cancer patients between 55 and 70 years of age. Ann Oncol 2010; 21:717-22.

Wittner BS, Sgroi DC, Ryan PD, Bruinsma TJ, Glas AM, Male A et al. Analysis of the MammaPrint breast cancer assay in a predominantly postmenopausal cohort. Clin Cancer Res 2008; 14:2988-93.

Bueno-de-Mesquita JM, van Harten WH, Retel VP, van ‘t Veer LJ, van Dam FS, Karsenberg K et al. Use of 70-gene signature to predict prognosis of patients with node-negative breast cancer: a prospective community-based feasibility study (RASTER). Lancet Oncol 2007; 8:1079-87.

Sapino A, Roepman P, Linn SC, Snel M.H.J., Delahaye LJ, van den Akker J. et al. MammaPrint molecular diagnostics on Formalin Fixed Paraffin Embedded tissue. J Mol Diagn. In press.

Retel VP, Joore MA, Knauer M, Linn SC, Hauptmann M, Harten WH. Cost-effectiveness of the 70gene signature versus St. Gallen guidelines and Adjuvant Online for early breast cancer. Eur J Cancer 2010; 46:1382-91.

Retel VP, Joore MA, Drukker CA, Bueno-de-Mesquita JM, Knauer M, van Tinteren H et al. Prospective cost-effectiveness analysis of genomic profiling in breast cancer. Eur J Cancer 2013.

Retel VP, Groothuis-Oudshoorn CG, Aaronson NK, Brewer NT, Rutgers EJ, van Harten WH. Association between genomic recurrence risk and well-being among breast cancer patients. BMC Cancer 2013; 13:295.

Dutch federation of Biomedical Scientific Societies. Codes of Conduct. www.federa.org.

Introduction and outline | 17

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39

Gene signature for risk stratification and treatment of brea Incorporating tumor biology in clinical decision-making

Chapter 2

of breast cancer

king

Voorspelling van de prognose van patiĂŤnten met vroeg stadium borstkanker: de bijdrage van een genexpressie-profiel

Accepted by Nederlands Tijdschrift voor Geneeskunde

Caroline A. Drukker Marjanka K. Schmidt Thijs van Dalen Jacobus J.M. van der Hoeven Sabine C. Linn Emiel J.Th. Rutgers

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39

Abstract •

Gene expression classifiers, such as the 70-gene signature, reflecting the biology of breast tumors, start finding their way into daily clinical practice.

•

Retrospective validation studies in breast cancer have established the prognostic value of the 70-gene signature (MammaPrint®).

•

The prospective observational RASTER study shows excellent 5-year distant-recurrencefree intervals in all subgroups. Patients with 70-gene signature low risk who had not received adjuvant chemotherapy despite poor clinicopathological factors, had an excellent 5-years distant-recurrence-free interval of 98.4%.

•

Especially for patients aged 45 years or older, with an estrogen receptor (ER)-positive, HER2-negative tumor, diameter 1-2 cm, grade 2 there is prospective evidence that the 70-gene signature has additional value for adjuvant chemotherapy decisions.

Samenvatting • Genexpressie-profielen, zoals het 70-genen profiel, die informatie geven over het biologisch gedrag van borstkanker, hebben hun intrede gedaan in de dagelijkse klinische praktijk. • Meerdere retrospectieve validatiestudies hebben de prognostische waarde van het 70-genen profiel (MammaPrint®) aangetoond. •

De prospectieve, observationele RASTER studie laat een uitstekende 5-jaars ziektevrije overleving zien voor klinisch hoog risico, maar 70-genen profiel laag risico patiënten, die geen adjuvante chemotherapie hadden gehad: een 5-jaars metastase-vrij interval van 98.4%.

•

Met name voor patiënten vanaf 45 jaar met een oestrogeen receptor (ER)-positieve, HER2-negatieve tumor, 1-2 cm, graad 2 is er nu ook prospectief bewijs dat het 70-genen profiel kan bijdragen in de besluitvorming om al dan niet adjuvant chemotherapie te adviseren.

20 | Chapter 2

Inleiding De daling in de mortaliteit van het mammacarcinoom in de afgelopen decennia wordt toegeschreven aan vroegere detectie door invoering van landelijke screeningsprogramma’s, toegenomen bewustzijn bij patiënten waardoor sneller medische hulp wordt gezocht, maar vooral door verbetering en frequenter gebruik van adjuvant systemische behandelmogelijkheden.1,2 De selectie van patiënten die in aanmerking komen voor adjuvante chemotherapie (ACT) wordt gebaseerd op klinisch-pathologische factoren, zoals leeftijd, tumorgrootte, lymfklierstatus, histologische graad, oestrogeen-receptor (ER) en Human Epidermal growth factor Receptor 2 (HER2) status.2 Aan de hand van deze factoren wordt een inschatting gemaakt van het risico op het ontwikkelen van recidief ziekte, op basis waarvan richtlijnen de indicatie voor ACT bepalen. De huidige NABON richtlijn (2012) geeft aan dat ACT alleen gerechtvaardigd is als er een absoluut overlevingsvoordeel kan worden behaald van meer dan 5% in de eerste 10 jaar.3 Voor de individuele patiënt blijft het echter moeilijk om een accurate risico-inschatting te maken, omdat patiënten met dezelfde klinisch-pathologische factoren een verschillend ziektebeloop kunnen hebben.4 Daardoor krijgen veel vrouwen ACT, terwijl ze er waarschijnlijk geen baat bij hebben. Een meer op het individu gerichte risico-inschatting en behandeladvies kan zowel overbehandeling, met de bijbehorende (soms) ernstige toxiciteit, als onderbehandeling voorkomen.4

Genexpressie-profielen Een mogelijkheid om de nauwkeurigheid van de risico-inschatting en het daaraan gekoppelde behandeladvies te verbeteren is het gebruik van genexpressie-profielen.4 Bekende genexpressieprofielen zijn: het 70-genen profiel, het 76-genen profiel, PAM 50, MapQuant Dx, EndoPredict, de Breast Cancer Index en het 21-genen profiel van Oncotype Dx. Informatie over groepen patiënten waarvoor deze testen voor wat betreft hun prognostische waarde zijn gevalideerd is te vinden in tabel 1.

Voorspelling prognose borstkanker: bijdrage genexpressie-profiel | 21

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39

22 | Chapter 2

Hoog/laag risico

Prognose van T1-2N0 pt, ER+/-,<61 jr

Weergave van uitslag

InitiĂŤle klinische doelgroep

Knauer et al. (BCRT 2010) MINDACT studie

n.v.t.

Martin et al. (BCRT 2013)

Prognose van N0 pt, ER+, behandeld met hormonale therapie n.v.t.

761 pt voor prognose 133 pt voor predictie ER+/-, HER2 +/-, T12N0-1 Continue variabele

189 pt, ER+/-, HER2 +/-, T1-2N0-1

Vries of paraffine

ARUP Laboratories (Salt Lake City, USA)

qRT-PCR

55-genen profiel

Paraffine

Sividon Diagnostics (Keulen, DE)

qRT-PCR

11-genen profiel

EndoPredict

Paik et al. (JCO 2006)

Prognose van ER+, N0 pt behandeld met tamoxifen mRNA levels ER, PR en HER2

Continue variabele verdeeld in 3 groepen; hoog, intermediair en laag risico

668 pt, ER+ uit NSABP B-14 studie (behandeld met tamoxifen) Continue variabele verdeeld in 2 groepen; hoog en laag risico (EP score)

1702 pt, ER+, HER2, behandeld met tamoxifen (2 series)

447 pt, ER+ uit NSABP 964 pt, ER+, HER2B-20 studie (uit arm die alleen tamoxifen kreeg)

Vries of paraffine

Genomic Health (Redwood City, USA)

qRT-PCR

21-genen recurrence score

Oncotype Dx

n.v.t.

Prognose van ER+, HER2- pt behandeld met tamoxifen mRNA levels ER, PR EP clin score wordt n.v.t. en HER2 (TargetPrint), verkregen door intrinsieke subtypes combinatie EP score (BluePrint) met klin. path. factoren Tot 3 pos. lymfklieren, ER+, N0 pt behandeld n.v.t. ER+ en 1-3 pos. ER+ tumoren in 55-70 jr, HER2+ met tamoxifen lymfklieren. Postmenop. postmenopauzale ER+ pt, behandeld met vrouwen aromatase-remmers van de Vijver et al. Foekens et al. Parker et al. Paik et al. Filipits et al. (NEJM 2002) (JCO 2006) (JCO 2009) (NEJM 2004) (Clin Cancer Res 2011) Buyse et al. (JNCI 2006) Desmedt et al. Nielsen et al. Paik et al. Dubsky et al. Bueno-de-Mesquita (Clin Cancer Res 2007) (Clin Cancer Res 2010) (JCO 2006) (Ann of Oncol 2013) et al. (BCRT 2008) MINDACT studie

Prognose van N0 pt

qRT-PCR= quantitative reverse transcrip!ase polymerase chain reaction pt=patiĂŤnten

Studies predictieve waarde

Studies prognostische waarde

Prognostische waarde in andere subgroepen

Additionele informatie

171 pt, ER+/-, N0

295 pt, T1-2N0-1, <53 jr, ER +/-

Validatie-cohort

Hoog/laag risico

115 pt, ER+, N0

Niet verkrijgbaar

Micro-array

Vries

Agendia (Amsterdam, NL)

Op de markt gebracht door

Cohort waarop test 78 pt, T1-2N0, <55 jr is ontwikkeld ER +/-

Micro-array

Analyse

76-genen profiel

Rotterdam signature Pam50

Weefsel preservatie Vries of paraffine

70-genen profiel

Assay

MammaPrint

Tabel 1. Overzicht van bekende genexpressie-profielen en hun kenmerken

Vries of paraffine

Ipsogen (Marseille, FR)

97-genen (micro-array) of 8-genen (qRT-PCR) profiel Micro-array/ qRT-PCR

MapQuant Dx

n.v.t.

Zhang et al (Clin Cancer Res 2013)

Mogelijk ook voorspellend voor late metastasen

Prognose van ER+, N0 pt behandeld met tamoxifen n.v.t.

Continue variabele verdeeld in 3 groepen; hoog, intermediair en laag risico

265 pt, ER+, N0, behandeld met tamoxifen

n.v.t.

Sotiriou et al. (JNCI 2006) Reyal et al. (PlosOne 2012)

ER+ pt behandeld met aromatase-remmers

n.v.t.

Moleculaire gradering van ER+, graad 2 pt

Genomic Grade Index (GGI) laag of GGI hoog-gradig

597 pt, ER+

588 pt, ER+, N0, 64 pt, ER+ behandeld met tamoxifen voor 2-genen ratio. 410 pt ER+, N0 voor MGI

Paraffine

Biotheranostics (San Diego, USA)

2-genen ratio HOXB13 en IL17R en moleculaire graad index (MGI) qRT-PCR

Breast Cancer Index

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39

Het 70-genen profiel (MammaPrint®) bepaalt de activiteit van de geselecteerde genen door de hoeveelheid messenger RNA (mRNA) te meten. Met behulp van een algoritme wordt de 70-genen profiel indexscore berekend, gelegen tussen -1 en 1.5 Hoog risico is gedefinieerd als een indexscore <0.4, laag risico als een indexscore >0.4. Figuur 1 laat zien hoe een 70-genen profiel testuitslag wordt weergegeven. De prognostische waarde van het profiel is uitgebreid gevalideerd in meerdere retrospectieve studies.5-7 De test kon aanvankelijk alleen op ingevroren tumorweefsel worden uitgevoerd, maar nu ook op formaline gefixeerd paraffine ingebed materiaal (FFPE).8 De Amerikaanse Food and Drug Administration (FDA) heeft het toepassingsgebied van het 70-genen profiel als prognostische marker goedgekeurd voor: vrouwen met een stadium 1 of 2 mammacarcinoom, <5 cm en een negatieve axillaire lymfklierstatus.9 Prospectieve evaluatie van het al dan niet voorschrijven van chemotherapie op basis van het 70-genen profiel (de predictieve waarde) wordt momenteel onderzocht in de multicentrische, gerandomiseerde MINDACT studie, waarvan de inclusie per juli 2011 gesloten is.10 Er zijn 6694 patiënten geincludeerd in de MINDACT, waarvan 2092 in 21 Nederlandse ziekenhuizen. In de studie is er, in geval van discordantie tussen de klinische risico-inschatting op basis van Adjuvant! Online en het 70-genen profiel, gerandomiseerd tussen behandeling conform Adjuvant! Online of conform het 70-genen profiel.10 Het 70-genen profiel wordt in Nederland veel toegepast en door het merendeel van de Nederlandse zorgverzekeraars vergoed. In andere landen wordt het 21-genen profiel ook veel gebruikt. Het 21-genen profiel van Oncotype Dx kent, naast een hoog en laag risico, ook een intermediaire risico-uitslag. De 10-jaars metastase-vrije-overleving van de laag, intermediair en hoog risico groep waren in het cohort waarop de test ontwikkeld is respectievelijk 93.2% (CI: 90.1-96), 85.7% (CI: 79.7-91.7) en 69.5% (CI: 62.6-76.4).11 De test is gevalideerd in 668 lymfklier-negatieve, ERpositieve patiënten die in de NSABP-B14 studie met tamoxifen waren behandeld.12 Het 21-genen profiel is niet getest bij ER-negatieve tumoren, noch bij onbehandelde patiënten. In Nederland is de afgelopen jaren vooral ervaring met het 70-genen profiel opgedaan. Hieronder geven wij een overzicht van een aantal patiëntengroepen voor wie het 70-genen profiel kan bijdragen aan de besluitvorming tot wel of geen aanvullende chemotherapie zoals gerapporteerd in de recente literatuur. Gepubliceerde studies zijn gecategoriseerd aan de hand van mate van bewijs zoals voorgesteld door Simon et al (tabel 2).13

Voorspelling prognose borstkanker: bijdrage genexpressie-profiel | 23

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39

Amended R eport

decoding breast cancer.

CUSTOMER

SPECIMEN

Doctor: Account:

Requisition #: Collection Date: Test Request Date: Date Received: Report Date: Specimen Type: Customer Ref.:

Address: City, St., Zip: Country:

PATIENT 19-Aug-2013 20-Aug-2013 20-Aug-2013 14-Sep-2013 FFPE, Needle Core

Patient: DOB: Patient #: Gender: Female SSN:

LOW RISK

The breast cancer tissue sample submitted was analyzed by MammaPrint, an IVDMIA 70 - Gene Pro e of Breast Cancer for Metastatic Risk that has been validated to correlate with high or low outcome risk for distant metastases in patients with invasive breast cancer. ¹ In the reference group as published, “Low Risk“ means that a lymph node negative breast cancer patient has a 10% chance (95% CI 4-15) that their cancer will recur within 10 years without any additional adjuvant treatment, either hormonal therapy or chemotherapy. ² T he analytical meas urement performed on the s ample fell within a pre-defined area around the clas s ification cut-off (i.e. borderline s ample). B orderline s amples have a les s than 90% clas s ification accuracy (i.e. > 10% chance of fals e clas s ification).

Clinicopathologic Findings Tumor Cell Percentage:

50 %

RNA Integrity : N/A

The reported tumor cell percentage and pathology comments serve as a quality control for Agendia’s genomic assays and should not be viewed as a diagnosis of the presence or absence of malignancy

Assay Description The U.S. Food and Drug Administration (FDA) has provided IVDMIA clearance of MammaPrint with fresh tissue for Stage I and II, lymph node negative, invasive breast cancer, for patients of all ages who have a tumor of 5 cm or less, independent of estrogen receptor status (ER+/-), based upon the development 2-5The test is and validation of the assay as reported in Nature, New England Journal of Medicine, Journal of the National Cancer Institute and BMC Genomics. performed using a microarray-based gene expression pro e that was independently validated on 10 year outcome data on an untreated patient cohort. 2 An unbiased, supervised analysis of the entire human genome, ~25,000 genes, followed by a leave-one-out cross-validation procedure, revealed the 70 critical genes that distinguish patients at High Risk vs. Low Risk of metastasis. 3 Based on the analytical performance of MammaPrint, the accuracy of classifying a sample as High Risk or Low Risk is 98.9% with reproducibility of the measurement being 98.5%. 1 MammaPrint has been validated in over 774 breast cancer patients and shown to provide information independent of clinicopathological risk assessment. 2,4,5

MammaP rint® B reas t C ancer G ene P rofile3

TRANSBIG Validation Results

Figuur 1. Resultaat formulier van het 70-genen profiel

R-ROW-013-V1

Figuur 1. Resultaat formulier van het 70-genen profiel

24 | Chapter 2

Tabel 2. Categorieën voor mate van bewijs wetenschappelijk onderzoek13 Categorie A Prospectieve, gecontroleerde studie, gericht op het onderzoeken van de betreffende tumor marker, waarbij de patiënt prospectief geïncludeerd, behandeld en gevolgd wordt. Ook weefsel wordt ten tijde van inclusie verzameld en geanalyseerd. De studie heeft genoeg power om de hypothese over de betreffende marker te toetsen. Het is onwaarschijnlijk dat het toeval de resultaten beïnvloedt, waardoor validatie gewenst, maar niet vereist is. B

Prospectieve studie, niet specifiek gericht maar wel te gebruiken voor het onderzoeken van de betreffende tumor marker, waarbij de patiënt prospectief geïncludeerd wordt en behandeld en gevolgd volgens de standaard. Ook weefsel wordt ten tijde van inclusie verzameld en geanalyseerd. De studie heeft genoeg power om de therapeutische vraag te beantwoorden, maar niet om de hypothese over de betreffende marker te toetsen. Er is vooraf een plan t.a.v. de statistische analyses om deze vragen te beantwoorden. Het is meer waarschijnlijk dat het toeval de resultaten beïnvloedt, waardoor een of meer validatie-studies nodig zijn.

Prospectieve observationele registratie. Patiënten zijn prospectief geïncludeerd in de registratie, maar behandeling en follow-up gaan volgens de standaard. Ook weefsel wordt ten tijde van inclusie verzameld, maar achteraf geanalyseerd. De studie heeft niet genoeg power om de hypothese over de betreffende marker te toetsen. Er is vooraf een plan t.a.v. de statistische analyses om deze vragen te beantwoorden. Het is vrij waarschijnlijk dat het toeval de resultaten beïnvloedt, waardoor opvolgende validatie-studies nodig zijn.

Geen prospectief aspect in de studie. Geen prospectieve registratie. Weefsels worden verzameld en retrospectief geanalyseerd. De studie heeft prospectief niet genoeg power. Er is vooraf geen plan t.a.v. de statistische analyses om deze vragen te beantwoorden. Het is vrij waarschijnlijk dat het toeval de resultaten beïnvloedt, waardoor opvolgende validatiestudies nodig zijn.

Klinisch nut in subgroepen op basis van klinisch-pathologische factoren Leeftijd Steeds meer postmenopauzale vrouwen komen in aanmerking voor ACT ondanks dat deze groep vaker gunstige biologische tumorkarakteristieken heeft.14,15 In een studie onder 148 systemisch onbehandelde patiënten tussen de 55 en 71 jaar met een T1-2N0M0 tumor, behandeld in het Nederlands Kanker Instituut (NKI) tussen 1984 en 1996, van wie 18% adjuvant hormonale therapie had gehad en niemand ACT, bleek er op basis van het 70-genen profiel een significant verschil in 5-jaars borstkanker-specifieke overleving tussen patiënten met een laag (99%, SE 1%) en een hoog risico (80%, SE 5%; p=0.036)(categorie C).14 Op basis van het genexpressie-profiel zou bij de eerste groep veilig kunnen worden afgezien van ACT. Ook in een cohort van 100 patiënten met een gemiddelde leeftijd van 62 jaar en T1-2N0M0 tumoren, behandeld in het Massachusetts General Hospital tussen 1985 en 1997, waarvan 15% van de laag risico groep (n=27) en 23% van de hoog risico groep (n=73) ACT kreeg, had het 70-genen profiel een zeer goede negatief voorspellende waarde.15 Na een mediane follow-up van 11.3 jaar ontwikkelde één van de patiënten met een 70-genen profiel laag risico tumor afstandsmetastasen (categorie D). Voorspelling prognose borstkanker: bijdrage genexpressie-profiel | 25

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39

Tumorgrootte Ondanks dat in vele richtlijnen is opgenomen dat kleinere tumoren geassocieerd zijn met een goede prognose,16 zien we in de praktijk dat kleine tumoren ook kunnen metastaseren.17 Recent werd naar het 70-genen profiel gekeken van 964 patiënten met een T1 tumor (<2 cm).18 In de 70-genen profiel laag risico groep (n= 525) was 10% behandeld met ACT en in de hoog risico groep (n=439) 37%. De 10-jaars borstkanker-specifieke-overleving van patiënten met een laag risico was 91% (SE 2%) en 72% (SE 3%) bij patiënten met een hoog risico (HR voor wat betreft borstkankerspecifieke sterfte na 10 jaar: 4.22 (95%CI: 2.70-6.60); p<0.001). Op basis van het genexpressie-profiel (hoog risico) zou bijna de helft van de patiënten wel in aanmerking komen voor ACT bij de overwegende kleine tumoren (categorie D). ER-status Het 70-genen profiel is zowel voor ER-positieve als -negatieve tumoren gevalideerd.5 ER-positieve tumoren zijn geassocieerd met een gunstiger 5-jaars prognose en hebben vaker een laag risico 70-genen profiel.5 ER-negatieve tumoren zijn geassocieerd met een minder gunstige 5-jaars prognose, doordat deze vaker een hogere proliferatie vertonen en er geen effect te zien is van adjuvant hormonale therapie (AHT). In verzamelde validatiestudies is gevonden dat 3-6% van de ER-negatieve tumoren toch een laag risico heeft volgens het 70-genen profiel. Er zal verder onderzoek moeten plaatsvinden om te kijken of bij deze subgroep van ER-negatieve patiënten ACT veilig achterwege gelaten kan worden. HER2-status Een andere ongunstige prognostische factor is overexpressie van HER2. Vrijwel alle patiënten met een HER2-positief mammacarcinoom worden behandeld met ACT in combinatie met trastuzumab.2 Trastuzumab is een duur middel, dat een lange behandelduur vraagt van een jaar en in combinatie met ACT gepaard gaat met een klein, maar niet verwaarloosbaar risico op ernstige cardiale toxiciteit in de eerste jaren na behandeling. Uit de HERA studie, waarin 1 of 2 jaar trastuzumab vergeleken werd met observatie bij 1698 HER2-positieve patiënten, is gebleken dat 72.2% (HR 0.76 (95% CI 0.66-0.87); p<0.0001) van de patiënten 4 jaar ziektevrij bleven na ACT zonder trastuzumab.19,20 In een recente studie identificeert het 70-genen profiel een laag risico groep met een relatief goede overleving binnen de HER2-positieve tumoren die niet behandeld zijn met ACT en/of trastuzumab (n=89).21 Deze patiënten hadden een 10-jaars metastase-vrije-overleving van 84% in geval van een laag risico 70-genen profiel, hetgeen op zich nog steeds zal leiden tot behandeling met ACT en trastuzumab. Echter, voor patiënten die naast een laag risico 70-genen profiel een zogeheten hoog endocriene responsieve tumor (ER >50%) hadden en die noch trastuzumab, noch ACT hadden gekregen, was de 10-jaars metastase-vrijeoverleving 100% (categorie D).21

26 | Chapter 2

Lymfklierstatus De aanwezigheid van axillaire lymfkliermetastasen is veelal een indicatie voor ACT, maar er zijn ook patiënten die zonder ACT een goede overlevingskans hebben.22 Een onafhankelijke validatiestudie werd uitgevoerd in een groep van 241 patiënten met een operabel mammacarcinoom en 1-3 positieve lymfklieren, behandeld in het NKI of het European Institute of Oncology in Milaan.23 In de 70-genen profiel laag risico groep (n=99) werd 41% behandeld met ACT en in de hoog risico (n=142) groep 67%. De 10-jaars metastase-vrije-overleving was 91% (SE 4%) in de laag risico groep en 76% (SE 4%) in de hoog risico groep (p=0.001). De 10-jaars borstkanker-specifiekeoverleving in dit cohort was 96% (SE 2%) in de laag risico groep en 76% (SE 4%) in de hoog risico groep (p<0.001). Met een multivariate HR van 7.17 (95%CI 1.81-28.43; p=0.005) is het 70-genen profiel significant beter in het voorspellen van de overleving dan de bekende klinischpathologische factoren bij deze patiënten.22 Het lastige van deze studie is dat een substantieel deel van de patiënten adjuvant systemisch behandeld is, waardoor het onduidelijk blijft wat de prognose van deze lymfklier-positieve patiëntengroep zou zijn geweest zonder ACT. Toch lijkt het binnen deze groep mogelijk om op basis van tumorload en genexpressie-profiel genuanceerd te denken over de indicatie voor ACT (categorie C). Veel gebruikte klinisch-pathologische richtlijnen, zoals de NABON richtlijn, Adjuvant! Online, de St. Gallen richtlijn en de Nottingham Prognostic Index (NPI) zijn discordant met het 70-genen profiel in 7-40% van de patiënten.23 Het profiel geeft aanvullende prognostische informatie bij een concordante laag risico-inschatting volgens de NABON richtlijn van 2004, St. Gallen en NPI of in geval van discordantie tussen de verschillende richtlijnen.24 Indien een patiënt een hoog risico op afstandsmetastasen heeft volgens de drie richtlijnen, geeft het 70-genen profiel geen aanvullende informatie.

Het grijze gebied In Nederland wordt bij gemiddeld 13200 vrouwen per jaar een invasief mammacarcinoom gediagnosticeerd. Jaarlijks zijn ongeveer 1400 van deze patiënten tussen 40-70 jaar, met een T1 tumor (<2 cm), graad 2, en een negatieve lymfklierstatus of alleen micrometastasen in de oksel, zonder afstandsmetastasen. In 2008 ontving 15% van deze patiënten ACT. In 2010, na aanpassing van de 2008 richtlijn, is dit percentage bijna verdubbeld naar 27% (tabel 3). De huidige tendens is om bij twijfel een patiënt met ACT te behandelen. De vraag is of al deze patiënten ook daadwerkelijk profijt hebben hiervan. Immers, een groot deel van deze patiënten krijgt ook zonder ACT nooit metastasen.

Voorspelling prognose borstkanker: bijdrage genexpressie-profiel | 27

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39

Tabel 3. Adjuvant systemische therapie in Nederland 2008-2010 voor patiënten 40-70 jaar met T1N0i-/+M0 tumor graad 2 AST Geen Hormonale Therapie (HT) Chemotherapie+/- HT Totaal

2008 1064 62% 387 23% 263 15% 1714

2009 666 36% 632 34% 550 30% 1848

2010 670 37% 653 36% 496 27% 1819

Naast het uitvoerige retrospectieve bewijs dat het 70-genen profiel toegevoegde prognostische waarde heeft, zijn er nu ook de resultaten van de prospectieve RASTER studie (categorie B).25 Deze observationele studie, waarbij het 70-genen profiel gebruikt werd in de besluitvorming om al dan niet adjuvant systemisch te behandelen, includeerde tussen 2004 en 2006 427 patiënten, < 61 jaar, met een T1-3N0M0 mammacarcinoom (70% T1, 29% T2, 1% T3). De 5-jaar follow-up resultaten laten zien dat patiënten met een laag risico profiel, waarvan 15% behandeld is met ACT, een uitstekende 5-jaars metastase-vrije-overleving hebben van 97% ten opzichte van 92% in de hoog risico groep, waarin 85% behandeld is met ACT.25 Binnen de gehele patiëntengroep classificeerde het 70-genen profiel 22% minder patiënten als hoog risico dan de NABON richtlijn (2012). Binnen de groep T1 tumoren (n=301) was een reductie van 18% te zien (ongepubliceerde data). Dit percentage discordantie tussen de klinische risico-inschatting en het 70-genen profiel wordt ook in de MINDACT gezien.10 De discordante groep bestond voornamelijk uit patiënten met een ER-positieve, HER2-negatieve tumoren van 1-2 cm, graad 2. Dit is juist de groep waarbinnen volgens gegevens van de Nederlandse Kanker Registratie de afgelopen jaren een forse stijging in behandeling met AST wordt gezien (tabel 3) ten gevolge van de recente aanscherping van de landelijke NABON-richtlijnen. Voor hen kan het 70-genen profiel dan ook het meest bijdragen in de besluitvorming om al dan niet adjuvant systemisch te behandelen. Ook werd een metastasevrije-overleving van 100% gezien in de groep die ACT noch AHT had gehad bij een laag risico volgens het 70-genen profiel, terwijl de huidige NABON-richtlijn deze patiënten als hoog risico had geclassificeerd.25 Deze resultaten suggereren dat ACT veilig achterwege gelaten kan worden bij een patiënte in dit grijze gebied in geval van een laag risico 70-genen profiel. Voor het veilig achterwege laten van AHT zijn langere follow-up data nodig, omdat twee-derde van de recidieven in de ER-positieve groep tussen 5 en 20 jaar follow-up optreedt.2 Ondanks dat er in de eerder genoemde RASTER studie niet gerandomiseerd was, geeft de studie wel belangrijke inzichten in het gebruik van het 70-genen profiel in de dagelijkse praktijk.

28 | Chapter 2

Conclusie Naast retrospectief is er nu ook het eerste prospectieve bewijs uit de RASTER studie dat het 70-genen profiel additionele informatie geeft voor patiënten waarbij er, ondanks minder gunstige klinisch-pathologische factoren, toch twijfel blijft bestaan over de geschatte winst van ACT. In afwachting van de resultaten van de MINDACT studie, welke definitief uitsluitsel zullen geven of ACT veilig achterwege gelaten kan worden in geval van een laag risico 70-genen profiel, lijkt op basis van dit overzicht de toegevoegde waarde van het 70-genen profiel het grootst voor patiënten voor wie het stellen van de indicatie voor adjuvant chemotherapie op basis van patiënt- en tumorkarakteristieken niet eenduidig is. Het gaat hier om patiënten tussen 45 en 55 jaar oud met een tumor van 1-2 cm, graad 2, ER-positief en HER2-negatief. Voor andere patiëntengroepen kan in individuele gevallen het 70-genen profiel als extra hulpmiddel ingezet worden bij de besluitvorming omtrent het nut van adjuvant systemische therapie. Dankwoord Met dank aan de Nederlandse Kankerregistratie beheerd door IKNL voor het verstrekken van de data aangaande het gebruik van adjuvant systemische therapie in Nederland. Met dank aan Stella Mook en Laura van ’t Veer voor hun bijdrage aan de vormgeving van dit manuscript.

Leerpunten •

Genexpressie-profielen geven belangrijke aanvullende informatie over het risico op afstandsmetastasen bij borstkanker patiënten.

•

Het 70-genen profiel kan voor zowel pre- als postmenopauzale patiënten, met of zonder aangedane axillaire lymfklieren en voor zowel HER2-positieve als -negatieve tumoren ingezet worden als hulpmiddel bij twijfel over de prognose inschatting en de daarmee gepaard gaande absolute overlevingswinst die met adjuvant systemische therapie verkregen kan worden.

•

Vooral voor patiënten met ER-positieve, HER2-negatieve tumoren van 1-2 cm, graad 2 kan het 70-genen profiel bijdragen in de besluitvorming om al dan niet adjuvant chemotherapie te adviseren, naast adjuvant hormonale therapie.

Voorspelling prognose borstkanker: bijdrage genexpressie-profiel | 29

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39

Referenties 1

Nationaal Borstkanker Overleg Nederland NABON, Kwaliteitsinstituut voor de Gezondheidszorg CBO, Vereniging van Integrale Kankercentra. Adjuvant Systemische Therapie. Richtlijn Mammacarcinoom 2012.

Mook S, van ‘t Veer LJ, Rutgers EJ, Piccart-Gebhart MJ, Cardoso F. Individualization of therapy using Mammaprint: from development to the MINDACT Trial. Cancer Genomics Proteomics 2007; 4:14755.

van de Vijver MJ, He YD, van ‘t Veer LJ, Dai H, Hart AA, Voskuil DW et al. A gene-expression signature as a predictor of survival in breast cancer. N Engl J Med 2002; 347:1999-2009.

Buyse M, Loi S, van ‘t Veer L, Viale G, Delorenzi M, Glas AM et al. Validation and clinical utility of a 70 gene prognostic signature for women with node-negative breast cancer. J Natl Cancer Inst 2006; 98:1183-92.

Sapino A, Roepman P, Linn SC, Snel MH, Delahaye LJ, van den Akker J et al. MammaPrint molecular diagnostics on Formalin Fixed Paraffin Embedded tissue. J of Mol Diagn 2013; in press.

FDA Label - USFDA Clearance; 2009. http://www.accessdata.fda.gov

Rutgers E, Piccart-Gebhart MJ, Bogaerts J, Delaloge S, van ‘t Veer LV, Rubio IT et al. The EORTC 10041/ BIG 03-04 MINDACT trial is feasible: results of the pilot phase. Eur J Cancer 2011; 47:2742-9.

Paik S, Shak S, Tang G, Kim C, Baker J, Cronin M et al. A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N Engl J Med 2004; 351:2817-26.

Tang G, Shak S, Paik S, Anderson SJ, Costantino JP, Geyer CE, Jr. et al. Comparison of the prognostic and predictive utilities of the 21-gene Recurrence Score assay and Adjuvant! for women with nodenegative, ER-positive breast cancer: results from NSABP B-14 and NSABP B-20. Breast Cancer Res Treat 2011; 127:133-42.

Simon RM, Paik S, Hayes DF. Use of archived specimens in evaluation of prognostic and predictive biomarkers. J Natl Cancer Inst 2009; 101:1446-52.

Wittner BS, Sgroi DC, Ryan PD, Bruinsma TJ, Glas AM, Male A et al. Analysis of the MammaPrint breast cancer assay in a predominantly postmenopausal cohort. Clin Cancer Res 2008; 14:2988-93.

Fitzgibbons PL, Page DL, Weaver D, Thor AD, Allred DC, Clark GM et al. Prognostic factors in breast cancer. College of American Pathologists Consensus Statement 1999. Arch Pathol Lab Med 2000; 124:966-78.

Foulkes WD, Reis-Filho JS, Narod SA. Tumor size and survival in breast cancer--a reappraisal. Nat Rev Clin Oncol 2010; 7:348-53.

30 | Chapter 2

Gianni L, Dafni U, Gelber RD, Azambuja E, Muehlbauer S, Goldhirsch A et al. Treatment with trastuzumab for 1 year after adjuvant chemotherapy in patients with HER2-positive early breast cancer: a 4-year follow-up of a randomised controlled trial. Lancet Oncol 2011; 12:236-44.

Untch M, Gelber RD, Jackisch C, Procter M, Baselga J, Bell R et al. Estimating the magnitude of trastuzumab effects within patient subgroups in the HERA trial. Ann Oncol 2008; 19:1090-6.

Bueno-de-Mesquita JM, van Harten WH, Retel VP, van â&#x20AC;&#x2DC;t Veer LJ, van Dam FS, Karsenberg K et al. Use of 70-gene signature to predict prognosis of patients with node-negative breast cancer: a prospective community-based feasibility study (RASTER). Lancet Oncol 2007; 8:1079-87.

24 Bueno-de-Mesquita JM, Sonke GS, van de Vijver MJ, Linn SC. Additional value and potential use of the 70-gene prognosis signature in node-negative breast cancer in daily clinical practice. Ann Oncol 2011; 22:2021-30. 25

Drukker CA, Bueno-de-Mesquita JM, Retel VP, van Harten WH, van Tinteren H, Wesseling J et al. A prospective evaluation of a breast cancer prognosis signature in the observational RASTER study. Int J Cancer 2013;133:929-936

Voorspelling prognose borstkanker: bijdrage genexpressie-profiel | 31

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39

Gene signature for risk stratification and treatment of brea Incorporating tumor biology in clinical decision-making

Chapter 3

of breast cancer

king

A prospective evaluation of a breast cancer prognosis signature in the observational RASTER study

International Journal of Cancer 2013;133:929-36

Caroline A. Drukker Jolien M. Bueno de Mesquita Valesca P. RetĂ¨l Wim H. van Harten Harm van Tinteren Jelle Wesseling Rudi M.H. Roumen Michael Knauer Laura J. van â&#x20AC;&#x2DC;t Veer Gabe S. Sonke Emiel J.Th. Rutgers Marc J. van de Vijver Sabine C. Linn

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39

Abstract The 70-gene signature (MammaPrint®) has been developed on retrospective series of breast cancer patients to predict the risk of breast cancer distant metastases. The microarRAy-prognoSTicsin-breast-cancER (RASTER) study was the first study designed to prospectively evaluate the performance of the 70-gene signature, which result was available for 427 patients (cT1-3N0M0). Adjuvant systemic treatment decisions were based on the Dutch CBO 2004 guidelines, the 70gene signature, and doctors’ and patients’ preferences. Five-year distant-recurrence-free-interval (DRFI) probabilities were compared between subgroups based on the 70-gene signature and Adjuvant! Online (AOL) (10-year survival probability <90% was defined as high risk). Median follow-up was 61.6 months. Fifteen percent (33/219) of 70-gene signature low risk patients received adjuvant chemotherapy (ACT) versus 81% (169/208) of 70-gene signature high risk patients. The 5-year DRFI probabilities for 70-gene signature low risk (n=219) and high risk (n=208) patients were 97.0% and 91.7%. The 5-year DRFI probabilities for AOL low risk (n=132) and high risk (n=295) patients were 96.7% and 93.4%. For 70-gene signature low risk – AOL high risk patients (n=124), of whom 76% (n=94) had not received ACT, 5-year DRFI was 98.4%. In the AOL high risk group, 32% (94/295) less patients would be eligible to receive ACT if the 70-gene signature was used. In this prospective community-based observational study, the 5-year DRFI probabilities confirmed the additional prognostic value of the 70-gene signature to clinic-pathological risk estimations such as AOL. Omission of adjuvant chemotherapy as judged appropriate by doctors and patients and instigated by a low risk 70-gene signature result, appeared not to compromise outcome.

34 | Chapter 3

Introduction Over the last two decades breast cancer mortality has declined in Western countries. This decline has been ascribed to early detection due to the implementation of population-based mammographic screening programs and the introduction of adjuvant systemic therapy (AST).1 Fifty percent of all breast cancer patients are cured with loco-regional therapy alone, while the other 50% recur in the absence of AST. The combination of adjuvant chemotherapy and adjuvant endocrine therapy halves the breast cancer mortality rate throughout the first 15 years after diagnosis.2 Selection of those patients at high risk of relapse for AST is based on clinico-pathologic factors, such as age, tumor size, nodal status, histological grade, and hormone receptor status. Current guidelines and clinical tools, such as Adjuvant! Online (AOL), use these factors to estimate the risk of recurrence and the benefit of AST. However, when using these standard clinico-pathologic factors, individual risk assessment remains challenging. Many women are treated with chemotherapy, without deriving significant benefit.3 To improve accuracy, several gene expression prognosis classifiers have been developed and validated on historic data to refine risk estimation based on current guidelines.4 One of these is the 70-gene signature (MammaPrint速), for which its accuracy to select the right patient for AST is being compared to the accuracy of AOL in a randomized trial called MINDACT, that completed accrual and primary results are awaited.5 Between 2004 and 2006 the 70-gene signature has been subjected to the first prospective study using a gene-expression prognosis classifier as a risk estimation tool, in addition to clinicopathological factors. The microarRAy prognoSTics in breast cancER (RASTER) study was conducted in 16 community hospitals in the Netherlands.6 The primary aim of this multicenter observational study were to assess the feasibility of implementing the 70-gene signature in a community-based setting and to study the clinical impact of the 70-gene signature test result on AST decision making.6 A secondary aim of the RASTER study was to assess the outcome of patients for whom a gene expression classifier was used to determine the need for adjuvant systemic treatment. Implementation of the 70-gene signature in daily clinical practice appeared feasible. A considerable discrepancy in risk estimations among different clinico-pathologic guidelines and the 70-gene signature was observed.6 The addition of the 70-gene signature test result to standard clinicopathological factors led to a change in AST advice in 19% of patients.6 Here, we report the 5-year follow-up data of the RASTER study.

Patients and methods The RASTER study design, patient eligibility criteria and study logistics have been described elsewhere.6 In short, 812 female patients were enrolled after having given written informed consent. 427 patients were postoperatively eligible and for them a 70-gene signature

5-year follow-up of the RASTER study | 35

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39

(MammaPrint®, Agendia NV) was obtained. All 427 patients were aged 18-61 years old and had a histologically confirmed unilateral, unifocal, primary operable, invasive adenocarcinoma of the breast (cT1-3N0M0). Exclusion criteria were a history of a malignancy (with exception of basal-cell carcinoma or cervical dysplasia) and neoadjuvant systemic treatment. After enrollment of 242 patients, the maximum allowed age was changed to 54 years, because the 70-gene signature had been developed in patients under 55 years of age. At that time, the validation of the prognostic value in patients aged over 55 years was not yet available.7 After enrollment, patients received surgery as their primary treatment. All patients underwent either breast conserving treatment or ablation of the breast. Within one hour after surgery, the tumor samples (stored without any preserving solution) were procured at the Pathology Department of the participating hospitals. To ensure (adapt to) routine clinical practice, the initial histopathology data were used for clinical risk assessment by the treating physician and in the statistical analysis, without central review of paraffin-embedded tumor samples. Details on tumor grading, assessment of hormone receptor status and HER2 status, RNA extraction and microarray analysis are described elsewhere.8, 9 The RASTER study is registered on the International Standard Randomised Controlled Trial Register, number ISRCTN71917916. A summary of the study protocol is outlined online (www.controlled-trials.com/ISRCTN71917916) Established clinical risk classification indexes AST decisions in this study were based on the Dutch Institute of Healthcare Improvement (CBO) guidelines of 2004,10 the 70-gene signature result, and doctors’ and patients’ preferences. The CBO guidelines used between 2004 and 2006 were more restrictive in selecting patients for AST as compared to other guidelines and were primarily based on the assumption that adjuvant chemotherapy is only justified if an absolute survival benefit of more than 5% at ten years can be expected. According to the 2004 CBO guidelines, low clinical risk was defined as age over 35 years, tumor of grade 1 and 30 mm or smaller, grade 2 and 20 mm or smaller, or grade 3 and 10 mm or smaller. Additionally, age less than 36 years with a grade 1 tumor of 10 mm or smaller was also defined as low risk. All other patients were defined as high risk. Notably, in the CBO guidelines, adjuvant endocrine treatment was advised only in clinically high risk patients with hormone-receptor-positive tumors in combination with chemotherapy.10 To study how the addition of the 70-gene signature to a risk prediction tool used today influences clinical practice we used AOL software, version 8.0 to calculate 10-year survival probabilities based on the patient’s age, tumor size, tumor grade, estrogen receptor status, and nodal status.11,12 Patients were assigned to a high clinical risk if their calculated 10-year survival probability was less than 90%.6 In addition, sensitivity analyses were performed for different AOL cutoffs ranging from 85% to 95%, including the cutoff used for the MINDACT trial.5

36 | Chapter 3

Statistical analysis For this analysis, we estimated 5-year distant-recurrence-free interval (DRFI), comprising distant recurrence and death from breast cancer. Overall survival (OS) and distant-disease-free-survival (DDFS) were also calculated.13 Survival curves were constructed using the Kaplan-Meier method and compared using the log-rank test. In case of ordinal variables (age, pT-stage of TNM, histological grade and nodal status) with more than two groups, we tested for trends (using the Cochran-Armitage test). A significant finding was defined as a p-value below 0.05. Analyses were performed using SAS version 9.2 and R version 2.14.0.

Results Follow-up data of all 427 patients who were enrolled in the RASTER study were updated until September 15th 2011. The first patient was enrolled January 22, 2004, the last patient December 18th, 2006. Median follow-up was 61.6 months. Patient characteristics, AST and outcome stratified by 70-gene signature Supplementary Table 1 summarizes the patient characteristics defined by the result of the 70-gene signature as reported by Bueno-de-Mesquita et al.6 70-gene signature high risk patients more often had large, poorly differentiated, estrogen receptor (ER)-negative, progesterone receptor (PR)-negative, and HER2-positive tumors than 70-gene signature low risk patients. Nineteen percent (9/47) of invasive lobular breast cancer patients had a high risk 70-gene signature, while 53% (183/345) of invasive ductal breast cancer patients had a high risk 70-gene signature result. Twelve percent (16/136) of grade 3 tumors were 70-gene signature low risk, while 83% (72/87) of grade 1 tumors were 70-gene signature low risk. After a median follow-up time of 61.6 months, 24 DRFI events and 11 deaths occurred. Nine patients died due to breast cancer. One patient died due to lung cancer and one patient due to cardiac disease (Supplementary Table 2). The 5-year DRFI probabilities for 70-gene signature low risk (n=219) and high risk (n=208) patients were 97.0% and 91.7% (p=0.03), respectively (Supplementary Figure 1). Importantly, this difference in outcome was observed despite the fact that in the 70-gene signature low risk group 15% (33/219) of the patients received adjuvant chemotherapy, versus 81% (169/208) in the high risk group. The administered chemotherapy regimens for low and high risk patients are described in Supplementary Table 1. Patient characteristics, AST and outcome stratified by 70-gene signature and AOL Table 1 shows the patient characteristics stratified by 70-gene signature and AOL risk prediction. Discordant risk estimations between 70-gene signature and AOL occurred in 38% of the cases (161/427). Most discordant cases were 70-gene signature low risk and AOL high risk (124/427=29%), while 37 cases (37/427=9%) had a high risk 70-gene signature result and a low

5-year follow-up of the RASTER study | 37

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39

risk AOL estimation. Figure 1 summarizes the AST received in the different categories defined by 70-gene signature result and AOL. Ninety-three percent (88/95) of patients who were 70gene signature low risk and AOL low risk did not receive any AST (chemotherapy nor endocrine therapy). Fifty-six percent (70/124) of patients who were 70-gene signature low risk and AOL high risk did not receive any AST. In Supplementary Figure 1 Kaplan-Meier plots for DRFI, DDFS and OS are given for the whole group of patients, according to 70-gene signature, and according to AOL risk estimation. The 5-year DRFI probabilities for AOL low risk (n=132) and high risk (n=295) patients were 96.7% and 93.4%, respectively (p=0.24) (Supplementary Figure 1). Table 2 shows DRFI and DDFS probabilities according to the combined risk categories. Table 1. Clinicopathological characteristics of patient groups defined by 70-gene signature (70-GS) and AOL risk estimations Total

Age

<35 36-40 41-45 46-50 51-55 >55 pT (TNM) pT1 (<20mm) pT2 (>20-50mm) pT3 (>50mm) Histological grade Good Intermediate Poor Histological type Ductal Lobular Other Unknown ER status Negative Positive PgR status Negative Positive Unknown HER2 status Negative Positive Unknown

(n = 427)

70-GS low- 70-GS high- 70-GS low- 70-GS highAOL low AOL low AOL high AOL high (n = 95) (n = 37) (n = 124) (n = 171)

26 (6%) 41 (10%) 84 (20%) 141 (33%) 100 (23%) 35 (8%) 301 (70%) 125 (29%) 1 (1%) 87 (20%) 204 (48%) 136 (32%) 345 (81%) 47 (11%) 31 (7%) 4 (1%) 85 (20%) 342 (80%) 133 (31%) 293 (69%) 1 (<1%) 358 (84%) 48 (11%) 21 (5%)

5 (5%) 12 (13%) 19 (20%) 28 (30%) 27 (28%) 4 (4%) 95 (100%) 0 (0%) 0 (0%) 60 (63%) 34 (36%) 1 (1%) 73 (77%) 14 (15%) 7 (7%) 1 (1%) 0 (0%) 95 (100%) 9 (9%) 86 (91%) 0 (0%) 86 (91%) 5 (5%) 4 (4%)

0 (0%) 7 (19%) 14 (38%) 8 (22%) 8 (22%) 0 (0%) 37 (100%) 0 (0%) 0 (0%) 12 (32%) 19 (51%) 6 (16%) 30 (81%) 2 (5%) 5 (13%) 0 (0%) 4 (11%) 33 (89%) 8 (21%) 29 (78%) 0 (0%) 29 (78%) 5 (14%) 3 (8%)

2 (2%) 2 (2%) 18 (14%) 58 (47%) 29 (23%) 15 (12%) 82 (66%) 42 (33%) 0 (0%) 12 (10%) 97 (78%) 15 (12%) 89 (72%) 24 (19%) 9 (7%) 2 (2%) 3 (2%) 121 (98%) 24 (19%) 100 (81%) 0 (0%) 111 (90%) 4 (3%) 9 (7%)

ER=estrogen receptor; PgR=progesterone receptor; HER2=Human Epidermal growth factor Receptor 2

38 | Chapter 3

19 (11%) 20 (12%) 33 (19%) 47 (28%) 36 (21%) 16 (9%) 87 (51%) 83 (48%) 1 (1%) 3 (2%) 54 (32%) 114 (67%) 153 (89%) 7 (4%) 10 (6%) 1 (1%) 78 (46%) 93 (54%) 92 (54%) 78 (46%) 1 (1%) 132 (77%) 34 (20%) 5 (3%)

1XPEHU RI SDWLHQWV

7KHUDS\

&7 HQGR HQGR &7 QR $67

P/2: Ă F/2:

P+,*+ Ă F/2:

P/2: Ă F+,*+

&RPELQDWLRQ

P+,*+ Ă F+,*+

Figure 1. Distribution of patients (n=427) over the four risk categories defined by 70-gene signature and AOL risk estimations and proportion and type of AST received per category mLow = 70-gene signature low; mHigh = 70-gene signature high; cLow = AOL low; cHigh = AOL high; CT=adjuvant chemotherapy; Endo=adjuvant endocrine therapy; AST=adjuvant systemic therapy.

Table 2. Kaplan-Meier risk estimations for DRFI and DDFS according to 70-gene signature and AOL stratification 70-gene signature Low High Low High

AOL Low Low High High

AST 5-year DRFI (%) (95% CI) 5-years DDFS (%) (95% CI) 7/95 (7%) 95.3 (90.9-100) 94.3 (89.5-99.3) 32/37 (86%) 100 (100-100) 94.6 (87.6-100) 54/124 (44%) 98.4 (96.1-100) 97.6 (94.9-100) 166/171 (97%) 89.8 (85.1-94.8) 88.7 (83.8-93.8)

The difference in overall survival outcome between 70-gene signature low risk and AOL low risk is partly due to the two cases who died of non-breast cancer causes (Supplementary Table 2) who were categorized as 70-gene signature low risk and AOL high risk. Sensitivity analyses were performed for different AOL cutoffs ranging from 85% to 95%, showing a shift in the proportion of low risk patients without a substantial effect on DRFI, DDFS or OS survival probabilities (Supplementary Table 3).

5-year follow-up of the RASTER study | 39

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39

Outcome at five years according to AST in patients with a low risk 70-gene signature result Five-year DRFI was 98.4% in patients with 70-gene signature low risk â&#x20AC;&#x201C; AOL high risk (n=124), of which 76% (n=94) had not received adjuvant chemotherapy. The group that had not received adjuvant chemotherapy had a 5-year DRFI of 98.9%. The group that did not receive any systemic therapy (chemotherapy nor endocrine therapy) (n=70) had a 5-year DRFI of 100% (Figure 2a and 2b). No significant difference (p=0.29) was seen between systemically untreated patients with a concordant low risk assessment and patients with a 70-gene signature low risk result even with a high risk assessment by AOL. Table 3 shows the patient characteristics of patients who had a low risk 70-gene signature result and who had received adjuvant endocrine therapy only or no AST at all, split by AOL risk assessment. Table 3. Clinicopathological characteristics of 70-gene signature low risk patients who received no AST or ET only 70-GS low- AOL low No AST No AST or ET only (n=88) (n=92) Age

<35 2 (2%) 36-40 11 (12%) 41-45 19 (22%) 46-50 26 (30%) 51-55 26 (30%) >55 4 (5%) pT (TNM) pT1 (<20mm) 88 (100%) pT2 (>20-50mm) 0 (0%) pT3 (>50mm) 0 (0%) 57 (65%) Histological Good grade Intermediate 30 (34%) Poor 1 (1%) 68 (77%) Histological Ductal type Lobulair 13 (14%) Other 7 (8%) Unknown 0 (0%) ER status Negative 0 (0%) Positive 88 (100%) PgR status Negative 9 (10%) Positive 79 (90%) Unknown 0 (0%) HER2 status Negative 79 (90%) Positive 5 (6%) Unknown 4 (4%)

40 | Chapter 3

3 (3%) 11 (12%) 19 (21%) 28 (30%) 27 (29%) 4 (4%) 92 (100%) 0 (0%) 0 (0%) 60 (65%) 31 (34%) 1 (1%) 72 (78%) 13 (14%) 7 (8%) 0 (0%) 0 (0%) 92 (100%) 9 (10%) 83 (90%) 0 (0%) 83 (90%) 5 (5%) 4 (4%)

70-GS low-AOL high No AST No AST or ET only (n=70) (n=94) 0 (0%) 0 (0%) 8 (11%) 32 (46%) 18 (26%) 12 (17%) 62 (89%) 8 (11%) 0 (0%) 8 (11%) 60 (86%) 2 (3%) 47 (67%) 16 (23%) 6 (9%) 1 (1%) 2 (3%) 68 (97%) 15 (21%) 55 (79%) 0 (0%) 63 (90%) 2 (3%) 5 (7%)

0 (0%) 1 (1%) 8 (9%) 44 (47%) 26 (28%) 15 (16%) 75 (80%) 19 (20%) 0 (0%) 9 (10%) 77 (82%) 8 (9%) 68 (72%) 19 (20%) 6 (6%) 1 (1%) 2 (2%) 92 (98%) 21 (22%) 73 (78%) 1 (1%) 85 (90%) 2 (2%) 7 (7%)

P/2: Ă F+,*+

P/2: Ă F/2:

P Ă P/2: Ă F/2: ? &, Ă

P Ă P/2: Ă F+,*+ ? &, Ă

/RJUDQN S

P Ă P/2: Ă F/2: ? &, Ă

P Ă P/2: Ă F+,*+ ? &, Ă

/RJUDQN S

'5), 3UREDELOLW\

P/2: Ă F/2:

P/2: Ă F+,*+

7LPH LQ PRQWKV

P/2: Ă F/2:

P/2: Ă F+,*+

7LPH LQ PRQWKV

A) 70-gene signature - AOL in pts B) 70-gene signature - AOL in pts without any form of AST without AST or with ET only Figure 2. Five-year outcome of chemotherapy-naĂŻve patients with a low risk 70-gene signature result

Discussion The RASTER study provides the first prospective data on the outcome of patients with breast cancer for whom a gene expression prognosis classifier was used to determine the need for adjuvant systemic treatment. This community-based observational study confirms the potential of the 70-gene signature towards better selection of breast cancer patients who can forego adjuvant chemotherapy without compromising outcome. Use of the 70-gene signature reduced the proportion of high risk patients as classified by AOL by 20% (87/427). In the AOL high risk group, 32% (94/295) less patients would have received ACT if they had been treated according to the 70-gene signature risk estimation. Overall, the 5-year outcome of the whole cohort was favorable, taking into consideration that 39% (168/427) had not received any form of AST. Most importantly, the 5-year DRFI probabilities were excellent for patients who were clinically high risk but had a low risk 70-gene signature, even in the absence of any AST. In addition, there was no difference in DRFI between 70-gene signature low risk patients who were either clinical high or low risk. Patients with a high risk AOL result, but a low risk 70-gene signature result who did not receive any AST (Table 2) more often had ER-positive tumors with less often poor but more often intermediate histological grade than the total group of study patients. This group of patients had a 100% DRFI at five years. One limitation of the comparison between the gene signature and AOL is that the actual treatment decisions in this study were based on the restrictive Dutch guidelines of 2004 and doctorâ&#x20AC;&#x2122;s and patientsâ&#x20AC;&#x2122; preferences. While this reflected clinical practice at the time of the study, equality of

5-year follow-up of the RASTER study | 41

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39

prognosis between groups that did or did not receive chemotherapy can not be guaranteed. Subtle selection mechanisms may therefore have influenced our results. The reduction in the number of patients eligible for AST when using the 70-gene signature can also be explained by the definition of low risk that was used for AOL. The cutoff we used here (â&#x2030;Ľ90% overall survival probability at ten years is defined as low risk), which is also used in the Dutch national guidelines of 2012, classifies a relatively large proportion of patients as high risk. A lower AOL cutoff (â&#x2030;Ľ85%) results in more low risk patients and thus fewer patients who require AST. Despite this lower cutoff, the outcome of patients in the AOL low risk group remained excellent. To our knowledge a cutoff below 90% is thusfar rarely used in clinical practice. Another possible limitation is that AOL risk estimations are based on 10-year outcomes, whereas we report on 5-year outcomes. The prognostic capacity of the 70-gene signature is best at a follow-up time of five years and has less discriminatory power in years 5-10.14 From recent Oxford Overview data it is known that the carry-over effect of adjuvant chemotherapy gradually fades after five years.2,15,16 Therefore, the data in this study can be considered relatively mature for the effect of adjuvant chemotherapy on outcome. The carry-over effect of five year adjuvant endocrine therapy remains present at ten years of follow-up.2,17 Thus, the data presented here is immature regarding the effect of adjuvant endocrine therapy on long term outcome and needs to be reevaluated at 10-years of follow-up. Consequently, only the effect on outcome of the decision to omit adjuvant chemotherapy based on a low risk 70-gene signature can be derived from the current study. Theoretically, the best survival for the entire group of breast cancer patients will be obtained by offering AST to all patients, as long as our prognostic tests are not 100% accurate.18 The mortality rate as a consequence of adjuvant chemotherapy toxicity is in the range of 1%.19 For adjuvant endocrine therapy, this is in the order of 0.3%. Hence, the real question is how many unnecessary deaths we are generally accepting by erroneously foregoing AST based on a false low risk estimation to spare the large majority of breast cancer patients the unnecessary toxicity of adjuvant chemotherapy and consequent deterioration in quality of life based on a true low risk estimation.20 In this study, 7 patients who developed distant metastases were low risk according to the 70-gene signature. Four of these patients were also low risk according to AOL. The other three patients were high risk according to AOL. One of these patients received both chemotherapy and endocrine therapy, one received endocrine therapy only, and one received no treatment at all. However, this AST untreated case developed a distant metastasis after 5 years (at 82 months of follow-up). Since 94 patients who had a 70-gene signature low risk - AOL high risk result did not receive chemotherapy and had a 98.9% (95%CI: 96.9-100%; Figure 2B) 5-year DRFI, one might infer that it costs about one avoidable distant recurrence (1.1%; 95%CI: 0-3.1%) to spare up to 94 patients unnecessary chemotherapy side-effects. When discussing the acceptable numbers-needed-to-treat and numbers-needed-to-harm, any prognostic factor that can improve the equation should be taken into account. The current data confirms that the 70gene signature is such a prognostic factor.

42 | Chapter 3

In conclusion, in a prospective community-based observational study, the 5-year follow-up data confirmed the additional prognostic value of the 70-gene signature to clinico-pathologic factors used in AOL risk estimations. Omission of chemotherapy as judged appropriate by doctors and patients and supported by a low risk 70-gene signature result appeared not to compromise outcome.

Contributors SL, MvdV, WvH and LvtV were responsible for the study design and development of the protocol. WvH ensured financing. This study was financially supported by the Dutch Health Care Insurance Board. The funding source had no role in the study design, data collection, data analysis, data interpretation, in writing the report, or in the decision to submit for publication. JBdM coordinated the study. ER and RR participated in the patient accrual. JBdM, VR, MK and CD took part in data collection. MvdV, JBdM and JW took part in collection and processing of tumor samples. HvT and GS performed the data analysis. CD, GS, ER and SL took part in data interpretation and manuscript writing. All authors were involved in reviewing the report. Conflict of interest The RASTER study was financially supported the Dutch Health Care Insurance Board (CVZ). LvtV and MvdV are named inventors on the patent for the 70-gene signature used in this study. LvtV reports being shareholder in and employed by Agendia NV, the commercial company that markets the 70-gene signature as MammaPrint®. WvH is a non-remunerated, non-stake holding member of the supervisory board of Agendia NV. MK received unrestricted educational grants from Agendia NV. and the Austrian Society of Surgery for his research. LvtV was supported by the Dutch Genomics Initiative ‘Cancer Genomics Centre’. Acknowledgements We are indebted to the women who participated in this study; to the doctors, nurses, and data managers from the participating hospitals in the Netherlands that enrolled patients in the RASTER-study and contributed to the collection of follow-up data. Principal and co-investigators of the RASTER study The following clinicians entered patients and/or participated in the study (between the brackets, the number of accrued patients is mentioned): J. Meijer, J. Klinkenbijl, J. Douma, Alysis Care Group, Arnhem (31); J. Wijsman, D. van der Meer, P. de Wit, O. Loosveld, Amphia Hospital, Breda (4); S. Veltkamp, A. Baan, G. Timmers, K. van der Hoeven, Amstelland Hospital, Amstelveen (66); J. van der Bijl, A.M. Lenssen, I. Snijders, M. Nap, J. Wals, M. Pannebakker, Atrium Medical Center,

5-year follow-up of the RASTER study | 43

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39

Heerlen (13); L. Strobbe, F. van den Wildenberg, R. Berry, B. Dekker, E. Thunnissen, A. Uyterlinde, C. Mandigers, Canisius-Wilhelmina Hospital, Nijmegen (21); J.W. Arends, H. de Vries, A. Hemelsvan der Lans, A. Imholz, Deventer Hospital, Deventer (40); I. Burgmans, C.I. Perre, T. van Dalen, J. van Gorp, D. ten Bokkel Huinink, P. Thunissen, Diakonessenhuis, Utrecht (4); J. Roussel, C. Bernhart, E. Weltevreden, S. Radema, Gelre Hospitals, Apeldoorn (21); R. Roumen, P. Reemst, A. Brands, K. Vercoelen, M. van Beek, W. Dercksen, G. Vreugdenhil, Maxima Medical Centre, Eindhoven/Veldhoven (114); T. van der Sluis, A. Stam, Lotus Sterk, Medisch Spectrum Twente, Enschede (6); M.J. Baas-Vrancken Peeters, H. Oldenburg, I. Eekhout, H. Hauer, J. Schornagel, Netherlands Cancer Institute-Antoni van Leeuwenhoek Hospital, Amsterdam (172); H. van der Mijle, D. de Vries, I. Kruithof, S. Hovenga, Nij Smellinghe Hospital; Drachten (18); B. de Valk, M. de Boer, P.J. Borgstein, A. Walter, Onze Lieve Vrouwe Gasthuis, Amsterdam (16); C. van Krimpen, P.W. de Graaf, C. van de Pol, N. van Holsteijn, A. van Leeuwen, M.M.E.M. Bos, E. Maartense, Reinier de Graaf Group, Delft (124); A. Zeillemaker, G. van Leeuwen, J. Calame, W. Molendijk, G. Jonkers, F. Cluitmans, Rijnland Hospital, Leiderdorp (59); and F. Bellot, G. Heuff, A. Tanka, P. Hoekstra, K. van de Stadt, J. Schrama, Spaarne Hospital, Hoofddorp (103).

44 | Chapter 3

References 1

Esserman LJ, Shieh Y, Rutgers EJ, Knauer M, Retel VP, Mook S, Glas AM, Moore DH, Linn S, van Leeuwen FE, van ‘t Veer. Impact of mammographic screening on the detection of good and poor prognosis breast cancers. Breast Cancer Res Treat 2011; 130: 725-34.

Bedard PL, Cardoso F. Can some patients avoid adjuvant chemotherapy for early-stage breast cancer? Nat Rev Clin Oncol 2011; 8: 272-79.

Ross JS, Hatzis C, Symmans WF, Pusztai L, Hortobagyi GN. Commercialized multigene predictors of clinical outcome for breast cancer. Oncologist 2008; 13: 477-93.

Bogaerts J, Cardoso F, Buyse M, Braga S, Loi S, Harrison JA, Bines J, Mook S, Decker N, Ravdin PM, Therasse P, Rutgers EJ et al. Gene signature evaluation as a prognostic tool: challenges in the design of the MINDACT trial. Nat Clin Pract Oncol 2006; 3: 540-51.

Bueno-de-Mesquita JM, van Harten WH, Retel VP, van ’t Veer LJ, van Dam FS, Karsenberg K, Douma KF, van Tinteren H, Peterse JL, Wesseling J, Wu TS, Atsma D et al. Use of 70-gene signature to predict prognosis of patients with node-negative breast cancer: a prospective community-based feasibility study (RASTER). Lancet Oncol 2007; 8: 1079-87.

Mook S, Schmidt MK, Weigelt B, Kreike B, Eekhout I, van de Vijver MJ, Glas AM, Floore A, Rutgers EJ, van ’t Veer LJ. The 70-gene prognosis signature predicts early metastasis in breast cancer patients between 55 and 70 years of age. Ann Oncol 2010; 21: 717-22.

Glas AM, Floore A, Delahaye LJ, Witteveer AT, Pover RC, Bakx N, Lahti-Domenici JS, Bruinsma TJ, Warmoes MO, Bernards R, Wessels LF, van ‘t Veer LJ. Converting a breast cancer microarray signature into a high-throughput diagnostic test. BMC Genomics 2006; 7: 278.

van de Vijver MJ, He YD, van ‘t Veer LJ, Dai H, Hart AA, Voskuil DW, Schreiber GJ, Peterse JL, Roberts C, Marton MJ, Parrish M, Atsma D et al. A gene-expression signature as a predictor of survival in breast cancer. N Engl J Med 2002; 347: 1999-2009.

Kwaliteitsinstituut voor de Gezondheidszorg CBO VvlK. Adjuvante Systemische Therapie voor het Operabel Mammacarcinoom. Richtlijn Behandeling van het Mammacarcinoom 2004; 46-70.

Olivotto IA, Bajdik CD, Ravdin PM, Speers CH, Coldman AJ, Norris BD, Davis GJ, Chia SK, Gelmon KA. Population-based validation of the prognostic model ADJUVANT! for early breast cancer. J Clin Oncol 2005; 23: 2716-25.

Ravdin PM, Siminoff LA, Davis GJ, Mercer MB, Hewlett J, Gerson N, Parker HL. Computer program to assist in making decisions about adjuvant therapy for women with early breast cancer. J Clin Oncol 2001; 19: 980-91.

Hudis CA, Barlow WE, Costantino JP, Gray RJ, Pritchart KI, Chapman JA, Sparano JA, Hunsberger S, Enos RA, Gelber RD, Zujewski JA. Proposal for standardized definitions for efficacy end points in adjuvant breast cancer trials: the STEEP system. J Clin Oncol 2007; 25: 2127-32.

Bueno-de-Mesquita JM, Linn SC, Keijzer R, Wesseling J, Nuyten DS, van Krimpen C, Meyers C, de Graaf PW, Bos MM, Hart AA, Rutgers EJ, Peterse JL et al. Validation of 70-gene prognosis signature in node-negative breast cancer. Breast Cancer Res Treat 2009; 117: 483-95.

Clarke M, Coates AS, Darby SC, Davies C, Gelber RD, Godwin J, Goldhirsch A, Gray R, Peto R, Pritchard KI, Wood WC. Adjuvant chemotherapy in oestrogen-receptor-poor breast cancer: patientlevel meta-analysis of randomised trials. Lancet 2008; 371: 29-40.

Peto R, Davies C, Godwin J, Pan HC, Clarke M, Cutter D, Darby S, McGale P, Taylor C, Wang YC, Bergh J, Di Leo A et al. Comparisons between different polychemotherapy regimens for early breast cancer: meta-analyses of long-term outcome among 100,000 women in 123 randomised trials. Lancet 2012; 379: 432-44.

5-year follow-up of the RASTER study | 45

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39

Davies C, Godwin J, Gray R, Clarke M, Cutter D, Darby S, McGale P, Pan HC, Taylor C, Wang YC, Dowsett M, Ingle J et al. Relevance of breast cancer hormone receptors and other factors to the efficacy of adjuvant tamoxifen: patient-level meta-analysis of randomised trials. Lancet 2011; 378: 771-84.

Colozza M, de Azambuja E, Cardoso F, Bernard C, Piccart MJ. Breast cancer: achievements in adjuvant systemic therapies in the pre-genomic era. Oncologist 2006; 11: 111-25.

Park BW, Lee S, Lee AR, Lee KH, Hwang SY. Quality of Life Differences between Younger and Older Breast Cancer Patients. J Breast Cancer 2011; 14: 112-18.

46 | Chapter 3

/RZ ULVN

/RZ ULVN +LJK ULVN

+LJK ULVN

P Ã /RZ ULVN ? &, Ã

P Ã +LJK ULVN ? &, Ã

/RJUDQN S

'')6 3UREDELOLW\

P Ã /RZ ULVN ? &, Ã

P Ã +LJK ULVN ? &, Ã

/RJUDQN S

/RZ ULVN +LJK ULVN

DDFS 70-gene signature

+LJK ULVN

P Ã /RZ ULVN ? &, Ã

P Ã +LJK ULVN ? &, Ã

/RJUDQN S

'5), 3UREDELOLW\

P Ã /RZ ULVN ? &, Ã

P Ã +LJK ULVN ? &, Ã

/RJUDQN S

/RZ ULVN +LJK ULVN

7LPH LQ PRQWKV

DRFI AOL

/RZ ULVN

+LJK ULVN

P Ã /RZ ULVN ? &, Ã

P Ã +LJK ULVN ? &, Ã

/RJUDQN S

26 3UREDELOLW\

/RZ ULVN +LJK ULVN

7LPH LQ PRQWKV

DRFI 70-gene signature

P Ã /RZ ULVN ? &, Ã

P Ã +LJK ULVN ? &, Ã

/RJUDQN S

7LPH LQ PRQWKV

/RZ ULVN

/RZ ULVN +LJK ULVN

DDFS AOL

7LPH LQ PRQWKV

/RZ ULVN +LJK ULVN

OS 70-gene signature

/RZ ULVN +LJK ULVN

7LPH LQ PRQWKV

OS AOL

Supplementary Figure 1. Kaplan-Meier plots for DDFS, DRFI and OS by 70-gene signature or AOL

5-year follow-up of the RASTER study | 47

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39

Supplementary Table 1. Clinicopathological characteristics by 70-gene signature result

Age

pT (TNM)

Histological grade

Histological type

ER status PgR status

HER2 status

CBO 2004 Adjuvant! Online Chemotherapy

70-gene signature Low Risk (219) <35 7 (3%) 36-40 14 (6%) 41-45 37 (17%) 46-50 86 (39%) 51-55 56 (26%) >55 19 (9%) pT1 (<20mm) 177 (81%) pT2 (>20-50mm) 42 (19%) pT3 (>50mm) 0 (0%) Good 72 (33%) Intermediate 131 (60%) Poor 16 (7%) Ductal 162 (74%) Lobular 38 (17%) Other 16 (7%) Unknown 3 (1%) Negative 3 (1%) Positive 216 (99%) Negative 33 (15%) Positive 186 (85%) Unknown 0 (0%) Negative 197 (90%) Positive 9 (4%) Unknown 13 (6%) Low Risk 167 (76%) High Risk 52 (24%) Low Risk 95 (43%) High Risk 124 (57%) None 186 (85%) FEC/FAC* 25 (11%) AC** 7 (3,5%) TAC*** 0 (0%) AC-Paclitaxel 1 (0,5%)

70-gene signature High Risk (208) 19 (9%) 27 (13%) 47 (23%) 55 (26%) 44 (21%) 16 (8%) 124 (60%) 83 (40%) 1 (0.5%) 15 (7%) 73 (35%) 120 (58%) 183 (88%) 9 (4%) 15 (7%) 1 (0.5%) 82 (39%) 126 (61%) 100 (48%) 107 (51.5%) 1 (0.5%) 161 (77%) 39 (19%) 8 (4%) 76 (37%) 132 (63%) 37 (18%) 171 (82%) 39 (19%) 108 (52%) 26 (12%) 20 (10%) 15 (7%)

Total 26 (6%) 41 (10%) 84 (20%) 141 (33%) 100 (23%) 35 (8%) 301 (70%) 125 (29%) 1 (0.2%) 87 (20%) 204 (48%) 136 (32%) 345 (81%) 47 (11%) 31 (7%) 4 (1%) 85 (20%) 342 (80%) 133 (31%) 293 (68.6%) 1 (0.2%) 358 (84%) 48 (11%) 21 (5%) 243 (57%) 184 (43%) 132 (31%) 295 (69%) 225 (53%) 133 (31%) 33 (8%) 20 (5%) 16 (4%)

*Chemotherapy regimen consisting of fluorouracil, cyclophosphamide and either adriamycine or epirubicine ** Adriamycine and cyclophosphamide *** Docetaxel, adriamycine and cyclophosphamide

48 | Chapter 3

Supplementary Table 2. Characteristics of patients with one or more events 70-gene signature low risk 4‡ 0 0 0 0 42 (27-46)

70-gene signature high risk 3 0 1 1 1 49 (33-59)

ER+HER2ER+HER2+ ER-HER2ER-HER2+ Median age at diagnosis (range)

6* 1* 0 0 46 (39-57)

5 2# 8 2 50 (34-59)

Breast cancer-specific death ER+HER2ER+HER2+ ER-HER2ER-HER2+ Median age at diagnosis (range)

2 0 0 0 ND (46-51)

2 0 4 1 51 (45-59)

2† 10‡ 2

0 4 7

Type of Event Locoregional event

IHC subtype ER+HER2ER+HER2+ ER-HER2ER-HER2+ ER+HER2 unknown Median age at diagnosis (range)

Distant metastasis event

Death due to other causes Contralateral breast cancerΔ Second primary tumor

IHC = immunohistochemistry ‡ One patient first developed an ipsilateral axillary recurrence, 10 months later followed by a contralateral breast cancer. *Of these 7 70-gene signature low risk cases, 4 cases were also low risk according to AOL. Of the three AOL high risk cases, only one did not receive any form of AST. This case developed a recurrence at 82 months of follow-up. # One case was AOL low risk and 70-gene signature high risk. No AST was administered. She had a recurrence at 5 months of follow-up. †One patient died due to a cardiac cause. This patient had only received adjuvant radiotherapy of the breast. She had no signs of breast cancer recurrence. She had had an ER+HER2- breast cancer at the age of 57. The second patient died of rightsided primary lung cancer, proven by histology and ER-negative immunohistochemistry, two years after the primary diagnosis of invasive lobular breast cancer, ER+HER2-. She had only received adjuvant radiotherapy on the right breast after breast conserving therapy. She had stopped smoking one year before the diagnosis of breast cancer. Δ Out of patients with low risk 70-gene signature, only one had received AST. Of 4 high risk 70-gene signature patients, 3 had received AST. One AML (no adjuvant chemotherapy) and one lung cancer in the 70-gene signature low risk group. Four lung cancers, 2 colorectal cancers and 1 carcinoid in the 70-gene signature high risk group.

5-year follow-up of the RASTER study | 49

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39

Supplementary Table 3. Kaplan-Meier risk estimations for DRFI using different AOL cutoffs AOL cutoff 10-years OS 85% 90% 88% (ER+) / 92% (ER-) 95%

50 | Chapter 3

AOL Low High Low High Low High Low High

Number of patients 253 (59.3%) 174 (40.7%) 132 (30.9%) 295 (69.1%) 194 (45.4%) 233 (54.6%) 19 (4.4%) 408 (95.6%)

5-years DRFI (%) (95% CI) 97 (95.1-99.5) 90 (85.8-94.9) 97 (93.5-100) 93 (90.4-96.4) 98 (88.0-95.0) 92 (96.0-100) 100 (100-100) 94 (91.8-96.6)

5-year follow-up of the RASTER study | 51

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39

Gene signature for risk stratification and treatment of brea Incorporating tumor biology in clinical decision-making

Chapter 4

of breast cancer

king

Optimized outcome prediction in breast cancer by combining the 70-gene signature with clinical risk prediction algorithms

Submitted

Caroline A. Drukker Matthijs V. Nijenhuis Jolien M. Bueno de Mesquita Valesca P. RetĂ¨l Wim H. van Harten Harm van Tinteren Marjanka K. Schmidt Laura J. van â&#x20AC;&#x2DC;t Veer Gabe S. Sonke Emiel J.Th. Rutgers Marc J. van de Vijver Sabine C. Linn

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39

Abstract Background Clinical guidelines for breast cancer treatment differ in their selection of patients at a high risk of recurrence who are eligible to receive adjuvant systemic treatment (AST). The 70-gene signature is a molecular tool to better guide AST decisions. The aim of this study was to evaluate whether adding the 70-gene signature to clinical risk prediction algorithms can optimize outcome prediction and consequently treatment decisions in early stage, node-negative breast cancer patients. Methods A 70-gene signature was available for 427 patients participating in the RASTER study (cT13N0M0). Median follow-up was 61.6 months. Based on 5-year distant-recurrence-free-interval (DRFI) probabilities survival Areas Under the Curve (AUC) were calculated and compared for risk estimations based on the six clinical risk prediction algorithms: Adjuvant! Online (AOL), Nottingham Prognostic Index (NPI), St. Gallen (2003), the Dutch National guidelines (CBO 2004 and NABON 2012) and PREDICT plus. Also, survival AUC were calculated after adding the 70gene signature to these clinical risk estimations. Results Systemically untreated patients with a high clinical risk estimation but a low risk 70-gene signature had an excellent 5-year DRFI varying between 97.1% and 100%, depending on the clinical risk prediction algorithms used in the comparison. The best risk estimation was obtained in this cohort by adding the 70-gene signature to CBO 2012 (AUC: 0.644) and PREDICT (AUC: 0.662). Clinical risk estimations by all clinical risk prediction algorithms improved by adding the 70-gene signature. Conclusion Patients with a low risk 70-gene signature have an excellent survival, independent of their clinical risk estimation. Adding the 70-gene signature to clinical risk prediction algorithms improves risk estimations and therefore might improve the identification of early stage node-negative breast cancer patients for whom AST has limited value. In this cohort, the PREDICT plus tool in combination with the 70-gene signature provided the best risk prediction.

54 | Chapter 4

Introduction For the past decade the selection of early stage breast cancer patients who are at a high risk of recurrence and eligible to receive adjuvant systemic treatment (AST) is based on clinicopathological factors, such as age, tumor size, nodal status, histological grade, and hormone receptor status. Several clinical risk prediction algorithms used in online tools and guidelines, such as Adjuvant! Online (AOL), the Nottingham Prognostic Index (NPI), the St. Gallen expert panel recommendations of 2003 and the Dutch National guidelines of 2004 and 2012 use these factors in specific algorithms for risk estimations and AST recommendations.1-6 A relatively new online tool for outcome prediction in breast cancer patients is PREDICT plus.7 This tool not only uses the clinicopathological factors mentioned above, but also incorporates Human Epidermal growth factor Receptor 2 (HER2) status and method of detection. Both of these factors have proven to be independent prognostic factors in overall and breast cancer specific survival.7,8 Even with the aid of these clinical risk prediction algorithms, individual risk assessment remains challenging. Each of these clinical risk prediction algorithms may define a slightly different group of patients at a low or high risk, which are partly non-overlapping. This indicates that it is unclear which tool or guideline has the highest prognostic accuracy for the individual patient.1,5,6,9 Moreover, online tools such as AOL provide a survival probability without stratification into high versus low risk. The choice for a specific cutoff point in risk clearly influences the concordance with other tools.10 Gene-expression classifiers have been developed and validated on historic data to refine clinical risk estimations and related AST recommendations.11,12 One of these classifiers is the 70-gene signature (MammaPrintÂŽ, Agendia NV, Amsterdam, the Netherlands).13,14 Between 2004 and 2006 the 70-gene signature has been assessed in the first prospective study using a gene-expression classifier as a risk estimation tool in addition to clinicopathological factors to determine the need for AST. A considerable discrepancy in risk estimations among different clinical guidelines and the 70-gene signature was observed.9,15 Recently, the 5-year follow-up data of the RASTER study were reported showing an excellent distant-recurrence-free interval (DRFI) of 97% for patients with a low risk 70-gene signature. Patients with a high risk 70-gene signature showed a DRFI of 92%.16 When compared to AOL, 70-gene signature low â&#x20AC;&#x201C; AOL high risk patients who did not receive any AST showed a DRFI of 100%. This indicates that omission of chemotherapy in these patients may not compromise outcome. Up to the evaluated 5 year median survival the number of events is small and the follow-up time relatively short. However, AOL is not the only risk estimation tool used in clinical practice today. Additionally, the 70-gene signature is more likely to be added to clinical risk prediction algorithms instead of replacing them. Therefore, we evaluated whether adding the 70-gene signature to clinical risk prediction algorithms can improve individual outcome prediction in early stage, node-negative breast cancer patients.

70-gene signature combined with clinical risk estimations | 55

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39

Patients and methods The RASTER study design, patient eligibility criteria and study logistics have been described elsewhere (www.controlled- trials.com/ISRCTN71917916).15 In short, 812 female patients were enrolled in 16 hospitals in the Netherlands. 427 patients were postoperatively eligible and for them a 70-gene signature (MammaPrint®, Agendia NV) was obtained. All patients were between 18-61 years old and had a histologically confirmed unilateral, unifocal, primary operable, invasive adenocarcinoma of the breast (cT1-3N0M0). All patients were primarily surgically treated with either breast conserving surgery or mastectomy. To ensure routine clinical practice, the initial histopathology data were used for clinical risk assessment by the treating physician and in the statistical analysis, without central review of the paraffin-embedded tumor samples. Details on tumor grading, assessment of hormone receptor status and HER2 status, RNA extraction and microarray analysis have been described elsewhere.15 Decisions on whether or not to treat with AST (comprising chemotherapy and/or endocrine therapy) in the RASTER study were based on the Dutch national guidelines of 2004, the 70-gene signature, and doctors’ and patients’ preferences.15 More detailed insight on the follow-up data of this cohort is described elsewhere.16 Clinical risk prediction algorithms Hereafter, risk assessment by use of clinicopathological factors is referred to as ‘clinical risk’. Guidelines used in this study to assess clinical risk were Adjuvant! Online (AOL), Nottingham Prognostic Index (NPI), the St. Gallen expert panel recommendations (2003, current at the time the RASTER study was conducted), the Dutch National guidelines (2004, current at the time the RASTER study was conducted, and 2012) and PREDICT plus. Adjuvant! Online software, version 8.0, calculates the 10-year survival probabilities based on the age of the patient, tumor size, tumor grade, estrogen receptor (ER) status, and nodal status.5,10 Patients were considered high risk if their calculated 10-year survival probability was less than 90%.15 This cutoff was also used in the RASTER study and similar to the cutoff used in the MINDACT trial. The NPI computes a score with the algorithm: 0.2*size (cm) + grade + nodal status. A moderate or high risk was defined as a score greater than 3.4.1,17 The St. Gallen expert panel of 2003 recommended to define low clinical risk as ER-positive or progesterone receptor(PR)-positive disease (or both) and all of the following criteria: tumor size of 2 cm or smaller, grade 1, and age 35 years or over. All other tumors were deemed to be associated with a moderate or high risk of distant metastasis and death.2 The 2004 Dutch National guidelines define high clinical risk for node-negative breast cancer as age 35 years or younger (except for tumors grade 1 of 10 mm or smaller), a tumor of grade 3 and 10 mm or larger, or grade 2 and 20 mm or larger, and every tumor larger than 30 mm. Adjuvant endocrine treatment was advised only in clinically high risk patients with hormone receptor-positive tumors in combination with chemotherapy.10 AST was justified for patients with a 10-year survival probability of less than 80%. The less restrictive Dutch guidelines of 2012

56 | Chapter 4

define high clinical risk for node-negative breast cancer as age under 35 years except for tumors grade 1 of 10 mm or smaller, or age 35 years or older with a tumor of grade 2 or higher and 10-20mm in size, and every tumor larger than 20 mm. According to this 2012 guideline AST was justified for patients with a 10-year survival probability of less than 85%. The online PREDICT plus tool estimates the 5 and 10-year survival probabilities based on the age of the patient, method of detection, tumor size, tumor grade, number of positive nodes, ER and HER2 status.7 We defined a 5-year survival probability of <95%, which is in line with the cutoffs used for Adjuvant! Online. All clinicopathological factors used by the guidelines mentioned above were summarized elsewhere.18 In our analyses, a moderate or high clinical risk was considered an indication for adjuvant systemic treatment. Statistical analysis We estimated 5-year distant-recurrence-free interval (DRFI), comprising distant recurrence and death from breast cancer.19 Survival curves were constructed using the Kaplan-Meier method and compared using the log-rank test. Survival ROC and AUC (c-index) analyses were performed to evaluate the additional value of the 70-gene signature to the clinical guidelines described in this manuscript. An ANOVA test was used to compare the model before and after adding the 70-gene signature. A significant finding was defined as a p-value below 0.05. Analyses were performed using SAS version 9.2 and R version 2.14.0.

Results Patient and tumor characteristics, AST and outcome stratified by 70-gene signature Patient and tumor characteristics were described elsewhere.15 After a median follow-up time of 61.6 months, 24 DRFI events occurred. Eleven patients died of whom nine due to breast cancer. The 5-year DRFI probabilities for 70-gene signature low risk (n=219) and high risk (n=208) patients were 97.0% (95%CI: 94.7-99.4) and 91.7% (95%CI: 87.9-95.7) (p=0.03), respectively (Supplementary Figure 1).16 Additional value of 70-gene signature to clinical risk assessment Adding the 70-gene signature to clinical risk prediction algorithms improved outcome prediction. For most guidelines this was a borderline significant improvement of the c-index (Table 1). The c-index was highest for PREDICT plus (0.627), followed by NPI (0.591) and the Dutch National guidelines of 2004 (0.586). Adding the 70-gene signature improved the model to 0.638 for NPI (p=0.05) and to 0.639 for the Dutch national guidelines of 2004 (p=0.04). The best risk predictions were achieved when using PREDICT plus (0.662) or the Dutch guidelines of 2012 (0.644) in combination with the 70-gene signature. The c-index for AOL was lowest, before (0.532) and after adding the 70-gene signature (0.619). 70-gene signature combined with clinical risk estimations | 57

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39

Table 1. Survival AUC and proportions of low risk for clinicopathological guidelines and in combination with the 70-gene signature

AOL NPI St. Gallen CBO 2004 CBO 2012 PREDICT plus

Low risk guideline

c-index guideline (95% CI)

132 (30.9%) 248 (58.1%) 73 (17.1%) 243 (56.9%) 124 (29.0%) 228 (53.4%)

0.532 (0.416-0.649 0.591 (0.454-0.728) 0.552 (0.465-0.64) 0.586 (0.449-0.724) 0.581 (0.477-0.685) 0.627 (0.538-0.717)

Low risk 70-gene c-index guideline + p-value signature 70-gene signature 219 (51.3%) 219 (51.3%) 219 (51.3%) 219 (51.3%) 219 (51.3%) 219 (51.3%)

0.619 (0.491-0.748) 0.638 (0.524-0.752) 0.631 (0.52-0.742) 0.639 (0.512-0.765) 0.644 (0.502-0.786) 0.662 (0.537-0.786)

0.03 0.05 0.05 0.04 0.05 0.27

Blue = proportion of low risk increased with the 70-gene signature Orange = proportion of low risk decreased with the 70-gene signature

Discordance between clinical risk assessment and the 70-gene signature Discordant risk estimations occurred in 37% of the cases (161/427) for AOL, 27% for NPI (117/427), 39% for St. Gallen (168/427), 30% for the Dutch National guidelines of 2004 (128/427), 39% for the guidelines of 2012 (167/427) and 25% for PREDICT plus (107/427)(Table 2; Figure 1). Most discordant cases were 70-gene signature low risk and clinically high risk; 29% for AOL (124/427), 10% for NPI (44/427), 37% for St. Gallen (157/427), 12% for the Dutch National guidelines of 2004 (52/427), 31% for the guidelines of 2012 (131/427) and 11% for PREDICT plus at 5 years (49/427). Table 2 summarizes the AST given in the different categories stratified by 70-gene signature and clinical risk. When the 70-gene signature was used, 20% less patients would be eligible to receive ACT compared to AOL, 34% less compared to St. Gallen, 6% less compared to the Dutch guidelines of 2004 and 22% less compared to the guidelines of 2012. The 70-gene signature identifies 7% more patients eligible to receive ACT compared to NPI and 2% more compared to PREDICT plus.

58 | Chapter 4

Figure 1. Risk estimations per case stratified by clinical risk prediction algorithms and the 70-gene signature. Cases were ordered according to their 70-gene signature. 70-gene signature combined with clinical risk estimations | 59

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39

Table 2. Distribution of patients (n=427) over the four risk categories defined by 70-gene signature and clinical risk and proportion and type of AST received per category 70-gene signature AOL No AST Low Low 88/95 (93%) High Low 5/37 (14%) Low High 70/124 (56%) High High 5/171 (3%) 70-gene signature NPI Low Low 153/175 (87%) High Low 7/73 (10%) Low High 5/44 (11%) High High 3/135 (2%) 70-gene signature St. Gallen Low Low 59/62 (95%) High Low 2/11 (18%) Low High 99/157 (63%) High High 8/196 (4%) 70-gene signature CBO 2004 Low Low 152/167 (91%) High Low 8/76 (11%) Low High 6/52 (12%) High High 2/132 (2%) 70-gene signature CBO 2012 Low Low 83/88 (94%) High Low 4/36 (11%) Low High 75/131 (57%) High High 6/172 (3%) 70-gene signature PREDICT plus Low Low 141/170 (83%) High Low 3/58 (5%) Low High 17/49 (35%) High High 7/150 (5%)

CT 0/95 (0%) 3/37 (8%) 1/124 (1%) 73/171 (43%)

ET 4/95 (4%) 11/37 (30%) 24/124 (19%) 18/171 (11%)

ET+CT 3/95 (3%) 18/37 (49%) 29/124 (23%) 75/171 (44%)

0/175 (0%) 7/73 (10%) 1/44 (2%) 69/135 (51%)

14/175 (8%) 23/73 (32%) 14/44 (32%) 6/135 (4%)

8/175 (5%) 36/73 (49%) 24/44 (55%) 57/135 (42%)

0/62 (0%) 0/11 (0%) 1/157 (1%) 76/196 (39%)

3/62 (5%) 5/11 (45%) 25/157 (16%) 23/196 (12%)

0/62 (0%) 4/11 (36%) 32/157 (20%) 89/196 (45%)

0/167 (0%) 10/76 (13%) 1/52 (2%) 66/132 (50%)

13/167 (8%) 25/76 (33%) 15/52 (29%) 4/132 (3%)

2/167 (1%) 33/76 (43%) 30/52 (58%) 60/132 (45%)

0/88 (0%) 6/36 (17%) 1/131 (1%) 70/172 (41%)

5/88 (6%) 14/36 (39%) 23/131 (18%) 15/172 (9%)

0/88 (0%) 12/36 (33%) 32/131 (24%) 81/172 (47%)

0/170 (0%) 1/58 (2%) 1/49 (2%) 75/150 (50%)

16/170 (9%) 22/58 (38%) 12/49 (25%) 7/150 (5%)

13/170 (8%) 32/58 (55%) 19/49 (39%) 61/150 (41%)

AST=adjuvant systemic therapy; CT=adjuvant chemotherapy; ET=adjuvant endocrine therapy; CBO=Dutch National guidelines

The 5-year DRFI probabilities for AOL low risk (n=132) and high risk (n=295) patients were 96.7% (95%CI: 93.5-100) and 93.4% (95%CI: 90.4-96.4), respectively (p=0.24). For NPI low risk (n= 248) and high risk (n=179) patients the 5-year DRFI probabilities were 96.7% (95%CI: 94.2-99.2) and 91.3% (95%CI: 87.2-95.6) (p=0.03). The St. Gallen low risk (n=73) and high risk (n=353) patients showed 5-year DRFI probabilities of 98.5% (95%CI: 95.7-100) and 93.5% (95%CI: 90.9-96.3)(p=0.08). For the Dutch National guidelines of 2004 low risk (n=243) and high risk (n=184) patients the 5-year DRFI probabilities were 96.6% (95%CI: 94.2-99.2) and 91.5% (95%CI: 87.4-95.7), respectively (p=0.11), while for the Dutch National guidelines of 2012 low risk (n=124) and high risk (n=303) patients the 5-year DRFI probabilities were 99.2% (95%CI: 97.6-100) and 92.4% (95%CI: 89.3-95.6)(p=0.02). The 5-year prediction of PREDICT plus low risk (n=228) and high risk (n=199) patients showed DRFI probabilities of 96.8% (95%CI: 94.299.4) and 91.7% (95%CI: 87.9-95.7), respectively (p=0.004)(Figure 2). Table 3 summarizes DRFI probabilities according to the combined risk categories. 60 | Chapter 4

P/2: Ă F+,*+

P/2: Ă F/2:

P Ă P/2: Ă F/2: ? &, Ă

P Ă P/2: Ă F+,*+ ? &, Ă

/RJUDQN S

'5), 3UREDELOLW\

P Ă P/2: Ă F/2: ? &, Ă

P Ă P/2: Ă F+,*+ ? &, Ă

/RJUDQN S

P/2: Ă F/2:

P/2: Ă F+,*+

7LPH LQ PRQWKV

70-gene signature â&#x20AC;&#x201C; AOL

70-gene signature â&#x20AC;&#x201C; NPI

P/2: Ă F/2:

P Ă P/2: Ă F/2: ? &, Ă

P Ă P/2: Ă F+,*+ ? &, Ă

/RJUDQN S

'5), 3UREDELOLW\

P/2: Ă F+,*+

P/2: Ă F/2:

P Ă P/2: Ă F/2: ? &, Ă

P Ă P/2: Ă F+,*+ ? &, Ă

/RJUDQN S

P/2: Ă F/2:

P/2: Ă F+,*+

7LPH LQ PRQWKV

P/2: Ă F/2:

7LPH LQ PRQWKV

70-gene signature â&#x20AC;&#x201C; St. Gallen

70-gene signature â&#x20AC;&#x201C; CBO 2004 '5), 1R $67 3UHGLFW ? P/2:ĂS /2:

P/2: Ă F/2:

P/2:ĂS +,*+

P/2: Ă F+,*+

P Ă P/2: Ă F/2: ? &, Ă

P Ă P/2: Ă F+,*+ ? &, Ă

/RJUDQN S

P Ă P/2:ĂS /2: ? &, Ă

P Ă P/2:ĂS +,*+ ? &, Ă

/RJUDQN S

'5), 3UREDELOLW\

P/2: Ă F/2:

P/2: Ă F+,*+

7LPH LQ PRQWKV

70-gene signature â&#x20AC;&#x201C; CBO 2012

P/2:ĂS /2:

P/2:ĂS +,*+

7LPH LQ PRQWKV

70-gene signature â&#x20AC;&#x201C; PREDICT plus

Figure 2. Five-year outcome of systemic therapy-naĂŻve patients with a low risk 70-gene signature AOL=Adjuvant! Online, NPI=Nottingham Prognostic Index; CBO=Dutch National guidelines

70-gene signature combined with clinical risk estimations | 61

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39

Table 3. Kaplan-Meier risk estimations for DRFI and DDFS according to 70-gene signature and clinical risk stratification 70-gene signature Low High Low High 70-gene signature Low High Low High 70-gene signature Low High Low High 70-gene signature Low High Low High 70-gene signature Low High Low High 70-gene signature Low High Low High

AOL Low Low High High NPI Low Low High High St. Gallen Low Low High High CBO 2004 Low Low High High CBO 2012 Low Low High High PREDICT plus Low Low High High

ACT 3/95 (3%) 21/37 (57%) 30/124 (24%) 148/171 (87%)

5-year DRFI (%) (95% CI) 95.3 (90.9-100) 100 (100-100) 98.4 (96.1-100) 89.8 (85.1-94.8)

8/175 (5%) 43/73 (59%) 25/44 (57%) 126/135 (93%)

97.4 (95.0-100) 95.3 (90.1-100) 95.5 (89.5-100) 89.9 (84.9-95.3)

0/62 (0%) 4/11 (36%) 33/157 (21%) 165/196 (84%)

98.3 (95.0-100) 100 (100-100) 96.5 (93.5-99.6) 91.2 (87.1-95.5)

2/167(1%) 43/76 (57%) 31/52 (60%) 126/132 (95%)

97.3 (94.8-100) 95.5 (90.6-100) 96.2 (91.1-100) 89.7 (84.5-95.2)

0/88 (0%) 18/36 (50%) 33/131 (25%) 151/172 (88%)

98.8 (96.5-100) 100 (100-100) 95.8 (92.3-99.5) 89.8 (85.2-94.8)

13/170 (8%) 33/58 (57%) 20/49 (41%) 136/150 (91%)

98.0 (95.7-100) 93.9 (87.5-100) 93.9 (87.4-100) 91.0 (86.5-95.8)

ACT = Adjuvant Chemotherapy; DRFI= Distant Recurrence Free Interval; DDFS= Distant Disease Free Survival; CBO=Dutch National guidelines

Subgroup analyses of therapy-na誰ve patients Of the patients who had a low risk 70-gene signature 85% did not receive adjuvant chemotherapy. Only 27% of the 70-gene signature low risk patients received adjuvant endocrine therapy. Among the low risk systemically untreated patients, no significant difference was seen for most clinical risk algorithms (p=0.29 for AOL, p=0.66 for NPI, p=0.37 for St. Gallen, p=0.65 for the 2004 and p=0.14 for the 2012 Dutch National guidelines) between patients with a concordant low risk assessment and patients with a 70-gene signature low risk result but a high risk assessment by one or more of the clinical indexes (Figure 1). Only the PREDICT plus tool shows that patients with a concordant low risk assessment (n=141) at 5 years have a significantly better DRFI survival probability compared to patients with a low risk 70-gene signature and a high risk according to PREDICT plus (n=17)(p=0.002).

62 | Chapter 4

Discussion The RASTER study was the first study to prospectively evaluate the outcome of patients for whom the 70-gene signature was used for risk estimations and AST recommendations. The recently published 5-year follow-up data of this study provide the opportunity to evaluate the additional value of a gene-expression classifier to risk estimations based on clinicopathological factors incorporated in clinical tools and guidelines. Of all clinical risk prediction algorithms used in this study, the online PREDICT plus tool provided the best risk estimation. Addition of the 70-gene signature to either the PREDICT plus tool or the Dutch National guidelines of 2012 resulted in the best risk estimations in this cohort. Interestingly, AOL showed the lowest c-index before and after adding the 70-gene signature. This might be explained by the fact that this guideline does not incorporate HER2 status, while the Dutch guidelines of 2012 and PREDICT plus do take this clinicopathological factor into account. In addition, as AOL does not provide a classification into high versus low risk, the choice for a specific cutoff point may influence these results. Previous analyses already showed that method of detection is an independent prognostic factor in breast cancer specific and overall survival. The fact that the PREDICT plus tool takes the method of detection into account may explain why this risk prediction algorithm performs so well in this cohort. When solely using the 70-gene signature, the number of patients at high risk of recurrence who are eligible for adjuvant chemotherapy would be reduced by 20% compared to AOL. As a similar comparison was made in the MINDACT trial (AOL in MINDACT does include HER2), one can hypothesize that a similar reduction in chemotherapy will be seen in this large, randomized controlled phase 3 trial. Analyses of the first 800 patients included in the MINDACT trial show a similar possible reduction in adjuvant chemotherapy of 18% (141/800). Overall, the 5-year outcome of this cohort of patients for whom the 70-gene signature result was prospectively used to guide AST decisions was favorable. One should take into consideration that a substantial proportion of patients, 39% (168/427) of this cohort, did not receive any form of AST. Most importantly, the 5-year DRFI probabilities were excellent for patients who were clinically at high risk but had a low risk 70-gene signature, even in the absence of any AST.16 Therefore, omission of chemotherapy in patients with a low risk 70-gene signature appeared safe, even in case of a high risk estimation by one or more of the clinical guidelines. A larger number of patients in the untreated subgroups and longer follow-up is needed to draw firm conclusions. The only tool that was able to select patients at a slightly higher risk of recurrence among the 70-gene signature low risk patients was the PREDICT plus tool. However, in this subgroup the number of patients (n=17) was too low to draw any firm conclusions. A larger cohort is necessary to evaluate the additional prognostic value of the 70-gene signature to PREDICT plus tool. An advantage, but also a limitation of this study is that the actual treatment decisions were based on the Dutch guidelines of 2004, the 70-gene signature result and preferences of doctors and patients. The study design provides an optimal reflection of daily clinical practice, but subtle selection mechanisms may

70-gene signature combined with clinical risk estimations | 63

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39

be present and may have influenced our results. Another possible limitation is that all clinical tools and guidelines included in our analyses use slightly different definitions of high and low risk. These differences create an additional group of patients for whom the guidelines provide discordant risk estimations. Also, some guidelines base their risk assessment on 5-year survival probabilities, while others on 10-year survival probabilities. In our analyses we were unable to adjust for these differences, which makes a head-to-head comparison more difficult to interpret. Still, the guidelines as used in this study reflect the way they are used in current daily clinical practice. The c-indexes reported here leave room for improvement and this again underlines the need for more accurate personalized breast cancer care. Also, it should be kept in mind that the results of this study are based on a case mix of relatively young (<61 years) breast cancer patients. Finally, central pathology revision might have changed the results, since an earlier report showed that for 8% of the patients AOL risk estimations would change based on revised pathology.20 In conclusion, our results indicate that adding the 70-gene signature clinical guidelines with the 70-gene signature improves risk estimations and therefore may help to identify early stage node-negative breast cancer patients for whom limited adjuvant systemic therapy might be appropriate and for whom overtreatment can be avoided. In this cohort, PREDICT plus appeared to be a promising tool to identify patients for whom limited adjuvant systemic therapy in case of early stage node-negative disease might be appropriate.

Contributors SL, MvdV, WvH and LvtV were responsible for the RASTER study design and development of the protocol. JBdM coordinated the RASTER study. JBdM, VR, CD and MN took part in data collection. CD and HvT performed the data analysis. CD, HvT, ER, MKS and SL took part in data interpretation and manuscript writing. All authors were involved in reviewing the manuscript. Conflict of Interest The RASTER study was financially supported the Dutch Health Care Insurance Board (CVZ). LvtV and MvdV are named inventors on the patent for the 70-gene signature used in this study. LvtV reports being shareholder in and part-time employed by Agendia NV, the commercial company that markets the 70-gene signature as MammaPrintÂŽ. LvtV was supported by the Dutch Genomics Initiative â&#x20AC;&#x2DC;Cancer Genomics Centreâ&#x20AC;&#x2122;. Acknowledgements We are indebted to the women who participated in the RASTER study; to the doctors, nurses, and data managers from the participating hospitals in the Netherlands that enrolled patients in the RASTER-study and contributed to the collection of follow-up data.

64 | Chapter 4

References 1 D’Eredita’ G, Giardina C, Martellotta M, Natale T, Ferrarese F (2001) Prognostic factors in breast cancer: the predictive value of the Nottingham Prognostic Index in patients with a long-term followup that were treated in a single institution. Eur J Cancer 37: 591-596. 2 Goldhirsch A, Glick JH, Gelber RD, Coates AS, Senn HJ (2001) Meeting highlights: International Consensus Panel on the Treatment of Primary Breast Cancer. Seventh International Conference on Adjuvant Therapy of Primary Breast Cancer. J Clin Oncol 19: 3817-3827. 3 Integraal Kankercentrum Nederland: NABON richtlijn mammacarcinoom 2012. 4 Kwaliteitsinstituut voor de Gezondheidszorg CBO VvlK: Adjuvante Systemische Therapie voor het Operabel Mammacarcinoom. Richtlijn Behandeling van het Mammacarcinoom. 2004; 46-70 5 Olivotto IA, Bajdik CD, Ravdin PM, Speers CH, Coldman AJ, Norris BD, Davis GJ, Chia SK, Gelmon KA (2005) Population-based validation of the prognostic model ADJUVANT! for early breast cancer. J Clin Oncol 23: 2716-2725. 6 Goldhirsch A, Wood WC, Gelber RD, Coates AS, Thurlimann B, Senn HJ (2003) Meeting highlights: updated international expert consensus on the primary therapy of early breast cancer. J Clin Oncol 21: 3357-3365. 7 Wishart GC, Bajdik CD, Dicks E, Provenzano E, Schmidt MK, Sherman M, Greenberg DC, Green AR, Gelmon KA, Kosma VM, Olson JE, Beckmann MW, Winqvist R, Cross SS, Severi G, Huntsman D, Pylkas K, Ellis I, Nielsen TO, Giles G, Blomqvist C, Fasching PA, Couch FJ, Rakha E, Foulkes WD, Blows FM, Begin LR, van ‘t Veer LJ, Southey M, Nevanlinna H, Mannermaa A, Cox A, Cheang M, Baglietto L, Caldas C, Garcia-Closas M, Pharoah PD (2012) PREDICT Plus: development and validation of a prognostic model for early breast cancer that includes HER2. Br J Cancer 107: 800-807. 8 Mook S, van ‘t Veer LJ, Rutgers EJ, Ravdin PM, van de Velde AO, van Leeuwen FE, Visser O, Schmidt MK (2011) Independent prognostic value of screen detection in invasive breast cancer. J Natl Cancer Inst 103: 585-597. 9 Bueno-de-Mesquita JM, Sonke GS, van de Vijver MJ, Linn SC (2011) Additional value and potential use of the 70-gene prognosis signature in node-negative breast cancer in daily clinical practice. Ann Oncol 22: 2021-2030. 10 Ravdin PM, Siminoff LA, Davis GJ, Mercer MB, Hewlett J, Gerson N, Parker HL (2001) Computer program to assist in making decisions about adjuvant therapy for women with early breast cancer. J Clin Oncol 19: 980-991. 11 Buyse M, Loi S, van ‘t Veer L, Viale G, Delorenzi M, Glas AM, d’Assignies MS, Bergh J, Lidereau R, Ellis P, Harris A, Bogaerts J, Therasse P, Floore A, Amakrane M, Piette F, Rutgers E, Sotiriou C, Cardoso F, Piccart MJ (2006) Validation and clinical utility of a 70-gene prognostic signature for women with node-negative breast cancer. J Natl Cancer Inst 98: 1183-1192. 12 Ross JS, Hatzis C, Symmans WF, Pusztai L, Hortobagyi GN (2008) Commercialized multigene predictors of clinical outcome for breast cancer. Oncologist 13: 477-493. 13 van ‘t Veer LJ, Dai H, van de Vijver MJ, He YD, Hart AA, Mao M, Peterse HL, van der Kooy K, Marton MJ, Witteveen AT, Schreiber GJ, Kerkhoven RM, Roberts C, Linsley PS, Bernards R, Friend SH (2002) Gene expression profiling predicts clinical outcome of breast cancer. Nature 415: 530-536. 14 van de Vijver MJ, He YD, van ‘t Veer LJ, Dai H, Hart AA, Voskuil DW, Schreiber GJ, Peterse JL, Roberts C, Marton MJ, Parrish M, Atsma D, Witteveen A, Glas A, Delahaye L, van der Velde T, Bartelink H, Rodenhuis S, Rutgers ET, Friend SH, Bernards R (2002) A gene-expression signature as a predictor of survival in breast cancer. N Engl J Med 347: 1999-2009. 15 Bueno-de-Mesquita JM, van Harten WH, Retel VP, van ‘t Veer LJ, van Dam FS, Karsenberg K, Douma KF, van Tinteren H, Peterse JL, Wesseling J, Wu TS, Atsma D, Rutgers EJ, Brink G, Floore AN, Glas AM, Roumen RM, Bellot FE, van Krimpen C, Rodenhuis S, van de Vijver MJ, Linn SC (2007) Use of 70-gene signature to predict prognosis of patients with node-negative breast cancer: a prospective community-based feasibility study (RASTER). Lancet Oncol 8: 1079-1087. 70-gene signature combined with clinical risk estimations | 65

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39

Drukker CA, Bueno-de-Mesquita JM, Retel VP, van Harten WH, van Tinteren H, Wesseling J, Roumen RM, Knauer M, â&#x20AC;&#x2DC;t Veer LJ, Sonke GS, Rutgers EJ, van de Vijver MJ, Linn SC (2013) A prospective evaluation of a breast cancer prognosis signature in the observational RASTER study. Int J Cancer 133: 929-936.

17 Todd JH, Dowle C, Williams MR, Elston CW, Ellis IO, Hinton CP, Blamey RW, Haybittle JL (1987) Confirmation of a prognostic index in primary breast cancer. Br J Cancer 56: 489-492. 18 Drukker CA, van den Hout HC, Sonke GS, Brain E, Bonnefoi H, Cardoso F, Goldhirsch A, Harbeck N, Honkoop A.H., Koornstra RH, van Laarhoven H.W.M., Portielje J.E.A., Schneeweiss A, Smorenburg C.H., Stouthard J., Linn SC, Schmidt MK: Risk estimations and treatment decisions in early stage breast cancer; agreement among oncologists and the impact of the 70-gene signature. (accepted EJC jan 2014) 19 Hudis CA, Barlow WE, Costantino JP, Gray RJ, Pritchard KI, Chapman JA, Sparano JA, Hunsberger S, Enos RA, Gelber RD, Zujewski JA (2007) Proposal for standardized definitions for efficacy end points in adjuvant breast cancer trials: the STEEP system. J Clin Oncol 25: 2127-2132. 20 Bueno-de-Mesquita JM, Nuyten DS, Wesseling J, van Tinteren H, Linn SC, van de Vijver MJ (2010) The impact of inter-observer variation in pathological assessment of node-negative breast cancer on clinical risk assessment and patient selection for adjuvant systemic treatment. Ann Oncol 21: 40-47.

66 | Chapter 4

/RZ ULVN

+LJK ULVN

'5), 3UREDELOLW\

P Ã /RZ ULVN ? &, Ã

P Ã +LJK ULVN ? &, Ã

/RJUDQN S

P Ã /RZ ULVN ? &, Ã

P Ã +LJK ULVN ? &, Ã

/RJUDQN S

/RZ ULVN +LJK ULVN

7LPH LQ PRQWKV

Adjuvant! Online

/RZ ULVN

+LJK ULVN

'5), 3UREDELOLW\

P Ã /RZ ULVN ? &, Ã

P Ã +LJK ULVN ? &, Ã

/RJUDQN S

P Ã /RZ ULVN ? &, Ã

P Ã +LJK ULVN ? &, Ã

/RJUDQN S

/RZ ULVN +LJK ULVN

Nottingham Prognostic Index

/RZ ULVN

+LJK ULVN

'5), 3UREDELOLW\

st. Gallen 2003

/RZ ULVN

/RZ ULVN +LJK ULVN

7LPH LQ PRQWKV

P Ã /RZ ULVN ? &, Ã

P Ã +LJK ULVN ? &, Ã

/RJUDQN S

7LPH LQ PRQWKV

70-gene signature

/RZ ULVN +LJK ULVN

P Ã /RZ ULVN ? &, Ã

P Ã +LJK ULVN ? &, Ã

/RJUDQN S

/RZ ULVN +LJK ULVN

7LPH LQ PRQWKV

/RZ ULVN +LJK ULVN

7LPH LQ PRQWKV

'5), S ? CBO 2004

CBO 2012

/RZ ULVN

+LJK ULVN

'5), 3UREDELOLW\

P Ã /RZ ULVN ? &, Ã

P Ã +LJK ULVN ? &, Ã

/RJUDQN S

/RZ ULVN

+LJK ULVN

7LPH LQ PRQWKV

PREDICT 5 year

Supplementary Figure 1. Five-year outcome of RASTER patients stratified by 70-gene signature and clinical risk prediction algorithms. 70-gene signature combined with clinical risk estimations | 67

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39

Gene signature for risk stratification and treatment of brea Incorporating tumor biology in clinical decision-making

Chapter 5

of breast cancer

king

Risk estimations and treatment decisions in early stage breast cancer: agreement among oncologists and the impact of the 70-gene signature

Accepted by European Journal of Cancer

Caroline A. Drukker Ella H.C. van den Hout Gabe S. Sonke Etienne Brain HervĂŠ Bonnefoi Fatima Cardoso Aaron Goldhirsch Nadia Harbeck Aafke H. Honkoop Rutger H.T. Koornstra Hanneke W.M. Laarhoven Johanna E.A. Portielje Andreas Schneeweiss Carolien H. Smorenburg Jacqueline Stouthard Sabine C. Linn* Marjanka K. Schmidt* *authors contributed equallly

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39

Abstract Background Clinical decision-making in patients with early stage breast cancer requires adequate risk estimation by medical oncologists. This survey evaluates the agreement among oncologists on risk estimations and adjuvant systemic treatment (AST) decisions and the impact of adding the 70-gene signature to known clinicopathological factors. Methods Twelve medical oncologists assessed 37 breast cancer cases (cT1-3N0M0) and estimated their risk of recurrence (high or low) and gave a recommendation for AST. Cases were presented in two written questionnaires sent four weeks apart. Only the second questionnaire included the 70-gene signature result. Results The level of agreement among oncologists in risk estimation (κ=0.57) and AST-recommendation (κ=0.57) was ‘moderate’ in the first questionnaire. Adding the 70-gene signature result significantly increased the agreement in risk estimation to ‘substantial’ (κ=0.61), while agreement in AST recommendations remained ‘moderate’ (κ=0.56). Overall, the proportion of high risk was reduced with 7.4% (range: 6.9-22.9%; p<0.001) and the proportion of chemotherapy that was recommended was reduced with 12.2% (range: 5.4-29.5%; p<0.001). Conclusion Oncologists’ risk estimations and AST recommendations vary greatly. Even though the number of participating oncologists is low, our results underline the need for a better standardization tool in clinical decision-making, in which integration of the 70-gene signature may be helpful in certain subgroups to provide patients with individualized, but standardized treatment.

70 | Chapter 5

Introduction Clinicopathological guidelines are used to guide adjuvant systemic treatment (AST) decisions in early stage breast cancer patients. These guidelines combine clinicopathological factors such as age, tumor size, grade, hormone-receptor status, and nodal status to estimate the risk of recurrence and provide an AST advice. Commonly used clinicopathological guidelines are Adjuvant! Online (AOL), the Sankt Gallen expert panel recommendations and the Nottingham Prognostic Index (NPI).1,2 In the Netherlands, the Dutch Institute of Healthcare Improvement (CBO) guidelines are used most often.3 Nevertheless, correctly estimating whether an individual patient has a high risk of recurrence and is likely to benefit from AST remains challenging.4 Most of the guidelines consider only a small proportion of patients at a low risk of recurrence. This may result in a substantial number of patients being treated with AST while they are unlikely to derive significant benefit.5 Each guideline mentioned above defines a partly non-overlapping group of patients at a low or high risk, which indicates that predictive accuracy for the individual patient is not high.1,6-8 Also, online tools such as AOL that provide a survival probability instead of a low/high risk estimation can be used with different cutoffs. Therefore, a variation in risk estimations made by oncologists who are guided by different guidelines is expected. The extent of this variation remains unclear. To refine risk estimations and provide a more tailored AST recommendation for the individual patient, gene expression prognosis classifiers have been developed.9 One of these gene expression classifiers is the 70-gene signature (MammaPrintÂŽ, Agendia NV, Amsterdam, the Netherlands).10 The first prospective study, in which the 70-gene signature was used in addition to clinical guidelines, was conducted in the Netherlands between 2004 and 2006. This microarRAy prognoSTics in breast cancER (RASTER) study showed discordance in risk estimation between the 70-gene signature and clinicopathological guidelines in one third of the patients.11 In daily clinical practice, medical oncologists are using the 70-gene signature the same way as it was used in the RASTER study, i.e. in addition to clinicopathological guidelines.1,11 However, the impact of the 70-gene signature on risk estimations and AST decisions in daily clinical practice is unknown. The aim of this survey was to determine the agreement among oncologistsâ&#x20AC;&#x2122; risk estimations and AST recommendations based on clinicopathological factors as are used in clinical guidelines, and to assess the impact of the 70-gene signature.

Methods Two written questionnaires were developed (CAD, SCL, HCvdH, MKS) and reviewed by an independent oncologist (GSS). Thirty-seven cases of breast cancer patients were presented to 29 medical oncologists specialised in breast cancer in Europe. The oncologists were chosen because

Agreement among oncologists | 71

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39

of their area of expertise and the country they work in. We included oncologists from all over Europe to not only demonstrate the situation among oncologists in one country, but for an entire continent. The oncologists were asked to indicate their use of clinical guidelines and to give their risk estimation (high/low) and recommendation of AST (none, endocrine therapy, chemotherapy, trastuzumab or a combination) for each case. Several weeks later, the same cases were presented in a randomly changed order in a second questionnaire. In this second questionnaire, the 70-gene signature result was provided along with clinical characteristics. Cases To provide a reflection of true clinical practice, thirty-seven cases of breast cancer patients were selected from the database of the RASTER study, with a 70-gene signature result. All cases involved women < 61 years, with unilateral, histological proven, operable breast cancer (cT13N0M0). Of each patient tumor size, histopathological grade, histological type, mitotic index, hormone-receptor status and Human Epidermal growth factor Receptor 2 (HER2) status were described (Supplementary Table 1). The actually received treatments were not mentioned in the questionnaire. Clinical risk estimation based on Adjuvant! Online Hereafter, risk estimations using clinicopathological factors will be referred to as ‘clinical risk’. In this survey, the clinical risk estimation was first assessed using AOL version 8.0. Patients were assigned to a high clinical risk if their AOL 10-year survival probability was less than 90% based on ‘minor problems’ regarding overall health status, which is the default item of the online program.11 Of the 37 cases, 10 cases were concordant high, 12 concordant low and, 15 discordant with the 70-gene signature result. The cases are a random selection from stratification of concordant low risk, discordant and concordant high risk with the 70-gene signature result. Clinical risk estimations by other guidelines Additional risk estimations according to the St. Gallen expert panel recommendations of 2003, NPI and CBO 2004 (all versions were used at the time of the RASTER study) were assessed previously.6,11-13 Differences among clinicopathological guidelines, tool and expert panel recommendations are summarized in Table 1. Risk estimations were concordant with the 70-gene signature and all clinical guidelines in 12 cases, six were concordant high risk and six concordant low risk. There was discordance between the 70-gene signature and at least one of the guidelines in 25 cases (68%).

72 | Chapter 5

Agreement among oncologists | 73

Not used

<35 or ≥ 35 Yes

NPI 6

CBO 2004 13

NABON 2012 3 Yes

Yes

Not used

Yes

ER & PR Yes

ER & PR No

Yes

None

Method of detection, CT regimen

Yes

Yes, more than Biological 3+ nodes is subtype high risk Yes None

10-years survival probability ≥85%. N0, <35, grade I tumor ≤ 1 cm OR ≥35 yrs, grade I tumor ≤ 2 cm. Not specified. Suggested: <3% survival benefit in 10-years no chemotherapy; 3-5% chemotherapy discussed as possible option

[0.2 x Size] + Number of nodes + Grade; low risk = score lower than 3.4 N0, ≥ 35 yrs, grade I tumor ≤ 1 cm OR >35 years, grade 1 ≤ 30mm OR grade 2, ≤ 20mm OR grade 3 ≤ 10mm

Luminal A; ER + and PR +, HER2 -, low Ki67

High ER and PR, grade I, low Ki67, node negative, absence of PVI, ≤20mm, low score on multigene assay.

Yes

PVI, multigene assays

ER + and PR +, grade I, ≤2cm and age ≥ 35 yrs

Other factors Low risk is defined as Co-morbidities, Not specified CT regimen

Node-negative None

HER2 Ki67 Nodal status No No Yes

ER & PR No

ER/PR ER

AOL=Adjuvant! Online; NPI=Nottingham Prognostic Index; CBO & NABON=Dutch guidelines; ER=estrogen receptor; PR=progesterone receptor; HER2=Human Epidermal growth factor Receptor 2; CT=chemotherapy; PVI=peritumoral vascular invasion

Yes

Pre- or post No menopausal

St. Gallen 2011 20

PREDICT

Not used

St. Gallen 2009 Yes

<35 or ≥ 35 Yes

Size Grade Hist. type Yes Yes Ductal, in case of other hist. type, information is available online

St. Gallen 2003 12

Guideline/tool Age AOL 8 Continuous

Table 1. Clinicopathological factors used by breast cancer guidelines and risk estimation tools to define patients at a low risk of recurrence

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39

Statistical analysis All data were analyzed using SPSS 20.0 (SPSS Inc.). Agreement among the oncologists as well as between each oncologist and the 70-gene signature result (low risk versus high risk) was assessed using kappa statistics. A kappa of 0 means random, 0.01-0.20 slight agreement, 0.210.40 fair agreement, 0.41-0.60 moderate agreement, 0.61-0.80 substantial agreement, 0.810.99 almost perfect agreement and a kappa of 1 is perfect agreement. The paired samples t-test was conducted to compare the kappa means between the oncologistsâ&#x20AC;&#x2122; risk estimations in the first and second questionnaire. Logistic regression models were used to assess the likelihood of the 70-gene signature result leading to changes in risk estimations and AST recommendations. Covariants included in this model were age, tumor size, grade, histological type, estrogen receptor (ER) and HER2 status. In case of an unanswered question in either the clinical risk estimation or the estimation based on the 70-gene signature, these risk estimations were both excluded from the analyses. A significant finding was defined as a two-sided p-value below 0.05.

Results Participants and case characteristics Nineteen oncologists completed the first questionnaire (66%). Twelve oncologists (41%) also completed the second questionnaire. Mean age of these oncologists was 49 years (36-66 years) and they were practicing their current profession on average for 18 years (2-35 years). Six of the oncologists came from the Netherlands and six from other European countries (Germany, France, Italy and Portugal). Patient and tumor characteristics of the 37 cases included in the analyses as well as their risk estimations according to the 70-gene signature, AOL and other clinical guidelines are summarized in Supplementary Table 1. On average, for each case two risk estimations and three AST recommendations were missing per oncologist, i.e. not answered in the two questionnaires. Risk estimations and AST recommendations On average, the oncologists classified 51% (range 24-65%) cases as clinically low risk and 47% (range 32-76%) as clinically high risk. After adding the 70-gene signature result, the oncologists classified 59% (range 22-78%) of the cases as low risk and 38% (range 22-78%) as high risk (Figure 1). On average, an oncologist changed the given clinical risk estimation in 14.2% of the cases. In 10.8 % of the cases high risk changed to low risk and in 3.4% of the cases low risk changed to high risk (Table 2). This leads to a net reduction of 7.4% (range 6.9-22.9%) in high risk classifications. In the 12 cases in which all guidelines and the 70-gene signature were concordant significantly less changes in risk estimations were made (3.5%) compared to the 25 cases in which one or more of the guidelines and the 70-gene signature were discordant (18%) (p<0.0001). 74 | Chapter 5

Figure 1. Changes in risk estimations per oncologist per case and risk estimations by clinicopathological guidelines and the 70-gene signature Table 2. Changes in risk estimation and AST recommendation after providing 70-gene signature result A. Changes in risk estimation (%) 70GS High risk CR $

Low risk

Total CR

High risk Low risk Total 70GS

45 (10.8) 208 (50) 253 (60.8)

194 (46.6) 222 (53.4) 416C (100)

149 (35.8) 14 (3.4) 163 (39.2)

B. Changes in AST-recommendation (%) 70GS No AST CR $ No AST ChemotherapyA Endocrine therapyB Total 70GS

16 (3.9) 2 (0.5) 10 (2.4) 28 (6.8)

ChemotherapyA

Endocrine therapyB

Total CR

1 (0.2) 144 (34.8) 8 (1.9) 153 (37)

5 (1.2) 57 (13.8) 171 (41.3) 233 (56.3)

22 (5.3) 203 (49) 189 (45.7) 414C (100)

CR = Clinical risk, estimations based on clinicopathological factors, 70GS = 70-gene signature, result included in the questionnaire. AChemotherapy alone or combined with endocrine therapy and / or trastuzumab. BEndocrine therapy alone. C Missing values not included

Agreement among oncologists | 75

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39

The oncologists recommended AST based on clinicopathological factors in 95% (range 76-100%) of the cases, chemotherapy (alone or combined) in 48% (range 30-70%) and endocrine therapy (alone) in 46% (range 0-70%) of the cases (Table 2, Figure 2). After adding the 70-gene signature result to the clinicopathological factors provided in the first questionnaire, they recommended AST in 93% (range 78-100%) of the cases, chemotherapy (alone or combined) in 37% (range 2268%) and endocrine therapy (alone) in 57% (range 11-78%). In 24% of the cases the oncologist adjusted the AST recommendation (Table 2). Adding the 70-gene signature resulted in 14.3% of the cases in a change from chemotherapy to either endocrine therapy or no AST at all. Only one oncologist advised more chemotherapy after knowledge of the 70-gene signature result. In 2.1% of the cases the advice of no AST or endocrine therapy only was changed to chemotherapy. This resulted in a reduction in chemotherapy use of 12.2% (range: 5.4-29.5%) after adding the 70gene signature to known clinicopathological factors in the second questionnaire. In the 12 cases in which all guidelines and the 70-gene signature were concordant significantly less changes in AST recommendations were made (4.2%) compared to the 25 cases in which one or more of the guidelines and the 70-gene signature were discordant (20.7%)(p<0.0001). Agreement among oncologists There was moderate level of agreement among oncologists in risk estimations based solely on clinicopathological factors (κ=0.57; range: 0.20-0.88) (Table 3). The level of agreement in AST recommendation was also moderate (κ=0.57; range: 0.24-0.84). After adding the 70-gene signature result to clinicopathological factors, agreement in risk estimation increases slightly, but significantly to substantial (κ=0.61; range: 0.14-1.00; p=0.035), while the level of agreement regarding AST recommendations remained moderate (κ=0.56; range: 0.18-1.00; p=0.59). The agreement among oncologists after adding the 70-gene signature remained moderate for risk estimations (κ=0.44; range: 0.05-0.84; p=0.39) as well as AST recommendations (κ=0.56; range: 0.18-1.00; p=0,76). Opinion of oncologists about the use of the 70-gene signature Seven oncologists (58%) indicated the 70-gene signature result had additional value and adding the 70-gene signature result led to a slightly, not significantly larger decrease in the use of AST in these oncologists. On average, in 19% of the cases the result of the 70-gene signature was decisive according to the oncologists who indicated the 70-gene signature had additional value.

76 | Chapter 5

Figure 2. Changes in AST recommendations per oncologist per case and the actual given treatment

Agreement among oncologists | 77

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39

Table 3. Levels of agreement among oncologists in risk estimations and AST recommendations before and after providing the 70-gene signature result to known clinicopathological factors Legend Kappa Kappa <0 0.01-0.20 0.21-0.40 0.41-0.60 0.61-0.80 0.81-0.99

Agreement Less than chance Slight Fair Moderate Substantial Almost perfect

Level of agreement among oncologists in risk estimation based solely on clinicopathological factors Oncologists 1 2 3 4 5 6 7 8 9 10 11 12

2 0,30

3 0,33 0,54

4 0,39 0,71 0,73

5 0,29 0,68 0,71 0,76

6 0,29 0,70 0,73 0,78 0,88

7 0,36 0,64 0,56 0,72 0,70 0,61

8 0,33 0,77 0,78 0,73 0,59 0,62 0,67

9 0,20 0,62 0,56 0,61 0,80 0,83 0,66 0,56

10 0,44 0,55 0,73 0,47 0,49 0,57 0,41 0,62 0,42

11 0,36 0,63 0,35 0,55 0,49 0,56 0,49 0,61 0,51 0,41

12 0,46 0,57 0,58 0,64 0,51 0,64 0,82 0,58 0,57 0,46 0,45

Level of agreement among oncologists in risk estimation after providing the 70-gene signature result Oncologists 1 2 3 4 5 6 7 8 9 10 11 12

78 | Chapter 5

2 0,26

3 0,23 0,46

4 0,21 0,75 0,69

5 0,21 0,93 0,65 0,93

6 0,19 0,81 0,61 0,80 0,92

7 0,21 0,86 0,65 0,78 0,92 1,00

8 0,32 0,54 0,60 0,65 0,73 0,59 0,68

9 0,14 0,68 0,73 0,79 0,92 0,54 0,92 0,58

10 0,35 0,61 0,43 0,60 0,73 0,85 0,60 0,61 0,53

11 0,30 0,48 0,54 0,49 0,52 0,44 0,48 0,58 0,35 0,52

12 0,36 0,73 0,66 0,72 0,83 0,86 0,92 0,74 0,71 0,74 0,57

Level of agreement among oncologists in AST recommendation based solely on clinicopathological factors Oncologists 1 2 3 4 5 6 7 8 9 10 11 12

2 0,53

3 0,37 0,70

4 0,30 0,67 0,69

5 0,28 0,55 0,69 0,65

6 0,30 0,66 0,71 0,77 0,79

7 0,26 0,51 0,62 0,67 0,79 0,72

8 0,37 0,70 0,84 0,69 0,69 0,62 0,70

9 0,24 0,47 0,65 0,70 0,83 0,76 0,84 0,65

10 0,42 0,67 0,66 0,56 0,48 0,54 0,66 0,66 0,43

11 0,44 0,42 0,50 0,25 0,52 0,40 0,65 0,59 0,35 0,52

12 0,34 0,58 0,47 0,51 0,56 0,66 0,66 0,47 0,60 0,51 0,41

Level of agreement among oncologists in AST recommendation after providing the 70-gene signature result Oncologists 1 2 3 4 5 6 7 8 9 10 11 12

2 0,40

3 0,45 0,48

4 0,28 0,75 0,54

5 0,23 0,63 0,44 0,88

6 0,25 0,64 0,49 0,83 0,82

7 0,27 0,65 0,51 0,81 0,80 1,00

8 0,36 0,54 0,51 0,63 0,56 0,58 0,64

9 0,18 0,52 0,51 0,75 0,73 0,80 0,86 0,51

10 0,34 0,72 0,48 0,52 0,44 0,42 0,50 0,49 0,39

11 0,41 0,50 0,59 0,50 0,44 0,46 0,48 0,62 0,34 0,47

12 0,30 0,55 0,51 0,72 0,75 0,88 0,93 0,65 0,69 0,44 0,53

Discussion Only a moderate level of agreement for both risk estimations and treatment decisions was observed between oncologists when using the clinicopathological factors that are used in current guidelines, such as age, tumor size, grade and hormone-receptor status. After providing the 70-gene signature result the level of agreement in risk estimations among oncologists increased slightly from moderate (κ=0.55) to substantial (κ=0.61; p=0.035), showing that classification of patients into high and low risk groups based on the 70-gene signature result may be useful to guide AST recommendations. The participating oncologists classified more patients as high risk compared to the 70-gene signature. This was followed by recommendations of AST in 92% of the cases. In 10.8% of the

Agreement among oncologists | 79

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39

cases a high risk estimation was changed into a low risk estimation after adding the 70-gene signature result. Overall, a reduction in the proportion of high risk patients of 7.4% and reduction of 12.2% in the use of chemotherapy was seen in this case-selection; these proportions may of course differ in populations with a different distribution of tumor characteristics. Previously reported specificity rates of the 70-gene signature (0.56) are higher than AOL (0.53) and St. Gallen (0.10) at 5 years of follow-up in a pooled dataset of 70-gene signature validation series of untreated patients with ER-positive, node-negative breast cancer.14 This suggests that the 70-gene signature is a useful tool to reduce the risk of falsely classifying a patient as high risk and that the 70-gene signature may help to reduce overtreatment. An important observation is the variation among oncologists in risk estimation and AST recommendation. A similar study, where the Oncotype DX recurrence score was used as a prognostic tool, showed comparable results, demonstrating that oncologists only have fair to moderate level of agreement when predicting the recurrence score.15 Adding the recurrence score resulted in a decrease in chemotherapy recommendation of 10.8%, which is comparable to the 12.2% seen in our survey. In our survey, 58% of the oncologists found the 70-gene signature of additional value. There are some limitations to this survey. The results of 12 oncologists are reported; 19 out of 29 responded to the first questionnaire and only 12 out of 29 also responded to the second questionnaire leading to a response rate of 41%. Unfortunately, because the number of participating oncologists was fairly low we were unable to perform subgroup analyses to evaluate if oncologists are adherent to the guidelines they indicated to use. The agreement among oncologists might also be partly explained by the presence of a few cases at such a high risk that chemotherapy might be considered standard of care. Even though not all guidelines included in this survey for example identify HER2-positive patients as high risk, the majority of the oncologists consider them eligible for chemotherapy. When excluding the HER2-positive cases from the analysis, the results show a moderate agreement in risk estimation and AST recommendation based on solely clinicopathological factors as well as after adding the 70-gene signature result. The changes in risk estimation and AST recommendations in this survey could also be due to practice patterns of oncologists and lack of adherence to guidelines in general.16 Only oncologists in Europe were invited to participate in this survey. A larger survey, including a larger number of oncologists not only from Europe, but also from other continents would provide more detailed information on differences in breast cancer treatment between countries and continents. In daily clinical practice, the oncologist is faced with the challenge of tailoring adjuvant systemic treatment for each patient, taking the clinicopathological features of the tumor, the 70-gene signature result, the patientsâ&#x20AC;&#x2122; co-morbidities and preferences into account. Proliferation markers, like Ki-67, menopausal status and co-morbidity were unknown in our case-selection and were not presented in the questionnaires. Providing this kind of extra information may have further improved the ability to discriminate between high and low risk cases and may have influenced AST recommendation. On the other hand, providing more proliferation markers and pathological

80 | Chapter 5

characteristics may not directly result in more agreement.17 In clinical practice, gene-expression profiles will likely be used in addition to clinicopathological guidelines, like the way the 70-gene signature was used in the RASTER study and presented in the cases in our survey.18 The follow-up of the RASTER study showed that patients treated according to the 70-gene signature who did not receive AST, despite poor clinicopathological factors, had a distant recurrence free interval of 100%.18 Based on these data, the reduction in chemotherapy resulting from knowledge of the 70-gene signature result as presented in this survey, may be justified. Especially, since in the RASTER study not only the 70-gene signature result was decisive, but also the doctors’ and patients’ preferences. The St. Gallen 2011 recommendations and ESMO practice guidelines include the 70-gene signature as an indicator for AST.19,20 In conclusion, this survey shows the variability in guidelines and oncologists’ risk estimations and recommendations of AST in early stage breast cancer patients. Providing the 70-gene signature result has a modest impact on risk estimation and AST recommendation. It may lead to a reduction in the classification of high risk patients and a decrease in the use of chemotherapy. Most importantly, this survey underlines the need for a better standardization tool in clinical decision-making.

5 Acknowledgements The authors thank all participants who filled out the first questionnaire, but were not able to complete their participation in this survey, for their time and efforts. We thank Jolien Bueno-deMesquita for collecting and providing the baseline characteristics of the RASTER patients, Emiel Rutgers for evaluating the impact of the 70-gene signature from a surgeon’s point of view and Philip Schouten for his efforts preparing the illustrations for this manuscript. Funding source This work was supported by BBMRI-NL (complementation project no. 45), TI Pharma (project no. T3‑502) and the Dutch Cancer Society (grant number NKI 2009-4363). The RASTER study was financially supported by the Dutch Health Care Insurance Board (CVZ). Conflict of interest statement The authors have declared no conflicts of interest.

Agreement among oncologists | 81

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39

References 1

Bueno-de-Mesquita JM, Sonke GS, van de Vijver MJ, Linn SC. Additional value and potential use of the 70-gene prognosis signature in node-negative breast cancer in daily clinical practice. Ann Oncol 2011;22:2021-2030.

van de Vijver MJ, He YD, van ‘t Veer LJ et al. A gene-expression signature as a predictor of survival in breast cancer. N Engl J Med 2002;347:1999-2009.

3 Integraal Kankercentrum Nederland: NABON richtlijn mammacarcinoom 2012. 4 Cardoso F. Microarray technology and its effect on breast cancer (re)classification and prediction of outcome. Breast Cancer Res 2003;5:303-304. 5

Cardoso F, van ‘t Veer L, Rutgers E, Loi S, Mook S, Piccart-Gebhart MJ. Clinical application of the 70gene profile: the MINDACT trial. J Clin Oncol 2008;26:729-735.

Olivotto IA, Bajdik CD, Ravdin PM et al. Population-based validation of the prognostic model ADJUVANT! for early breast cancer. J Clin Oncol 2005;23:2716-2725.

Azim HA, Jr., Michiels S, Zagouri F et al. Utility of prognostic genomic tests in breast cancer practice: The IMPAKT 2012 Working Group Consensus Statement. Ann Oncol 2013;24:647-654.

van ‘t Veer LJ, Dai H, van de Vijver MJ et al. Gene expression profiling predicts clinical outcome of breast cancer. Nature 2002;415:530-536.

Bueno-de-Mesquita JM, van Harten WH, Retel VP et al. Use of 70-gene signature to predict prognosis of patients with node-negative breast cancer: a prospective community-based feasibility study (RASTER). Lancet Oncol 2007;8:1079-1087.

13 Kwaliteitsinstituut voor de Gezondheidszorg CBO VvlK. Adjuvante Systemische Therapie voor het Operabel Mammacarcinoom. Richtlijn Behandeling van het Mammacarcinoom 2004;46-70. 14

Kamal AH, Loprinzi CL, Reynolds C et al. Breast medical oncologists’ use of standard prognostic factors to predict a 21-gene recurrence score. Oncologist 2011;16:1359-1366.

Foster JA, Abdolrasulnia M, Doroodchi H, McClure J, Casebeer L. Practice patterns and guideline adherence of medical oncologists in managing patients with early breast cancer. J Natl Compr Canc Netw 2009;7:697-706.

Mehta R, Jain RK, Badve S. Personalized medicine: the road ahead. Clin Breast Cancer 2011;11:2026.

Drukker CA, Bueno-de-Mesquita JM, Retel VP et al. A prospective evaluation of a breast cancer prognosis signature in the observational RASTER study. Int J Cancer 2013;133:929-936.

Aebi S, Davidson T, Gruber G, Cardoso F. Primary breast cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol 2011;22 Suppl 6:vi12-vi24.

82 | Chapter 5

Goldhirsch A, Wood WC, Coates AS, Gelber RD, Thurlimann B, Senn HJ. Strategies for subtypes-dealing with the diversity of breast cancer: highlights of the St. Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2011. Ann Oncol 2011;22:1736-1747.

Agreement among oncologists | 83

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39

Ductal Ductal Ductal Ductal Ductal Ductal Lobular Ductal Ductal Ductal Ductal Ductal Ductal Ductal Ductal Lobular Ductal Lobular Ductal lobular lobular ductolobular ductal ductal ductal ductal lobular ductal ductal

13 12 10 19 15 15 17 11 11 14 10 22 13 12 18 25 35 12 18 19 12 16 19 6 14 18 15 15 7

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29

41 50 45 38 53 52 59 51 49 57 50 57 37 50 53 43 54 54 41 52 46 58 33 54 39 48 57 43 27

Histology

Case Age Size yrs mm 3 2 3 1 2 1 2 1 2 2 2 3 2 2 2 1 1 2 2 2 2 2 2 1 2 2 2 1 2

Gradea 30 low 16 5 12 1 unknown 4 unknown unknown unknown 23 3 unknown 5 2 1 4 16 11 4 5 18 1 5 8 5 2 1

84 | Chapter 5 per 10 HPF per 2mm2 per 2mm2 per 2mm2

per 10 HPF per 10 HPF per 10 HPF per 10 HPF per 2mm2

per 2mm2

per 10 HPF

per 8 HPF

per 2mm2

Mitotic index

PRb

80% 100% + + + 100% 100% + + + + + + 70% 100% 100% 50% + + + + + + 30% 90% 100% 30% 100% 10% 40% + + + 90% 90% 100% 100% 90% + + + + + + 100% 100%

ERb Neg Neg 3+ Neg Neg Neg Neg Neg 3+ Neg Neg 3+ Neg Neg Neg Neg Neg Neg Neg Neg Neg Neg Neg Neg Neg 3+ Neg Neg Neg

HER2c BCT + Rtx BCT + Rtx Ablation BCT + Rtx BCT + Rtx BCT + Rtx BCT + Rtx BCT + Rtx BCT + Rtx BCT + Rtx BCT + Rtx BCT + Rtx BCT + Rtx BCT+ Rtx BCT+ Rtx BCT+ Rtx BCT+ Rtx Ablation BCT+ Rtx BCT+ Rtx Ablation Ablation BCT+ Rtx BCT+ Rtx BCT+ Rtx BCT+ Rtx BCT+ Rtx BCT+ Rtx Ablation

Primary treatment

Supplementary Table 1. Characteristics of the 37 breast cancer cases presented in the questionnaires

High Low High Low Low Low Low Low High Low Low High Low Low Low Low High Low High Low Low Low High Low Low High Low Low Low

70GS High High High Low High Low High Low High High Low High Low High High High High High High High High High High High Low High High Low Low

AOL High Low High Low Low Low Low Low Low Low Low High Low Low Low Low Low Low High Low Low Low Low Low Low Low Low Low Low

NPI High High High Low High Low High Low High High High High High High High High High High High High High High High Low High High High Low High

St. Gallen 2003 High Low High Low Low Low Low Low Low Low Low High Low Low Low Low High Low High Low Low Low High Low Low Low Low Low High

CBO 2004

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39

32 46 51 43 51 51 46 43

32 ductal 14 mucinous ductal 11 invasive 11 ductal 13 ductal 8 ductal 23 ductal 18 ductal

2 2 2 2 1 1 3 2

2 per 10 HPF 6 2 4 unknown 3 per 2mm2 9 per 2mm2 17

50% 50% + + 100% 100% + + + + 60% 50% + + 100% 100%

3+ Neg Neg Neg Neg Neg 3+ Neg

Ablation BCT+ Rtx Ablation + Rtx Ablation BCT+ Rtx BCT+ Rtx BCT+ Rtx BCT+ Rtx

High Low Low Low Low Low High Low

High High High Low Low Low High Low

High Low Low Low Low Low High Low

High High High High Low Low High High

High Low Low Low Low Low High Low

Histological tumor grade according to Elston and Ellis. bAccording to Dutch guidelines, oestrogen and progesterone receptors (ER, PR) were deemed positive if at least 10% of tumor cells stained positive in immunohistochemical assay. cSamples were deemed HER2-positive if the score was 3+ in immunohistochemical assay. If the score was 2+ in immunohistochemical assay and a fluorescent in-situ hybridisation result (FISH) was available, the FISH result (positive or negative) was used. Abbreviations: Neg or - = negative, + = positive, BCT = breast conserving therapy, Rtx = radiotherapy, 70GS = 70-gene signature, AOL= Adjuvant! Online version 8.0, NPI= Nottingham Prognostic Index, CBO= the Dutch Institute of Healthcare Improvement guidelines

30 31 32 33 34 35 36 37

Agreement among oncologists | 85

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39

Gene signature for risk stratification and treatment of brea Incorporating tumor biology in clinical decision-making

Chapter 6

of breast cancer

king

Long-term impact of the 70-gene signature on breast cancer outcome

Breast Cancer Research and Treatment 2014;143:587-92

Caroline A. Drukker Harm van Tinteren Marjanka K. Schmidt Emiel J.Th. Rutgers Marc J. van de Vijver Laura J. van â&#x20AC;&#x2DC;t Veer

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39

Abstract Background Several studies have validated the prognostic value of the 70-gene prognosis signature (MammaPrint速), but long-term outcome of these patients has not been previously reported. Methods The follow-up of the consecutively treated cohort of 295 patients (<53 years) with invasive breast cancer (T1-2N0-1M0; n=151 N0, n=144 N1) diagnosed between 1984 and 1995, in which the 70-gene signature was previously validated, was updated. Results The median follow-up for this series is now extended to 18.5 years. A significant difference is seen in long-term distant-metastasis-free-survival (DMFS) for the patients with a low and a high risk 70-gene signature (DMFS p<0.0001), as well as separately for node-negative (DMFS p<0.0001) and node-positive patients (DMFS p=0.0004). The 25-year Hazard Ratios (HR) for all patients for DMFS and OS were 3.1 (95%CI: 2.02-4.86) and 2.9 (95%CI: 1.90-4.28), respectively. The HRs for DMFS and OS were largest in the first five years after diagnosis: 9.6 (95%CI: 4.2-22.1) and 11.3 (95%CI: 3.5-36.4), respectively. The 25-year HR in the subgroup of node-negative patients for DMFS and OS were 4.57 (95%CI: 2.31-9.04) and 4.73 (95%CI: 2.46-9.07), respectively, and for node-positive patients for DMFS and OS were 2.24 (95%CI: 1.25-4.00) and 1.83 (95%CI: 1.07-3.11), respectively. Conclusion The 70-gene signature remains prognostic at longer follow-up in patients < 53 years of age with stage I and II breast cancer. The 70-gene signature strongest prognostic power is seen in the first 5 years after diagnosis.

88 | Chapter 6

Introduction Gene-expression signatures, such as the 70-gene signature (MammaPrint速), were developed to assess the risk of distant recurrence in the first five years after diagnosis to predict outcome of breast cancer patients.1 The 70-gene signature was extensively validated in several retrospective studies.2-4 The test was mainly validated in systemically untreated patients with estrogenreceptor (ER) positive and negative invasive breast cancer, <55 or 60 years, with no axillary nodal involvement. Subsequently, multiple studies validated this signature for additional subgroups such as postmenopausal patients, patients with up to three positive lymph nodes and for Human Epidermal growth factor Receptor 2 (HER2) positive disease.5-7 More recently, the first prospective data on the 70-gene signature was published showing an excellent overall survival for patients with a low risk for recurrence estimation by the 70-gene signature. Even when these low risk patients did not receive adjuvant chemotherapy, despite poor clinicopathological factors, they had a 5-year distant recurrence free interval of 100%.8 In studies on the prognostic value of the 70-gene signature published so far, the median followup was between 5 to 13.6 years. To our knowledge, no data on long-term survival of patients for whom gene-expression data is available has been published. We therefore set out to update the follow-up of the previously published 70-gene signature consecutive 295 patient cohort, as published by van de Vijver et al. in 2002, to investigate the long-term outcome of breast cancer patients and to evaluate the effect of the 70-gene signature after longer follow-up.4

Patients and Methods Follow-up was updated until September 2013 for a cohort of 295 consecutive patients diagnosed with primary breast cancer. Study design, patient eligibility and study logistics of the study have been described before.4 In short, all patients were female, younger than 53 years with histologically proven, operable, invasive breast cancer (T1-2N0-1M0). All were diagnosed at the Netherlands Cancer Institute between 1984 and 1995. 151 of the 295 patients had node-negative disease, 144 patients had node-positive disease. All patients were primarily treated with breast-conserving surgery or mastectomy. Adjuvant treatment consisted of radiotherapy, chemotherapy and/or endocrine therapy as indicated by guidelines used at the time of treatment. 70-gene signature For all patients included in these analyses a 70-gene signature result was available. Frozen tumor samples from each patient were processed at the Netherlands Cancer Institute and Rosetta Inpharmatics for RNA isolation, amplification, and labelling as described elsewhere.1,4,9 Tumors were classified as a 70-gene signature low or high risk at the time of the initial studies. Low risk

Long-term impact of the 70-gene signature | 89

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39

was defined as an index-score greater than 0.4. High risk was defined as an index-score lower than 0.4.1,9 Statistical Analysis For this analysis, we estimated overall survival (OS) and distant metastasis free survival (DMFS). DMFS was defined as time from diagnosis to distant metastasis as first event. Data on all other patients was censored on the last date of follow-up, in the event of a second primary tumor including contralateral breast cancer, in case of death from any cause other than breast cancer or if there was a locoregional recurrence of the disease. In case a locoregional recurrence was followed by distant metastasis within 6 months, the event of distant metastasis was included in the analysis. Survival curves were constructed using the Kaplan-Meier method and compared using the log-rank test. Competing risk analyses were performed to adjust for patients having a type of event (for example death due to another cause than breast cancer) that makes them unable to develop the event of interest. The Hazard Ratioâ&#x20AC;&#x2122;s (HR) of the 70-gene signature were calculated for the full follow-up as well as per 5-year intervals. A significant finding was defined as a p-value below 0.05. Analyses were performed using SAS version 9.2 and R version 2.14.0.

Results Patient and tumor characteristics Patient characteristics are described in Supplementary Table 1.4 Of the 295 patients, 115 patients had a low risk 70-gene signature and 180 had a high risk 70-gene signature. Patients with a low risk 70-gene signature were more often of older age and had more often smaller estrogenreceptor (ER)-positive tumors with lower grade. No significant difference between 70-gene signature high and low risk patients was seen for number of positive nodes, vascular invasion and treatment (type of surgery, adjuvant chemotherapy nor adjuvant endocrine therapy). Thirty seven percent of the patients received adjuvant chemotherapy and 14% received adjuvant endocrine therapy. After a median follow up of 18.5 years, 121 patients developed distant metastasis as first event. One hundred and twenty seven patients have died, of whom 114 due to breast cancer. Long-term prognostic value of the 70-gene signature Figure 1 shows DMFS and OS for the entire cohort (1A), and separately for node-negative (1B), and node-positive patients (1C). The Kaplan-Meier curves showed a significant absolute difference in DMFS and OS at 25 years between the patients with a low risk 70-gene signature (60.4% and 57.3% respectively) and the patients with a high risk 70-gene signature (41.6% and 44.5% respectively; p<0.0001 for both OS and DMFS). This significant difference was observed for node-negative (p<0.0001 for both OS and DMFS) as well as node-positive patients (p=0.03 for OS and p=0.0004 for DMFS). 90 | Chapter 6

/RZ ULVN

26 3UREDELOLW\

'0)567 3UREDELOLW\

+LJK ULVN

/RJÃ5DQN S

/RZ ULVN

+LJK ULVN

/RZ ULVN

+LJK ULVN

7LPH \HDU

OS All patients

/RZ ULVN

26 3UREDELOLW\

+LJK ULVN

/RJÃ5DQN S

/RZ ULVN

+LJK ULVN

/RZ ULVN

+LJK ULVN

DMFS Node-negative patients

/RZ ULVN

26 3UREDELOLW\

+LJK ULVN

/RJÃ5DQN S

/RZ ULVN

+LJK ULVN

7LPH \HDU

OS Node-negative patients

'0)567 3UREDELOLW\

7LPH \HDU

/RZ ULVN

DMFS All Patients

'0)567 3UREDELOLW\

7LPH \HDU

DMFS Node-positive patients

/RZ ULVN +LJK ULVN

7LPH \HDU

OS Node-positive patients

Figure 1. Overall Survival (OS) and Distant Metastasis Free Survival (DMFS) for all patients and stratified by nodal status.

Long-term impact of the 70-gene signature | 91

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39

Conditional survival probabilities for all patients and both subgroups for 5, 10, 15, 20 and 25 years are summarized in Table 1A. The 25-year HR for all patients for DMFS and OS were 3.1 (95%CI: 2.02-4.86) and 2.9 (95%CI: 1.90-4.28), respectively. The HR for DMFS in the first 5 years after diagnosis was 9.6 (95%CI: 4.2-22.1) and 11.3 (95%CI: 3.5-36.4) for OS. After 5 years the effect of the 70-gene signature on DMFS diminished, while the effect on OS from years 5 to 10 after diagnosis was still very significant with a HR 6.1 (95%CI: 2.4-15.6). After 15 years, the effect of the 70-gene signature on OS slowly diminished (Table 1B). The 25-year HR for nodenegative patients for DMFS and OS were 4.57 (95%CI: 2.31-9.04) and 4.73 (95%CI: 2.46-9.07), respectively. The 25-year HR for node-positive patients for DMFS and OS were 2.24 (95%CI: 1.254.00) and 1.83 (95%CI: 1.07-3.11), respectively. Distant metastases and competing events Figure 2 demonstrates how competing events that occurred in this cohort in addition to distant metastases are divided over the 70-gene signature low and high risk groups. The 70-gene signature low versus high risk in this cohort was only significant for prediction of distant metastases, as shown in Figure 1 and Table 1. For the DMFS analyses, locoregional recurrence was considered a competing event and therefore follow-up was censored if occurred first, except when the locoregional event took place within 6 months prior to the distant metastasis. Reanalysing the data without including the locoregional events that occur within 6 months before the patient is diagnosed with distant metastases, gives no substantial difference in survival probabilities (data not shown).

ORFRUHJLRQDO GLVWDQW &%& 6HF SULP 'HDWK RWKHU

3UREDELOLW\

ORFRUHJLRQDO GLVWDQW &%& 6HF SULP 'HDWK RWKHU

3UREDELOLW\

0DPPD3ULQW +LJK 5LVN

0DPPD3ULQW /RZ 5LVN

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39

<HDUV

70-gene signature low risk

Figure 2. Competing risk analyses

92 | Chapter 6

<HDUV

70-gene signature high risk

Long-term impact of the 70-gene signature | 93

295 115 180 151 60 91 144 55 89

All patients Low risk High risk Node-negative Low risk High risk Node-positive Low risk High risk

DMFS (95%CI) 15 yr 78.1 (70.3-86.8) 47.1 (39.7-55.8) 85.6 (76.8-95.4) 44.0 (34.4-56.3) 70.3 (58.1-85.1) 50.1 (39.7-63.3) OS (95%CI) 15 yr 83.0 (75.9-90.8) 47.7 (70.7-56) 89.1 (81.2-97.8) 44.3 (34.9-56.1) 76.6 (65.2-89.9) 51.1 (41.6-63.8)

10 yr 82.0 (75-89.7) 50.0 (42.8-58.4) 85.6 (76.8-95.4) 45.6 (36-57.8) 78.6 (68.1-90.7) 54.3 (44.2-66.6) 10 yr 92.8 (88.2-97.8) 55.7 (48.7-63.6) 93.2 (87-99.9) 52.7 (43.2-64.2) 92.5 (85.7-99.9) 58.7 (49.1-70.3)

94.7 (90.7-98.9) 58.5 (51.6-66.4) 94.9 (89.5-100) 52.4 (42.8-64.2) 94.5 (88.7-100) 64.7 (55.3-75.8) 5yr 97.4 (94.5-100) 74.0 (67.8-80.7) 96.7 (92.2-100) 71.1 (62.4-81.1) 98.2 (94.7-100) 76.9 (68.5-86.3)

5yr

Table 1B. Hazard Ratioâ&#x20AC;&#x2122;s for the 70-gene signature for OS and DMFS At risk Events HR 95% CI DMFS 0-25 years 295 111 3.1 2.02-4.86 0-5 years 295 74 9.6 4.2-22.1 5-10 years 196 23 1.1 0.5-2.5 10-15 years 145 6 1.2 0.2-6.0 15-20 years 94 2 1.1 0.1-17.9 20-25 years 40 6 0.3 0-2.9 OS 0-25 years 295 127 2.9 1.9-4.28 0-5 years 295 49 11.3 3.5-36.4 5-10 years 240 36 6.1 2.4-15.6 10-15 years 191 21 1.5 0.6-3.5 15-20 years 131 15 0.6 0.2-1.7 20-25 years 64 6 0.2 0-2.1

295 115 180 151 60 91 144 55 89

No. of patients

All patients Low risk High risk Node-negative Low risk High risk Node-positive Low risk High risk

Group

54.5 (40-74.2) 47.1 (36.8-60.3)

82.1 (72-93.6) 37.8 (28-50.9)

69.4 (60-80.2) 42.0 (34.6-51.1)

20 yr

70.3 (58.1-85.1) 50.1 (39.7-63.3)

81.3 (70.1-94.3) 39.6 (28.7-54.6)

75.9 (67.3-85.5) 44.8 (36.9-54.5)

20 yr

Tabel 1A. Distant Metastasis Free Survival (DMFS) and Overall Survival (OS) probabilities for all patients and stratified by nodal status.

42.2 (26.6-67.5) 47.1 (36.8-60.3)

69.5 (52.3-92.2) 33.6 (23-49)

57.3 (44.8-73.2) 39.7 (31.7-49.8)

25 yr

No pt at risk 44.5 (32.1-61.8)

73.2 (56.7-94.3) 39.6 (28.7-54.6)

60.4 (45.3-80.5) 41.6 (32.6-53.1)

25 yr

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39

Discussion This update of the consecutive 295 patient cohort shows that the 70-gene signature is able to accurately differentiate between patients at a low and a high risk of distant metastases up to 25 years after diagnosis. This gene signature was designed to predict the risk of distant metastases in the first 5 years after diagnosis. Previous analyses by Buyse et al. already confirmed that the 70-gene signature has prognostic value in the first five years after diagnosis.3 Their analyses also suggested that this effect might be present up to ten years after diagnosis. In our analyses the 70-gene signature has the largest prognostic value for DMFS and OS in the first 5 years (HRs 9.6 (95%CI: 4.2-22.1) and 11.3 (95% CI: 3.5-36.4) respectively). The significant prognostic value per 5 year intervals for OS remained from 5 years after diagnosis onwards and becomes smaller after 15 years. Meta-analyses of patients with breast cancer have shown that adjuvant chemotherapy reduces the rate of recurrence almost exclusively in the first 5 years.10 Consequently, one would expect that patients with relapse in the first 5 years after surgery will benefit most from adjuvant chemotherapy. Thus, for the question of who should receive adjuvant chemotherapy, it is most relevant to identify the patients with relapse in the first 5 years after surgery. That the 70-gene signature has the highest HR for recurrence in the first 5 years supports the notion that this test can help identify those patients that are most likely to benefit from adjuvant chemotherapy. The Food and Drug Administration (FDA) 510-(k) cleared intended use of the 70-gene signature for prognosis prediction in the node-negative, systemically untreated patient population (IVDMIA k101454). The node-negative subgroup of this consecutive series, of whom over 85% did not receive adjuvant systemic treatment, most closely represents this population; it is shown here that for node-negative patients long term outcome can also be predicted using the 70-gene prognosis signature. Patients included in this cohort were all diagnosed between 1984 and 1995. Due to improvement of adjuvant systemic therapy and the introduction of nation-wide screening programs, which resulted in an increase in early stage breast cancer and a decrease in breast cancer mortality rates, one could hypothesize that the survival probabilities of this cohort if diagnosed today would be even better than shown here.10 Also of note, the patients included were all younger than 53 years old, who tend to have a poorer prognosis compared to patients diagnosed at older age.11,12 In conclusion, an update of the 70-gene signature consecutive 295 patient cohort shows that the 70-gene signature has long-term prognostic value in patients < 53 years old with stage I and II breast cancer.

94 | Chapter 6

Contributors CD updated the follow-up data of this 70-gene signature validation study. CD and HvT performed the statistical analysis. CD, HvT, ER, MKS, and LvtV took part in data interpretation and manuscript writing. All authors were involved in reviewing the report. Funding sources This work was supported by the EORTC Breast Cancer Group (type 3 grant 2011/2012), BBMRI-NL, a research infrastructure financed by the Dutch Government (NWO 184.021.007, complementation project 45), and the Dutch Genomics Initiative ‘Cancer Genomics Centre’. Conflict of interest RB, MvdV and LvtV are named inventors on the patent for the 70-gene signature used in this study. RB and LvtV report being shareholder in and employed by Agendia NV, the commercial company that markets the 70-gene signature as MammaPrint®. Acknowledgements We acknowledge the efforts of Sjoerd Elias, Stella Mook and Michael Knauer to keep the database used for this study updated. We are indebted to all women who participated in this 70-gene signature validation study.

Long-term impact of the 70-gene signature | 95

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39

References 1

van ‘t Veer LJ, Dai H, van de Vijver MJ, He YD, Hart AA, Mao M et al. Gene expression profiling predicts clinical outcome of breast cancer. Nature 2002; 415:530-6.

van de Vijver MJ, He YD, van ‘t Veer LJ, Dai H, Hart AA, Voskuil DW et al. A gene-expression signature as a predictor of survival in breast cancer. N Engl J Med 2002; 347:1999-2009.

Glas AM, Floore A, Delahaye LJ, Witteveen AT, Pover RC, Bakx N et al. Converting a breast cancer microarray signature into a high-throughput diagnostic test. BMC Genomics 2006; 7:278.

Adami HO, Malker B, Holmberg L, Persson I, Stone B. The relation between survival and age at diagnosis in breast cancer. N Engl J Med 1986; 315:559-63.

Chung M, Chang HR, Bland KI, Wanebo HJ. Younger women with breast carcinoma have a poorer prognosis than older women. Cancer 1996; 77:97-103.

96 | Chapter 6

Supplementary Table 1. Patient and tumor characteristics stratified by 70-gene signature

Age <40 yrs 40-44 yrs 45-49 yrs >=50yrs No. of positive nodes 0 1-3 >=4 Tumor size =<20 mm >20 mm Histological grade I II III Lymphovascular invasion Absent 1-3 vessels >3 vessels ER status Negative Positive Surgery Breast Conserving Mastectomy Chemotherapy No Yes Endocrine therapy No Yes

70-gene signature low risk n=115 (39%)

70-gene signature high risk n=180 (61%)

11 (10) 44 (38) 43 (37) 17 (15)

52 (29) 41 (23) 55 (31) 32 (18)

60 (52) 43 (37) 12 (10)

91 (51) 63 (35) 26 (14)

71 (62) 44 (38)

84 (47) 96 (53)

56 (49) 45 (39) 14 (12)

19 (11) 56 (31) 105 (58)

77 (67) 12 (10) 26 (23)

108 (60) 18 (10) 54 (30)

3 (3) 112 (97)

66 (37) 114 (63)

64 (56) 51 (44)

97 (54) 83 (46)

71 (62) 44 (38)

114 (63) 66 (37)

98 (85) 17 (15)

157 (87) 23 (13)

p-value <0.001

0.60

0.012

<0.001

0.38

<0.001

0.63

0.79

0.63

ER=estrogen receptor

Long-term impact of the 70-gene signature | 97

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39

Gene signature for risk stratification and treatment of brea Incorporating tumor biology in clinical decision-making

Chapter 7

of breast cancer

king

Mammographic screening detects low risk tumor biology breast cancers

Breast Cancer Research and Treatment 2014 Jan 28. Epub ahead of print

Caroline A. Drukker Marjanka K. Schmidt Emiel J.Th. Rutgers Fatima Cardoso Karla Kerlikowske Laura J. Esserman Flora E. van Leeuwen Ruud M. Pijnappel Leen Slaets Jan Bogaerts Laura J. van â&#x20AC;&#x2122;t Veer

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39

Abstract Background Overdiagnosis of breast cancer, i.e. the detection of slow growing tumors that would never have caused symptoms or death, became more prevalent with the implementation of populationbased screening. Only rough estimates have been made of the proportion of patients that are overdiagnosed and identification of those patients is difficult. Therefore, the aim of this study is to evaluate whether tumor biology can help identify patients with screen-detected tumors at such a low risk of recurrence that they are likely to be overdiagnosed. Furthermore, we wish to evaluate the impact of the transition from film-screen mammography (FSM) to the more sensitive full-field digital mammography (FFDM) on the biology of the tumors detected by each screening-modality. Methods All Dutch breast cancer patients enrolled in the MINDACT trial (EORTC-10041) accrued 20072011, who participated in the national screening program (biennial screening ages 50-75) were included (n=1165). We calculated the proportions of high, low and among those the ultralow risk tumors according to the 70-gene signature for patients with screen-detected (n= 775) and interval (n=390) cancers for FSM and FFDM. Results Screen-detected cancers had significantly more often a low risk tumor biology (68%) of which 54% even an ultralow risk compared to interval cancers (53% low, of which 45% ultralow risk (p=0.001) with an OR of 2.33 (p<0.0001; 95%CI: 1.73-3.15). FFDM detected significantly more high risk tumors (35%) compared to FSM (27%)(p=0.011). Conclusion Aside from favorable clinicopathological factors, screen-detected cancers were also more likely to have a biologically low risk or even ultralow risk tumor. Especially for patients with screendetected cancers the use of tools, such as the 70-gene signature, to differentiate breast cancers by risk of recurrence may minimize overtreatment. The recent transition in screening-modalities led to an increase in the detection of biologically high risk cancers using FFDM.

100 | Chapter 7

Introduction The increasing incidence in breast cancer after implementation of population-based mammographic screening programs has been suggested to be partly due to the detection of slow growing tumors that would never have caused symptoms or death, i.e. breast cancer overdiagnosis.1 This lead time bias is related to the phenomenon of length time bias, as slow growing tumors have a longer window of opportunity to be detected in screening and therefore they are overrepresented in screen-detected cancers.1 Whether this actually results in an increase in the detection of low risk tumors or even clinically indolent disease is still being investigated.2,3 The concept of overdiagnosis due to screening was first reported in 1982 by Lundgren et al.4 Estimates of the proportion of overdiagnosis were made by different study groups and are reported between 1% and 54%, depending on the denominators that are used.5,6 In the Netherlands, there is an estimated 2.8% overdiagnosis.6 Previous analyses, including our own, reported that screendetection is associated with a better prognosis for overall and breast-cancer-specific survival, independent of other favorable prognostic clinicopathological factors.7 Screen-detected cancers are more often tumors of smaller size, lymph node-negative, low grade, and estrogen-receptor positive than interval cancers.7 Identification of the patients with screen-detected cancers that are likely to be overdiagnosed based on clinicopathological factors remains difficult. Therefore, the hypothesis was generated that knowledge of the biological background of the tumor may be helpful in the identification of patients with screen-detected tumors at such a low risk of recurrence that they are likely to be overdiagnosed. Nowadays, gene-expression classifiers are used in addition to clinicopathological factors to identify patients with a favorable prognosis based on the biology of their tumor.8 One of these gene-expression classifiers is the 70-gene signature (MammaPrint速), developed to improve the selection of those patients who may benefit from adjuvant systemic treatment.8 The prognostic value of the 70-gene signature has been validated in several studies, both retrospectively and prospectively.9-13 We previously reported on the tumor biology of screen-detected cancers and suggested that screen-detection might also be associated with a higher likelihood of a biologically low risk or even ultralow risk tumor assessed by the 70-gene signature.2 Over the past decade a transition in diagnostic imaging has occurred. Most screening facilities switched from film-screen mammography (FSM) to full-field digital mammography (FFDM). In the Netherlands this transition started in 2008 and as of 2010, 94% of the women participating in the Dutch screening program have been screened using FFDM.14 Several studies have evaluated the performance of FFDM compared to FSM and showed comparable or even better results for FFDM in the detection of clinically relevant tumors.15,16 FFDM showed a higher sensitivity compared to FSM and detects more ductal carcinoma in situ (DCIS) and invasive cancers17, especially in women under the age of 50 years and in pre- or perimenopausal women with radiographically

Screening detects low risk tumor biology breast cancers | 101

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39

dense breasts.16,17 Recent studies indicate that FFDM-detected cancers are more often estrogenreceptor-negative tumors.17,18 A more sensitive screening-modality such as FFDM may also lead to an increase in the detection of biologically high risk tumors as assessed by the 70-gene signature. No differences in other clinicopathological factors, such as tumor size or grade, are described in literature.15,16 The aim of this study is to determine the proportion of biologically high, low, and among those ultralow risk tumors among the screen-detected and interval tumors and to evaluate the impact of the transition from FSM to the more sensitive FFDM on the biology of the tumors detected by each screening-modality.

Patients and Methods Patients and Clinicopathological characteristics All Dutch breast cancer patients enrolled in the MINDACT trial (EORTC-10041)19,20, who were invited for the Dutch screening program, were included in this study. The MINDACT trial enrolled women aged 18â&#x20AC;&#x201C;70 years with histologically proven operable invasive breast cancer, no distant metastases, and for whom a frozen tumor sample was available between 2007 and 2011.19,20 Eligibility criteria included tumor stage T1, T2, or operable T3, and unilateral; DCIS or lobular carcinoma in situ (LCIS) provided invasive cancer is present; surgery options included breastconserving surgery or mastectomy combined with either a sentinel node procedure or full axillary clearance; WHO performance status of 0 or 1 and adequate bone marrow, liver and renal functions. Main exclusion criteria were: previous or concurrent cancer, previous chemotherapy, anticancer endocrine therapy or radiotherapy, and clinically significant impaired cardiac function. The protocol was amended in April 2008 to allow inclusion of 1â&#x20AC;&#x201C;3 lymph node positive (N1) disease and genomic test in samples containing >30% of tumor cells.19,20 Clinicopathological characteristics were obtained from the EORTC-10041 trial database. In case of discordance between a patientsâ&#x20AC;&#x2122; clinical risk estimation (based on Adjuvant! Online) and 70-gene signature result, the patient was randomized between treatment according to their clinical risk estimation or according to the 70-gene signature result. Screening Program The Dutch Screening Program started April 1, 1990. First, women aged 50-69 years old and from 1998 women up to 75 years old were invited to participate in the screening program based on area code regions. Full coverage was achieved in 1997.7,21 Women were invited for biennial mammography. Screening mammograms were performed in independent and (mostly) mobile screening units (3-8 units per region). The images are read double-blind by trained radiologists.

102 | Chapter 7

The current attendance rate is around 80%.7,14 FFDM was rolled out as from 2008 and fully implemented in 2011. From each patient in this study data was collected on whether the most recent screening was by FSM or FFDM. Method of Detection Data on the method of detection was retrieved from the database of the Dutch screening organization. Data of all five regions is centrally collected in the iBob database.14 The screening data for the eligible Dutch MINDACT patients was derived from the iBob database based on demographic information. Patients were eligible if they were 49 years or older at the time of diagnoses and were invited to participate in the Dutch screening program (n=1475). One hospital excluded their patients (n=4) from the linkage protocol and 62 patients could not be matched to the iBob database, due to incomplete demographic information. Of the 1409 patients that were matched to the iBob database, 1165 were identified as participants of the screening program. Two types of breast cancer were identified based on the method of detection. First, the screendetected cancers, defined as breast cancers that were mammographically detected in the first (prevalent cancers, n=115) or a subsequent screening round (incident cancers, n=660) (total n=775). Second, the interval cancers, defined as symptomatic cancers that were diagnosed within 30 months of a negative screening (n=390). Screening is biennial, giving a window of 24 months for an interval cancer to become symptomatic after a negative screening mammography. When a woman moves to another area-code, her next screening could be delayed up to 6 months. Therefore the interval of 30 months was chosen. 70-gene signature In this study we used the 70-gene signature to evaluate tumor biology. For all patients included in the MINDACT trial a 70-gene signature result was available. The 70-gene signature, MammaPrint速 (Agendia NV, Amsterdam, the Netherlands), is a gene-expression classifier used to estimate the risk of developing distant metastasis. The result of the 70-gene signature is presented as a binary result (good or poor prognosis), which is derived from an index score (-1 to 1).9,10 An index-score greater than 0.4 is classified as good prognosis (low risk) and an index-score less than 0.4 is classified as poor prognosis (high risk). For this study we also applied the previously set threshold to identify patients with an ultralow risk of distant recurrence (index-score >0.6).2 Within the low risk group of the original 78 patients used to develop this classifier, no distant metastases were observed at five years in patients who had an index-score greater than 0.6.2,9

Screening detects low risk tumor biology breast cancers | 103

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39

Statistical Analysis Baseline characteristics for Screen-detected and interval cancers were compared and the proportions of 70-gene signature high, low, and among the latter the ultralowrisk were calculated. We performed separate analyses for FSM and FFDM. Prognostic factors, such as age, tumor size, histological type, estrogen-receptor (ER), progesterone-receptor (PR), and HER2/neu-oncoprotein (ERBB2) were evaluated in a logistic regression model. Hereafter, tumor biology related factors are referred to as â&#x20AC;&#x2DC;prognostic factorsâ&#x20AC;&#x2122;. Only factors that resulted in <10% change in the coefficient of association of the 70-gene signature with the method of detection were included in the multivariate analyses. Calculations were done using SPSS (version 19.0). A two-sided p-value of less than 0.05 was considered statistically significant.

Results Patient characteristics The clinicopathological characteristics of the 1165 included patients are described in Table 1, stratified by method of detection, and in Supplementary Table 1 also stratified by 70-gene signature result. Screen-detected cancers were more often of smaller size (<2 cm), ER- and PRpositive, HER2-negative, grade I, without nodal involvement compared to interval cancers.

104 | Chapter 7

Table 1. Breast cancer patients eligible to participate in the Dutch screening program: Patient and tumor characteristics stratified by method of detection

70-gene signature High risk Low risk Ultralow risk Age (years) 49-54 55-59 60-64 65-69 Size T1 (<20 mm) T2 (20-50 mm) T3 (>50 mm) Lymph node status Negative 1-3 positive nodes Histological type Ductal Lobular Mixed Other Grade Grade I Grade II Grade III Undefined ER status Negative Positive PR status Negative Positive Unknown HER2 status Negative Positive Unknown

Screen-detected cancers (SD) n=775

Interval cancers (IC) n=390

p-value# SD vs IC

244 (32%) 242 (31%) 289 (37%)

185 (47%) 111 (29%) 94 (24%)

<0.0001

103 (26%) 104 (27%) 94 (24%) 88 (23%) 247 (63%) 139 (36%) 4 (1%) 315 (81%) 75 (19%) 316 (81%) 49 (13%) 9 (2%) 16 (4%) 59 (15%) 170 (44%) 160 (41%) 1 80 (21%) 310 (80%) 138 (35%) 242 (62%) 10 325 (83%) 64 (16%) 1

0.896

208 (27%) 193 (25%) 200 (26%) 170 (22%) 613 (79%) 160 (21%) 2 (0.3%) 680 (88%) 95 (12%) 643 (83%) 76 (10%) 28 (4%) 28 (4%) 244 (32%) 356 (46%) 174 (23%) 1 77 (10%) 698 (90%) 188 (24%) 573 (74%) 14 680 (88%) 94 (12%) 1

<0.0001

0.001

0.390

<0.0001

7 <0.0001

<0.0001

0.043

Chi-square test ER=estrogen receptor; PR=progesterone receptor; HER2=Human Epidermal growth factor Receptor 2.

Screening detects low risk tumor biology breast cancers | 105

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39

70-gene signature for screen-detected and interval cancers Among the screen-detected cancers, 32% had a 70-gene signature high risk and 68% a low risk tumor, of which 54% had a ultralow risk tumor (37% of total) (Figure 1A and Table 1). Among the interval cancers, 47% had a high risk and 53% a low risk tumor, of which 46% could be defined as ultralow risk tumor (24% of total). A significant difference was seen between screen-detected and interval cancers (pX2 test = 0.001) in 70-gene signature high, low, and ultralow risk groups. Of the prevalent tumors, detected in the first screening round, 19% had a 70-gene signature high risk and 81% a low risk tumor. Among the low risk prevalent tumors about 63% even had an ultralow risk tumor (51% of total)(Figure 1B). Of the incident tumors, detected in subsequent screening rounds, 34% had a 70-gene signature high risk and 66% a low risk tumor. Among the low risk incident tumors, 52% could be defined as ultralow risk (35% of total)(pX2 test prevalent vs incident < 0.0001) (Figure 1B). When excluding the prevalent cancers from these analyses the significant difference between screen-detected and interval cancers remained (Supplementary Table 2). In a univariate analyses, patients with screen-detected cancers were two-times more likely to have an ultralow risk tumor compared to patients with an interval cancer (OR high vs ultralow: 2.33 (95%CI: 1.73-3.15; p<0.0001)(Table 3A). When adjusting for intermediate factors such as ER-status and tumor size, this significant association remained (Table 3A). However, when adjusting for grade the 70-gene signature was no longer a significant factor; likely due to a substantial correlation between the 70-gene signature and grade (ρ=0.393). The analyses mentioned above lead to similar conclusions in ER-positive patients only (data not shown). Figure 1A. Proportions of 70-gene signature result among screen-detected and interval cancers

ϭϬϬй ϵϬй ϴϬй

Ŷсϳϳϱ

ŶсϯϵϬ

ϲϬй

ϳϬй

ϱϬйϮϵй

ϱϯй

54% ultralow among low

46% ultralow among low

^ĐƌĞĞŶͲĚĞƚĞĐƚĞĚ ĐĂŶĐĞƌ

/ŶƚĞƌǀĂů ĐĂŶĐĞƌ

Figure 1A. Proportions of 70-gene signature result among screen-detected and interval cancers

106 | Chapter 7

ŶсϲϲϬ

ϭϵй

ϯϰй

ϴϭй

,ŝŐŚ ƌŝƐŬ

ϯϬй ϮϬй ϯϰй ϭϬй Ϭй

ŚŝŐŚ ƌŝƐ Ŭ

>Žǁ ƌŝƐŬ

ůŽǁ ƌŝƐ Ŭ

ϰϬй

ϮϬй

Ŷсϭϭϱ

ϴϬйϯϳй

ϲϬй

ϲϴй ϲϴй

ϯϬй

Ϭй

ϰϳй

ϱϬй

ϭϬй

ŶсϮϰϰ

ϭϬϬй ϵϬй

ϯϮй

ϳϬй

ϰϬй

Figure 1B. Screen-detected cancers detected in first vs subsequent screening rounds

ϲϲй

63% ultralow among low

WƌĞǀĂůĞŶƚ ĐĂŶĐĞƌƐ EŽŶͲƐĐƌĞĞŶŝŶŐ ƌĞůĂƚĞĚ ĐĂŶĐĞƌ

52% ultralow among low

/ŶĐŝĚĞŶƚ ĐĂŶĐĞƌƐ

Figure 1B. Screen-detected cancers detected in first vs subsequent screening rounds

Table 2. Breast cancer patients with screen-detected cancers: Patient and tumor characteristics stratified by film-screen or digital mammography

70-gene signature High risk Low risk Ultralow risk Age (years) 49-54 yrs 55-59 yrs 60-64 yrs 65-69 yrs Tumor size T1 (< 20 mm) T2 (20-50 mm) T3 (>50 mm) Lymph node status Negative Positive Histological type Ductal Lobular Mixed Other Grade I II III Unknown ER status Negative Positive PR status Negative Positive Unknown HER2 status Negative Positive Unknown

Film screen mammography n=315

Full field digital mammography n=459

85 (27%) 98 (31%) 132 (42%)

159 (35%) 143 (31%) 157 (34%)

0.04

77 (24%) 83 (26%) 81 (26%) 74 (24%)

130 (28%) 110 (24%) 119 (26%) 96 (21%)

0.633

254 (81%) 60 (19%) 1 (0路3%)

358 (78%) 100 (22%) 1 (0路2%)

0.633

290 (92%) 25 (8%)

389 (85%) 70 (15%)

0.002

269 (85%) 28 (9%) 11 (4%) 7 (2%)

373 (81%) 48 (11%) 17 (4%) 20 (4%)

0.406

111 (35%) 143 (45%) 61 (19%) 0

133 (29%) 213 (46%) 112 (24%) 1

0.160

28 (9%) 287 (91%)

49 (11%) 410 (89%)

0.415

79 (25%) 233 (74%) 3

109 (24%) 339 (74%) 11

0.169

271 (86%) 43 (14%) 1

409 (89%) 50 (11%) 0

0.242

p-value#

Chi-square test ER=estrogen receptor; PR=progesterone receptor; HER2=Human Epidermal growth factor Receptor 2.

Screening detects low risk tumor biology breast cancers | 107

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39

Table 3A. Unadjusted and adjusted* Oddâ&#x20AC;&#x2122;s Ratioâ&#x20AC;&#x2122;s of the tumor biology among screen-detected vs interval cancers# Unadj. OR (95%CI)

p-value#

Adj. OR* (95%CI)

p-value#

1.41 (1.02-1.95) 2.33 (1.73-3.15)

0.037 <0.0001

1.18 (0.84-1.65) 1.26 (0.86-1.84)

0.339 0.230

1.40 (1.01-1.93) 1.95 (1.40-2.71) 1.68 (1.14-2.47)

0.044 <0.0001 0.008

1.19 (0.86-1.67) 1.37 (0.95-1.97) 1.84 (1.30-2.61) 3.15 (2.07-4.80)

0.299 0.090 0.001 <0.0001

1.38 (0.99-1.91) 2.15 (1.58-2.91) 1.97 (1.49-2.59) 5.4 (0.96-30.33)

0.057 <0.0001 <0.0001 0.056

70-gene signature ultralow vs low ultralow vs high 70-gene signature + ER status 70-gene signature ultralow vs low 70-gene signature ultralow vs high ER status positive vs negative 70-gene signature + Grade 70-gene signature ultralow vs low 70-gene signature ultralow vs high Grade I vs II Grade I vs III 70-gene signature + Tumor size 70-gene signature ultralow vs low 70-gene signature ultralow vs high T1 vs T2 T1 vs T3

Logistic regression model * Adjusted for grade, estrogen receptor status and tumor size

Film screen versus full field digital mammography Between 2007 and 2011 a transition was seen in screening-modality used for the last screening before diagnoses. Supplementary Figure 1 displays this transition in this cohort over time. Among the screen-detected cancers, 41% were detected using FSM (n=315) and 59% were detected using FFDM (n=459). FSM detected 27% high risk and 73% low risk tumors of whom 57% could be defined as ultralow risk (42% of total). This is significantly different compared to cancers detected using FFDM (p X2 test = 0.011), which detected 35% high risk and 65% low risk tumors of whom 51% could be defined ultralow risk (34% of total)(Figure 2 and Table 2). Aside from a difference in tumor biology in tumors detected by FSM versus FFDM, there is also a difference in nodal involvement. For tumors detected by FSM 8% had one or more positive lymph nodes, while for tumors detected by FFDM 15% had one or more positive lymph nodes (pX2 test = 0.002). For other patient and tumor characteristics, such as age, size, histological type, grade, ER, PR, and HER2 status, no significant differences were seen between the two screening-modalities (Table 2). The association of nodal status with FFDM was at least partly attributable to the amendment of the MINDACT study in 2008, which allowed patients with 1-3 positive nodes to be included in the trial. This leads to an increase of nodal positive patients over the years (data not shown), however, nodal status was not associated with the 70-gene signature result (Supplementary Table 1).

108 | Chapter 7

Figure 2. Screen-detected cancers using film-screen vs digital mammography

ϭϬϬй ϵϬй ϴϬй

Ŷсϯϭϱ

Ŷсϰϱϵ

Ϯϳй

ϯϱй

ϳϬй

ϲϬй ϱϬй ϰϬй

ϳϯй

ϯϬй

Ϭй

>Žǁ ƌŝƐŬ

ϲϱй

ϮϬй ϭϬй

,ŝŐŚ ƌŝƐŬ

57% ultralow among low

51% ultralow among low

&^D

&& D

Figure 2. Screen-detected cancers using film-screen vs digital mammography

Overall, the proportion of interval cancers among the screened women within the Dutch MINDACT cohort was 33% (390/1164). In the FSM-screened population (n=624) the proportion of interval cancers was 49.5% (309/624), while for the FFDM-screened population (n= 540) the interval rate was 15% (81/540). Among the FSM interval cancers, which became symptomatic within 30 months after a negative FSM (n=309), 46% had a high risk and 54% had a low risk tumor of whom 54% had an ultralow risk tumor (Figure 3A). Among the FFDM interval cancers, which became symptomatic within 30 months after a negative FFDM (n=81), 54% had a high risk and 46% had a low risk tumor of whom 46% an ultralow risk tumor (Figure 3B). Odd’s ratios for FSM and FFDM are shown in Table 3B. There was no effect-modification of screening-modality in the association between the 70-gene signature and screen-detected vs interval cancers. These proportions in tumor biology remained the same for FSM and FFDM when only including those patients that were diagnosed after the amendment. Sensitivity analyses in the period when FFDM screening was implemented in at least half of the population and potentially two years had passed for women with a negative FFDM screen in order for interval cancers to become manifest, i.e. 2009 and 2010, showed similar proportions of high risk tumors among FSM- and FFDMscreened patients (26.2% FSM and 33.0% FFDM).

Screening detects low risk tumor biology breast cancers | 109

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39

Figure 3A. 70-gene signature proportions among screen-detected Figure 3B. 70-gene signature proportions among screen-detected and interval cancers after film-screen mammography and interval cancers after full field digital mammography

ϭϬϬй ϵϬй ϴϬй ϳϬй ϲϬй ϱϬй ϰϬй ϯϬй ϮϬй ϭϬй Ϭй

Ŷсϯϭϱ

Ϯϲй

ŶсϯϬϵ

Ŷсϰϱϵ

ϭϬϬй ϵϬй

ϯϴй

ϴϬй

ϰϲй

ϳϬй

Ŷсϴϭ

ϱϰй

,ŝŐŚ ƌŝƐŬ ϲϬй

,ŝŐŚ ƌŝƐŬ

ϱϬй >Žǁ ƌŝƐŬ

ϳϰй

ϰϬй

ϱϰй

ϯϬй

57% ultralow among low

54% ultralow among low

ϭϬй

^ĐƌĞĞŶͲĚĞƚĞĐƚĞĚ ĐĂŶĐĞƌ

/ŶƚĞƌǀĂů ĐĂŶĐĞƌ

>Žǁ ƌŝƐŬ

ϲϮй

ϮϬй Ϭй

ϰϲй

57% ultralow among low

46% ultralow among low

^ĐƌĞĞŶͲĚĞƚĞĐƚĞĚ ĐĂŶĐĞƌ

/ŶƚĞƌǀĂů ĐĂŶĐĞƌ

Figure 3. 70-gene signature proportions among screen-detected and interval cancers after A. film-screen mammography or B. full field digital mammography

Table 3B. Unadjusted and adjusted* Odd’s Ratio’s of the tumor biology among interval vs screen-detected cancers for film-screen and digital mammography#

Last screen FSM 70-gene signature ultralow vs low 70-gene signature ultralow vs high Last screen FFDM 70-gene signature ultralow vs low 70-gene signature ultralow vs high

Unadj. OR (95%CI)

p-value#

Adj. OR* (95%CI)

p-value#

1.59 (1.07-2.38) 2.84 (1.93-4.20)

0.023 <0.0001

1.27 (0.84-1.94) 1.45 (0.88-2.38)

0.308 0.261

1.29 (0.65-2.56) 2.56 (1.40-4.66)

0.464 0.002

1.15 (0.57-2.32) 1.68 (0.82-3.45)

0.695 0.158

Logistic regression model * Adjusted for grade, estrogen receptor status and tumor size

Discussion The effectiveness of breast cancer screening is extensively debated, particularly regarding the estimated proportion of overdiagnosed cancers.3,22 Identification of these overdiagnosed screendetected cancers is challenging. Screen-detected cancers have shown to have more favorable clinicopathological factors and better outcome compared to interval cancers.7 Our results also show that the majority of the cancers detected in screening (68%) are biologically low risk and over half of the low risk tumors are even ultralow risk. This indicates that knowledge of the biological background may help to identify those screen-detected breast cancers at such a low risk of recurrence that concerns about overdiagnosis can be raised. Especially for this subgroup of patients overtreatment with chemotherapy should be avoided. To determine whether the group with screen-detected ultralow-risk tumors is indeed overdiagnosed, a randomized controlled trial would provide further insight. Mammographic screening on the other hand, has proven to be an effective way to detect breast cancer at an early stage.23 Our results confirm that screening

110 | Chapter 7

also detects cancers with poor prognosis tumor biology, which are at a high risk of recurrence. Almost one third of the patients with a tumor detected in the screening program had a high risk 70-gene signature result. The 70-gene signature is likely to be a useful tool to separate patients at a high risk from those at a low or even an ultralow risk of recurrence. Patients with a screendetected cancer are two-times more likely to have an ultralow risk tumor compared to interval cancers. Even when adjusting for other prognostic factors with a substantial association with method of detection (in our population tumor size, grade, and ER status), the 70-gene signature remained an important prognostic factor. Previous analyses showed that the proportion of low and ultralow risk tumors among screen-detected cancers is higher compared to symptomatic cancers diagnosed before the introduction of screening.2 Our current results validate this finding in a larger cohort, showing 68% low risk among screen-detected cancers of whom 54% had an ultralow risk. In literature it is still debated whether the prevalent screen-detected cancers should be included when analyzing screen-detected cancers.23 In this study, we aimed to look at screen-detected cancers from a different, more biologically oriented perspective to evaluate the type of tumors that are detected in screening programs. Since prevalent cancers are also screen-detected and a substantial proportion of overdiagnosis may be present in this subgroup, they were included in our analyses. Good prognosis for prevalent cancers has been suggested by others1, and our observation on the biological level support that notion, albeit not significant. The number of prevalent cancers in this cohort is low and in univariate analyses the screening round was not a significant prognostic factor. The recent transition from FSM to FFDM resulted in a larger proportion of high risk tumors among the screen-detected cancers, which may indicate that the introduction of FFDM leads to the detection of more aggressive cancers with a worse prognosis. It may also indicate that breast cancer screening using FFDM is even more effective than when using solely FSM. Given the possibility that high risk tumors that used to be missed in screening are now detected with FFDM, the introduction of FFDM might be responsible for an increase in the proportion of high risk tumors among the screen-detected cancers and decrease in the number of interval cancers. The fact that the proportion of interval cancers among FFDM-screened patients was low (15%) may therefore be a result of more sensitive screening, but can also explained by the fact that the accrual of women to FFDM was in transition from 2008 till 2010. Hence, for many women insufficient time had passed after a negative FFDM for the development of interval cancers. Thus, the ratio between the number of women at risk for a screen-detected tumor versus an interval cancer, is lower for FSM compared to FFDM. Therefore, no conclusions regarding the relative amount of interval cancers for FFDM versus FSM can be drawn based on the data presented here. Since the Dutch screening program is still collecting data on the effect of the transition from FSM to FFDM, we were not able compare our result to those of the entire screened population in the Netherlands. Of note is that the MINDACT trial currently only has available data of the tumor

Screening detects low risk tumor biology breast cancers | 111

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39

samples provided by the local pathology departments. Tumor-characteristics, especially grade, may change after central review of the samples. A limitation is the possibility of selection bias in the MINDACT trial itself. The novelty of gene-signatures and the limited experience of doctors with this new prognostic tool may have resulted in the inclusion of patients with more favorable tumor characteristics in the beginning of the trial. In conclusion, screen-detection was found to be associated with a higher likelihood of a 70-gene signature biologically low risk tumor, which prospectively validates our previous analyses.2 Half of all screen-detected low risk tumors even had an ultralow risk of distant metastases. Especially for this screen-detected patient group the use of tools to differentiate breast cancers by risk of recurrence may minimize overtreatment. Second, the transition from FSM to FFDM resulted in the detection of a larger proportion of high risk tumors, which may indicate that FFDM is a more effective screening-modality than FSM.

Contributors CD, MKS, LvtV, ER, FC, and JB were responsible for the study design and development of the protocol. CD coordinated the study and collected the data on method of detection. FC, LS and JB provided the MINDACT baseline characteristic data. CD and MKS performed the data analysis. CD, MKS, LE, KK, ER, LS, JB, and LvtV took part in data interpretation and manuscript writing. All authors were involved in reviewing the report. This study was approved by the MINDACT steering committee, and is confirmed to be in line with the principles of the sponsor of the trial, EORTC POL008 (“Release of data from EORTC studies for use in External Research Projects”). All patients have given written informed consent before enrolment in the MINDACT trial (EORTC 10041/BIG 3-04).This informed consent allowed linkage to the Dutch screening facilities. Funding source This work was supported by the EORTC Breast Cancer Group (type 3 grant 2011/2012), the Dutch Cancer Society (NKI 2009-4363), BBMRI-NL (NWO 184.021.007, complementation project 45) and the Dutch Genomics Initiative ‘Cancer Genomics Centre’. The funding sources had no role in the study design, data collection, data analysis, data interpretation, in writing the report, or in the decision to submit for publication. CD, MKS and LvtV had full access to all the data. CD, MKS, ER and LvtV had final responsibility for the decision to submit for publication. Conflict of interest We have read and understood the BMJ Group policy on declaration of interests and declare the following interests: LvtV is named inventor on the patent for the 70-gene signature used in this study. LvtV reports being shareholder in and employed by Agendia NV, the commercial company that markets the 70-gene signature as MammaPrint®. 112 | Chapter 7

Acknowledgements We acknowledge the contribution of the European Organization for Research and Treatment of Cancer (EORTC) and the TransBig Consortium. We thank the Dutch Screening Facilities, Frank Yntema in particular, for providing the screening data used in this study. We thank Annuska Glas from Agendia for providing value information on the 70-gene signature results. We are indebted to all the Dutch women who participated in the MINDACT trial.

Screening detects low risk tumor biology breast cancers | 113

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39

References 1

Nagtegaal ID, Allgood PC, Duffy SW, Kearins O, Sullivan EO, Tappenden N, et al. Prognosis and pathology of screen-detected carcinomas: how different are they? Cancer 2011;117(7):1360-8.

Esserman LJ, Shieh Y, Rutgers EJ, Knauer M, Retel VP, Mook S, et al. Impact of mammographic screening on the detection of good and poor prognosis breast cancers. Breast Cancer Res Treat 2011;130(3):725-34.

Esserman, LJ, Thompson IM, and Reid B. Overdiagnosis and Overtreatment in Cancer: an Opportunity for Improvement. JAMA 2013;310(8):797-8.

Lundgren B, Helleberg A. Single oblique-view mammography for periodic screening for breast cancer in women. J Natl Cancer Inst 1982;68(3):351-5.

5 Puliti D, Duffy SW, Miccinesi G, de Koning H, Lynge E, Zappa M, et al. Overdiagnosis in mammographic screening for breast cancer in Europe: a literature review. J Med Screen 2012;19 Suppl 1: 42-56. 6 de Gelder R, Heijnsdijk EA, van Ravesteyn NT, Fracheboud J, Draisma G, De Koning HJ. Interpreting overdiagnosis estimates in population-based mammography screening. Epidemiol Rev 2011;33: 111121. 7

Mook S, van ‘t Veer LJ, Rutgers EJ, Ravdin PM, van de Velde AO, van Leeuwen FE, et al. Independent prognostic value of screen detection in invasive breast cancer. J Natl Cancer Inst 2011;103(7):585-97.

Azim HA, Jr., Michiels S, Zagouri F, Delaloge S, Filipits M, Namer M, et al. Utility of prognostic genomic tests in breast cancer practice: The IMPAKT 2012 Working Group Consensus Statement. Ann Oncol 2013;24:647-654.

van ‘t Veer LJ, Dai H, van de Vijver MJ, He YD, Hart AA, Mao M, et al. Gene expression profiling predicts clinical outcome of breast cancer. Nature 2002;415(6871):530-6.

van de Vijver MJ, He YD, van ‘t Veer LJ, Dai H, Hart AA, Voskuil DW et al. A gene-expression signature as a predictor of survival in breast cancer. N Engl J Med 2002;347(25):1999-2009.

Buyse M, Loi S, van ‘t Veer L, Viale G, Delorenzi M, Glas A, et al. Validation and clinical utility of a 70-gene prognostic signature for women with node-negative breast cancer. J Natl Cancer Inst 2006;98:1183-1192.

Bueno-de-Mesquita JM, Linn SC, Keijzer R, Wesseling J, Nuyten DS, van Krimpen C, et al. Validation of 70-gene prognosis signature in node-negative breast cancer. Breast Cancer Res Treat 2009;117:483495.

Drukker CA, Bueno-de-Mesquita JM, Retel VP, van Harten WH, van Tinteren H, Wesseling J, et al. A prospective evaluation of a breast cancer prognosis signature in the observational RASTER study. Int J Cancer 2013;133(4):929-36.

National Evaluation Team for Breast Cancer Screening in the Netherlands. NETB report 2012.

Bluekens AM, Holland R, Karssemeijer N, Broeders MJ, den Heeten GJ. Comparison of digital screening mammography and screen-film mammography in the early detection of clinically relevant cancers: a multicenter study. Radiology 2012;265(3):707-14.

16 Vinnicombe S, Pinto Pereira SM, McCormack VA, Shiel S, Perry N, Dos Santos Silva IM. Full-field digital versus screen-film mammography: comparison within the UK breast screening program and systematic review of published data. Radiology 2009;251(2):347-58. 17 Pisano ED, Gatsonis C, Hendrick E, Yaffe M, Baum JK, Acharyya S et al. Diagnostic performance of digital versus film mammography for breast-cancer screening. N Engl J Med 2005;353(17):1773-83. 18 Kerlikowske K, Hubbard RA, Miglioretti DL, Geller BM, Yankaskas BC, Lehman CD, et al. Comparative effectiveness of digital versus film-screen mammography in community practice in the United States: a cohort study. Ann Intern Med 2011;155(8):493-502.

114 | Chapter 7

19 Cardoso F, van â&#x20AC;&#x2DC;t Veer L, Rutgers E, Loi S, Mook S, Piccart-Gebhart MJ. Clinical application of the 70gene profile: the MINDACT trial. J Clin Oncol 2008;26(5):729-35. 20 Rutgers E, Piccart-Gebhart MJ, Bogaerts J, Delaloge S, Veer LV, Rubio IT, et al. The EORTC 10041/BIG 03-04 MINDACT trial is feasible: results of the pilot phase. Eur J Cancer 2011;47(18):2742-9. 21 De Koning HJ, Fracheboud J, Boer R, Verbeek AL, Collette HJ, Hendriks JH et al. Nation-wide breast cancer screening in The Netherlands: support for breast-cancer mortality reduction. National Evaluation Team for Breast Cancer Screening (NETB). Int J Cancer 1995;60(6):777-80. 22 Esserman L, Shieh Y, Thompson I. Rethinking screening for breast cancer and prostate cancer. JAMA 2009;302(15):1685-92. 23

Marmot MG, Altman DG, Cameron DA, Dewar JA, Thompson SG, Wilcox M. The benefits and harms of breast cancer screening: an independent review. Br J Cancer 2013;108(11):2205-40.

Screening detects low risk tumor biology breast cancers | 115

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39

Supplementary Table 1. Patient characteristics by method of detection and 70-gene signature# Screen-detected (n=775) High risk Low risk Ultralow risk 70-gene signature n=244 n=242 n=289 Age p=0.136 49-54 55 (23%) 60 (25%) 93 (32%) 55-59 64 (26%) 67 (28%) 62 (21%) 60-64 66 (27%) 61 (25%) 73 (25%) 65-69 59 (24%) 51 (21%) 60 (21%) >70 0 (0%) 0 (0%) 0 (0%) Size p=0.025 T1 <20 mm 176 (72%) 199 (82%) 238 (82%) T2 20-50 mm 67 (27%) 43 (18%) 50 (17%) T3 >50 mm 1 (0%) 0 (0%) 1 (0%) Histological type p=0.006 Ductal 216 (89%) 209 (86%) 218 (75%) Lobular 15 (6%) 19 (8%) 42 (15%) Mixed 5 (2%) 9 (4%) 14 (5%) Other 8 (3%) 5 (2%) 14 (5%) Grade p<0.0001 Grade I 15 (6%) 80 (33%) 149 (52%) Grade II 97 (40%) 133 (55%) 126 (44%) Grade III 132 (54%) 28 (12%) 14 (5%) Undefined 0 (0%) 1 (0%) 0 (0%) ER status p<0.0001 Negative 69 (28%) 8 (3%) 0 (0%) Positive 175 (72%) 234 (97%) 289 (100%) PR status p<0.0001 Negative 111 (45%) 46 (19%) 31 (11%) Positive 128 (52%) 190 (79%) 255 (88%) Unknown 5 (2%) 6 (2%) 3 (1%) HER2 status p<0.0001 Negative 191 (78%) 214 (88%) 275 (95%) Positive 53 (22%) 28 (12%) 13 (4%) Unknown 0 (0%) 0 (0%) 1 (0%) LN status p=0.649 N0 218 (89%) 211 (87%) 251(87%) 26 (11%) 31 (13%) 38 (13%) N1

High risk n=185 49 (26%) 53 (29%) 44 (24%) 38 (21%) 0 (0%) 107 (58%) 77 (42%) 1 (1%) 164 (89%) 10 (5%) 2 (1%) 9 (5%) 7 (4%) 50 (27%) 128 (69%) 0 (0%) 79 (43%) 106 (57%) 115 (62%) 68 (37%) 2 (1%) 136 (74%) 49 (26%) 0 (0%) 155 (84%) 30 (16%)

Interval (n=390) Low risk Ultralow risk n=111 n=94 p=0.572 27 (24%) 27 (29%) 24 (22%) 27 (29%) 27 (24%) 23 (24%) 33 (30%) 17 (18%) 0 (0%) 0 (0%) p=0.014 68 (61%) 72 (77%) 42 (38%) 20 (21%) 1 (1%) 2 (2%) p=0.001 87 (78%) 65 (69%) 17 (15%) 22 (23%) 3 (3%) 4 (4%) 4 (4%) 3 (3%) p<0.0001 23 (21%) 29 (31%) 59 (53%) 61 (65%) 28 (25%) 4 (4%) 1 (1%) 0 (0%) p<0.0001 0 (0%) 1 (1%) 111 (100%) 93 (99%) p<0.0001 12 (11%) 11 (12%) 94 (85%) 80 (85%) 5 (5%) 3 (3%) p<0.0001 100 (90%) 89 (95%) 10 (9%) 5 (5%) 1 (1%) 0 (0%) p=0.354 87 (78%) 73 (78%) 24 (22%) 21 (22%)

Chi-square test ER=estrogen receptor; PR=progesterone receptor; HER2=Human Epidermal growth factor Receptor 2.

116 | Chapter 7

Supplementary Table 2. 70-gene signature results for screen-detected and interval cancers with and without prevalent cases 70-gene signature High risk Low risk Ultralow risk Total

Screen-detected Interval Screen-detected Interval p-value# p-value# incl prevalent incl prevalent excl prevalent excl prevalent 244 (31.5%) 242 (31.2%) 289 (37.3%) 775

185 (47.4%) 111 (28.5%) 94 (24.1%) 390

<0.0001

221 (33.6%) 207 (31.5%) 230 (35.0%) 658

154 (47.8%) 88 (27.3%) 80 (24.8%) 322

<0.0001

Chi-Square test

^ƵƉƉůĞŵĞŶƚĂƌǇ ĮŐƵƌĞ ϭ͘ ŝƐƚƌŝďƵƟŽŶ ŽĨ ĮůŵͲƐĐƌĞĞŶ ǀƐ ĚŝŐŝƚĂů ŵĂŵŵŽŐƌĂƉŚǇ ŽǀĞƌ ƟŵĞ

ϯϰ͘ϰй

ϳϱ͘ϱй

ϭϯ͘ϴй

ϲϱ͘ϲй

ϵϰ͘ϰй

ϴϲ͘Ϯй

Ϯϰ͘ϱй ϵϳ͘Ϯй

Supplementary figure 1. Distribution of film-screen vs digital mammography over time

Screening detects low risk tumor biology breast cancers | 117

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39

Gene signature for risk stratification and treatment of brea Incorporating tumor biology in clinical decision-making

Chapter 8

of breast cancer

king

Gene-expression profiling to predict the risk of locoregional recurrence in breast cancer

To be submitted

Caroline A. Drukker* Sjoerd G. Elias* Matthijs V. Nijenhuis* Jelle Wesseling Harry Bartelink Paula Elkhuizen Barbara Fowble Pat Whitworth Rakesh Patel Laura J. van â&#x20AC;&#x2DC;t Veer Peter D. Beitsch Emiel J.Th. Rutgers *authors contributed equally

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39

Abstract Background The 70-gene signature (MammaPrint速) has been developed to predict the risk of distant metastases in breast cancer and select those patients who may benefit from adjuvant treatment. Given the strong association between locoregional and distant recurrence, we hypothesize that the 70-gene signature will also be able to predict the risk of locoregional recurrence (LRR). Methods 1053 breast cancer patients primarily treated with breast conserving treatment (BCT) or mastectomy at the Netherlands Cancer Institute between 1984-2006 were included. Adjuvant treatment consisted of radiotherapy, chemotherapy and/or endocrine therapy as indicated by guidelines used at the time. All patients were included in various 70-gene signature studies. Results After a median follow-up of 8.96 years, patients with a high risk 70-gene signature (n=492) had a LRR risk of 12.6 % (95%CI: 9.7-15.8) at 10 years, compared to 6.1% (95%CI: 4.1-8.5) for low risk patients (n=561)(p<0.0001), respectively. Adding the 70-gene signature to a Coxproportional-hazard model including clinicopathological factors, such as age, tumor size, grade, hormone receptor status, lymphovascular invasion, axillary lymph node involvement, surgical treatment and systemic treatment (endocrine and chemotherapy) resulted in a multivariable HR of 1.73 (95%CI: 1.02-2.93; p=0.042). An increase of the C index from 0.731 (95% CI: 0.6820.782) in a prediction model with solely clinicopathological factors to 0.741 (95% CI: 0.6930.790) after adding the 70-gene signature is seen. Conclusion The 70-gene signature is able to predict the risk of LRR. A significantly lower incidence of LRR in patients with a low risk 70-gene signature result compared to those with high risk 70-gene signature result, independent of known risk factors was seen.

120 | Chapter 8

Introduction For the majority of breast cancer patients locoregional recurrence (LRR) is becoming a less common problem. Improvements in patient selection, surgical treatment, radiotherapy techniques and (neo)adjuvant systemic therapy have led to a substantial decrease in LRR incidence rates.1,2 Still, on average 3% of the patients do experience a LRR within 5 years after diagnosis. After 10 years of diagnosis this is around 6%. The favorable LRR rates raise the issue of overtreatment in women with a low or limited probability to recur locally and on the other hand undertreatment in those patients at a higher risk of LRR. Ideally, one would aim to identify patients at a high risk of LRR to better guide optimal locoregional treatment with more extensive surgery, radiotherapy and adjuvant systemic treatment, while at the same time identification of patients at a low risk of LRR can help to avoid unnecessary adjuvant radiotherapy in these patients. Currently, traditional clinicopathological factors such as age, grade, tumor size, lymphovascular invasion (LVI), hormone receptor status and involvement of axillary lymph nodes are used to predict the risk of LRR. Based on these factors, LRR rates as described earlier can be achieved, but further discrimation to assess LRR risk and to evaluate possible overtreatment seems not possible. Aside from these clinicopathological factors, the biological background of breast cancer may be of help in further assessing the risk of recurrence.3 Gene expression classifiers, such as the 70gene signature (MammaPrint速, Agendia NV, Amsterdam, the Netherlands), have proven to be a useful additional tool for assessing the risk of distant recurrence in breast cancer.4-6 The 70-gene signature has been extensively validated on historic data and has recently been prospectively evaluated in the microarRAy-prognoSTics-in-breast-cancER (RASTER) study.5-8 The results show a favorable distant-recurrence-free interval for patients with a low risk 70-gene signature result after 5 years, even in the absence of adjuvant systemic treatment and despite poor clinicopathological factors.5 The test has proven to be able to predict the risk of distant metastases in the individual patient based on the biological background of the tumor.4,6 Since locoregional recurrence is an independent predictor of subsequent distant metastases9, we hypothesize that the 70-gene signature will be able to predict the risk of LRR as well. The aim of this study is to evaluate the performance of the 70-gene signature in the prediction of LRR and its additional value to clinicopathological factors that are currently used.

Patients and methods Patients All 1053 individual breast cancer patients included in eight 70-gene signature studies, who were not included in the 70-gene signature training set and diagnosed and treated at the Netherlands Cancer Institute (NKI), were eligible for the current study (Study flow chart, Figure 1). Only NKI

70-gene signature predicts locoregional recurrence | 121

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39

treated patients were included to facilitate standardized ascertainment of locoregional events during the extended follow-up period and to allow an update of radiotherapy information. Details of study design, rationale, and patient eligibility of seven of the included studies have been described elsewhere.6,7,10-14 In short, all patients were women with histologically proven, operable, invasive breast cancer (T1-3N0-1M0), diagnosed between 1984 and 2006. All patients were primarily treated with mastectomy or breast conserving therapy (BCT), comprising lumpectomy followed by whole breast irradiation. All patients had tumor free resection margins. Radiotherapy comprised whole breast irradiation, mostly consisting of 50 Gy in 25 fractions, with or without a boost dose of 16 Gy after BCT and chestwall and/or internal mammary chain radiotherapy after mastectomy. Systemic adjuvant treatment consisted of chemotherapy and/or endocrine therapy as indicated by guidelines used at the time. One of the studies included in this pooled analyses was not yet published at the time these analyses were performed. In this study Bedard et al. included 252 women aged 65 years and older diagnosed with early stage breast cancer (T1-3N01M0) at the NKI between 1987 and 2003. None of the patients received adjuvant chemotherapy. All individual studies complied with Ethical Review Board standards. The 78 patients included in the cohort used to develop the 70-gene signature were not included in these analyses. Median follow-up of this cohort of 1053 individual patients was 8.96 years.

Figure 1. Study flow chart

122 | Chapter 8

Clinicopathological factors and the 70-gene signature Information on age, grade, estrogen receptor (ER), progesterone receptor (PR), LVI, tumor size and involvement of axillary lymph nodes were derived from the original study data that included pathological review by an expert (Hans Peterse). LVI was not documented in all studies. Therefore, tumor samples of 150 patients were revised by an experienced breast pathologist (JW) for this study. Frozen tumor samples from each patient were processed at Agendia’s laboratory (Amsterdam, the Netherlands) for RNA isolation, amplification, and labeling as described elsewhere. 4,6,15 To assess the mRNA expression level of the 70 genes, RNA was hybridized to a custom-designed array, commercially available as MammaPrint®. Agendia NV is ISO17025-certified, CLIA accredited and FDA-cleared. Tumors were classified as a 70-gene signature low or high risk at the time of the initial studies. Low risk was defined as an index-score greater than 0.4. High risk was defined as an index-score lower than 0.4.4,15 Follow-up Follow-up for locoregional recurrence and death was updated through November 2011 using data from the NKI Tumor Registry complemented with review of the original patient records. Locoregional recurrence was defined as reappearance of breast cancer in the ipsilateral breast or chest wall or ipsilateral regional lymph node involvement, six months or longer after diagnosis. Ipsilateral supraclavicular lymph node involvement was included as regional recurrence throughout the follow-up period, although TNM-editions 4 and 5 considered such recurrences M1 instead of N3. Statistical analysis Detailed description of the statistical analyses is described in appendix 1. In short, analyses for time to LRR were performed using competing risk analyses as not to overestimate the absolute LRR risk.16 For this, follow-up time started at diagnosis and ended at the first manifestation of LRR (event) or death (competing event), or at the end of follow-up without LRR or death (censored). Occurrences of distant metastases, contralateral breast cancer, or second primary tumors were not considered censoring events nor competing risks. The univariable 5- and 10-year absolute risk of LRR for the 70-gene signature high and low risk groups was estimated using the cumulative incidence function,17 and compared using Gray’s test.18 Multivariable analyses were performed using Fine and Gray competing risk regression.19 A multivariable model was constructed comprising solely of routine clinicopathological factors and treatment. To this model the 70-gene signature was added to evaluate its additional and independent prognostic value. The combined prognostic performance of the multivariable models was evaluated with regard to discrimination (Harrell’s C index adapted to competing risk analyses16) and calibration. Model improvement upon addition of the 70-gene signature was tested by the pLR test, by the improvement in C index. Analyses were performed using R version 3.0.1. All statistical tests were two-sided with a

70-gene signature predicts locoregional recurrence | 123

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39

cutoff for statistical significance of 5%. Estimates are reported together with 95% confidence intervals. For the C index, 2000-fold bootstrapping was used for statistical testing and standard error estimation.

Results Patient and tumor characteristics by 70-gene signature 70-gene signature low risk patients (n=561) were more often of older age at the time of diagnosis, had smaller tumors of a lower grade, being ER-positive, PR-positive and HER2-negative as compared to the 70- gene signature high risk patients (n=495)(Table 1). More chemotherapy was administered to 70-gene signature high risk patients, while no significant difference was seen in the administration of endocrine therapy between high and low risk patients. No significant difference was seen between high and low risk patients regarding their initial type of surgical treatment and no significant difference in the administration of radiotherapy after both types of surgical treatment. Type of LRR Through 10 years of follow-up (median 8.96 years; IQR 5.3-10), 87 LRR events occurred; in 29 patients who had a low risk 70-gene signature primary cancer and 58 who had a high risk 70-gene signature cancer. Thirty-nine of the 470 patients treated with BCT developed an LRR event compared to 47 out of 555 patients treated with mastectomy. Of one patient with a LRR event data on surgical treatment is missing. Most common site for regional recurrence was the supraclavicular area for both patients treated with BCT and with mastectomy. Association between 70-gene signature and risk of LRR Patients with a low risk 70-gene signature tumor had a LRR risk of 2.7% (95%CI: 1.6-4.3) at 5 years and 6.1% (95%CI: 4.1-8.5) at 10 years (Figure 2A). Patients with a 70-gene signature high risk tumor had a LRR risk of 9.1% (95%CI: 6.8-11.9) at 5 years and 12.6% (95%CI: 9.7-15.8) at 10 years (p<0.001). Univariable probabilities of risk of LRR stratified by surgery and radiotherapy are shown in Figure 2B, 2C and 2D.

124 | Chapter 8

Table 1. Patient and tumor characteristics stratified by 70-gene signature high and low risk. 70-GS low risk n=561 (%) Age ≤ 50 year > 50 year Tumor size Nodal status Node positive Node negative Missing Grade 1 2 3 Missing ER status Positive Negative Missing PR status Positive Negative Missing HER2 status Positive Negative Missing Surgical treatment Mastectomy Local RT Breast conserving surgery Local RT Missing Radiotherapy RT RT + boost No RT Missing Endocrine therapy Chemotherapy

188 373

(3.5) (66.5)

70-GS high risk n=492 (%) 232 260

22 mm

(47.2) (52.8) 24 mm

p-value

<0.001 0.002

271 286 4

(48) (51)

266 224 1

(54) (45.5)

233 259 48 21

(41.5) (46.1) (8.6) (3.7)

52 154 275 11

(10.5) (31.3) (55.9) (2.2)

542 16 3

(96.6) (2.9) (0.5)

318 173 1

(64.4) (35.2) (0.2)

<0.001

448 97 16

(79.8) (17.3) (2.9)

223 255 14

(45.3) (51.8) (2.8)

<0.001

20 441 100

(3.6) (78.6) (17.8)

100 320 72

(20.3) (65) (14.7)

<0.001

298 151

(53.2) (51% of MST)

257 147

(52.2) (57% of MST)

253 249 10

(45) (98% of BST) (1.8)

217 216 18

(44.1) (100% of BST) (3.7)

246 153 151 11 263 105

(43.9) (27.3) (26.9) (1.9) (46.9) (18.7)

232 129 112 19 212 188

(47.1) (26.6) (22.8) (3.9) (43.1) (38.2)

0.073

<0.001

0.13 0.38

0.175 0.238 <0.001

ER=estrogen receptor; PR=progesterone receptor; HER2=Human Epidermal growth factor Receptor 2

70-gene signature predicts locoregional recurrence | 125

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39

Figure 2. Risk of locoregional recurrence for the entire cohort stratified for 70-gene signature low risk and high risk.

Additional value of the 70-gene signature to clinicopathological factors Patients with a high risk 70-gene signature had an 2.40 times higher risk of LRR than patients with a low risk 70-gene signature (univariable hazard ratio (HR) 2.40; 95%CI: 1.54-3.74)(Table 2). Other significant prognostic factors in the univariable model were age (HR non-linear; p<0.001), grade (HR 2.91 (grade 3 vs 1); p<0.001), LVI (HR 1.83; p=0.008), ER status (HR 0.55; p=0.014) and endocrine therapy (HR 0.51; p=0.004). In a multivariable Cox proportional Hazard model including the prognostic factors mentioned earlier (model 1), the factors age (HR nonlinear; p<0.001) and LVI (HR 1.94; 95%CI: 1.16-3.25; p=0.012) were prognostic factors for the prediction of LRR based solely on clinicopathological factors. After adding the 70-gene signature

126 | Chapter 8

to model 1, the 70-gene signature showed to be an independent prognostic factor for LRR with a multivariable HR of 1.73 (95%CI: 1.02-2.93; p=0.042)(Table 2; model 2). Other significant prognostic factors in model 2 were age (HR non-linear; p<0.001), LVI (HR 1.87; 95%CI: 1.123.15; p=0.018) and adjuvant chemotherapy (HR 0.51; 95%CI: 0.26-0.98; p=0.042). Adding the 70-gene signature to clinicopathological factors resulted in an increase of the C index from 0.731 (95%CI: 0.682-0.782) in a model with solely clinicopathological factors (model 1) to 0.741 (95%CI: 0.693-0.790) after adding the 70-gene signature in model 2 (Table 2).

70-gene signature predicts locoregional recurrence | 127

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39

128 | Chapter 8 HR (95% CI)

HR (95% CI)

0.75 0.24 0.082 <0.001 0.008 0.47 0.014 0.71 0.004 0.25 0.46

1.00 ― 1.07 (0.70-1.63) 1.04 (0.97-1.12) 1.00 ― 1.76 (0.93-3.33) 2.91 (1.56-5.43) 1.00 ― 1.83 (1.17-2.85) 1.04 (0.94-1.14) 1.00 ― 0.55 (0.34-0.88) 1.00 ― 1.09 (0.69-1.72) 1.00 ― 0.51 (0.32-0.80) 1.00 ― 0.76 (0.48-1.21) 1.00 ― 0.83 (0.50-1.36)

5.2% (13) 8.8% (33) 13.0% (40) 7.3% (47) 12.8% (40) Continuous 13.3% (24) 8.2% (63) 8.9% (61) 9.8% (26) 11.6% (61) 6.1% (26) 10.5% (26) 8.6% (61) 9.4% (66) 8.5% (21)

1.00 ― 0.84 (0.47-1.49) 0.731 (0.682-0.782)

1.00 ― 0.69 (0.32-1.48)

1.00 ― 0.67 (0.40-1.11)

1.00 ― 0.53 (0.27-1.02)

1.00 ― 0.86 (0.47-1.58)

1.00 ― 1.94 (1.16-3.25) 1.07 (0.95-1.21)

1.00 ― 1.57 (0.82-3.00) 2.12 (1.01-4.45)

1.00 ― 0.72 (0.39-1.33) 1.03 (0.93-1.14)

<0.001 <0.001 Non-linear

9.3% (39) 9.0% (48) Continuous

6.1% (29) 1.00 ― 12.6% (58) 2.40 (1.54-3.74) Continuous Non-linear

10-yr risk (N events)1 P

HR (95% CI)

0.54

0.34

0.12

0.058

0.63

0.012 0.28

0.17 0.047

0.29 0.60

1.00 ― 0.86 (0.48-1.52) 0.741 (0.693-0.790)

1.00 ― 0.68 (0.32-1.44)

1.00 ― 0.67 (0.40-1.12)

1.00 ― 0.51 (0.26-0.98)

1.00 ― 0.96 (0.53-1.74)

1.00 ― 1.87 (1.12-3.15) 1.07 (0.95-1.21)

1.00 ― 1.41 (0.72-2.78) 1.60 (0.72-3.56)

1.00 ― 0.72 (0.39-1.34) 1.03 (0.93-1.15)

0.60

0.31

0.12

0.042

0.89

0.018 0.26

0.32 0.25

0.31 0.56

0.042 0.001

Multivariable Model 2 Traditional + 70-gene signature

1.00 ― 1.73 (1.02-2.93) <0.001 Non-linear

Multivariable Model 1 Traditional predictors

Events may not add-up due to averaging over multiple imputation datasets; 2Modelled with linear tail-restricted cubic spline function with 4 degrees of freedom (see Supplementary Figure 1)

70-gene signature Low risk High risk Age at surgery (years)2 Surgical procedure Breast conserving Mastectomy Tumor size (per 5 mm) Tumor grade Grade 1 Grade 2 Grade 3 Lymphovascular invasion Absent Present Axillary status (per positive node) Estrogen-receptor status Negative Positive Adjuvant chemotherapy No Yes Adjuvant endocrine therapy No Yes Local radiotherapy No Yes Radiotherapy boost No Yes C-index

Parameter

Univariable analysis

Table 2. Risk of locoregional recurrence within 10 years of breast cancer diagnosis according to the 70-gene signature and routine clinicopathological factors – Fine and Gray competing risk regression analysis.

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39

Discussion The 70-gene signature is an independent prognostic factor in the prediction of LRR in breast cancer after adequate primary treatment with BCT or mastectomy. A significantly higher risk of LRR after 10 years is seen in patients with a high risk 70-gene signature result (12.6%) compared to patients with a low risk 70-gene signature (6.1%; p<0.001). In a multivariable competing risk model including known prognostic factors and treatment, the 70-gene signature is an independent significant predictor of LRR (HR 1.73; 95%CI: 1.02-2.93; p=0.042). Other prognostic factors include age, LVI and adjuvant chemotherapy. Adding the 70-gene signature to clinicopathological factors improved the C index of this model, indicating the additional value of the 70-gene signature as a prognostic factor for LRR. The results of our study show potential clinical value for the 70-gene signature in the prediction of LRR. For instance, it is arguable whether there is added value of whole breast irradiation for 70-gene signature low risk patients treated with breast conserving surgery. A recent study already showed that whole breast irradiation after BCT might be of limited value in specific subgroups of patients.20 In our study the LRR risk for 70-gene signature low risk patients treated with BCT was as low as 5.8% after 10 year after receiving whole breast irradiation. Even though their risk of LRR is lowered by half due to radiotherapy 21, the annual estimated risk of recurrence in this group would still be around 1%, which is a generally accepted risk of LRR.22 Especially for those patients with ER-positive disease, who will also receive adjuvant endocrine therapy, the risk of LRR will be low.23,24 Therefore, the 70-gene signature may aid in the identification of patients for whom radiotherapy can be safely omitted after BCT. On the other hand, the question rises whether 70-gene signature high risk patients who were treated with mastectomy without receiving radiotherapy should have received radiotherapy to reduce their risk of LRR which is 13.8% at 10 years in this cohort. This suggests that the 70-gene signature may also be helpful to identify patients at a high risk of recurrence who are eligible for radiotherapy after mastectomy. Validation of this finding is planned to evaluate whether these suggested clinical implications are supported in a larger, independent cohort. This study was conducted in the largest known patient cohort with long-term follow-up for whom gene-expression data as well as data on LRR was collected. There are two other studies reporting on the use of a gene-expression classifier to predict the risk of LRR. Mamounas et al. describe 73 LRR events among 895 patients for whom a 21-gene recurrence score was available from the NSABP B-14 and B-20 trials25. Their results show a 10-year LRR risk of 15.8% for patients with a high risk recurrence score after treatment with tamoxifen and 4.3% among patients with a low risk recurrence score. Another study by Solin et al. describes only 30 LRR events among 388 patients for whom a 21-gene recurrence score was available from the Eastern Cooperative Oncology Group E2197 study.26 This study also reports 10-year LRR rates in the 21-gene recurrence score high risk group of 8.7% and 3.7% in the low risk group. In the NSABP study all patients included were treated with tamoxifen and a large proportion of these patients (n=227) were also

70-gene signature predicts locoregional recurrence | 129

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39

used as a training set to develop the 21-gene recurrence score, which increases the probability of overfitting.27 Both studies included only patients who were treated with BCT. The novelty in our study is the additional subgroup analyses of patients treated with mastectomy with and without receiving radiotherapy, showing additional value of the 70-gene signature especially in BCT treated patients and patients who received radiotherapy after mastectomy. Missing data in our database was imputed, which makes the analyses more reliable. Still, imputing missing data creates a margin of uncertainty. The studies that were included in this pooled dataset were conducted between 1984 and 2006. During these 22 years the management of breast cancer changed. Not only adjuvant systemic treatment options improved, but also population-based screening programs were introduced, leading to an increasing incidence of tumors with favorable prognostic factors.28 The definitions for high and low risk used by clinical guidelines to guide adjuvant systemic treatment decisions were adjusted accordingly. It is not possible to adjust for all these time-related factors. In conclusion, the 70-gene signature is able to predict the risk of LRR, independent of known clinicopathological factors including the involvement of axillary lymph nodes. With this study the first step is taken in the search for a gene-expression profile that is of added value to traditional clinicopathological factors and can help select those patients who will have benefit of limited locoregional treatment and those who will have benefit of a more extensive locoregional treatment.

Contributors CD, ER, and LvtV were responsible for the study design. No financial support was needed to perform this study. SE, CD and LvtV collected follow-up data on all validation studies collected in the 70-gene signature database used for this study. CD and MN collected data on surgical treatment, radiotherapy and resection margins. SE performed data analyses. CD, MN, SE, ER, PB, LvtV, HB, PE, and BF took part in data interpretation. CD, MN, SE, and ER took part in manuscript writing. All authors were involved in reviewing the report. Conflict of interest LvtV is named inventor on the patent for the 70-gene signature used in this study. LvtV reports being shareholder in and employed by Agendia NV, the commercial company that markets the 70-gene signature as MammaPrintÂŽ. LvtV was supported by the Dutch Genomics Initiative â&#x20AC;&#x2DC;Cancer Genomics Centreâ&#x20AC;&#x2122;. HB is a non-remunerated, non-stake holding member of the supervisory board of Agendia NV.

130 | Chapter 8

Acknowledgements We acknowledge the enormous efforts of M. van de Vijver, M. Buyse, P. Bedard, J. Bueno-deMesquita, S. Mook, M. Kok, M. Saghatchian, and colleagues to perform the various 70-gene signature studies included in this study. We thank F. de Snoo for her input in this collaboration and N. Russell for her input on the interpretation of the preliminary data. We especially thank the data-managers at the Netherlands Cancer Institute for all their efforts in collection of the follow-up data.

70-gene signature predicts locoregional recurrence | 131

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39

References 1

Clemons M, Danson S, Hamilton T, Goss P. Locoregionally recurrent breast cancer: incidence, risk factors and survival. Cancer Treat Rev 2001; 27:67-82.

van der Heiden-van der Loo, Ho VK, Damhuis RA, Siesling S, Menke MB, Peeters PH et al. [Percentage of local recurrence following treatment for breast cancer is not a suitable performance indicator]. Ned Tijdschr Geneeskd 2010; 154:A1984.

van ‘t Veer LJ, Paik S, Hayes DF. Gene expression profiling of breast cancer: a new tumor marker. J Clin Oncol 2005; 23:1631-5.

van ‘t Veer LJ, Dai H, van de Vijver MJ, He YD, Hart AA, Mao M et al. Gene expression profiling predicts clinical outcome of breast cancer. Nature 2002; 415:530-6.

van de Vijver MJ, He YD, van ‘t Veer LJ, Dai H, Hart AA, Voskuil DW et al. A gene-expression signature as a predictor of survival in breast cancer. N Engl J Med 2002; 347:1999-2009.

Wapnir IL, Anderson SJ, Mamounas EP, Geyer CE, Jr., Jeong JH, Tan-Chiu E et al. Prognosis after ipsilateral breast tumor recurrence and locoregional recurrences in five National Surgical Adjuvant Breast and Bowel Project node-positive adjuvant breast cancer trials. J Clin Oncol 2006; 24:2028-37.

Glas AM, Floore A, Delahaye LJ, Witteveen AT, Pover RC, Bakx N et al. Converting a breast cancer microarray signature into a high-throughput diagnostic test. BMC Genomics 2006; 7:278.

Wolbers M, Koller MT, Witteman JC, Steyerberg EW. Prognostic models with competing risks: methods and application to coronary risk prediction. Epidemiology 2009; 20:555-61.

Aalen O. Nonparametric estimation of partial transition probabilities in multiple decrement models. Annals of Statistics 1978;534-45.

Gray RJ. A class of K-sample tests for comparing the cumulative incidence of a competing risk. Annals of Statistics 1988;1141-54.

132 | Chapter 8

Fine JP, Gray RJ. A proportional hazards model for the subdistribution of a competing risk. JASA 1999; 94:496-509.

Gatzemeier W, Andreoli C, Costa A, Gentilini MA, Tinterri C, Zanini V et al. Multi-centre randomised prospective trial on breast conservative surgery (BCS) with or without whole breast irradiation (WBI) in postmenopausal women aged 55-75 years and low in-breast-recurrence risk: analysis after 9 years median follow-up - RT 55-75 Study Group. [Abstract no. 2007 ECCO 2013]. 2013. Ref Type: Generic

Darby S, McGale P, Correa C, Taylor C, Arriagada R, Clarke M et al. Effect of radiotherapy after breast-conserving surgery on 10-year recurrence and 15-year breast cancer death: meta-analysis of individual patient data for 10,801 women in 17 randomised trials. Lancet 2011; 378:1707-16.

Rutgers EJ. Quality control in the locoregional treatment of breast cancer. Eur J Cancer 2001; 37:44753.

Fyles AW, McCready DR, Manchul LA, Trudeau ME, Merante P, Pintilie M et al. Tamoxifen with or without breast irradiation in women 50 years of age or older with early breast cancer. N Engl J Med 2004; 351:963-70.

Hughes KS, Schnaper LA, Bellon JR, Cirrincione CT, Berry DA, McCormick B et al. Lumpectomy plus tamoxifen with or without irradiation in women age 70 years or older with early breast cancer: longterm follow-up of CALGB 9343. J Clin Oncol 2013; 31:2382-7.

Mamounas EP, Tang G, Fisher B, Paik S, Shak S, Costantino JP et al. Association between the 21gene recurrence score assay and risk of locoregional recurrence in node-negative, estrogen receptorpositive breast cancer: results from NSABP B-14 and NSABP B-20. J Clin Oncol 2010; 28:1677-83.

Solin LJ, Gray R, Goldstein LJ, Recht A, Baehner FL, Shak S et al. Prognostic value of biologic subtype and the 21-gene recurrence score relative to local recurrence after breast conservation treatment with radiation for early stage breast carcinoma: results from the Eastern Cooperative Oncology Group E2197 study. Breast Cancer Res Treat 2012; 134:683-92.

Azim HA, Jr., Michiels S, Zagouri F, Delaloge S, Filipits M, Namer M et al. Utility of prognostic genomic tests in breast cancer practice: The IMPAKT 2012 Working Group Consensus Statement. Ann Oncol 2013; 24:647-54.

Mook S, van â&#x20AC;&#x2DC;t Veer LJ, Rutgers EJ, Ravdin PM, van de Velde AO, van Leeuwen FE et al. Independent prognostic value of screen detection in invasive breast cancer. J Natl Cancer Inst 2011; 103:585-97.

van Buuren S., Groothuis-Oudshoorn K. mice: Multivariate Imputation by Chained Equa- tions in R. Journal of Statistical Software 2011; 45:1-67.

Moons KG, Donders RA, Stijnen T, Harrell FE, Jr. Using the outcome for imputation of missing predictor values was preferred. J Clin Epidemiol 2006; 59:1092-101.

Marshall A, Altman DG, Holder RL, Royston P. Combining estimates of interest in prognostic modelling studies after multiple imputation: current practice and guidelines. BMC Med Res Methodol 2009; 9:57.

70-gene signature predicts locoregional recurrence | 133

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39

Supplementary Figure 1. Relation between age at diagnosis and locoregionaal recurrence risk (linear+linear tail-restricted cubic spline function with 4df)

134 | Chapter 8

Appendix 1. Detailed description of statistical analyses Standard descriptive statistics were used to describe the study population and to compare the distribution of routine clinicopathological factors between 70-gene signature high and low risk patients. Analyses for time to LRR were performed using competing risk analyses as not to overestimate the absolute LRR risk.16 For this, follow-up time started at diagnosis and ended at the first manifestation of LRR (event) or death (competing event), or at the end of follow-up without LRR or death (censored). Follow-up beyond 10 years was truncated. Occurrences of distant metastasis, contralateral breast cancer, or second primary tumors were not considered censoring events nor competing risks. The univariable 5- and 10-year absolute risk of LRR for the 70-gene signature high and low risk groups was estimated using the cumulative incidence function,17 and compared using Grayâ&#x20AC;&#x2122;s test.18 This was performed for the entire cohort, but also in subgroups according to primary locoregional treatment (BCT, or mastectomy with or without radiotherapy). Multivariable analyses were performed using Fine and Gray competing risk regression.19 Following univariable regression analyses, a multivariable model was constructed comprising solely of routine clinicopathological factors and treatment. To this model the 70-gene signature (high vs low risk) was added to evaluate its additional and independent prognostic value. The primary model with routine clinicopathological factors consisted of age (continuous), grade (2 vs 1 and 3 vs 1), tumor size (continuous), estrogen receptor (positive vs negative), number of tumor involved lymph nodes (continuous), surgery (mastectomy vs BCT), adjuvant chemo-, endocrine- and radiotherapy (all yes vs no), and radiotherapy boost (yes vs no). HER2 (positive vs negative) was only included in auxiliary analyses, particularly because none of the study patients received trastuzumab, hampering the generalizibility of these results to contemporary patients. Furthermore, HER2 is not regularly considered in locoregional treatment decisions. At least one routine clinicopathological factor was missing for 24% of patients (30% including HER2). As analyses leaving out patients with missing data is less efficient and may lead to biased results, multiple imputation by chained equation was used to account for missing data (10 imputation datasets, 25 iterations, healthy convergence).29 The imputation model included all available clinicopathological factors including patient outcome data.30 All regression analyses were performed separately in each imputation dataset and then combined using Rubinâ&#x20AC;&#x2122;s rules.31 Evaluation of the linearity assumption with restricted cubic splines showed that a spline for age significantly improved the primary model (pseudo-likelihood ratio (pLR) test P=0.049), and this spline was retained in all analyses. Tumor size and number of involved lymph nodes showed no departure from linearity. As previous analyses of the 70-gene signature and distant recurrence risk and survival suggested that its prognostic value may decrease with time since diagnosis, a time-covariate interaction (tâ&#x2030;Ľ5 years*70-gene signaturehigh risk) was added to the 70-gene signature extended primary model. This significantly improved the model (pLR test P=0.019), and this interaction was retained. The above regression analyses were performed in the entire cohort.

70-gene signature predicts locoregional recurrence | 135

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39

There was no indication that the effect of the 70-gene signature on LRR risk differed between subgroups according to primary locoregional treatment (interaction terms were not statistically significant). Besides assessing the independent prognostic value of the 70-gene signature for LRR (i.e. the evaluation of the adjusted hazard ratio (HR)), the combined prognostic performance of the multivariable models was evaluated with regard to discrimination (Harrellâ&#x20AC;&#x2122;s C index adapted to competing risk analyses16) and calibration. A C index of 1 indicates perfect discrimination (i.e. all patients with LRR have higher predicted recurrence risk than those without), whereas 0.5 means as poor discrimination as predictions based on just the average LRR risk. Calibration was assessed by plotting model predicted versus actual observed risk. Model improvement upon addition of the 70-gene signature was tested by the pLR test, by the improvement in C index. Discrimination and calibration were evaluated at 5 and 10 years following diagnosis, for the entire cohort, as well as in primary locoregional treatment subgroups (using predicted probabilities derived from the models fitted in the entire cohort). Analyses were performed using R version 3.0.1. All statistical tests were two-sided with a cutoff for statistical significance of 5%. Estimates are reported together with 95% confidence intervals. For the C index, 2000-fold bootstrapping was used for statistical testing and standard error estimation.

136 | Chapter 8

70-gene signature predicts locoregional recurrence | 137

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39

Gene signature for risk stratification and treatment of brea Incorporating tumor biology in clinical decision-making

Chapter 9

of breast cancer

king

General discussion and future prospects

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39 140 | Chapter 9

Several years ago the ultimate goal to personalize medicine was set by the medical society worldwide. Breast cancer treatment should be tailored to fit the individual patient and the unique characteristics of the tumors they are diagnosed with. Understanding the biology of breast cancer and translating this knowledge into new treatment options that will change clinical practice are the steps to be taken in the journey towards reaching this ultimate goal. Personalizing medicine starts by further stratifying patients in subgroups based on clinicopathological factors and molecular subtypes and adjusting screening, risk assessment and treatment decisions accordingly. Prognostic factors and clinical guidelines Until recently, solely clinicopathological factors such as age, tumor size, grade, hormone receptor status, HER2 status and involvement of the axillary lymph nodes were used to estimate a patients risk of recurrence and the benefit that could be derived from adjuvant treatment.1-3 Most of these clinicopathological factors are included in clinical guidelines and risk estimation tools such as Adjuvant! Online (AOL), the Nottingham Prognostic Index (NPI) and the St. Gallen expert panel recommendations.1-3 The definition of high and low risk and the importance of the individual clinicopathological factors can be debated, as described in chapter 2, 3 and 4. Currently, only 15% to 20% of the node-negative breast cancer patients are identified as low risk based on risk assessments by clinical guidelines, while the other 80% to 85% is classified as high risk and therefore considered eligible to receive adjuvant chemotherapy.4 An overview by the Early Breast Cancer Trialistsâ&#x20AC;&#x2122; Collaborative Group shows that approximately 70% of the node-negative breast cancer patients remains free of distant metastases at 10 years in the absence of adjuvant systemic therapy.4 This indicates that a substantial number of patients is currently treated with adjuvant chemotherapy without deriving significant benefit from it.5 The potential benefit of adjuvant systemic therapy is most debatable in patients who balance on the edges of the definitions of high and low risk. Often these patients are between 45 and 55 years old at the time of diagnosis and have a grade II, ER-positive, HER2-negative breast cancer of 1-2 cm. To improve accuracy in the selection of patients who will benefit from adjuvant chemotherapy, further stratification of patients into specific subgroups is necessary. Stratifying solely based on clinicopathological factors has proven to be insufficient. A new approach on breast cancer from a more biological point of view may be helpful. Insight in the biology of breast cancer: using gene signatures An important step towards personalized or stratified medicine was the introduction of microarray analyses. At first, breast tumors were stratified into molecular subtypes, as first identified by Perou et al. Four types of breast cancer related to different features of molecular tumor biology were identified from 65 specimens from 42 different patients using unsupervised analysis: Luminal A, Luminal B, Basal-like and ERBB2 type breast cancers.6 Soon after, supervised analysis was used to compare gene-expression data from patients with known clinical outcomes to identify

General discussion and future prospects | 141

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39

genes associated with prognosis. Based on the knowledge derived from these analyses, gene signatures were developed. The 70-gene signature was the first gene signature described and the results were published in 2002.7 Other gene signatures, such as the Oncotype Dx recurrence score (2004), PAM50 (2009) and EndoPredict (2011), followed a few years later (chapter 1).8-10 A cause of confusion among clinicians is that the group of patients for whom the different gene signatures that are commercially available can be used is inconsistent. For example, the Oncotype Dx test is only validated in patients with ER-positive, node-negative disease who are treated with Tamoxifen, while the Pam50 test can be used in patients with ER-positive, node-negative disease treated with any type of adjuvant endocrine therapy.8,9 The 70-gene signature is validated for patients with ER-positive or ER-negative, node-negative disease.11,12 Because the 70-gene signature is the most commonly used gene signature in the Netherlands and extensive experience is gained with this prognostic tool in our country, this gene signature was used in the studies described in this thesis. Gene signatures, like the 70-gene signature, are able to identify patients at a high risk of recurrence based on the biological background of the tumor, and are therefore creating a new perspective on risk assessment. As one can anticipate, for a subgroup of patients discordance is seen between risk assessment by clinical guidelines and by a gene signature. A substantial proportion of breast cancer patients is considered high risk based on clinicopathological factors, but low risk according to a gene signature and the other way around. This discordancy is especially prevalent in the subgroup of patients described earlier who are balancing on the thresholds for clinically high and low risk (chapter 3). This suggests that gene signatures can be especially helpful in case of uncertainty about the benefit that an individual patient will derive from adjuvant systemic treatment. Initially, the 70-gene signature was developed for patients < 55 years old with stage 1 or 2, ER-positive or -negative, node-negative breast cancer. In the many additional validation studies performed in the last decade, the test was also retrospectively validated for breast cancer patients who are 55-70 years of age, patients with HER2-positive disease, and patients with 1-3 positive nodes in the axilla (chapter 2).13-16 Recently, the 5-year follow-up results of the RASTER study were became available (chapter 3). This is the first prospective data on patients for whom the 70-gene signature was used in the decision whether or not to treat a patient with adjuvant chemotherapy. An excellent survival is seen in patients who did not receive adjuvant chemotherapy based on their low risk 70-gene signature result. Even patients who had unfavorable clinicopathological factors and therefore a high clinical risk assessment according to AOL, had a distant-recurrence-free-interval of 100%. Even though no randomization was included in the design of the RASTER study, this study provides an accurate reflection of daily clinical practice where the 70-gene signature will be used in addition to clinical guidelines. The RASTER study provides the first prospective data suggesting that chemotherapy can safely be omitted in case of a low risk 70-gene signature. These results might justify use of the 70-gene signature in daily

142 | Chapter 9

clinical practice when in doubt whether a patient will benefit from adjuvant systemic therapy, even though this assumption is based on data with a relatively short follow-up time. While the prognostic value of the 70-gene signature was thoroughly investigated, knowledge about longterm outcome was limited. An update of the consecutive validation series of van de Vijver et al (chapter 6) showed even after 25 years a significantly better survival in patients with a low risk 70-gene signature compared to patients with a high risk 70-gene signature (p<0.0001). The 25year Hazard Ratios (HR) for distant-metastasis-free-survival (DMFS) and Overall Survival (OS) were 3.1 (95%CI: 2.02-4.86) and 2.9 (95%CI: 1.90-4.28), respectively. Both HRâ&#x20AC;&#x2122;s were largest in the first five years after diagnosis (9.6 (95%CI: 4.2-22.1) and 11.3 (95%CI: 3.5-36.4) respectively), indicating that the 70-gene signature is a strong prognostic factor for DMFS in the first five years after diagnosis. After the first 5 years this effect slowly diminished. Personalized or stratified medicine not only consists of adjuvant therapy for systemic control, but also locoregional treatment with radiotherapy to achieve optimal locoregional control. The estimations of the risk of locoregional recurrence (LRR) are currently also based on clinicopathological factors, especially age, lymphovascular invasion and axillary lymph node involvement. LRR rates of 6% after 10 years can be achieved using these conventional factors, which is reasonable but still suboptimal.17 Further stratification based on the previously mentioned molecular subtypes defined by Perou et al. does not improve LRR risk estimations as was shown in a recent study by Metzger-Filho et al. In this study 1951 node-negative early stage breast cancer patients were stratified into molecular subtypes and their risk of LRR was estimated.18 The results show no significant difference in LRR risk after 13 years for all four subtypes, indicating the need for better estimation methods for the risk of LRR. Even though the 70-gene signature was developed to predict the risk of distant metastases,7 given the strong association between locoregional and distant recurrence19, we hypothesized that the 70-gene signature will also be able to predict the risk of LRR. Incorporating tumor biology in the risk assessment of LRR enables us to identify the outliers with a very low risk of recurrence after breast conserving surgery who might not benefit from whole breast irradiation. On the other hand, it might also identify a subgroup of patients at such a high risk of recurrence after mastectomy that these patients are eligible to receive chest wall irradiation after surgery (chapter 8). A randomized controlled trial to validate the findings reported in this thesis and to evaluate whether for patients with a low risk 70-gene signature result whole breast irradiation can safely be avoided is currently considered. A trial like this could be the first step towards more restrictive adjuvant locoregional treatment with radiotherapy. Whether the 70-gene signature in its current form is sufficient or a gene signature especially developed to predict LRR is required to optimize the prediction of LRR is unknown. This optimization of LRR risk prediction will not only lower LRR rates, but may eventually lead to more restrictive and minimally invasive surgical treatment for specific subgroups of patients.

General discussion and future prospects | 143

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39

Low or high: who knows? As described earlier, one of the main issues in current clinical practice is the large amount of discordance between clinical guidelines. Adding another prognostic factor, like the 70-gene signature, showed a slight improvement in agreement among oncologists regarding their risk estimations. Nevertheless, the most important conclusion of chapter 5 is that the agreement among oncologists regarding risk estimations and adjuvant systemic treatment recommendations is lower than expected and desired. This again underlines the need for more standardization in breast cancer treatment. The use different and sometimes multiple guidelines by a single oncologist might (partially) explain why the agreement is not as high as we would like it to be. To evaluate whether this lack of agreement among these oncologists in these cases is representative for clinical practice, a similar survey will be performed among a larger number of oncologists. The performance of the most commonly used guidelines and the additional value of the 70-gene signature was investigated to further evaluate the magnitude of the variance in risk estimations and whether the 70-gene signature can contribute in resolving this lack of agreement among oncologists (chapter 4). Clinical guidelines, such as Adjuvant! Online (AOL), Nottingham Prognstic Index (NPI), the St. Gallen expert panel recommendations, the Dutch National guidelines of 2004 and the updated version of 2012, and the relatively new online risk estimation tool PREDICT plus, have similar but slightly different definitions of high and low risk.20 The use of clinicopathological factors among these clinical guidelines differs. For example, NPI uses tumor size, number of involved axillary lymph nodes and grade, while AOL also incorporates age, co-morbidities and ERstatus.1,3 PREDICT plus is the only risk estimation tool which takes both HER2-status and method of detection into account.21 Among all tools and guidelines included in the analyses, NPI, the Dutch CBO 2004 guidelines and the PREDICT plus tool are the most restrictive, meaning they classify a relatively large proportion of patients as low risk. The results of our analyses show that adding the 70-gene signature to clinical guidelines improves risk predictions. The most accurate risk predictions were seen when using the PREDICT plus tool in combination with the 70-gene signature, suggesting that a more restrictive guideline combined with a gene signature may be the best way to select patients for whom adjuvant chemotherapy can safely be omitted. One should consider that incorporating HER2-status and method of detection might attribute to the improvement of clinical risk estimations made by PREDICT plus. The importance of especially the method of detection is also addressed in chapter 7. Breast cancer screening Already decades ago, it was hypothesized that to optimize breast cancer treatment the disease needs to be diagnosed as early as possible. Therefore, the population-based screening programs were introduced. Screening entails the examination of a group of asymptomatic individuals to detect disease at an earlier stage.22 The rationale for the introduction of nation-wide screening programs is that if breast cancer is detected at an earlier stage, more treatment options would

144 | Chapter 9

be available, prognosis would improve and mortality would decline. The latter was achieved, alongside with a remarkable increase in incidence.22,23 This increased incidence has been suggested to be due to the detection of slow growing tumors that would never have caused symptoms or death, i.e. overdiagnosis (Figure 1).24 The possibility of overdiagnosis in breast cancer due to screening was already described in 1982 by Lundgren et al.25 Several studies have tried to quantify the proportion of patients that are overdiagnosed.26 The estimated proportion of overdiagnosis is Europe ranges from 1% to 10%. In the Netherlands this estimate is 2.8% after adjustment for breast cancer risk and lead time.26 Identification of the patients that are overdiagnosed is difficult. We hypothesized that the 70-gene signature would be able to identify a subgroup of patients with screen-detected cancers with such a low risk of recurrence that these cancers are not likely to have become symptomatic without screening, even though they are invasive cancers. Since screening has proven to detect a substantial number of high risk cancers at an early stage, screening programs will continue to exist. Therefore, overdiagnosis can not be avoided, but overtreatment can. Identifying those individual patients that are currently overdiagnosed will help to avoid overtreatment in this subgroup. Previous analyses showed that screen-detected cancers are associated with favorable survival, independent of known prognostic factors.23 The question was raised whether screen-detected cancers would also have a more favorable tumor biology. Our group previously evaluated the 70-gene signature result of a group of patients diagnosed before the introduction of screening and compared this to the 70-gene signatures of screen-detected cancers and concluded that the introduction of screening leads to the detection of tumors with a more favorable tumor biology.27 In our recent analyses, described in chapter 7, we showed that screen-detected cancers are more likely to have a low or even ultralow risk tumor biology, which prospectively validates our previous analyses.27 Especially for this screen-detected patients the use of tools to differentiate breast cancers by risk of recurrence may minimize overtreatment. The ultralow risk tumors were defined as having a 70-gene signature index-score >0.6. In the original 78 patients from the van â&#x20AC;&#x2DC;t Veer training set, no distant metastasis were seen in this group after 5 years.7,27 Whether this ultralow risk group has a significantly better survival than patients with an index-score between 0.4 and 0.6, first needs to be evaluated in a larger, independent cohort before the results of our study can be translated into changes in the treatment of these screen-detected ultralow risk tumors. Preliminary analyses evaluating outcome of a small group of patients with screen-detected cancers for whom a 70-gene signature (n=107) was available shows that patients with an ultralow risk 70-gene signature have a better breast cancer specific survival (96.7%) compared to patients with a low (91.3%) or high risk (78.8%; p=0.06) 70-gene signature result (data not published). The group of patients with data on method of detection and biological background was too small to draw any firm conclusion, but it shows a trend towards excellent survival in patients with an ultralow risk tumor biology.

General discussion and future prospects | 145

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39

Size Size at which cancer causes death Size at which cancer causes symptoms

Fast

Slow Very Slow Non-progressive

Overdiagnosis is detecting any one of these

Regressive Abnormal cell

Time

Death from other causes

Figure 1. Overdiagnosis due to breast cancer screening. Adapted with permission from prof. dr. H.G. Welch

Another issue that needs to be addressed in the near future is the selection of women eligible for screening outside of the national screening programs. Currently, women with a BRCA1 or BRCA2 mutation and women with a strong hereditary predisposition are offered annual inhospital screening. If a large proportion of the genetically high risk tumors has a strong hereditary component, one could hypothesize that more extensive breast cancer screening in these families might be appropriate. While at the same time more limited screening might be sufficient in families were no cases of breast cancer or a single genetically low risk case have presented. More customized risk-adapted screening should also be based on the presence of lifestyle- and environmental risk factors. Diagnostic imaging During implementation of the national screening programs, all screening mammographies were conducted using film-screen mammography (FSM). Over past couple of years a transition to full-field digital mammography (FFDM) has taken place. In 2010, 94% of the Dutch women participating in the screening program were screened using FFDM.28 Several studies have shown that FFDM is comparable or even better than FSM in the detection of clinically relevant tumors, especially in pre- or perimenopausal women with dense breasts.29-31 Whether the introduction of this new screening modality has led to the detection of different types of tumors from a biological point of view was unknown. Our results suggest that the transition from FSM to FFDM resulted in the detection of a larger proportion of biologically high risk tumors, which may indicate that FFDM is a more effective screening-modality than FSM. One should be aware that this study involves only patients were diagnosed with breast cancer. The true impact of the transition from FSM to FFDM can only be evaluated in a group of patients representative for the entire screened

146 | Chapter 9

population, including patients who developed breast cancer and patients who did not. The report of the Dutch screening facilities, who are currently collecting data on both screening modalities, is still awaited. Aside from conventional mammography, several other imaging techniques are used for evaluating suspicious lesions of the breast, such as magnetic resonance imaging (MRI), ultrasound and molecular imaging of the breast using positron emission tomography (PET-CT). Several studies have shown that for monitoring BRCA-mutated women the MRI is the most accurate modality. MRI has also been shown to be useful in monitoring response to neoadjuvant chemotherapy, especially in patients with triple-negative and HER2-positive tumors.32 On the other hand, this technique has proven to be less accurate in monitoring the response in patients with ER-positive, HER2-negative tumors.32 This indicates that stratification into specific subtypes is also relevant when choosing the imaging techniques that will be used for screening and monitoring of breast cancer. Gene signatures in current clinical practice Gene signatures are already increasingly used in daily clinical practice. Especially when in doubt of the benefit that the individual patient can derive from adjuvant therapy, locoregional as well as systemic, the 70-gene signature can be a useful tool. Knowledge of the heterogeneity of breast cancer and the increasing insight in the biological background of this disease, enables us to move forward towards personalized medicine by incorporating this knowledge in clinical decision-making. In the thesis of Jolien Bueno de Mesquita and Stella Mook the first steps were taken towards the implementation of gene signatures in clinical practice. They retrospectively validated the 70-gene signature for early stage, node-negative breast cancer as well as for different subgroups of patients, confirmed its feasibility in an observational study and evaluated its additional value and potential use in the clinic. The cost-effectiveness of the 70-gene signature was confirmed by Valesca RetĂ¨l and in a head-to-head comparison to the 21-gene assay of Oncotype DX, the 70-gene signature was most cost-effective in terms of quality adjusted life years.33 In this thesis, we showed that the 70-gene signature accurately predicts outcome and has clinical relevance in estimating the risk of distant metastasis as well as locoregional recurrence. We evaluated the impact on risk assessment and AST recommendations, and evaluated its additional value to established clinical guidelines used in breast cancer treatment. In addition, we used the 70-gene signature to evaluate the biological background of tumors detected in a populationbased screening program. Even though full incorporation of gene signatures in clinical practice appears close, some areas need further investigation. In the process of further stratifying patients into different subgroups, a group of patients with triple-negative breast cancer originated. Triple-negative breast cancer is associated with a relatively poor prognosis in the first five years of follow-up. Yet, around 60% of node-negative triple-negative breast cancer patients are cured by locoregional therapy alone,

General discussion and future prospects | 147

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39

without additional adjuvant chemotherapy.34 Unfortunately, the 70-gene signature labels only a few percent of all triple-negative breast cancers as low risk, illustrating the inability of this gene signature to identify the majority of triple negative breast cancers with a good prognosis. Also, the majority of breast cancers evaluated in the numerous validation studies were invasive ductal carcinomas. Other histological types and especially the lobular carcinomas were underrepresented and further evaluation of these specific histological types is necessary, but challenging because of the low prevalence of these types of cancer. Currently, data from randomized studies evaluating the predictive value of the 70-gene signature is not yet available. Of course, the results of the MINDACT trial will provide a definite answer to the question whether chemotherapy can safely be omitted in case of a low risk 70-gene signature result. In many recently presented trial outcomes, such as the 5-year results of the AMAROS trial, we see that studies are underpowered due to very low numbers of events.35 This results in longer follow-up time necessary to meet the criteria for accurate statistical analyses, which in turn leads to a longer delay for clinicians to translate the results into clinical practice. Whether the improvement in overall, breast cancer specific and distant disease free survival among breast cancer patients in Europe will have an impact on the time needed to accurately evaluate the endpoints of the MINDACT trial is still unclear. Another consequence of the improvement of survival among breast cancer patients is the increasing number of late recurrences, especially among patients with ER-positive disease. The currently available gene signatures can not be used to predict these late recurrences. Future prospects Over the past few years, clinicians are more and more incorporating gene signatures in their clinical practice. Gene signatures like the 70-gene signature are nowadays used in combination with clinicopathological factors. Incorporating the result of these signatures in clinical guidelines as an additional prognostic factor is still ongoing. Once the predictive value of the 70-gene signature has been scrutinized, this gene signature may also be used to tailor adjuvant systemic treatment. Researchers at the Netherlands Cancer Institute have the ambition to change breast cancer treatment in a way that in a few decades breast cancer will be more like a chronic disease for 90% of the patients, who would otherwise have died of their metastatic disease. Targeted therapies for early stage breast cancer as well as metastatic disease will become available, because of the increasing insight in tumor biology that will be acquired over the next couple of years. Screening methods will be optimized and fitted to the personalized character of breast cancer treatment. In patient subgroups at a high risk of developing breast cancer due to hereditairy predisposition increased screening will create the possibility to detect tumors at an even earlier stage. At the same time, limited screening in women with a low risk of ever developing breast cancer will reduce overdiagnosis and overtreatment. The 70-gene signature not only created more insight in the biology of the individual tumor, but was the first step to incorporate tumor biology in clinical decision making. This revolutionary

148 | Chapter 9

approach to cancer is likely to further evolve over the next couple of years. The introduction of next generation (massive parallel) sequencing will enable us to personalize medicine faster, cheaper and more accurate than ever before.36 In a future where cancer becomes a chronic disease, the focus is likely to shift towards primary prevention. Primary prevention focuses on modifiable risk factors, such as decreasing maternal age at first childbirth, obesity, alcohol consumption and physical inactivity.37-39 To provide more individualized primary prevention strategies there is need to further investigate the relationship between known lifestyle- and environmental risk factors and tumor biology. The MINDACT lifestyle study was conducted by our group to evaluate lifestyle- and environmental risk factors within the Dutch MINDACT cohort. The results of this study are still awaited. Perhaps this revolution towards genome-guided medicine will evolve as far as genome-based screening, which will enable us to determine the perfect screening intervals and diagnostic imaging modalities, anticipate on the type of cancer that might lay ahead and adjust lifestyle- and environmental risk factors accordingly to minimize risk and maximize the chance of early detection. Concluding remarks Previous studies showed that the 70-gene signature is a promising tool for risk estimation in breast cancer based on the biology of the tumor. The results described in this thesis expand our knowledge on the use of the 70-gene signature in clinical practice and the impact it has on clinical decision-making. Not only did the results show that the 70-gene signature is an important prognostic factor in long-term retrospective and observational prospective data, the results also revealed new areas in which the 70-gene signature can be used. Using the 70-gene signature to estimate of the risk of loco-regional recurrence and to identify screen-detected cancers at a low risk of recurrence needs further evaluation, but will likely have an important influence on clinical practice.

General discussion and future prospects | 149

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39

References 1

Cardoso F. Microarray technology and its effect on breast cancer (re)classification and prediction of outcome. Breast Cancer Res 2003; 5:303-4.

Perou CM, Sorlie T, Eisen MB, van de Rijn M, Jeffrey SS, Rees CA et al. Molecular portraits of human breast tumors. Nature 2000; 406:747-52.

van ‘t Veer LJ, Dai H, van de Vijver MJ, He YD, Hart AA, Mao M et al. Gene expression profiling predicts clinical outcome of breast cancer. Nature 2002; 415:530-6.

Filipits M, Rudas M, Jakesz R, Dubsky P, Fitzal F, Singer CF et al. A new molecular predictor of distant recurrence in ER-positive, HER2-negative breast cancer adds independent information to conventional clinical risk factors. Clin Cancer Res 2011; 17:6012-20.

Paik S, Shak S, Tang G, Kim C, Baker J, Cronin M et al. A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N Engl J Med 2004; 351:2817-26.

Parker JS, Mullins M, Cheang MC, Leung S, Voduc D, Vickery T et al. Supervised risk predictor of breast cancer based on intrinsic subtypes. J Clin Oncol 2009; 27:1160-7.

van de Vijver MJ, He YD, Van’t Veer LJ, Dai H, Hart AA, Voskuil DW et al. A gene-expression signature as a predictor of survival in breast cancer. N Engl J Med 2002; 347:1999-2009.

Wittner BS, Sgroi DC, Ryan PD, Bruinsma TJ, Glas AM, Male A et al. Analysis of the MammaPrint breast cancer assay in a predominantly postmenopausal cohort. Clin Cancer Res 2008; 14:2988-93.

150 | Chapter 9

Metzger-Filho O, Sun Z, Viale G, Price KN, Crivellari D, Snyder RD et al. Patterns of Recurrence and outcome according to breast cancer subtypes in lymph node-negative disease: results from international breast cancer study group trials VIII and IX. J Clin Oncol 2013; 31:3083-90.

Gershon-Cohen J, Ingleby H, Moore L. Can mass x-ray surveys be used in detection of early cancer of the breast? J Am Med Assoc 1956; 161:1069-71.

Welch HG. Overdiagnosis and mammography screening. BMJ 2009; 339:b1425.

Lundgren B, Helleberg A. Single oblique-view mammography for periodic screening for breast cancer in women. J Natl Cancer Inst 1982; 68:351-5.

Puliti D, Duffy SW, Miccinesi G, de Koning H, Lynge E, Zappa M et al. Overdiagnosis in mammographic screening for breast cancer in Europe: a literature review. J Med Screen 2012; 19 Suppl 1:42-56.

National Evaluation Team for Breast Cancer Screening in the Netherlands. NETB report 2012.

Pisano ED, Gatsonis C, Hendrick E, Yaffe M, Baum JK, Acharyya S et al. Diagnostic performance of digital versus film mammography for breast-cancer screening. N Engl J Med 2005; 353:1773-83.

Vinnicombe S, Pinto Pereira SM, McCormack VA, Shiel S, Perry N, Dos Santos Silva IM. Full-field digital versus screen-film mammography: comparison within the UK breast screening program and systematic review of published data. Radiology 2009; 251:347-58.

Loo CE, Straver ME, Rodenhuis S, Muller SH, Wesseling J, Vrancken Peeters MJ et al. Magnetic resonance imaging response monitoring of breast cancer during neoadjuvant chemotherapy: relevance of breast cancer subtype. J Clin Oncol 2011; 29:660-6.

Retel VP, Joore MA, van Harten WH. Head-to-head comparison of the 70-gene signature versus the 21-gene assay: cost-effectiveness and the effect of compliance. Breast Cancer Res Treat 2012; 131:627-36.

Criscitiello C, Azim HA, Jr., Schouten PC, Linn SC, Sotiriou C. Understanding the biology of triplenegative breast cancer. Ann Oncol 2012; 23 Suppl 6:vi13-vi18.

Rutgers E, Donker M., Straver ME, Meijnen P., van de Velde C.J.H., Mansel R.E. et al. Radiotherapy or surgery of the axilla after a positive sentinel node in breast cancer patients: Final analysis of the EORTC AMAROS trial (10981/22023). J Clin Oncol 31. 2013.

Mardis ER. A decadeâ&#x20AC;&#x2122;s perspective on DNA sequencing technology. Nature 2011; 470:198-203.

Sprague BL, Trentham-Dietz A, Newcomb PA, Titus-Ernstoff L, Hampton JM, Egan KM. Lifetime recreational and occupational physical activity and risk of in situ and invasive breast cancer. Cancer Epidemiol Biomarkers Prev 2007; 16:236-43.

General discussion and future prospects | 151

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39

van den Brandt PA, Spiegelman D, Yaun SS, Adami HO, Beeson L, Folsom AR et al. Pooled analysis of prospective cohort studies on height, weight, and breast cancer risk. Am J Epidemiol 2000; 152:51427.

Smith-Warner SA, Spiegelman D, Yaun SS, van den Brandt PA, Folsom AR, Goldbohm RA et al. Alcohol and breast cancer in women: a pooled analysis of cohort studies. JAMA 1998; 279:535-40.

152 | Chapter 9

General discussion and future prospects | 153

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39

Gene signature for risk stratification and treatment of brea Incorporating tumor biology in clinical decision-making

Chapter 10

of breast cancer

king

Summary Samenvatting PhD portfolio Acknowledgements (Dankwoord) Curriculum Vitae

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39 156 | Chapter 10

Breast cancer is the most frequently diagnosed cancer among women worldwide. Over the past two decades, the incidence rate in the Netherlands increased from 56 per 100.000 women diagnosed with invasive breast cancer in 1991 to 83 per 100.000 in 2011. This increasing incidence may (partly) be explained by the introduction of population-based screening programs, which resulted in an increase in the detection of early stage breast cancer. Another important observation is a decrease in mortality rates, which may be explained by early detection, but also by the improvement and more extensive use of adjuvant systemic treatment. Adjuvant systemic therapy (AST) improves survival, but not all patients who receive AST derive significant benefit from it. This indicates that the selection of patients eligible to receive AST based on clinicopathological factors as used nowadays is not sufficient. Knowledge about the biological background of breast cancer has grown tremendously over the past years. This knowledge was used to develop gene signatures, such as the prognostic 70-gene signature. Chapter 1 provides an introduction to breast cancer, clinicopathological factors used to predict the risk of recurrence and the introduction of gene signatures. Furthermore, the rationale and outline of this thesis are described. The first part of this thesis focuses on the current applicability of the 70-gene signature in daily clinical practice and the impact of the 70-gene signature on clinical decision-making. In Chapter 2 we provide an overview of the prognostic value of the 70-gene signature in different subgroups of patients as described in recently published, retrospective studies. This overview shows that the 70-gene signature was initially developed for patients < 55 years old with stage 1 or 2, ER-positive or -negative, node-negative breast cancer. Shortly after, the test was retrospectively validated for breast cancer patients who are 55-70 years of age, patients with HER2-positive disease, and patients with 1-3 positive nodes in the axilla. At this point in time, only retrospective data on patient outcome was available for the 70-gene signature. Fortunately, as described in chapters 2 and 3, the 5-year follow-up results of the RASTER study became available, providing the first prospective data on patients for whom the 70-gene signature was used in the decision whether or not to treat a patient with adjuvant chemotherapy. A total of 427 women with early stage, node-negative breast cancer were included in this observational study conducted between 2004 and 2006. After 5 years of follow-up an excellent distant-recurrence-free-interval of 100% is seen in patients who did not receive adjuvant chemotherapy based on their low risk 70-gene signature result, despite unfavorable clinicopathological factors and therefore a high risk assessment by Adjuvant! Online (AOL). All clinical risk estimations in the first analyses were based on AOL. To put the results of the RASTER study in a wider context, the analyses were not only performed compared to AOL, but also compared to Nottingham Prognostic Index (NPI), the St. Gallen expert panel recommendations of 2003, the Dutch National guidelines of 2004 and the updated version of 2012, and the relatively new online risk estimation tool PREDICT plus (chapter 4). The results of our analyses show that adding the 70-gene signature to clinical guidelines significantly improves

Summary | 157

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39

risk predictions. The most accurate risk predictions were seen when using the PREDICT plus tool in combination with the 70-gene signature (c-index of 0.662). In chapter 5, we evaluated the agreement among oncologists and the impact of the 70-gene signature on risk estimations and treatment recommendations in early stage, node-negative breast cancer. Only a moderate agreement on risk estimation was seen (Îş=0.55; range: 0.20-0.88). This could slightly be improved by providing the 70-gene signature result (Îş=0.61; range: 0.14-1.00; p=0.035), indicating that the 70-gene signature may be a useful tool to provide patients with more standardized, but individualized treatment. Overall, when adding the 70-gene signature the proportion of patients classified as high risk was reduced with 7.4% (range: 6.9-22.9%; p<0.001) and the proportion of chemotherapy that was recommended was reduced with 12.2% (range: 5.4-29.5%; p<0.001). To evaluate the effect of the 70-gene signature on long-term outcome, we updated follow-up for the 295 patients included in the consecutive validation cohort of van de Vijver et al. in chapter 6. The median follow-up for this cohort was prolonged to 18.5 years. A significant difference was seen in distant-metastasis-free-survival (DMFS) for patients with a low and a high risk 70-gene signature (p<0.0001), for node-negative (n=151; p<0.0001) as well as node-positive patients (n=144; p=0.0004). The 25-year Hazard Ratio (HR) for DMFS and OS were 3.1 (95%CI: 2.024.86) and 2.9 (95%CI: 1.90-4.28), respectively. The HR for DMFS and OS was largest in the first five years after diagnosis (9.6 (95%CI: 4.2-22.1) and 11.3 (95%CI: 3.5-36.4) respectively). The second part of this thesis focuses on new areas in which the 70-gene signature may be helpful. A current problem is the increasing incidence of breast cancer after implementation of population-based mammographic screening programs. This has been suggested to be partly due to the detection of slow growing tumors that would never have caused symptoms or death, i.e. breast cancer overdiagnosis. Only rough estimates have been made of the proportion of patients that are overdiagnosed and identification of those patients is difficult. Therefore, the aim of the study described in chapter 7 was to evaluate whether tumor biology can help identify patients with screen-detected tumors at such a low risk of recurrence that concerns regarding overdiagnosis might be raised. We hypothesized that screen-detected cancers have a more favorable tumor biology, aside from more favorable clinicopathological factors. Our results show that screen-detected cancers had significantly more often a low (68%), of which 54% even an ultralow risk tumor biology compared to interval cancers (53% low, of which 45% ultralow risk (p=0.001) with an OR of 2.33 (p<0.0001; 95% CI: 1.73-3.15). Especially for patients with screendetected cancers the use of tools, such as the 70-gene signature, to differentiate breast cancers by risk of recurrence may minimize overtreatment.

158 | Chapter 10

Second, we evaluated the impact of the recent transition from film-screen mammography (FSM) to full-field digital mammography (FFDM) on the biological background of the tumors detected in the nation-wide screening program. FFDM detected significantly more high risk tumors (35%) compared to FSM (27%; p=0.011), suggesting that screening with this imaging modality is more efficient. The 70-gene signature was developed to predict the risk of distant metastases. Given the strong association between loco-regional and distant recurrence, we hypothesized that the 70-gene signature will also be able to predict the risk of loco-regional recurrence (LRR). In chapter 8 is shown that a high risk 70-gene signature is associated with an 2.89 times higher risk of LRR compared to a low risk 70-gene signature (95%CI: 1.80-4.63). Adding the 70-gene signature to known clinicopathological factors significantly improved the risk prediction model (multivariable HR 2.27 (95%CI: 1.24-4.15); p=0.007). This effect was seen in patients treated with breast conserving surgery as well as patients treated with mastectomy. In chapter 9 the results presented in this thesis are discussed and put in perspective of current clinical practice. In general, the results described in this thesis expand our knowledge on the use of the 70-gene signature in clinical practice and the impact this gene-signature has on clinical decision-making. Not only did the results show that the 70-gene signature is an important prognostic factor in long-term retrospective and observational prospective data, they also revealed new areas in which the 70-gene signature can be used.

10 Summary | 159

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39

Gene signature for risk stratification and treatment of brea Incorporating tumor biology in clinical decision-making

Chapter 10

of breast cancer

king

Summary Samenvatting PhD portfolio Acknowledgements (Dankwoord) Curriculum Vitae

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39 162 | Chapter 10

Borstkanker is wereldwijd de meest voorkomende vorm van kanker onder vrouwen. Gedurende de laatste twee decennia is de incidentie van borstkanker in Nederland gestegen van 56 per 100.000 vrouwen in 1991 naar 83 per 100.000 vrouwen in 2011. Deze stijging in incidentie wordt deels toegeschreven aan de introductie van screening-programma’s, wat geleid heeft tot een toename in de detectie van vroeg stadium borstkanker. Een andere belangrijke observatie is de daling in de mortaliteit van borstkanker. Deze observatie wordt verklaard door vroege detectie, als ook verbetering en toenemend gebruik van adjuvant systemische therapieën. Adjuvant systemische therapie (AST) verbeterd de overleving, maar niet alle patiënten die behandeld worden met AST hebben hier ook daadwerkelijk baat bij. Dit suggereert dat het selecteren van patiënten op basis van klinisch-pathologische factoren, zoals we dat heden ten dage doen, ontoereikend is. De kennis over de biologische achtergrond van borstkanker is de laatste jaren enorm gegroeid. Deze kennis is onder andere gebruikt om genexpressie profielen te ontwikkelen, zoals het prognostische 70-genen profiel. Hoofdstuk 1 van dit proefschrift geeft een introductie over borstkanker, klinisch-pathologische factoren die gebruikt worden om het risico op afstandsmetastasen in te schatten en het gebruik van genexpressie profielen. Daarnaast geeft dit hoofdstuk een beknopt overzicht van het onderzoek beschreven in dit proefschrift en de klinische vraagstellingen die er aan ten grondslag liggen. Het eerste deel van dit proefschrift richt zich met name op de huidige toepasbaarheid van het 70-genen profiel in de dagelijkse klinische praktijk. Daarnaast is ook gekeken naar de invloed die het 70-genen profiel heeft op de klinische besluitvorming. In hoofdstuk 2 wordt een overzicht gegeven van de recente literatuur over de prognostische waarde van het 70-genen profiel in verschillende subgroepen patiënten. Dit overzicht laat zien dat het 70-genen profiel, dat oorspronkelijk ontwikkeld werd voor patiënten <55 jaar met zowel ER-positieve als -negatieve, stadium 1 of 2, lymfklier-negatieve borstkanker, ook in andere subgroepen hoog en laag risico patiënten kan onderscheiden. De test is ook gevalideerd voor patiënten tussen de 55 en 70 jaar, patiënten met HER2-positieve ziekte en patiënten met 1 tot 3 positieve klieren in de oksel. Tot 2 jaar geleden waren er alleen retrospectieve gegevens beschikbaar. Nu zijn er de 5 jaar follow-up data van de RASTER studie beschikbaar, zoals beschreven in hoofdstuk 2 en 3. Dit zijn de eerste prospectieve data van patiënten voor wie het 70-genen profiel is gebruikt in de besluitvorming om een patiënt al dan niet te behandelen met adjuvante chemotherapie. In totaal zijn 427 vrouwen met vroeg stadium, lymfklier-negatieve borstkanker tussen 2004 en 2006 geincludeerd in deze observationele studie. Vijf jaar na diagnose wordt een uitstekende afstandsmetastasevrije-overleving van 100% gezien bij patiënten die geen adjuvante chemotherapie hebben gekregen op basis van een laag risico 70-genen profiel, ondanks dat hun tumor ongunstige klinisch-pathologische factoren liet zien. Deze patiënten hadden vanwege deze ongunstige

Samenvatting | 163

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39

klinisch-pathologische factoren een hoog risico inschatting volgens Adjuvant! Online (AOL). Alle inschattingen van het klinisch risico waren in de eerste analyses van de RASTER data gebaseerd op AOL. Om de resultaten van deze studie in een bredere context te kunnen plaatsen zijn deze analyses naast AOL ook uitgevoerd voor de Nottingham Prognostic Index (NPI), de st. Gallen expert panel aanbevelingen van 2003, de Nederlandse richtlijnen van 2004 en 2012 en de relatief nieuwe online tool PREDICT-plus (hoofdstuk 4). Voor alle klinische richtlijnen geldt dat het toevoegen van het 70-genen profiel de risico-inschatting verbeterd. De meest accurate risicoinschattingen werden gezien bij het gebruik van PREDICT-plus in combinatie met het 70-genen profiel (c-index = 0.662). In hoofdstuk 5 is de overeenstemming geëvalueerd onder medisch oncologen over de risicoinschatting en behandeladviezen voor vroeg stadium, lymfklier-negatieve borstkanker en de invloed van het 70-genen profiel hier op. Er werd een matige overeenstemming gezien wat betreft de risico-inschatting (κ=0.55; range: 0.20-0.88). Dit verbeterde iets nadat aan deze oncologen ook bekend werd gemaakt wat de uitslag was van het 70-genen profiel (κ=0.61; range: 0.141.00; p=0.035). Dit laat zien dat het 70-genen profiel mogelijk kan bijdragen in het geven van meer gestandaardiseerde, maar toch gepersonaliseerde behandeling. Over het algemeen nam het aantal patiënten met een hoog risico inschatting af met 7.4% (range: 6.9-22.9%; p<0.001) na bekendmaking van de uitslag van het 70-genen profiel. De proportie aanbevolen chemotherapie werd gereduceerd met 12.2% (range: 5.4-29.5%; p<0.001). Om het effect van het 70-genen profiel op de overleving op de lange termijn te kunnen beoordelen werd de follow-up bijgewerkt van de 295 patiënten die geincludeerd waren in de oorspronkelijk gepubliceerde consecutieve validatie serie, waarop de voorspellingskracht tot 10 jaar na diagnose werd gebaseerd. De mediane follow-up van deze serie is door deze update verlengd naar 18.5 jaar. Zoals beschreven in hoofdstuk 6, werd er een significant verschil in afstandsmetastase-vrijeoverleving gezien voor patiënten met een laag en hoog risico 70-genen profiel (p<0.0001), voor zowel de lymfklier-negatieve patiënten (n=151;p<0.0001) als de lymfklier-positieve patiënten (n=144;p=0.0004). De 25-jaar Hazard Ratio (HR) voor metastase-vrije-overleving en algemene overleving was respectievelijk 3.1 (95%CI: 2.02-4.86) en 2.9 (95%CI: 1.90-4.28). Zowel voor metastase-vrije als algemene overleving was de Hazard Ratio het grootst in de eerste vijf jaar na diagnose (9.6 (95%CI: 4.2-22.1) en 11.3 (95%CI: 3.5-36.4) respectievelijk). In het tweede deel van dit proefschrift ligt de focus op nieuwe gebieden waarin het 70-genen profiel van toegevoegde waarde kan zijn. Een van de redenen voor de toenemende incidentie van borstkanker is de introductie van het bevolkingsonderzoek borstkanker, ofwel de borstkanker screening. Deze toenemende incidentie kan gedeeltelijk worden verklaard door de detectie van traag groeiende tumoren die zonder de screening nooit symptomatisch zouden zijn geworden

164 | Chapter 10

noch geleid zouden hebben tot overlijden. Deze overdiagnose is een gekend probleem van de screening. Er zijn alleen schattingen van de proportie van patiënten waarbij sprake is van overdiagnose. Identificatie van deze patiënten is zeer moeilijk. Daarom was het doel van de studie beschreven in hoofdstuk 7 te evalueren of tumor biologie kan helpen een groep patiënten met een middels de screening ontdekte tumor te identificeren die een dusdanig laag risico heeft op uitzaaiingen dat er bij deze patiënten mogelijk sprake is van overdiagnose. Onze hypothese was dat middels de screening ontdekte tumoren vaker een gunstige tumor biologie hebben, onafhankelijk van hun klinisch-pathologische factoren. Onze resultaten laten zien dat deze tumoren inderdaad significant vaker een laag risico 70-genen profiel hebben (68%) -waarvan 54% zelfs een ultralaag risico tumor biologie bleek te hebben- in vergelijking met intervaltumoren (53% laag, waarvan 45% ultralaag risico (p= 0.001) met een Odds Ratio van 2.33 (p<0.0001; 95%CI: 1.73-3.15). Vooral voor patiënten met een middels de screening ontdekte tumor zou het gebruik van het 70-genen profiel kunnen helpen om te differentiëren tussen patiënten met een laag en ultralaag risico op afstandsmetastasen en daarmee helpen overbehandeling te minimaliseren. Daarnaast hebben we gekeken naar de gevolgen van de recente overgang van analoge naar digitale mammografie op de biologische achtergrond van de tumoren die ontdekt worden in de screening. Digitale mammografie bleek significant meer hoog risico tumoren te detecteren (35%) vergeleken met analoge mammografie (27%; p=0.011). Dit suggereert dat de screening met deze nieuwe beeldvorming efficiënter is geworden, maar dit dient bevestigd te worden in landelijke ongeselecteerde data. Het 70-genen profiel is ontwikkeld om het risico op afstandsmetastasen in te schatten. Gezien de sterke associatie tussen locoregionaal recidief en afstandsmetastasen, denken wij dat het 70-genen profiel ook voorspellend zal zijn voor het risico op locoregionaal recidief (LRR). In hoofdstuk 8 laten wij zien dat een hoog risico 70-genen profiel geassocieerd is met een 2.89 keer hoger risico op LRR vergeleken met een laag risico 70-genen profiel (95%CI: 1.80-4.63). Het toevoegen van het 70-genen profiel aan bekende klinisch-pathologische factoren verbetert het risico-predictie-model significant (multivariabele HR 2.27 (95%CI: 1.24-4.15); p=0.007). Dit effect was zowel te zien in patiënten behandeld met borstsparende therapie als patiënten behandeld met een mastectomie. In hoofdstuk 9 worden de resultaten van dit proefschrift bediscussieerd, gerelateerd aan de dagelijkse klinische praktijk en wordt onze visie op het gebruik van genexpressie profielen in de toekomst geschetst. In het algemeen verbreden de resultaten beschreven in dit proefschrift onze kennis over het gebruik van genexpressie-profielen in de praktijk en de invloed die dit heeft op de klinisch besluitvorming. Het 70-genen profiel bleek zowel prospectief als op de lange termijn een belangrijke prognostische factor. Daarnaast werden ook nieuwe gebieden waarin het 70-genen profiel gebruikt kan worden geëxploreerd.

Samenvatting | 165

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39

Gene signature for risk stratification and treatment of brea Incorporating tumor biology in clinical decision-making

Chapter 10

of breast cancer

king

Summary Samenvatting PhD portfolio Acknowledgements (Dankwoord) Curriculum Vitae

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39

PhD student: Caroline A. Drukker PhD period: March 2011- March 2014 PhD supervisors: Prof. dr. E.J.T. Rutgers Prof. dr. L.J. van â&#x20AC;&#x2122;t Veer Prof. dr. S.C. Linn Dr. M.K. Schmidt

168 | Chapter 10

Publications Prognose van een patiënte met vroeg stadium borstkanker: de bijdrage van een gen-expressie profiel Drukker CA, Schmidt MK, van Dalen T, van der Hoeven JJM, Linn SC, Rutgers EJT Nederlands Tijdschrift voor Geneeskunde 2013, Dec 11. Accepted. A prospective evaluation of a breast cancer prognosis signature in the observational RASTER study Drukker CA, Retel VP, Bueno de Mesquita JM, van Harten WH, van Tinteren H, Wesseling J, Roumen RM, Knauer M, van ’t Veer LJ, Sonke GS, Rutgers EJ, van de Vijver MJ, Linn SC. International Journal of Cancer 2013;133(4):929-936. Evaluation of the performance of the 70-gene signature in addition to clinical risk prediction algorithms in node-negative breast cancer Drukker CA, Nijenhuis MV, Bueno-de-Mesquita JM, Retel VP, van Harten WH, van Tinteren H, Schmidt MK, van ’t Veer LJ, Sonke GS, Rutgers EJT, van de Vijver MJ, Linn SC Submitted. Risk estimations and treatment decisions in early stage breast cancer: agreement among oncologists and the impact of the 70-gene signature Drukker CA, van den Hout HC, Sonke GS, Brain E, Bonnefoi H, Cardoso F, Goldhirsch A, Harbeck N, Honkoop AH, Koornstra RHT, van Laarhoven HWM, Portielje JEA, Schneeweiss A, Smorenburg CH, Stouthard J, SC Linn, Schmidt MK European Journal of Cancer 2014, Jan 7. Accepted. Long-term impact of the 70-gene signature on breast cancer outcome Drukker CA, van Tinteren H, Schmidt MK, Rutgers EJT, van de Vijver MJ, van ’t Veer LJ Breast Cancer Research and Treatment 2014;143:587-92. Mammographic screening detect low-risk tumor biology breast cancers Drukker CA, Schmidt MK, Rutgers EJT, Cardoso F, Kerlikowske K, Esserman LJ, Pijnappel RM, Slaets L, Bogaerts J, van ‘t Veer LJ Breast Cancer Research and Treatment 2014, Jan 28. Epub ahead of print. Gene expression profiling to predict the risk of locoregional recurrence in breast cancer Drukker CA, Elias SG, Nijenhuis MV, Wesseling J, Bartelink H, Elkhuizen P, Fowble B, Whitworth P, Patel R, van ‘t Veer LJ, Beitsch P, Rutgers EJT To be submitted PhD portfolio | 169

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39

Marking Axillary Lymph Nodes with Radioactive Iodine seeds for Axillary Staging After Neoadjuvant Systemic Treatment in Breast Cancer Patients: the MARI-procedure Donker M, Straver ME, Wesseling J, Loo CE, Schot M, Drukker CA, van Tinteren H, Sonke GS, Rutgers EJT, Vrancken Peeters MTFD. Annals of Surgery 2013, Nov 18. Accepted. Prospective cost-effectiveness analysis of genomic profiling in breast cancer, based on 5 year survival data, actual compliance rates and recommended subgroups. RetĂ¨l VP, Joore MA, Drukker CA, Bueno de Mesquita JM, Linn SC, van Tinteren H, van Harten WH. European Journal of Cancer 2013;49(18):3773-9. Guiding Breast-Conserving Surgery in Patients after Neo-adjuvant Systemic Therapy for Breast Cancer: A Comparison of Radioactive Seed Localisation with the ROLL Technique. Donker M, Drukker CA, Valdes Olmos RA, Rutgers EJT, Loo CE, Sonke GS, Wesseling J, Alderliesten T, Vrancken Peeters MTFD. Annals of Surgical Oncology 2013;20(8):2569-75. Necrotizing Fasciitis of the leg: a rare complication of intra-abdominal pathology. Drukker CA, Poelman MM, Langenhorst BL, Wiarda BM, Schreurs WH. Nederlands Tijdschrift voor Heelkunde, oktober 2010. Paraneoplastic gastro-intestinal anti-Hu syndrome in neuroblastoma. Drukker CA, Heij HA, Wijnaendts LC, Verbeke JI, Kaspers GJ. Pediatr Blood Cancer 2009;52(3):396-8.

170 | Chapter 10

Presentations Optimized prediction of clinical outcome by the PREDICT plus tool and 70-gene signature in early stage node-negative breast cancer Drukker CA, Nijenhuis MV, Bueno-de-Mesquita JM, Retèl VP, van Tinteren H, Schmidt MK, van Harten WH, Sonke GS, van ‘t Veer LJ, Rutgers EJT, van de Vijver MJ, Linn SC San Antonio Breast Cancer Symposium, December 2013, San Antonio, USA. Poster presentation. Comparing the 70-gene signature to the Dutch Breast Cancer guidelines in the prospective RASTER study Drukker CA, Bueno-de-Mesquita JM, Retèl VP, van Harten WH, van Tinteren H, Wesseling J, van ’t Veer LJ, Rutgers EJT, van de Vijver MJ, Linn SC European Cancer Congress, September 2013, Amsterdam, the Netherlands. Poster discussion. Risk estimations and treatment decisions in early stage breast cancer; agreement among oncologists and the impact of the 70-gene signature Drukker CA, van den Hout HC, Sonke GS, Linn SC, Schmidt MK European Cancer Congress, September 2013, Amsterdam, the Netherlands. Poster presentation. Breast Cancer Screening: Biology of tumors detected by analog and digital mammography Drukker CA, Schmidt MK, Rutgers EJ, Cardoso F, Kerlikowske K, Esserman LJ, Slaets L, Bogaerts J, van ‘t Veer LJ. American Society of Clinical Oncology Annual Meeting, May 2013, Chicago, USA. Poster presentation. Mammographic screening: Good Prognosis Tumor Biology in Screen-detected Breast Cancers Drukker CA, Schmidt MK, Rutgers EJ, Cardoso F, Kerlikowske K, Esserman LJ, Slaets L, Bogaerts J, van ‘t Veer LJ. San Antonio Breast Cancer Symposium, December 2012, San Antonio, USA. Poster presentation. Dutch Society of Surgery Annual Meeting, May 2013, Veldhoven, the Netherlands. Oral Presentation. Breast Oncology Program Retreat UCSF, January 2013, San Francisco, USA. Poster presentation. Staff meeting Department of Surgical Oncology, February 2013, Amsterdam, the Netherlands. Oral Presentation.

PhD portfolio | 171

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39

MammaPrint.. Wat is dat nu eigenlijk? Mammodag, January and March 2013, Amersfoort, the Netherlands. Invited Speaker. Gene expression profiling to predict the risk of locoregional recurrence in breast cancer Drukker CA, Nijenhuis MV, Elias SG, Wesseling J, Russel NS, de Snoo F, van ‘t Veer LJ, Beitsch PD, Rutgers EJ. San Antonio Breast Cancer Symposium, December 2012, San Antonio, USA. Late breaking abstract, poster presentation. Masterclass Mammacarcinoom: ‘Tussen RASTER en MINDACT: welke patiente een MammaPrint?’ Masterclass Mammacarcinoom, October 2012, Eindhoven, the Netherlands. Invited speaker. First prospective validation of a breast cancer gene expression signature: the RASTER study Drukker CA, Bueno-de-Mesquita JM, Retèl VP, van Harten WH, van Tinteren H, Wesseling J, Roumen RM, Knauer M, van ’t Veer LJ, Sonke GS, Rutgers EJ, van de Vijver MJ, Linn SC. Personalized Cancer Care Symposium, September 2012, Oslo, Norway. Poster presentation. MINDACT investigators meeting, September 2011, Amsterdam, the Netherlands. Oral presentation. Staff meeting Department of Medical Oncology, September 2012, Amsterdam, the Netherlands. Oral presentation. Meeting Intergraal Kankercentrum Nederland (IKNL), March 2012, Amsterdam, the Netherlands. Oral presentation. MammaPrint 70-gene Assay Predicts Risk of Local-Regional Recurrence Beitsch PD, Jia A, de Snoo F, Whitworth P, Drukker CA, Rutgers EJ, Patel R. American Society of Breast Surgeons Annual Meeting, May 2012, Phoenix, USA. Poster presentation. 70-genen profiel voorspelt de prognose van patiënten met vroeg stadium mammacarcinoom Drukker CA, Retèl VP, Bueno-de-Mesquita JM, van Harten WH, van Tinteren H, Wesseling J, Roumen RM, Knauer M, van ’t Veer LJ, Sonke GS, Rutgers EJ, van de Vijver MJ, Linn SC. Dutch Society of Surgery Annual Meeting, May 2012, Veldhoven, the Netherlands. Oral presentation. Dutch Society of Internal Medicine Annual Meeting, April 2012, Maastricht, the Netherlands. Oral presentation. presentation. Dutch Society of Pathology Annual Meeting, April 2012, Zeist, the Netherlands. Poster discussion.

172 | Chapter 10

Het genexpressieprofiel vindt zijn weg in de klinische praktijk Voorjaarscongres Vereniging Analisten Pathologie, March 2012, Nunspeet, the Netherlands. Invited Speaker. Assessment of risk factor information for gene-environment-tumortype interaction studies within the randomized breastcancer clinical trial ‘MINDACT’ – study design Drukker CA, van ’t Veer LJ, Cardoso F, Rutgers EJT, Schmidt MK BBMRI-NL Connecting Biobanks, November 2011, Rotterdam, the Netherlands. Poster presentation. MINDACT Investigators Meeting, September 2011, Amsterdam, the Netherlands. Oral presentation. Hoe precies is PREZIES? Drukker CA, Emous M, Eerenberg JP, Hendriks ER. Dutch Society of Surgery Annual Meeting, May 2011, Veldhoven, NL. Poster presentation.

Courses -

Experimental Oncology Course

2011

Acces Course (basic & advanced)

2011

Basic Medical Statistics

2012

English writing and presenting Course

2012

Introduction to EORTC trials

2012

Good Clinical Practice Course

2013

Other (inter)national conferences attended - Chirurgendagen

2011

EBCC-8, Vienna, Austria

2012

10e Bossche mammacongres

2012

NKI-AVL mamma symposium

2012 and 2013

DONAMO mammacarcinoom

2012 and 2013

NVCO Scholingscursus XI Mammacarcinoom

2012 and 2013

Annual Graduate Student Retreat OOA

2011 and 2013

10 PhD portfolio | 173

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39

Supervising Ella van den Hout, medical student VUmc. June - October 2012. ‘Guidelines in clinical decision making in early stage breast cancer patients and the impact of the 70-gene signature’. Matthijs Nijenhuis, medical student LUMC. June - October 2012. ’70-gene signature predicts risk of loco-regional recurrence in breast cancer’. Fatma Al Sofi, medical student LUMC. October 2012 - June 2013. ‘BMI and breast cancer’.

Grants Co-author, BCWG type 3 grant, EORTC. Assessment of genetic, lifestyle and environment information for gene-environment-tumor subtype interaction studies within the randomized breast cancer trial ‘MINDACT’ (EORTC 10041/BIG 3-04)

Awards and Prizes SABCS Clinical Scholar-in-training Award San Antonio Breast Cancer Symposium 2012 Conquer Cancer Foundation of ASCO Merit Award ASCO annual meeting 2013

Other Founder of the EORTC BCG Young Investigators Group

174 | Chapter 10

10 PhD portfolio | 175

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39

Gene signature for risk stratification and treatment of brea Incorporating tumor biology in clinical decision-making

Chapter 10

of breast cancer

king

Summary Samenvatting PhD portfolio Acknowledgements (Dankwoord) Curriculum Vitae

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39 178 | Chapter 10

The past three years have been a wonderful journey. Many have travelled with me, supported me and guided me while writing this thesis. It has been a rollercoaster; with more ups than downs. I would like to thank all of you who have contributed to this thesis. Some of you I would like to thank in particular. Prof. dr. E.J.Th. Rutgers, beste Emiel, De perfecte balans tussen kliniek en wetenschap, het lijkt alsof jij hem gevonden hebt. Je gedrevenheid en passie voor het vak zijn inspirerend. Ik ben heel dankbaar dat je me de kans hebt gegeven om onder jouw begeleiding te promoveren. Je had altijd het beste met me voor, bracht me terug bij de hoofdlijnen als ik was afgedreven en voelde feilloos aan als ik het eigenlijk te druk had. Je leerde me niet bang te zijn voor grote dromen. Ieder idee voor onderzoek was bespreekbaar en er werd altijd gezocht naar mogelijkheden om het idee uitvoerbaar te maken. De eerste droom is waargemaakt, een proefschrift om trots op te zijn. Er zijn nog vele wetenschappelijke dromen na te jagen en ik hoop dat we in de toekomst nog eens samen mogen werken aan een groot project. Prof. dr. L.J. van ’t Veer, beste Laura, Wat was ik zenuwachtig toen ik je voor het eerst ontmoette: de professor uit Amerika. Al zoveel bereikt in het leven en nog steeds waanzinnig gedreven om de behandeling van borstkanker te verbeteren. Ondanks de afstand en het tijdsverschil was je altijd heel dichtbij en betrokken bij alle projecten. Je bracht rust en orde als ik het overzicht even kwijt was. Na mijn bezoek aan San Francisco begreep ik waarom je daar gebleven bent. Het is een fantastische stad, je hebt een prachtige eigen afdeling en een heel leuk team om je heen. Ik hoop dat we elkaar nog regelmatig tegen zullen komen, hier of daar. Dr. M.K. Schmidt, beste Marjanka, De afgelopen drie jaar was jij de stabiele factor. Wekelijks kon ik bij je terecht met al mijn vragen. Op PSOE of C2, liever ’s avonds laat dan ’s ochtends vroeg, maakte je me wegwijs in de wereld van de wetenschap. Waar in eerste instantie veel plannen nog uit grote lijnen bestonden, bracht jij de verfijning aan. We hebben bergen verzet om alle projecten logistiek haalbaar te maken. Van jou leerde ik heel veel over statistiek en epidemiologie. Dank je wel. Prof. dr. S.C. Linn, beste Sabine, Om ‘out of the box’ te kunnen denken heb je iemand nodig die je laat zien wat de grenzen zijn van de box waar je in zit. Je stimuleerde me om grenzen te verleggen, maar ook grenzen te trekken. Je leerde me groots te denken, kritisch te zijn op de wetenschap en op mezelf zonder de lol in het werk te verliezen. Dank dat je mijn co-promotor wilde zijn.

Acknowledgements (Dankwoord) | 179

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39

De leden van mijn promotie commissie, prof. dr. S. Rodenhuis, prof. dr. ir. F.E. van Leeuwen, prof. dr. G.J. den Heeten, prof. dr. ir. J.J.M. van der Hoeven, prof. dr. R. Versteeg en prof. dr. J.H.G. Klinkenbijl wil ik graag bedanken voor het beoordelen van mijn manuscript. @Jolien, mijn digitale steun en toeverlaat. Het begon met 1 email en het werden er 114, met uiteenlopende, vaak niet eens wetenschapgerelateerde onderwerpen. Dank voor je bemoedigende woorden, scherpe observaties en diplomatiek correcte oplossingen. Lieve Olga en Ilse, dank jullie wel. Voor alles! Van taartjes tot printjes, van afspraken tot contracten, jullie regelden het. Zonder jullie was het niet gelukt. Judith, Hebon en MINDACT lifestyle gingen hand in hand de wereld in, dus wij ook. Jij wist het altijd, ik regelmatig niet. Maar met jouw nummer op speed-dial verliep alles volgens plan. Ik vond het ontzettend leuk om dit allemaal samen met jou mee te maken! Harm, je was niet alleen het statistisch brein achter de RASTER, maar ook regelmatig een klankbord. Wat wil je in de toekomst? Hoe zie je het voor je, kliniek en wetenschap, je zal toch ergens moeten inleveren? Door jou heb ik nagedacht over de wetenschap als deel van mijn huidige carriĂ¨re en deel van mijn toekomst. Dank daarvoor. Gabe, ik heb enorme bewondering voor de manier waarop jij een stuk tekst met een aantal zinnen en net iets andere woordkeuze om kan toveren tot een mooi manuscript. Dank voor je bijdrage aan veel van mijn manuscripten. Jelle, je kritische blik, organisatorisch vermogen en je gezelligheid waren onmisbaar voor mijn promotie-traject. Inge en Jolanda, jullie weten van de hoed en de rand als het om MINDACT gaat en hadden op iedere vraag een antwoord. Dank voor al jullie hulp bij het opzetten van de MINDACT side studies. Alle anderen uit de mamma-werkgroep, Hester, Marie-Jeanne, Nicola, Paula en Jacqueline in het bijzonder, bedankt voor jullie interesse en bijdrage aan mijn onderzoek. Lieve mamma-onderzoekers, Bas en Mila het was genoegen om met jullie aan mijn zijde mijn eerste stapjes te zetten in de wereld van congressen, borrels en netwerken. Lot, er zijn er weinig met zoâ&#x20AC;&#x2122;n focus en drive als jij. Ik hoop dat we in de toekomst samen nog eens een project mogen doen!

180 | Chapter 10

Matthijs, ik ben heel blij dat jij het AvL binnen wandelde op zoek naar een promotieplek net toen ik verzoop in het werk. Ik wens je heel veel succes met het afmaken van je promotie! Lieve (oud-)collega arts-onderzoekers, dank voor de gezelligheid gedurende de vele koffiepauzes, borrels en feestjes. Ik kijk uit naar alle promotie-feestjes die nog in het verschiet liggen. Some of my projects were performed in collaboration with the EORTC. Jan and Leen, it has been an honor to work with you on the two MINDACT related studies. Fatima and David, thank you for your guidance in my research and the opportunity to set up the Young Breast Cancer Group within the EORTC. Lotte, Leen, Kostas, Berta and Fei, thank you for believing in the idea of a ‘baby dinosaurs’ meeting. It has been a pleasure to work with you on the Young BCG and I am looking forward to our next meeting. For two studies we worked closely with Karla Kerlikowske, Barbara Fowble and Laura Esserman from the University of California, San Francisco. Thank you for your time and dedication to these projects. Beste oud-collega’s uit Tergooi, jullie hebben me laten zien hoe mooi het is om dokter te zijn en dan met name dokter op de afdeling chirurgie. Beste dr. Eerenberg en dr. van Geloven, bedankt dat jullie me aangespoord hebben om me wetenschappelijk verder te ontwikkelen. Lieve Nicole, toen ik na 1 week onderzoek als een berg op zag tegen alles wat komen ging en geen idee had waar ik moest beginnen, zei jij: ‘oh, valt wel mee hoor, een METC aanvraag, cursus statistiek, dit boek moet je lezen, die website bezoeken en dan komt het allemaal wel goed!’. Je had gelijk. Bedankt voor al je hulp! Lieve Ro, Fem en Soof, alle ups en downs binnen en buiten onze medische werelden hebben we gedeeld en er staat nog zoveel leuks op de planning. Ik kijk er naar uit! Eveline, lieve Eef, een van de highlights van mijn promotietijd was ook zeker het moment dat wij samen over de finish liepen in Berlijn! Gedurende de 989 km die we in de aanloop naar de marathon toe hebben gelopen hebben we gelachen, gevloekt, gehuild en gescholden, maar vooral een hele bijzondere vriendschap op gebouwd. Ik ben heel blij dat je mijn paranimf bent!

10 Acknowledgements (Dankwoord) | 181

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39

Emelie, lieve Emo, huisgenoot, jaargenoot, vriendinnetje. 1 blik, meer is niet nodig. Dank voor je steun en luisterend oor, maar vooral ook dank dat je me er af en toe aan herinnerde dat er meer is dan promoveren. ‘Bubbels?’, want alles moet gevierd worden! Een dag niet gelachen is een dag niet geleefd, dat is het motto en ik hoop dat dat voor altijd zo blijft! Je bent de beste! Lieve opa en oma, ‘jong geleerd is oud gedaan’ zegt men wel eens. Toen ik nog een klein meisje was bladerden we al samen door grote encyclopedieën. Die encyclopedie is inmiddels vervangen door google, brieven werden emails en tegenwoordig lezen jullie op de iPad wat er allemaal in de kranten geschreven wordt over borstkankeronderzoek. Bedankt voor jullie grenzeloze interesse in mijn onderzoek. Ik kom snel weer langs. Lieve Henriëtte en Reinier, we waren de drie musketiers de afgelopen jaren. Samen sterk en samen kunnen we de wereld aan. Har, ik ben onder de indruk van je kracht en positieve kijk op het leven. Leon en jij vormen een mooi team! Rein, broer, je bent een echte doorzetter. Ik ben trots op jou! Lieve pap en mam, een goede basis is het allerbelangrijkste. Mijn basis, dat zijn jullie. Jullie hebben me de vrijheid gegeven mijn dromen na te jagen, me gestimuleerd door te zetten en gesteund als ik het nodig had. Een rustige, fijne thuishaven waar ik altijd even kan bijtanken. Ik ben blij dat jullie samen aan het roer staan. Pieter, liefste, wat ben ik blij dat ik jou 1,5 jaar geleden tegen kwam. Samen met jou is het leven een groot feest! Jij bent de reden om doordeweeks een tandje bij te zetten, zodat we in het weekend leuke dingen kunnen doen. Jij bent degene die me op onze tweede date vertelde dat MammaPrint ook door jouw verzekering vergoed wordt. Jij bent degene die me er af en toe aan herinnert gas terug te nemen. Dank je wel voor alle steun, begrip en liefde. Jij maakt me gelukkig en ik heb heel veel zin om samen op reis te gaan!

182 | Chapter 10

10 Acknowledgements (Dankwoord) | 183

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39

Gene signature for risk stratification and treatment of brea Incorporating tumor biology in clinical decision-making

Chapter 10

of breast cancer

king

Summary Samenvatting PhD portfolio Acknowledgements (Dankwoord) Curriculum Vitae

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39 186 | Chapter 10

Caroline Drukker was born on April 8th 1984 in Amsterdam, the Netherlands. She graduated from the scholengemeenschap ‘de Breul’ in Zeist in 2002 and started medical school at the VU University in 2003. She graduated medical school in May 2010 after senior internships in surgical oncology at the Netherlands Cancer Institute-Antoni van Leeuwenhoek Hospital and general surgery at the Sint Lucas Andreas Hospital in Amsterdam. In June 2010, Caroline started working as a surgical resident-not-in-training at Tergooi in Hilversum (dr. J.P. Eerenberg). She started working as a clinical investigator on MINDACT-related projects at the Netherlands Cancer Institute in March 2011. After one year, she became a PhD student and worked on multiple studies focussing on the additional value of the 70-gene signature in breast cancer patients. She worked under supervision of prof. dr. Emiel J.Th. Rutgers, prof. dr. Laura J. van ’t Veer, prof. dr. Sabine C. Linn and dr. Marjanka K. Schmidt. Her work was awarded with a Scholar-in-training Award during the San Antonio Breast Cancer Symposium in 2012 and an ASCO Merit Award during the American Society of Clinical Oncology annual meeting in 2013.

10 Curriculum Vitae | 187

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39

To all my Friends You’re my army of fortune, You win every war I walk on air whenever I’m with you, You’re where the happiness begins.

Stellingen behorende bij het proefschrift

Gene signature for risk stratification and treatment of breast cancer Incorporating tumor biology in clinical decision-making

1. Patiënten met een laag risico 70-genen profiel hebben ook zonder adjuvante chemotherapie een uitstekende overleving, zelfs in geval van ongunstige klinisch-pathologische factoren. (dit proefschrift) 2. Er bestaat een grote variatie in risico-inschatting en keuze voor adjuvante behandeling van vroeg stadium borstkanker patiënten door medisch oncologen in Europa. (dit proefschrift) 3. We can not change the cards we are dealt, just the way we play the hand. (Randy Pausch) 4. Het gebruik van het 70-genen profiel leidt tot een reductie in aantal klinischpathologisch hoog risico patiënten en daarmee een reductie in gebruik van adjuvante chemotherapie. (dit proefschrift) 5. Perfection is not when there is nothing more to add, but when there is nothing left to take away. (Antoine de Saint-Exupery) 6. De biologie van de tumor kan bij patiënten met middels de screening ontdekte tumoren laten zien of deze mogelijk tot de categorie ‘overdiagnose’ behoren. (dit proefschrift) 7. Its your road and yours alone. Others may walk it with you, but no one can walk it for you. (Buddha) 8. Het 70-genen profiel voorspelt niet alleen het risico op afstandsmetastasen, maar geeft ook een inschatting van het risico op een locoregionaal recidief. (dit proefschrift) 9. In geval van een 70-genen profiel ultra-laag risico kan men terughoudend zijn met adjuvant systemische therapie. (dit proefschrift)