G. Poste, Bring on the biomarkers, Nature, vol.94, issue.7329, pp.156-157, 2011.
DOI : 10.1038/469156a

D. Schneider, G. Bianchini, and D. Horgan, Establishing the Evidence Bar for Molecular Diagnostics in Personalised Cancer Care, Public Health Genomics, vol.18, issue.6, pp.349-358, 2015.
DOI : 10.1159/000441556

S. Koscielny, Why Most Gene Expression Signatures of Tumors Have Not Been Useful in the Clinic, Science Translational Medicine, vol.455, issue.7215, pp.14-26, 2010.
DOI : 10.1038/455847a

M. Dowsett, I. Sestak, and E. Lopez-knowles, DX and IHC4 for Predicting Risk of Distant Recurrence After Endocrine Therapy, Journal of Clinical Oncology, vol.31, issue.22, pp.2783-2790, 2013.
DOI : 10.1200/JCO.2012.46.1558

J. Bartlett, J. Bayani, and A. Marshall, Comparing Breast Cancer Multiparameter Tests in the OPTIMA Prelim Trial: No Test Is More Equal Than the Others, Journal of the National Cancer Institute, vol.108, issue.9, 2016.
DOI : 10.1093/jnci/djw050

H. Azim, . Jr, S. Michiels, and F. Zagouri, Utility of prognostic genomic tests in breast cancer practice: The IMPAKT 2012 Working Group Consensus Statement???, Annals of Oncology, vol.24, issue.3, pp.647-654, 2013.
DOI : 10.1093/annonc/mds645

G. Evaluation, P. Applications-in, P. Working, and G. , Recommendations from the Extended group name: Evaluation of Genomic Applications in Practice and Prevention Working Group: does the use of Oncotype DX tumor gene expression profiling to guide treatment decisions improve outcomes in patients with breast cancer, Genet Med, vol.18, pp.770-779, 2015.

L. Harris, N. Ismaila, L. Mcshane, and D. Hayes, Use of Biomarkers to Guide Decisions on Adjuvant Systemic Therapy for Women With Early-Stage Invasive Breast Cancer: American Society of Clinical Oncology Clinical Practice Guideline, Journal of Clinical Oncology, vol.34, issue.10, pp.384-389, 2016.
DOI : 10.1200/JCO.2015.65.2289

M. Buyse, S. Michiels, and D. Sargent, Integrating biomarkers in clinical trials, Expert Review of Molecular Diagnostics, vol.11, issue.2, pp.171-182, 2011.
DOI : 10.1586/erm.10.120

P. Rothwell, Subgroup analysis in randomised controlled trials: importance, indications, and interpretation, The Lancet, vol.365, issue.9454, pp.176-186, 2005.
DOI : 10.1016/S0140-6736(05)17709-5

J. Windeler, Prognosis - what does the clinician associate with this notion?, Statistics in Medicine, vol.90, issue.4, pp.425-430, 2000.
DOI : 10.1002/(SICI)1097-0258(20000229)19:4<425::AID-SIM347>3.0.CO;2-J

C. Early-breast, R. Trialists-'collaborative-grouppeto, and C. Davies, Comparisons between different polychemotherapy regimens for early breast cancer: meta-analyses of long-term outcome among 100,000 women in 123 randomised trials, Lancet, vol.379, pp.432-444, 2012.

C. Early-breast, C. Trialists-'collaborative-groupdavies, and J. Godwin, Relevance of breast cancer hormone receptors and other factors to the efficacy of adjuvant tamoxifen: patient-level meta-analysis of randomised trials, Lancet, vol.378, pp.771-784, 2011.

L. Stewart and M. Parmar, The results of a quantitative overview of chemotherapy in advanced ovarian cancer: what can we learn?, Bull Cancer, vol.80, pp.146-151, 1993.

S. Michiels, S. Koscielny, and C. Hill, Prediction of cancer outcome with microarrays: a multiple random validation strategy, The Lancet, vol.365, issue.9458, pp.488-492, 2005.
DOI : 10.1016/S0140-6736(05)17866-0

N. Ternes, M. Arnedos, and S. Koscielny, Statistical methods applied to omics data, Current Opinion in Oncology, vol.26, issue.6, pp.576-583, 2014.
DOI : 10.1097/CCO.0000000000000134

N. Ternes, F. Rotolo, and S. Michiels, Empirical extensions of the lasso penalty to reduce the false discovery rate in high-dimensional Cox regression models, Statistics in Medicine, vol.96, issue.456, pp.2561-2573, 2016.
DOI : 10.1002/sim.6927

S. Teutsch, L. Bradley, and G. Palomaki, The Evaluation of Genomic Applications in Practice and Prevention (EGAPP) initiative: methods of the EGAPP Working Group, Genetics in Medicine, vol.156, issue.1, pp.3-14, 2009.
DOI : 10.1097/GIM.0b013e318184137c

S. Michiels, S. Koscielny, and C. Hill, Interpretation of microarray data in cancer, British Journal of Cancer, vol.365, issue.8, pp.1155-1158, 2007.
DOI : 10.1038/nm0803-999b

S. Michiels, A. Kramar, and S. Koscielny, Multidimensionality of microarrays: Statistical challenges and (im)possible solutions, Molecular Oncology, vol.10, issue.2, pp.190-196, 2011.
DOI : 10.1016/j.molonc.2011.01.002

R. Simon, S. Paik, and D. Hayes, Use of Archived Specimens in Evaluation of Prognostic and Predictive Biomarkers, JNCI Journal of the National Cancer Institute, vol.101, issue.21, pp.1446-1452, 2009.
DOI : 10.1093/jnci/djp335

I. Miller, J. Ashton-chess, and H. Spolders, Market access challenges in the EU for high medical value diagnostic tests, Personalized Medicine, vol.8, issue.2, pp.137-148, 2011.
DOI : 10.2217/pme.11.2

E. Rakha, J. Reis-filho, and F. Baehner, Breast cancer prognostic classification in the molecular era: the role of histological grade, Breast Cancer Research, vol.55, issue.Suppl 2, p.207, 2010.
DOI : 10.1111/j.1365-2559.2009.03429.x

M. Ignatiadis, S. Singhal, and C. Desmedt, Gene Modules and Response to Neoadjuvant Chemotherapy in Breast Cancer Subtypes: A Pooled Analysis, Journal of Clinical Oncology, vol.30, issue.16, pp.1996-2004, 2012.
DOI : 10.1200/JCO.2011.39.5624

S. Michiels and F. Rotolo, Evaluation of clinical utility and validation of gene signatures in clinical trials Design and Analysis of Clinical Trials for Predictive Medicine, Boca Raton, pp.187-203, 2015.

A. Vickers, A. Cronin, and C. Begg, One statistical test is sufficient for assessing new predictive markers, BMC Medical Research Methodology, vol.172, issue.4, p.13, 2011.
DOI : 10.1111/j.1467-985X.2009.00592.x

URL : http://doi.org/10.1186/1471-2288-11-13

M. Pepe, K. Kerr, G. Longton, and Z. Wang, Testing for improvement in prediction model performance, Statistics in Medicine, vol.31, issue.1, pp.1467-1482, 2013.
DOI : 10.1002/sim.5727

URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3625503

M. Pencina, D. Rbsr, and L. Song, Quantifying discrimination of Framingham risk functions with different survival C statistics, special articles Annals of Oncology 2166 | Michiels et al, pp.1543-1553, 2012.
DOI : 10.1002/sim.4508

P. Heagerty, T. Lumley, and M. Pepe, Time-Dependent ROC Curves for Censored Survival Data and a Diagnostic Marker, Biometrics, vol.39, issue.2, pp.337-344, 2000.
DOI : 10.1111/j.0006-341X.2000.00337.x

D. Dunkler, S. Michiels, and M. Schemper, Gene expression profiling: Does it add predictive accuracy to clinical characteristics in cancer prognosis?, European Journal of Cancer, vol.43, issue.4, pp.745-751, 2007.
DOI : 10.1016/j.ejca.2006.11.018

M. Ignatiadis, H. Azim, . Jr, and C. Desmedt, The Genomic Grade Assay Compared With Ki67 to Determine Risk of Distant Breast Cancer Recurrence, JAMA Oncology, vol.2, issue.2, pp.217-224, 2015.
DOI : 10.1001/jamaoncol.2015.4377

K. Mcgeechan, P. Macaskill, and L. Irwig, Assessing New Biomarkers and Predictive Models for Use in Clinical Practice, Archives of Internal Medicine, vol.168, issue.21, pp.2304-2310, 2008.
DOI : 10.1001/archinte.168.21.2304

E. Steyerberg, M. Pencina, and H. Lingsma, Assessing the incremental value of diagnostic and prognostic markers: a review and illustration, European Journal of Clinical Investigation, vol.27, issue.2, pp.216-228, 2012.
DOI : 10.1111/j.1365-2362.2011.02562.x

E. Steyerberg, M. Vedder, and M. Leening, Graphical assessment of incremental value of novel markers in prediction models: From statistical to decision analytical perspectives, Biometrical Journal, vol.26, issue.4, pp.556-570, 2015.
DOI : 10.1002/bimj.201300260

L. Mcshane, M. Cavenagh, and T. Lively, Criteria for the use of omics-based predictors in clinical trials: explanation and elaboration, BMC Medicine, vol.8, issue.1, p.220, 2013.
DOI : 10.1186/bcr1412

P. Bossuyt, J. Lijmer, and B. Mol, Randomised comparisons of medical tests: sometimes invalid, not always efficient, The Lancet, vol.356, issue.9244, pp.1844-1847, 2000.
DOI : 10.1016/S0140-6736(00)03246-3

J. De-graaff, D. Ubbink, J. Tijssen, and D. Legemate, The diagnostic randomized clinical trial is the best solution for management issues in critical limb ischemia, Journal of Clinical Epidemiology, vol.57, issue.11, pp.1111-1118, 2004.
DOI : 10.1016/j.jclinepi.2004.02.020

B. Lu and C. Gatsonis, Efficiency of study designs in diagnostic randomized clinical trials, Statistics in Medicine, vol.85, issue.9, pp.1451-1466, 2013.
DOI : 10.1002/sim.5655

M. Rodger, T. Ramsay, and D. Fergusson, Diagnostic randomized controlled trials: the final frontier, Trials, vol.356, issue.1, p.137, 2012.
DOI : 10.1016/S0140-6736(00)03246-3

URL : http://doi.org/10.1186/1745-6215-13-137

M. Buyse and S. Michiels, Omics-based clinical trial designs, Current Opinion in Oncology, vol.25, pp.289-295, 2013.
DOI : 10.1097/CCO.0b013e32835ff2fe

J. Bogaerts, F. Cardoso, and M. Buyse, Gene signature evaluation as a prognostic tool: challenges in the design of the MINDACT trial, Nature Clinical Practice Oncology, vol.14, issue.10, pp.540-551, 2006.
DOI : 10.1038/ncponc0591

R. Hooper, K. Diaz-ordaz, A. Takeda, and K. Khan, Comparing diagnostic tests: trials in people with discordant test results, Statistics in Medicine, vol.49, issue.iii, pp.2443-2456, 2013.
DOI : 10.1002/sim.5676

J. Sparano, TAILORx: Trial Assigning Individualized Options for Treatment (Rx), Clinical Breast Cancer, vol.7, issue.4, pp.347-350, 2006.
DOI : 10.3816/CBC.2006.n.051

J. Sparano, R. Gray, and D. Makower, Prospective Validation of a 21-Gene Expression Assay in Breast Cancer, New England Journal of Medicine, vol.373, issue.21, pp.2005-2014, 2015.
DOI : 10.1056/NEJMoa1510764

J. Bonastre, S. Marguet, and B. Lueza, Cost Effectiveness of Molecular Profiling for Adjuvant Decision Making in Patients With Node-Negative Breast Cancer, Journal of Clinical Oncology, vol.32, issue.31, pp.3513-3519, 2014.
DOI : 10.1200/JCO.2013.54.9931

S. Yusuf, R. Collins, and R. Peto, Why do we need some large, simple randomized trials?, Statistics in Medicine, vol.34, issue.4, pp.409-422, 1984.
DOI : 10.1002/sim.4780030421

R. Simon, New challenges for 21st century clinical trials, Clinical Trials, vol.353, issue.2, pp.167-169, 2007.
DOI : 10.1177/1740774507076800

R. Betensky, D. Louis, and J. Cairncross, Influence of Unrecognized Molecular Heterogeneity on Randomized Clinical Trials, Journal of Clinical Oncology, vol.20, issue.10, pp.2495-2499, 2002.
DOI : 10.1200/JCO.2002.06.140

K. Pogue-geile, C. Kim, and J. Jeong, Predicting Degree of Benefit From Adjuvant Trastuzumab in NSABP Trial B-31, JNCI Journal of the National Cancer Institute, vol.105, issue.23, pp.1782-1788, 2013.
DOI : 10.1093/jnci/djt321

E. Perez, E. Thompson, and K. Ballman, Genomic Analysis Reveals That Immune Function Genes Are Strongly Linked to Clinical Outcome in the North Central Cancer Treatment Group N9831 Adjuvant Trastuzumab Trial, Journal of Clinical Oncology, vol.33, issue.7, pp.701-708, 2015.
DOI : 10.1200/JCO.2014.57.6298

A. Dupuy and R. Simon, Critical Review of Published Microarray Studies for Cancer Outcome and Guidelines on Statistical Analysis and Reporting, JNCI Journal of the National Cancer Institute, vol.99, issue.2, pp.147-157, 2007.
DOI : 10.1093/jnci/djk018

S. Paik, G. Tang, and S. Shak, Gene Expression and Benefit of Chemotherapy in Women With Node-Negative, Estrogen Receptor???Positive Breast Cancer, Journal of Clinical Oncology, vol.24, issue.23, pp.3726-3734, 2006.
DOI : 10.1200/JCO.2005.04.7985

K. Albain, W. Barlow, and S. Shak, Prognostic and predictive value of the 21-gene recurrence score assay in postmenopausal women with node-positive, oestrogen-receptor-positive breast cancer on chemotherapy: a retrospective analysis of a randomised trial, The Lancet Oncology, vol.11, issue.1, pp.55-65, 2010.
DOI : 10.1016/S1470-2045(09)70314-6

W. Barlow, Design of a clinical trial for testing the ability of a continuous marker to predict therapy benefit Handbook of Statistics in Clinical Oncology, pp.293-304, 2012.

B. Freidlin and E. Korn, Biomarker enrichment strategies: matching trial design to biomarker credentials, Nature Reviews Clinical Oncology, vol.31, issue.2, pp.81-90, 2014.
DOI : 10.4155/cli.12.137

S. Michiels, R. Potthoff, and S. George, Multiple testing of treatment-effect-modifying biomarkers in a randomized clinical trial with a survival endpoint, Statistics in Medicine, vol.371, issue.13, pp.1502-1518, 2011.
DOI : 10.1002/sim.4022

S. Matsui, R. Simon, and P. Qu, Developing and Validating Continuous Genomic Signatures in Randomized Clinical Trials for Predictive Medicine, Clinical Cancer Research, vol.18, issue.21, pp.6065-6073, 2012.
DOI : 10.1158/1078-0432.CCR-12-1206

M. Polley, E. Polley, and E. Huang, Two-stage adaptive cutoff design for building and validating a prognostic biomarker signature, Statistics in Medicine, vol.11, issue.1, pp.5097-5110, 2014.
DOI : 10.1002/sim.6310

URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4227914

B. Freidlin, W. Jiang, and R. Simon, The Cross-Validated Adaptive Signature Design, Clinical Cancer Research, vol.16, issue.2, pp.691-698, 2010.
DOI : 10.1158/1078-0432.CCR-09-1357

R. Simon, Clinical trials for predictive medicine, Statistics in Medicine, vol.48, issue.5, pp.3031-3040, 2012.
DOI : 10.1002/sim.5401

N. Ternè-s, F. Rotolo, G. Heinze, and S. Michiels, Identification of biomarker-by-treatment interactions in randomized clinical trials with survival outcomes and high-dimensional spaces, Biometrical Journal, vol.67, pp.10-1002, 2016.
DOI : 10.1002/bimj.201500234