Blows, F. M. et al. Subtyping of breast cancer by immunohistochemistry to investigate a relationship between subtype and short and long term survival: a collaborative analysis of data for 10,159 cases from 12 studies. PLoS Med. 7, e1000279 (2010).
Davies, C. et al. Long-term effects of continuing adjuvant tamoxifen to 10 years versus stopping at 5 years after diagnosis of oestrogen receptor-positive breast cancer: ATLAS, a randomised trial. Lancet 381, 805–816 (2013).
Sestak, I. et al. Factors predicting late recurrence for estrogen receptor-positive breast cancer. J. Natl Cancer Inst. 105, 1504–1511 (2013).
Sgroi, D. C. et al. Prediction of late distant recurrence in patients with oestrogen-receptor-positive breast cancer: a prospective comparison of the breast-cancer index (BCI) assay, 21-gene recurrence score, and IHC4 in the TransATAC study population. Lancet Oncol. 14, 1067–1076 (2013).
Pan, H. et al. 20-year risks of breast-cancer recurrence after stopping endocrine therapy at 5 years. N. Engl. J. Med. 377, 1836–1846 (2017).
Dowsett, M. et al. Integration of clinical variables for the prediction of late distant recurrence in patients with estrogen receptor-positive breast cancer treated with 5 years of endocrine therapy: CTS5. J. Clin. Oncol. 36, 1941–1948 (2018).
Harris, L. N. et al. 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. J. Clin. Oncol. 34, 1134–1150 (2016).
Sledge, G. W. et al. Past, present, and future challenges in breast cancer treatment. J. Clin. Oncol. 32, 1979–1986 (2014).
Richman, J. & Dowsett, M. Beyond 5 years: enduring risk of recurrence in oestrogen receptor-positive breast cancer. Nat. Rev. Clin. Oncol. 1, https://doi.org/10.1038/s41571-018-0145-5 (2018).
Perou, C. M. et al. Molecular portraits of human breast tumours. Nature 406, 747–752 (2000).
Parker, J. S. et al. Supervised risk predictor of breast cancer based on intrinsic subtypes. J. Clin. Oncol. 27, 1160–1167 (2009).
Curtis, C. et al. The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature 486, 346–352 (2012).
Ali, H. R. et al. Genome-driven integrated classification of breast cancer validated in over 7,500 samples. Genome Biol. 15, 431 (2014).
Putter, H., van der Hage, J., de Bock, G. H., Elgalta, R. & van de Velde, C. J. H. Estimation and prediction in a multi-state model for breast cancer. Biom. J. 48, 366–380 (2006).
Fisher, B. et al. Significance of ipsilateral breast tumour recurrence after lumpectomy. Lancet 338, 327–331 (1991).
Insa, A. et al. Prognostic factors predicting survival from first recurrence in patients with metastatic breast cancer: analysis of 439 patients. Breast Cancer Res. Treat. 56, 67–78 (1999).
Putter, H., Fiocco, M. & Geskus, R. B. Tutorial in biostatistics: competing risks and multi-state models. Stat. Med. 26, 2389–2430 (2007).
Wishart, G. C. et al. PREDICT: a new UK prognostic model that predicts survival following surgery for invasive breast cancer. Breast Cancer Res. 12, R1 (2010); erratum 12, 401 (2010).
Michaelson, J. S. et al. Improved web-based calculators for predicting breast carcinoma outcomes. Breast Cancer Res. Treat. 128, 827–835 (2011).
Ormandy, C. J., Musgrove, E. A., Hui, R., Daly, R. J. & Sutherland, R. L. Cyclin D1, EMS1 and 11q13 amplification in breast cancer. Breast Cancer Res. Treat. 78, 323–335 (2003).
Sanchez-Garcia, F. et al. Integration of genomic data enables selective discovery of breast cancer drivers. Cell 159, 1461–1475 (2014).
Shrestha, Y. et al. PAK1 is a breast cancer oncogene that coordinately activates MAPK and MET signaling. Oncogene 31, 3397–3408 (2012).
Holland, D. G. et al. ZNF703 is a common luminal B breast cancer oncogene that differentially regulates luminal and basal progenitors in human mammary epithelium. EMBO Mol. Med. 3, 167–180 (2011).
Reis-Filho, J. S. et al. FGFR1 emerges as a potential therapeutic target for lobular breast carcinomas. Clin. Cancer Res. 12, 6652–6662 (2006).
Liu, H. et al. Pharmacologic targeting of S6K1 in PTEN-deficient neoplasia. Cell Reports 18, 2088–2095 (2017).
Delmore, J. E. et al. BET bromodomain inhibition as a therapeutic strategy to target c-Myc. Cell 146, 904–917 (2011).
Pearson, A. et al. High-level clonal FGFR amplification and response to FGFR inhibition in a translational clinical trial. Cancer Discov. 6, 838–851 (2016).
Wapnir, I. L. et al. A randomized clinical trial of adjuvant chemotherapy for radically resected locoregional relapse of breast cancer: IBCSG 27-02, BIG 1-02, and NSABP B-37. Clin. Breast Cancer 8, 287–292 (2008).
Clark, G. M., Sledge, G. W. Jr, Osborne, C. K. & McGuire, W. L. Survival from first recurrence: relative importance of prognostic factors in 1,015 breast cancer patients. J. Clin. Oncol. 5, 55–61 (1987).
Kennecke, H. et al. Metastatic behavior of breast cancer subtypes. J. Clin. Oncol. 28, 3271–3277 (2010).
Fix, E. & Neyman, J. A simple stochastic model of recovery, relapse, death and loss of patients. Hum. Biol. 23, 205–241 (1951).
Broët, P. et al. Analyzing prognostic factors in breast cancer using a multistate model. Breast Cancer Res. Treat. 54, 83–89 (1999).
Meier-Hirmer, C. & Schumacher, M. Multi-state model for studying an intermediate event using time-dependent covariates: application to breast cancer. BMC Med. Res. Methodol. 13, 80 (2013).
Therneau, T. M. & Grambsch, P. M. Modeling Survival Data: Extending the Cox Model (Springer, New York, 2000).
de Wreede, L. C., Fiocco, M. & Putter, H. mstate: an R package for the analysis of competing risks and multi-state models. J. Stat. Software 38, 1–30 (2011).
Klein, J. P., Keiding, N. & Copelan, E. A. Plotting summary predictions in multistate survival models: probabilities of relapse and death in remission for bone marrow transplantation patients. Stat. Med. 12, 2315–2332 (1993).
Aalen, O., Borgan, O. & Gjessing, H. Survival and Event History Analysis—A Process Point of View (Springer, New York, 2008).
Fiocco, M., Putter, H. & van Houwelingen, H. C. Reduced-rank proportional hazards regression and simulation-based prediction for multi-state models. Stat. Med. 27, 4340–4358 (2008).
Hothorn, T., Bretz, F. & Westfall, P. Simultaneous inference in general parametric models. Biom. J. 50, 346–363 (2008).
Dunnett, C. W. A multiple comparison procedure for comparing several treatments with a control. J. Am. Stat. Assoc. 50, 1096–1121 (1955).
Prentice, R. L., Williams, B. J. & Peterson, A. V. On the regression analysis of multivariate failure time data. Biometrika 68, 373–379 (1981).
Harrell, F. E. J. Regression Modeling Strategies (Springer, 2001).
Li, Y. et al. Amplification of LAPTM4B and YWHAZ contributes to chemotherapy resistance and recurrence of breast cancer. Nat. Med. 16, 214–218 (2010).
Clarke, C. et al. Correlating transcriptional networks to breast cancer survival: a large-scale coexpression analysis. Carcinogenesis 34, 2300–2308 (2013).
Loi, S. et al. Predicting prognosis using molecular profiling in estrogen receptor-positive breast cancer treated with tamoxifen. BMC Genomics 9, 239 (2008).
Nagalla, S. et al. Interactions between immunity, proliferation and molecular subtype in breast cancer prognosis. Genome Biol. 14, R34 (2013).
Schmidt, M. et al. The humoral immune system has a key prognostic impact in node-negative breast cancer. Cancer Res. 68, 5405–5413 (2008).
Desmedt, C. et al. Strong time dependence of the 76-gene prognostic signature for node-negative breast cancer patients in the TRANSBIG multicenter independent validation series. Clin. Cancer Res. 13, 3207–3214 (2007).
Miller, L. D. et al. An expression signature for p53 status in human breast cancer predicts mutation status, transcriptional effects, and patient survival. Proc. Natl Acad. Sci. USA 102, 13550–13555 (2005); correction 102, 17882 (2005).
Gautier, L., Cope, L., Bolstad, B. M. & Irizarry, R. A. affy—analysis of Affymetrix GeneChip data at the probe level. Bioinformatics 20, 307–315 (2004).
Leek, J. T., Johnson, W. E., Parker, H. S., Jaffe, A. E. & Storey, J. D. The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics 28, 882–883 (2012).
Gendoo, D. M. A. et al. Genefu: an R/Bioconductor package for computation of gene expression-based signatures in breast cancer. Bioinformatics 32, 1097–1099 (2016).
Schröder, M. S., Culhane, A. C., Quackenbush, J. & Haibe-Kains, B. survcomp: an R/Bioconductor package for performance assessment and comparison of survival models. Bioinformatics 27, 3206–3208 (2011).
R Core Team. R: A Language and Environment for Statistical Computing. http://www.r-project.org/ (2015).