Background: Predictive models are an integral part of current clinical practice and help determine optimal treatment strategies for individual patients. A drawback is that covariates are assumed to have constant effects on overall survival (OS), when in fact, these effects may change during follow-up (FU). Furthermore, breast cancer (BC) patients may experience events that alter their prognosis from that time onwards. We investigated the 'dynamic' effects of different covariates on OS and developed a nomogram to calculate 5-year dynamic OS (DOS) probability at different prediction timepoints (t<inf>P</inf>) during FU. Methods: Dutch and Belgian postmenopausal, endocrine-sensitive, early BC patients enrolled in the TEAM trial were included. We assessed time-varying effects of specific covariates and obtained 5-year DOS predictions using a proportional baselines landmark supermodel. Covariates included age, histological grade, hormone receptor and HER2 status, T-and N-stage, locoregional recurrence (LRR), distant recurrence, and treatment compliance. A nomogram was designed to calculate 5-year DOS based on individual characteristics. Results: A total of 2602 patients were included (mean FU 6.2 years). N-stage, LRR, and HER2 status demonstrated time-varying effects on 5-year DOS. Hazard ratio (HR) functions for LRR, high-risk N-stage (N2/3), and HER2 positivity were HR = (8.427 × 0.583<sup>tP</sup>), HR = (3.621 × 0.816<sup>tP</sup>), and HR = (1.235 × 0.851<sup>tP</sup>), respectively. Treatment discontinuation was associated with a higher mortality risk, but without a time-varying effect [HR 1.263 (0.867-1.841)]. All other covariates were time-constant. Discussion: The current nomogram accounts for elapsed time since starting adjuvant endocrine treatment and optimizes prediction of individual 5-year DOS during FU for postmenopausal, endocrine-sensitive BC patients. The nomogram can facilitate in determining whether further therapy will benefit an individual patient, although validation in an independent dataset is still needed.

Breast cancer, Dynamic prediction, Landmark analysis, Personalized therapy, Survival probability
dx.doi.org/10.1093/annonc/mdv146, hdl.handle.net/1765/85060
Annals of Oncology
Erasmus MC: University Medical Center Rotterdam

Fontein, D.B.Y, Klinten Grand, M, Nortier, J.W.R, Seynaeve, C.M, Kranenbarg, E.M.-K, Dirix, L.Y, … Putter, H. (2015). Dynamic prediction in breast cancer: Proving feasibility in clinical practice using the TEAM trial. Annals of Oncology, 26(6), 1254–1262. doi:10.1093/annonc/mdv146