One of the objectives of precision oncology is to identify patient's responsiveness to a given treatment and prevent potential overtreatments through molecular profiling. Predictive gene expression biomarkers are a promising and practical means to this purpose. The overall response rate of paclitaxel drugs in breast cancer has been reported to be in the range of 20-60% and is in the even lower range for ER-positive patients. Predicting responsiveness of breast cancer patients, either ER-positive or ER-negative, to paclitaxel treatment could prevent individuals with poor response to the therapy from undergoing excess exposure to the agent. In this study, we identified six sets of gene signatures whose gene expression profiles could robustly predict nonresponding patients with precisions more than 94% and recalls more than 93% on various discovery datasets (n = 469 for the largest set) and independent validation datasets (n = 278), using the previously developed Multiple Survival Screening algorithm, a random-sampling-based methodology. The gene signatures reported were stable regardless of half of the discovery datasets being swapped, demonstrating their robustness. We also reported a set of optimizations that enabled the algorithm to train on small-scale computational resources. The gene signatures and optimized methodology described in this study could be used for identifying unresponsiveness in patients of ER-positive or ER-negative breast cancers.
Keywords: breast cancer; drug resistance; microarray gene expression profile; predictor; signature genes.