Predictive performance of microarray gene signatures: impact of tumor heterogeneity and multiple mechanisms of drug resistance

Abstract

Gene signatures have failed to predict responses to breast cancer therapy in patients to date. In this study, we used bioinformatic methods to explore the hypothesis that the existence of multiple drug resistance mechanisms in different patients may limit the power of gene signatures to predict responses to therapy. In addition, we explored whether substratification of resistant cases could improve performance. Gene expression profiles from 1,550 breast cancers analyzed with the same microarray platform were retrieved from publicly available sources. Gene expression changes were introduced in cases defined as sensitive or resistant to a hypothetical therapy. In the resistant group, up to five different mechanisms of drug resistance causing distinct or overlapping gene expression changes were generated bioinformatically, and their impact on sensitivity, specificity, and predictive values of the signatures was investigated. We found that increasing the number of resistance mechanisms corresponding to different gene expression changes weakened the performance of the predictive signatures generated, even if the resistance-induced changes in gene expression were sufficiently strong and informative. Performance was also affected by cohort composition and the proportion of sensitive versus resistant cases or resistant cases that were mechanistically distinct. It was possible to improve response prediction by substratifying chemotherapy-resistant cases from actual datasets (non-bioinformatically perturbed datasets) and by using outliers to model multiple resistance mechanisms. Our work supports the hypothesis that the presence of multiple resistance mechanisms in a given therapy in patients limits the ability of gene signatures to make clinically useful predictions.