Discovery of predictive biomarkers for cancer therapy through computational approaches

Precision oncology involves the use of predictive biomarkers to personalize treatment. However, for most cancer therapeutics or combination regimens, effective biomarkers have been elusive. This challenge has fuelled efforts to interrogate increasingly diverse and complex clinical and molecular determinants of treatment response. Some molecular predictors have been identified (for example, based on analysis of transcriptomic or imaging data), although the limited reproducibility and robustness of many of these candidate biomarkers make them difficult to apply in clinical practice. Moreover, different types of predictor must often be combined to optimize treatment selection (for example, gene signatures plus patient characteristics). Computational methods, including machine learning and artificial intelligence approaches, provide opportunities to identify predictive patterns in both clinical data and preclinical datasets and to predict treatment response for individual patients. Such approaches also offer opportunities to predict the efficacy or synergy of drug combinations, for example, via extrapolation from correlations of monotherapy responses or by linking the cellular responses observed in preclinical drug screens with molecular and clinical data from patients. In this Review, we describe the application of computational methods to predictive biomarker discovery, including current progress, key challenges facing this field, and future opportunities.