A context-aware deconfounding autoencoder for robust prediction of personalized clinical drug response from cell-line compound screening

Abstract Accurate and robust prediction of patient-specific responses to a new compound is critical to personalized drug discovery and development. However, patient data are often too scarce to train a generalized machine learning model. Although many methods have been developed to utilize cell-line screens for predicting clinical responses, their performances are unreliable owing to data heterogeneity and distribution shift. Here we have developed a novel context-aware deconfounding autoencoder (CODE-AE) that can extract intrinsic biological signals masked by context-specific patterns and confounding factors. Extensive comparative studies demonstrated that CODE-AE effectively alleviated the out-of-distribution problem for the model generalization and significantly improved accuracy and robustness over state-of-the-art methods in predicting patient-specific clinical drug responses purely from cell-line compound screens. Using CODE-AE, we screened 59 drugs for 9,808 patients with cancer. Our results are consistent with existing clinical observations, suggesting the potential of CODE-AE in developing personalized therapies and drug response biomarkers.