Pisces: A multi-modal data augmentation approach for drug combination synergy prediction

Abstract Drug combination therapy is promising for cancer treatment through simultaneously reducing resistance and improving efficacy. Machine learning approaches to drug combination response prediction can prioritize experiments and discover new combinations, but require lots of training data in order to fit the nonlinearity of synergistic effect. Here, we propose Pisces, a novel machine learning approach for drug combination synergy prediction. The key idea of Pisces is to augment the sparse drug combination dataset by creating multiple views for each drug combination based on its different modalities. We combined eight different modalities of a single drug to create 64 augmented views for a pair of drugs, effectively expanding the size of the original data 64 times. Pisces obtained state-of-the-art results on cell-line-based drug synergy prediction, xenograft-based drug synergy prediction, and drug-drug interaction prediction. By interpreting Pisces’s predictions using a genetic interaction network, we further identified a breast cancer drug-sensitive pathway from BRCA cell lines in GDSC. We validated this pathway on an independent TCGA-BRCA tumor dataset and found that patients with this pathway activated had substantially longer survival time. Collectively, Pisces effectively predicts drug synergy and drug-drug interactions through augmenting the original dataset 64 times, and can be broadly applied to various biological applications that involve a pair of drugs.