Network Propagation Predicts Drug Synergy in Cancers

Combination therapies are commonly used to treat patients with complex diseases that respond poorly to single-agent therapies. In vitro high-throughput drug screening is a standard method for pre-clinical prioritization of synergistic drug combinations, but it can be impractical for large drug sets. Computational methods are thus being actively explored; however, most published methods were built on a limited size of cancer cell lines or drugs, and it remains a challenge to predict synergism at a large scale where the diversity within the data escalates the difficulty of prediction. Here we present a state-of-the-field synergy prediction algorithm, which ranked first in all sub-challenges in the AstraZeneca-Sanger Drug Combination Prediction DREAM Challenge. The model was built and evaluated using the largest drug combination screening dataset at the time of the competition, consisting of ~11,500 experimentally tested synergy scores of 118 drugs in 85 cancer cell lines. We developed a novel feature extraction strategy by integrating the cross-cell and cross-drug information with a novel network propagation method and then assembled the information in monotherapy and simulated molecular data to predict drug synergy. This represents a significant conceptual advancement of synergy prediction, using extracted features in the form of simulated post-treatment molecular profiles when only the pre-treatment molecular profile is available. Our cross-tissue synergism prediction algorithm achieves promising accuracy comparable to the correlation between experimental replicates and can be applied to other cancer cell lines and drugs to guide therapeutic choices.

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