Network Propagation Predicts Drug Synergy in Cancers

Abstract

Combination therapies are commonly used to treat patients with complex diseases that respond poorly to single-agent therapies. high-throughput drug screening is a standard method for preclinical 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 subchallenges 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 approximately 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 posttreatment molecular profiles when only the pretreatment molecular profile is available. Our cross-tissue synergism prediction algorithm achieves promising accuracy comparable with the correlation between experimental replicates and can be applied to other cancer cell lines and drugs to guide therapeutic choices. This study presents a novel network propagation-based method that predicts anticancer drug synergy to the accuracy of experimental replicates, which establishes a state-of-the-field method as benchmarked by the pharmacogenomics research community involving models generated by 160 teams. .

Predicting synergism of cancer drug combinations using NCI-ALMANAC data

“…AstraZeneca carried out a screening study, spanning 910 drug combinations over 85 cancer cell lines (over 11,000 measured synergy scores), which was subsequently used for a DREAM challenge 37,38 . Very recently, the largest publicly available cancer drug combination dataset has been provided by the US National Cancer Institute (NCI).…”

Drug combination sensitivity scoring facilitates the discovery of synergistic and efficacious drug combinations in cancer

“…On the other hand, as the focus of the current study is to propose the new experimental design and to justify its associated drug combination scoring methods, we tested the predictability of CSS using conventional machine learning methods, and showed that CSS can be accurately inferred from the pharmacological features of drug combinations. More advanced machine learning methods such as Deep Learning [16] or network-based methods [22] may further improve the prediction accuracy, which will be tested as future work.…”

Machine Learning for Cancer Drug Combination

“…The winning algorithm introduced a novel network propagation method to simulate the posttreatment genomic profile from the pretreatment profile of a cancer cell line based on drug target information and the gene-gene interaction network. 2 Together with the simulated genomic profiles, the monotherapy data were used to build treebased conventional ML models for predicting drug synergy ( Figure 1a). When tested on a sizeable held-out dataset, this method ranked first among 160 teams in the challenge and established a new stateof-the-field algorithm in the pharmacogenomics research community.…”

“…Hidden prior knowledge is often crucial for developing a powerful prediction model and a better understanding of the mechanism underlying drug synergism. For instance, Li et al 2 leveraged prior information of the gene-gene interaction network and drug target genes to improve prediction accuracy, and, in DeepSynergy, 3 both genomic profiles and chemical compounds were considered. However, it remains unknown to what extent the hidden biological information is needed to perfectly predict drug synergy.…”