DTF: Deep Tensor Factorization for Predicting Anticancer Drug Synergy

TL;DR

The paper introduces a Deep Tensor Factorization model that combines tensor factorization and deep neural networks to effectively predict anticancer drug synergies, outperforming existing methods and identifying novel combinations.

Contribution

A novel Deep Tensor Factorization model integrating tensor factorization with deep neural networks for improved drug synergy prediction.

Findings

DTF outperforms tensor-based methods with PR AUC of 0.57 vs. 0.24

DTF achieves comparable performance to DeepSynergy

Predicted drug synergies include some validated in vivo or in vitro

Abstract

Motivation: Combination therapies have been widely used to treat cancers. However, it is cost- and time-consuming to experimentally screen synergistic drug pairs due to the enormous number of possible drug combinations. Thus, computational methods have become an important way to predict and prioritize synergistic drug pairs. Results: We proposed a Deep Tensor Factorization (DTF) model, which integrated a tensor factorization method and a deep neural network (DNN), to predict drug synergy. The former extracts latent features from drug synergy information while the latter constructs a binary classifier to predict the drug synergy status. Compared to the tensor-based method, the DTF model performed better in predicting drug synergy. The area under the precision-recall curve (PR AUC) was 0.57 for DTF and 0.24 for the tensor method. We also compared the DTF model with DeepSynergy and logistic regression models and found that the DTF outperformed the logistic regression model and achieved almost the same performance as DeepSynergy using several typical metrics for the classification task. Applying the DTF model to predict missing entries in our drug-cell line tensor, we identified novel synergistic drug combinations for 10 cell lines from the 5 cancer types. A literature survey showed that some of these predicted drug synergies have been identified in vivo or in vitro. Thus, the DTF model could be valuable in silico tool for prioritizing novel synergistic drug combinations.