Name
Title | ||
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Transynergy: Mechanism-Driven Interpretable Deep Neural Network For The Synergistic Prediction And Pathway Deconvolution Of Drug Combinations |
Abstract | ||
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Drug combinations have demonstrated great potential in cancer treatments. They alleviate drug resistance and improve therapeutic efficacy. The fast-growing number of anti-cancer drugs has caused the experimental investigation of all drug combinations to become costly and time-consuming. Computational techniques can improve the efficiency of drug combination screening. Despite recent advances in applying machine learning to synergistic drug combination prediction, several challenges remain. First, the performance of existing methods is suboptimal. There is still much space for improvement. Second, biological knowledge has not been fully incorporated into the model. Finally, many models are lack interpretability, limiting their clinical applications. To address these challenges, we have developed a knowledge-enabled and self-attention transformer boosted deep learning model, TranSynergy, which improves the performance and interpretability of synergistic drug combination prediction. TranSynergy is designed so that the cellular effect of drug actions can be explicitly modeled through cell-line gene dependency, gene-gene interaction, and genome-wide drug-target interaction. A novel Shapley Additive Gene Set Enrichment Analysis (SA-GSEA) method has been developed to deconvolute genes that contribute to the synergistic drug combination and improve model interpretability. Extensive benchmark studies demonstrate that TranSynergy outperforms the state-of-the-art method, suggesting the potential of mechanism-driven machine learning. Novel pathways that are associated with the synergistic combinations are revealed and supported by experimental evidence. They may provide new insights into identifying biomarkers for precision medicine and discovering new anti-cancer therapies. Several new synergistic drug combinations have been predicted with high confidence for ovarian cancer which has few treatment options. The code is available at .Author summaryThe number of anti-cancer drugs has been consistently and quickly growing. They are mainly used as standardized mono-therapy. Drug combinations show substantial advantages over the anti-cancer mono-therapy. Cancer cells treated with the mono-therapy could later activate bypassing pathways and harbor drug resistances. Drug combinations can alleviate this issue by using a smaller doses of each anti-cancer drug or targeting multiple oncogenic pathways. However, the investigation of all anti-cancer drug combinations using experimental methods is costly and time-consuming. Machine learning provides an attractive solution to screening synergistic drug combinations, but it is a black-box and not easy to explain. We have developed a knowledge-enabled deep learning model, TranSynergy, to predict synergistic drug combinations and have demonstrated that our model outperformed other state-of-the-art methods. A novel Shapley Additive Gene Set Enrichment Analysis (SA-GSEA) method is introduced to improve the interpretability of the machine learning model. Using TransSynergy and SA-GSEA, we can deconvolute genes responsible for the synergistic drug combination, suggesting the potential of machine learning in developing precision anti-cancer therapy. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1371/journal.pcbi.1008653 | PLOS COMPUTATIONAL BIOLOGY |
DocType | Volume | Issue |
Journal | 17 | 2 |
ISSN | Citations | PageRank |
1553-734X | 0 | 0.34 |
References | Authors | |
17 | 2 | |