Anticancer drug synergy prediction in understudied tissues using transfer learning.

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ID: 128163
2020
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Ranked #20 of 77 articles by views in Journal of the American Medical Informatics Association : JAMIA

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Abstract
Drug combination screening has advantages in identifying cancer treatment options with higher efficacy without degradation in terms of safety. A key challenge is that the accumulated number of observations in in-vitro drug responses varies greatly among different cancer types, where some tissues are more understudied than the others. Thus, we aim to develop a drug synergy prediction model for understudied tissues as a way of overcoming data scarcity problems.We collected a comprehensive set of genetic, molecular, phenotypic features for cancer cell lines. We developed a drug synergy prediction model based on multitask deep neural networks to integrate multimodal input and multiple output. We also utilized transfer learning from data-rich tissues to data-poor tissues.We showed improved accuracy in predicting synergy in both data-rich tissues and understudied tissues. In data-rich tissue, the prediction model accuracy was 0.9577 AUROC for binarized classification task and 174.3 mean squared error for regression task. We observed that an adequate transfer learning strategy significantly increases accuracy in the understudied tissues.Our synergy prediction model can be used to rank synergistic drug combinations in understudied tissues and thus help to prioritize future in-vitro experiments. Code is available at https://github.com/yejinjkim/synergy-transfer.
Reference Key
kim2020anticancerjournal Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Kim, Yejin;Zheng, Shuyu;Tang, Jing;Jim Zheng, Wenjin;Li, Zhao;Jiang, Xiaoqian;
Journal Journal of the American Medical Informatics Association : JAMIA
Year 2020
DOI
ocaa212
URL
Keywords
ranking drug sensitivity external validation data paucity data scarcity multitask learning

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