Molecular pathways enhance drug response prediction using transfer learning from cell lines to tumors and patient-derived xenografts.

ABSTRACT: Computational models have been successful in predicting drug sensitivity in cancer cell line data, creating an opportunity to guide precision medicine. However, translating these models to tumors remains challenging. We propose a new transfer learning workflow that transfers drug sensitivity predicting models from large-scale cancer cell lines to both tumors and patient derived xenografts based on molecular pathways derived from genomic features. We further compute feature importance to identify pathways most important to drug response prediction. We obtained good performance on tumors (AUROC = 0.77) and patient derived xenografts from triple negative breast cancers (RMSE = 0.11). Using feature importance, we highlight the association between ER-Golgi trafficking pathway in everolimus sens

SUBMITTER: Tang YC 

PROVIDER: S-EPMC9515168 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature