Predicting patient response with models trained on cell lines and patient-derived xenografts by nonlinear transfer learning.

ABSTRACT: Preclinical models have been the workhorse of cancer research, producing massive amounts of drug response data. Unfortunately, translating response biomarkers derived from these datasets to human tumors has proven to be particularly challenging. To address this challenge, we developed TRANSACT, a computational framework that builds a consensus space to capture biological processes common to preclinical models and human tumors and exploits this space to construct drug response predictors that robustly transfer from preclinical models to human tumors. TRANSACT performs favorably compared to four competing approaches, including two deep learning approaches, on a set of 23 drug prediction challenges on The Cancer Genome Atlas and 226 metastatic tumors from the Hartwig Medical Foundation. We de

SUBMITTER: Mourragui SMC 

PROVIDER: S-EPMC8670522 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature