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

Significance Cell lines have been extensively used to study anticancer agents, thereby establishing vast molecular and drug response datasets. Unfortunately, the translation of cell line–derived biomarkers often fails. To bridge this gap between model systems and clinical practice, we developed a mathematical framework to capture gene expression patterns shared between model systems and human tumors in a consensus space. In this space, we trained drug response predictors on a panel of 1,000 cell lines and successfully predicted drug response on approximately 1,300 human tumors. Finally, we derived an approach to interpret the predictors, and we propose potential mechanisms mediating the cytotoxic effects of two drugs. Experimental validation is required to confirm these results. 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 demonstrate that response predictions deliver a robust performance for a number of therapies of high clinical importance: platinum-based chemotherapies, gemcitabine, and paclitaxel. In contrast to other approaches, we demonstrate the interpretability of the TRANSACT predictors by correctly identifying known biomarkers of targeted therapies, and we propose potential mechanisms that mediate the resistance to two chemotherapeutic agents.