Machine learning approach informs biology of cancer drug response.

ABSTRACT:

Background

The mechanism of action for most cancer drugs is not clear. Large-scale pharmacogenomic cancer cell line datasets offer a rich resource to obtain this knowledge. Here, we present an analysis strategy for revealing biological pathways that contribute to drug response using publicly available pharmacogenomic cancer cell line datasets.

Methods

We present a custom machine-learning based approach for identifying biological pathways involved in cancer drug response. We test the utility of our approach with a pan-cancer analysis of ML210, an inhibitor of GPX4, and a melanoma-focused analysis of inhibitors of BRAF^V600. We apply our approach to reveal determinants of drug resistance to microtubule inhibitors.

Results

Our method implicated lipid metabolism

SUBMITTER: Zhu EY 

PROVIDER: S-EPMC9112473 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature