Dataset Information

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 and Rac1/cytoskeleton signaling in the context of ML210 and BRAF inhibitor response, respectively. These findings are consistent with current knowledge of how these drugs work. For microtubule inhibitors, our approach implicated Notch and Akt signaling as pathways that associated with response.

Conclusions

Our results demonstrate the utility of combining informed feature selection and machine learning algorithms in understanding cancer drug response.

SUBMITTER: Zhu EY

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

REPOSITORIES: biostudies-literature

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Publications

Machine learning approach informs biology of cancer drug response.

Zhu Eliot Y EY Dupuy Adam J AJ

BMC bioinformatics 20220517 1

<h4>Background</h4>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.<h4>Methods</h4>We present a custom machine-learning based approach for identifying biological pathways involved in cancer drug response. We test t ...[more]

PMID: 35581546

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Dataset Information

Machine learning approach informs biology of cancer drug response.

Background

Methods

Results

Conclusions

Publications

Machine learning approach informs biology of cancer drug response.

Similar Datasets

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