Enhanced Co-Expression Extrapolation (COXEN) Gene Selection Method for Building Anti-Cancer Drug Response Prediction Models

Zhu, Yitan; Brettin, Thomas; Evrard, Yvonne A.; Xia, Fangfang; Partin, Alexander; Shukla, Maulik; Yoo, Hyunseung; Doroshow, James H.; Stevens, Rick L.

doi:10.3390/genes11091070

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Enhanced Co-Expression Extrapolation (COXEN) Gene Selection Method for Building Anti-Cancer Drug Response Prediction Models

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Computing, Environment and Life Sciences, Argonne National Laboratory, Lemont, IL 60439, USA

Leidos Biomedical Research, National Laboratory for Cancer Research, Inc. Frederick, Frederick, MD 21702, USA

Developmental Therapeutics Branch, National Cancer Institute, Bethesda, MD 20892, USA

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Department of Computer Science, The University of Chicago, Chicago, IL 60637, USA

Author to whom correspondence should be addressed.

Genes 2020, 11(9), 1070; https://doi.org/10.3390/genes11091070

Received: 15 July 2020 / Revised: 27 August 2020 / Accepted: 9 September 2020 / Published: 11 September 2020

(This article belongs to the Special Issue Selected Papers from the International Conference on Intelligent Biology and Medicine (ICIBM 2020))

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Abstract

The co-expression extrapolation (COXEN) method has been successfully used in multiple studies to select genes for predicting the response of tumor cells to a specific drug treatment. Here, we enhance the COXEN method to select genes that are predictive of the efficacies of multiple drugs for building general drug response prediction models that are not specific to a particular drug. The enhanced COXEN method first ranks the genes according to their prediction power for each individual drug and then takes a union of top predictive genes of all the drugs, among which the algorithm further selects genes whose co-expression patterns are well preserved between cancer cases for building prediction models. We apply the proposed method on benchmark in vitro drug screening datasets and compare the performance of prediction models built based on the genes selected by the enhanced COXEN method to that of models built on genes selected by the original COXEN method and randomly picked genes. Models built with the enhanced COXEN method always present a statistically significantly improved prediction performance (adjusted p-value ≤ 0.05). Our results demonstrate the enhanced COXEN method can dramatically increase the power of gene expression data for predicting drug response. View Full-Text

Keywords: general drug response prediction model; gene selection; co-expression extrapolation (COXEN); precision oncology

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Zhu, Y.; Brettin, T.; Evrard, Y.A.; Xia, F.; Partin, A.; Shukla, M.; Yoo, H.; Doroshow, J.H.; Stevens, R.L. Enhanced Co-Expression Extrapolation (COXEN) Gene Selection Method for Building Anti-Cancer Drug Response Prediction Models. Genes 2020, 11, 1070. https://doi.org/10.3390/genes11091070

AMA Style

Zhu Y, Brettin T, Evrard YA, Xia F, Partin A, Shukla M, Yoo H, Doroshow JH, Stevens RL. Enhanced Co-Expression Extrapolation (COXEN) Gene Selection Method for Building Anti-Cancer Drug Response Prediction Models. Genes. 2020; 11(9):1070. https://doi.org/10.3390/genes11091070

Chicago/Turabian Style

Zhu, Yitan, Thomas Brettin, Yvonne A. Evrard, Fangfang Xia, Alexander Partin, Maulik Shukla, Hyunseung Yoo, James H. Doroshow, and Rick L. Stevens. 2020. "Enhanced Co-Expression Extrapolation (COXEN) Gene Selection Method for Building Anti-Cancer Drug Response Prediction Models" Genes 11, no. 9: 1070. https://doi.org/10.3390/genes11091070

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Enhanced Co-Expression Extrapolation (COXEN) Gene Selection Method for Building Anti-Cancer Drug Response Prediction Models

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