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Enhanced Co-Expression Extrapolation (COXEN) Gene Selection Method for Building Anti-Cancer Drug Response Prediction Models
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 buildin...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7565427/ https://www.ncbi.nlm.nih.gov/pubmed/32933072 http://dx.doi.org/10.3390/genes11091070 |
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author | Zhu, Yitan Brettin, Thomas Evrard, Yvonne A. Xia, Fangfang Partin, Alexander Shukla, Maulik Yoo, Hyunseung Doroshow, James H. Stevens, Rick L. |
author_facet | Zhu, Yitan Brettin, Thomas Evrard, Yvonne A. Xia, Fangfang Partin, Alexander Shukla, Maulik Yoo, Hyunseung Doroshow, James H. Stevens, Rick L. |
author_sort | Zhu, Yitan |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7565427 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-75654272020-10-26 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. Genes (Basel) Article 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. MDPI 2020-09-11 /pmc/articles/PMC7565427/ /pubmed/32933072 http://dx.doi.org/10.3390/genes11091070 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Zhu, Yitan Brettin, Thomas Evrard, Yvonne A. Xia, Fangfang Partin, Alexander Shukla, Maulik Yoo, Hyunseung Doroshow, James H. Stevens, Rick L. Enhanced Co-Expression Extrapolation (COXEN) Gene Selection Method for Building Anti-Cancer Drug Response Prediction Models |
title | Enhanced Co-Expression Extrapolation (COXEN) Gene Selection Method for Building Anti-Cancer Drug Response Prediction Models |
title_full | Enhanced Co-Expression Extrapolation (COXEN) Gene Selection Method for Building Anti-Cancer Drug Response Prediction Models |
title_fullStr | Enhanced Co-Expression Extrapolation (COXEN) Gene Selection Method for Building Anti-Cancer Drug Response Prediction Models |
title_full_unstemmed | Enhanced Co-Expression Extrapolation (COXEN) Gene Selection Method for Building Anti-Cancer Drug Response Prediction Models |
title_short | Enhanced Co-Expression Extrapolation (COXEN) Gene Selection Method for Building Anti-Cancer Drug Response Prediction Models |
title_sort | enhanced co-expression extrapolation (coxen) gene selection method for building anti-cancer drug response prediction models |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7565427/ https://www.ncbi.nlm.nih.gov/pubmed/32933072 http://dx.doi.org/10.3390/genes11091070 |
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