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Feature selection translates drug response predictors from cell lines to patients

Targeted therapies and chemotherapies are prevalent in cancer treatment. Identification of predictive markers to stratify cancer patients who will respond to these therapies remains challenging because patient drug response data are limited. As large amounts of drug response data have been generated...

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Autores principales: Yuan, Shinsheng, Chen, Yen-Chou, Tsai, Chi-Hsuan, Chen, Huei-Wen, Shieh, Grace S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10382684/
https://www.ncbi.nlm.nih.gov/pubmed/37519889
http://dx.doi.org/10.3389/fgene.2023.1217414
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author Yuan, Shinsheng
Chen, Yen-Chou
Tsai, Chi-Hsuan
Chen, Huei-Wen
Shieh, Grace S.
author_facet Yuan, Shinsheng
Chen, Yen-Chou
Tsai, Chi-Hsuan
Chen, Huei-Wen
Shieh, Grace S.
author_sort Yuan, Shinsheng
collection PubMed
description Targeted therapies and chemotherapies are prevalent in cancer treatment. Identification of predictive markers to stratify cancer patients who will respond to these therapies remains challenging because patient drug response data are limited. As large amounts of drug response data have been generated by cell lines, methods to efficiently translate cell-line-trained predictors to human tumors will be useful in clinical practice. Here, we propose versatile feature selection procedures that can be combined with any classifier. For demonstration, we combined the feature selection procedures with a (linear) logit model and a (non-linear) K-nearest neighbor and trained these on cell lines to result in LogitDA and KNNDA, respectively. We show that LogitDA/KNNDA significantly outperforms existing methods, e.g., a logistic model and a deep learning method trained by thousands of genes, in prediction AUC (0.70–1.00 for seven of the ten drugs tested) and is interpretable. This may be due to the fact that sample sizes are often limited in the area of drug response prediction. We further derive a novel adjustment on the prediction cutoff for LogitDA to yield a prediction accuracy of 0.70–0.93 for seven drugs, including erlotinib and cetuximab, whose pathways relevant to anti-cancer therapies are also uncovered. These results indicate that our methods can efficiently translate cell-line-trained predictors into tumors.
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spelling pubmed-103826842023-07-30 Feature selection translates drug response predictors from cell lines to patients Yuan, Shinsheng Chen, Yen-Chou Tsai, Chi-Hsuan Chen, Huei-Wen Shieh, Grace S. Front Genet Genetics Targeted therapies and chemotherapies are prevalent in cancer treatment. Identification of predictive markers to stratify cancer patients who will respond to these therapies remains challenging because patient drug response data are limited. As large amounts of drug response data have been generated by cell lines, methods to efficiently translate cell-line-trained predictors to human tumors will be useful in clinical practice. Here, we propose versatile feature selection procedures that can be combined with any classifier. For demonstration, we combined the feature selection procedures with a (linear) logit model and a (non-linear) K-nearest neighbor and trained these on cell lines to result in LogitDA and KNNDA, respectively. We show that LogitDA/KNNDA significantly outperforms existing methods, e.g., a logistic model and a deep learning method trained by thousands of genes, in prediction AUC (0.70–1.00 for seven of the ten drugs tested) and is interpretable. This may be due to the fact that sample sizes are often limited in the area of drug response prediction. We further derive a novel adjustment on the prediction cutoff for LogitDA to yield a prediction accuracy of 0.70–0.93 for seven drugs, including erlotinib and cetuximab, whose pathways relevant to anti-cancer therapies are also uncovered. These results indicate that our methods can efficiently translate cell-line-trained predictors into tumors. Frontiers Media S.A. 2023-07-14 /pmc/articles/PMC10382684/ /pubmed/37519889 http://dx.doi.org/10.3389/fgene.2023.1217414 Text en Copyright © 2023 Yuan, Chen, Tsai, Chen and Shieh. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Genetics
Yuan, Shinsheng
Chen, Yen-Chou
Tsai, Chi-Hsuan
Chen, Huei-Wen
Shieh, Grace S.
Feature selection translates drug response predictors from cell lines to patients
title Feature selection translates drug response predictors from cell lines to patients
title_full Feature selection translates drug response predictors from cell lines to patients
title_fullStr Feature selection translates drug response predictors from cell lines to patients
title_full_unstemmed Feature selection translates drug response predictors from cell lines to patients
title_short Feature selection translates drug response predictors from cell lines to patients
title_sort feature selection translates drug response predictors from cell lines to patients
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10382684/
https://www.ncbi.nlm.nih.gov/pubmed/37519889
http://dx.doi.org/10.3389/fgene.2023.1217414
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