Predicting drug sensitivity of cancer cells based on DNA methylation levels.

Cancer cell lines, which are cell cultures derived from tumor samples, represent one of the least expensive and most studied preclinical models for drug development. Accurately predicting drug responses for a given cell line based on molecular features may help to optimize drug-development pipelines...

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Autores principales:	Sofia P Miranda, Fernanda A Baião, Julia L Fleck, Stephen R Piccolo
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/cda66912ff464280b536bf53c158d2e1
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spelling	oai:doaj.org-article:cda66912ff464280b536bf53c158d2e12021-12-02T20:06:20ZPredicting drug sensitivity of cancer cells based on DNA methylation levels.1932-620310.1371/journal.pone.0238757https://doaj.org/article/cda66912ff464280b536bf53c158d2e12021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0238757https://doaj.org/toc/1932-6203Cancer cell lines, which are cell cultures derived from tumor samples, represent one of the least expensive and most studied preclinical models for drug development. Accurately predicting drug responses for a given cell line based on molecular features may help to optimize drug-development pipelines and explain mechanisms behind treatment responses. In this study, we focus on DNA methylation profiles as one type of molecular feature that is known to drive tumorigenesis and modulate treatment responses. Using genome-wide, DNA methylation profiles from 987 cell lines in the Genomics of Drug Sensitivity in Cancer database, we used machine-learning algorithms to evaluate the potential to predict cytotoxic responses for eight anti-cancer drugs. We compared the performance of five classification algorithms and four regression algorithms representing diverse methodologies, including tree-, probability-, kernel-, ensemble-, and distance-based approaches. We artificially subsampled the data to varying degrees, aiming to understand whether training based on relatively extreme outcomes would yield improved performance. When using classification or regression algorithms to predict discrete or continuous responses, respectively, we consistently observed excellent predictive performance when the training and test sets consisted of cell-line data. Classification algorithms performed best when we trained the models using cell lines with relatively extreme drug-response values, attaining area-under-the-receiver-operating-characteristic-curve values as high as 0.97. The regression algorithms performed best when we trained the models using the full range of drug-response values, although this depended on the performance metrics we used. Finally, we used patient data from The Cancer Genome Atlas to evaluate the feasibility of classifying clinical responses for human tumors based on models derived from cell lines. Generally, the algorithms were unable to identify patterns that predicted patient responses reliably; however, predictions by the Random Forests algorithm were significantly correlated with Temozolomide responses for low-grade gliomas.Sofia P MirandaFernanda A BaiãoJulia L FleckStephen R PiccoloPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 9, p e0238757 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Sofia P Miranda Fernanda A Baião Julia L Fleck Stephen R Piccolo Predicting drug sensitivity of cancer cells based on DNA methylation levels.
description	Cancer cell lines, which are cell cultures derived from tumor samples, represent one of the least expensive and most studied preclinical models for drug development. Accurately predicting drug responses for a given cell line based on molecular features may help to optimize drug-development pipelines and explain mechanisms behind treatment responses. In this study, we focus on DNA methylation profiles as one type of molecular feature that is known to drive tumorigenesis and modulate treatment responses. Using genome-wide, DNA methylation profiles from 987 cell lines in the Genomics of Drug Sensitivity in Cancer database, we used machine-learning algorithms to evaluate the potential to predict cytotoxic responses for eight anti-cancer drugs. We compared the performance of five classification algorithms and four regression algorithms representing diverse methodologies, including tree-, probability-, kernel-, ensemble-, and distance-based approaches. We artificially subsampled the data to varying degrees, aiming to understand whether training based on relatively extreme outcomes would yield improved performance. When using classification or regression algorithms to predict discrete or continuous responses, respectively, we consistently observed excellent predictive performance when the training and test sets consisted of cell-line data. Classification algorithms performed best when we trained the models using cell lines with relatively extreme drug-response values, attaining area-under-the-receiver-operating-characteristic-curve values as high as 0.97. The regression algorithms performed best when we trained the models using the full range of drug-response values, although this depended on the performance metrics we used. Finally, we used patient data from The Cancer Genome Atlas to evaluate the feasibility of classifying clinical responses for human tumors based on models derived from cell lines. Generally, the algorithms were unable to identify patterns that predicted patient responses reliably; however, predictions by the Random Forests algorithm were significantly correlated with Temozolomide responses for low-grade gliomas.
format	article
author	Sofia P Miranda Fernanda A Baião Julia L Fleck Stephen R Piccolo
author_facet	Sofia P Miranda Fernanda A Baião Julia L Fleck Stephen R Piccolo
author_sort	Sofia P Miranda
title	Predicting drug sensitivity of cancer cells based on DNA methylation levels.
title_short	Predicting drug sensitivity of cancer cells based on DNA methylation levels.
title_full	Predicting drug sensitivity of cancer cells based on DNA methylation levels.
title_fullStr	Predicting drug sensitivity of cancer cells based on DNA methylation levels.
title_full_unstemmed	Predicting drug sensitivity of cancer cells based on DNA methylation levels.
title_sort	predicting drug sensitivity of cancer cells based on dna methylation levels.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/cda66912ff464280b536bf53c158d2e1
work_keys_str_mv	AT sofiapmiranda predictingdrugsensitivityofcancercellsbasedondnamethylationlevels AT fernandaabaiao predictingdrugsensitivityofcancercellsbasedondnamethylationlevels AT julialfleck predictingdrugsensitivityofcancercellsbasedondnamethylationlevels AT stephenrpiccolo predictingdrugsensitivityofcancercellsbasedondnamethylationlevels
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Predicting drug sensitivity of cancer cells based on DNA methylation levels.

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