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Systematic assessment of multi-gene predictors of pan-cancer cell line sensitivity to drugs exploiting gene expression data
Background: Selected gene mutations are routinely used to guide the selection of cancer drugs for a given patient tumour. Large pharmacogenomic data sets, such as those by Genomics of Drug Sensitivity in Cancer (GDSC) consortium, were introduced to discover more of these single-gene markers of drug...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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F1000Research
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5310525/ https://www.ncbi.nlm.nih.gov/pubmed/28299173 http://dx.doi.org/10.12688/f1000research.10529.2 |
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author | Nguyen, Linh Dang, Cuong C Ballester, Pedro J. |
author_facet | Nguyen, Linh Dang, Cuong C Ballester, Pedro J. |
author_sort | Nguyen, Linh |
collection | PubMed |
description | Background: Selected gene mutations are routinely used to guide the selection of cancer drugs for a given patient tumour. Large pharmacogenomic data sets, such as those by Genomics of Drug Sensitivity in Cancer (GDSC) consortium, were introduced to discover more of these single-gene markers of drug sensitivity. Very recently, machine learning regression has been used to investigate how well cancer cell line sensitivity to drugs is predicted depending on the type of molecular profile. The latter has revealed that gene expression data is the most predictive profile in the pan-cancer setting. However, no study to date has exploited GDSC data to systematically compare the performance of machine learning models based on multi-gene expression data against that of widely-used single-gene markers based on genomics data. Methods: Here we present this systematic comparison using Random Forest (RF) classifiers exploiting the expression levels of 13,321 genes and an average of 501 tested cell lines per drug. To account for time-dependent batch effects in IC (50) measurements, we employ independent test sets generated with more recent GDSC data than that used to train the predictors and show that this is a more realistic validation than standard k-fold cross-validation. Results and Discussion: Across 127 GDSC drugs, our results show that the single-gene markers unveiled by the MANOVA analysis tend to achieve higher precision than these RF-based multi-gene models, at the cost of generally having a poor recall (i.e. correctly detecting only a small part of the cell lines sensitive to the drug). Regarding overall classification performance, about two thirds of the drugs are better predicted by the multi-gene RF classifiers. Among the drugs with the most predictive of these models, we found pyrimethamine, sunitinib and 17-AAG. Conclusions: Thanks to this unbiased validation, we now know that this type of models can predict in vitro tumour response to some of these drugs. These models can thus be further investigated on in vivo tumour models. R code to facilitate the construction of alternative machine learning models and their validation in the presented benchmark is available at http://ballester.marseille.inserm.fr/gdsc.transcriptomicDatav2.tar.gz. |
format | Online Article Text |
id | pubmed-5310525 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | F1000Research |
record_format | MEDLINE/PubMed |
spelling | pubmed-53105252017-03-14 Systematic assessment of multi-gene predictors of pan-cancer cell line sensitivity to drugs exploiting gene expression data Nguyen, Linh Dang, Cuong C Ballester, Pedro J. F1000Res Research Article Background: Selected gene mutations are routinely used to guide the selection of cancer drugs for a given patient tumour. Large pharmacogenomic data sets, such as those by Genomics of Drug Sensitivity in Cancer (GDSC) consortium, were introduced to discover more of these single-gene markers of drug sensitivity. Very recently, machine learning regression has been used to investigate how well cancer cell line sensitivity to drugs is predicted depending on the type of molecular profile. The latter has revealed that gene expression data is the most predictive profile in the pan-cancer setting. However, no study to date has exploited GDSC data to systematically compare the performance of machine learning models based on multi-gene expression data against that of widely-used single-gene markers based on genomics data. Methods: Here we present this systematic comparison using Random Forest (RF) classifiers exploiting the expression levels of 13,321 genes and an average of 501 tested cell lines per drug. To account for time-dependent batch effects in IC (50) measurements, we employ independent test sets generated with more recent GDSC data than that used to train the predictors and show that this is a more realistic validation than standard k-fold cross-validation. Results and Discussion: Across 127 GDSC drugs, our results show that the single-gene markers unveiled by the MANOVA analysis tend to achieve higher precision than these RF-based multi-gene models, at the cost of generally having a poor recall (i.e. correctly detecting only a small part of the cell lines sensitive to the drug). Regarding overall classification performance, about two thirds of the drugs are better predicted by the multi-gene RF classifiers. Among the drugs with the most predictive of these models, we found pyrimethamine, sunitinib and 17-AAG. Conclusions: Thanks to this unbiased validation, we now know that this type of models can predict in vitro tumour response to some of these drugs. These models can thus be further investigated on in vivo tumour models. R code to facilitate the construction of alternative machine learning models and their validation in the presented benchmark is available at http://ballester.marseille.inserm.fr/gdsc.transcriptomicDatav2.tar.gz. F1000Research 2017-03-14 /pmc/articles/PMC5310525/ /pubmed/28299173 http://dx.doi.org/10.12688/f1000research.10529.2 Text en Copyright: © 2017 Nguyen L et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Nguyen, Linh Dang, Cuong C Ballester, Pedro J. Systematic assessment of multi-gene predictors of pan-cancer cell line sensitivity to drugs exploiting gene expression data |
title | Systematic assessment of multi-gene predictors of pan-cancer cell line sensitivity to drugs exploiting gene expression data |
title_full | Systematic assessment of multi-gene predictors of pan-cancer cell line sensitivity to drugs exploiting gene expression data |
title_fullStr | Systematic assessment of multi-gene predictors of pan-cancer cell line sensitivity to drugs exploiting gene expression data |
title_full_unstemmed | Systematic assessment of multi-gene predictors of pan-cancer cell line sensitivity to drugs exploiting gene expression data |
title_short | Systematic assessment of multi-gene predictors of pan-cancer cell line sensitivity to drugs exploiting gene expression data |
title_sort | systematic assessment of multi-gene predictors of pan-cancer cell line sensitivity to drugs exploiting gene expression data |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5310525/ https://www.ncbi.nlm.nih.gov/pubmed/28299173 http://dx.doi.org/10.12688/f1000research.10529.2 |
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