Discordancy Partitioning for Validating Potentially Inconsistent Pharmacogenomic Studies

Abstract The Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE) are two major studies that can be used to mine for therapeutic biomarkers for cancers of a large variety. Model validation using the two datasets however has proved challenging. Both predictions and s...

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Autores principales:	J. Sunil Rao, Hongmei Liu
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/bb1b8528f0514b0fbef8c0d00fe38054
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spelling	oai:doaj.org-article:bb1b8528f0514b0fbef8c0d00fe380542021-12-02T11:40:50ZDiscordancy Partitioning for Validating Potentially Inconsistent Pharmacogenomic Studies10.1038/s41598-017-15590-42045-2322https://doaj.org/article/bb1b8528f0514b0fbef8c0d00fe380542017-11-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-15590-4https://doaj.org/toc/2045-2322Abstract The Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE) are two major studies that can be used to mine for therapeutic biomarkers for cancers of a large variety. Model validation using the two datasets however has proved challenging. Both predictions and signatures do not consistently validate well for models built on one dataset and tested on the other. While the genomic profiling seems consistent, the drug response data is not. Some efforts at harmonizing experimental designs has helped but not entirely removed model validation difficulties. In this paper, we present a partitioning strategy based on a data sharing concept which directly acknowledges a potential lack of concordance between datasets and in doing so, also allows for extraction of reproducible novel gene-drug interaction signatures as well as accurate test set predictions. We demonstrate these properties in a re-analysis of the GDSC and CCLE datasets.J. Sunil RaoHongmei LiuNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-12 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q J. Sunil Rao Hongmei Liu Discordancy Partitioning for Validating Potentially Inconsistent Pharmacogenomic Studies
description	Abstract The Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE) are two major studies that can be used to mine for therapeutic biomarkers for cancers of a large variety. Model validation using the two datasets however has proved challenging. Both predictions and signatures do not consistently validate well for models built on one dataset and tested on the other. While the genomic profiling seems consistent, the drug response data is not. Some efforts at harmonizing experimental designs has helped but not entirely removed model validation difficulties. In this paper, we present a partitioning strategy based on a data sharing concept which directly acknowledges a potential lack of concordance between datasets and in doing so, also allows for extraction of reproducible novel gene-drug interaction signatures as well as accurate test set predictions. We demonstrate these properties in a re-analysis of the GDSC and CCLE datasets.
format	article
author	J. Sunil Rao Hongmei Liu
author_facet	J. Sunil Rao Hongmei Liu
author_sort	J. Sunil Rao
title	Discordancy Partitioning for Validating Potentially Inconsistent Pharmacogenomic Studies
title_short	Discordancy Partitioning for Validating Potentially Inconsistent Pharmacogenomic Studies
title_full	Discordancy Partitioning for Validating Potentially Inconsistent Pharmacogenomic Studies
title_fullStr	Discordancy Partitioning for Validating Potentially Inconsistent Pharmacogenomic Studies
title_full_unstemmed	Discordancy Partitioning for Validating Potentially Inconsistent Pharmacogenomic Studies
title_sort	discordancy partitioning for validating potentially inconsistent pharmacogenomic studies
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/bb1b8528f0514b0fbef8c0d00fe38054
work_keys_str_mv	AT jsunilrao discordancypartitioningforvalidatingpotentiallyinconsistentpharmacogenomicstudies AT hongmeiliu discordancypartitioningforvalidatingpotentiallyinconsistentpharmacogenomicstudies
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Discordancy Partitioning for Validating Potentially Inconsistent Pharmacogenomic Studies

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