Improved prediction of PARP inhibitor response and identification of synergizing agents through use of a novel gene expression signature generation algorithm

Personalized medicine: Signature-guided cancer therapy Personalized cancer therapy is one of the holy grails of oncology, as the ability to determine what treatment would best benefit a patient would serve not only to improve outcomes, but also mitigate side effects from less effective treatments. H...

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Autores principales:	Daniel J. McGrail, Curtis Chun-Jen Lin, Jeannine Garnett, Qingxin Liu, Wei Mo, Hui Dai, Yiling Lu, Qinghua Yu, Zhenlin Ju, Jun Yin, Christopher P. Vellano, Bryan Hennessy, Gordon B. Mills, Shiaw-Yih Lin
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/b2d7262023a5464f9e1788ca4d9c8cbf
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spelling	oai:doaj.org-article:b2d7262023a5464f9e1788ca4d9c8cbf2021-12-02T11:41:50ZImproved prediction of PARP inhibitor response and identification of synergizing agents through use of a novel gene expression signature generation algorithm10.1038/s41540-017-0011-62056-7189https://doaj.org/article/b2d7262023a5464f9e1788ca4d9c8cbf2017-03-01T00:00:00Zhttps://doi.org/10.1038/s41540-017-0011-6https://doaj.org/toc/2056-7189Personalized medicine: Signature-guided cancer therapy Personalized cancer therapy is one of the holy grails of oncology, as the ability to determine what treatment would best benefit a patient would serve not only to improve outcomes, but also mitigate side effects from less effective treatments. Here, we develop algorithms to predict what patients will respond to a given therapeutic modality, as well as ways to specifically target any observed phenotype, by integrating large scale data sets that profile cancer cell line gene expression and sensitivity to hundreds of drugs. Furthermore, we show how these gene expression signatures can be used to predict novel synergizing agents to further enhance the efficacy of these therapeutics. Taken together, this work stands to advance the era of personalized medicine by enabling precision medicine approaches in the clinic.Daniel J. McGrailCurtis Chun-Jen LinJeannine GarnettQingxin LiuWei MoHui DaiYiling LuQinghua YuZhenlin JuJun YinChristopher P. VellanoBryan HennessyGordon B. MillsShiaw-Yih LinNature PortfolioarticleBiology (General)QH301-705.5ENnpj Systems Biology and Applications, Vol 3, Iss 1, Pp 1-12 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Daniel J. McGrail Curtis Chun-Jen Lin Jeannine Garnett Qingxin Liu Wei Mo Hui Dai Yiling Lu Qinghua Yu Zhenlin Ju Jun Yin Christopher P. Vellano Bryan Hennessy Gordon B. Mills Shiaw-Yih Lin Improved prediction of PARP inhibitor response and identification of synergizing agents through use of a novel gene expression signature generation algorithm
description	Personalized medicine: Signature-guided cancer therapy Personalized cancer therapy is one of the holy grails of oncology, as the ability to determine what treatment would best benefit a patient would serve not only to improve outcomes, but also mitigate side effects from less effective treatments. Here, we develop algorithms to predict what patients will respond to a given therapeutic modality, as well as ways to specifically target any observed phenotype, by integrating large scale data sets that profile cancer cell line gene expression and sensitivity to hundreds of drugs. Furthermore, we show how these gene expression signatures can be used to predict novel synergizing agents to further enhance the efficacy of these therapeutics. Taken together, this work stands to advance the era of personalized medicine by enabling precision medicine approaches in the clinic.
format	article
author	Daniel J. McGrail Curtis Chun-Jen Lin Jeannine Garnett Qingxin Liu Wei Mo Hui Dai Yiling Lu Qinghua Yu Zhenlin Ju Jun Yin Christopher P. Vellano Bryan Hennessy Gordon B. Mills Shiaw-Yih Lin
author_facet	Daniel J. McGrail Curtis Chun-Jen Lin Jeannine Garnett Qingxin Liu Wei Mo Hui Dai Yiling Lu Qinghua Yu Zhenlin Ju Jun Yin Christopher P. Vellano Bryan Hennessy Gordon B. Mills Shiaw-Yih Lin
author_sort	Daniel J. McGrail
title	Improved prediction of PARP inhibitor response and identification of synergizing agents through use of a novel gene expression signature generation algorithm
title_short	Improved prediction of PARP inhibitor response and identification of synergizing agents through use of a novel gene expression signature generation algorithm
title_full	Improved prediction of PARP inhibitor response and identification of synergizing agents through use of a novel gene expression signature generation algorithm
title_fullStr	Improved prediction of PARP inhibitor response and identification of synergizing agents through use of a novel gene expression signature generation algorithm
title_full_unstemmed	Improved prediction of PARP inhibitor response and identification of synergizing agents through use of a novel gene expression signature generation algorithm
title_sort	improved prediction of parp inhibitor response and identification of synergizing agents through use of a novel gene expression signature generation algorithm
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/b2d7262023a5464f9e1788ca4d9c8cbf
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Improved prediction of PARP inhibitor response and identification of synergizing agents through use of a novel gene expression signature generation algorithm

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