Revealing protein networks and gene-drug connectivity in cancer from direct information

Abstract The connection between genetic variation and drug response has long been explored to facilitate the optimization and personalization of cancer therapy. Crucial to the identification of drug response related genetic features is the ability to separate indirect correlations from direct correl...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Xian-Li Jiang, Emmanuel Martinez-Ledesma, Faruck Morcos
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/4709e04e25cf472b984a02fd84ae8513
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:4709e04e25cf472b984a02fd84ae8513
record_format	dspace
spelling	oai:doaj.org-article:4709e04e25cf472b984a02fd84ae85132021-12-02T16:06:30ZRevealing protein networks and gene-drug connectivity in cancer from direct information10.1038/s41598-017-04001-32045-2322https://doaj.org/article/4709e04e25cf472b984a02fd84ae85132017-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-04001-3https://doaj.org/toc/2045-2322Abstract The connection between genetic variation and drug response has long been explored to facilitate the optimization and personalization of cancer therapy. Crucial to the identification of drug response related genetic features is the ability to separate indirect correlations from direct correlations across abundant datasets with large number of variables. Here we analyzed proteomic and pharmacogenomic data in cancer tissues and cell lines using a global statistical model connecting protein pairs, genes and anti-cancer drugs. We estimated this model using direct coupling analysis (DCA), a powerful statistical inference method that has been successfully applied to protein sequence data to extract evolutionary signals that provide insights on protein structure, folding and interactions. We used Direct Information (DI) as a metric of connectivity between proteins as well as gene-drug pairs. We were able to infer important interactions observed in cancer-related pathways from proteomic data and predict potential connectivities in cancer networks. We also identified known and potential connections for anti-cancer drugs and gene mutations using DI in pharmacogenomic data. Our findings suggest that gene-drug connections predicted with direct couplings can be used as a reliable guide to cancer therapy and expand our understanding of the effects of gene alterations on drug efficacies.Xian-Li JiangEmmanuel Martinez-LedesmaFaruck MorcosNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-13 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Xian-Li Jiang Emmanuel Martinez-Ledesma Faruck Morcos Revealing protein networks and gene-drug connectivity in cancer from direct information
description	Abstract The connection between genetic variation and drug response has long been explored to facilitate the optimization and personalization of cancer therapy. Crucial to the identification of drug response related genetic features is the ability to separate indirect correlations from direct correlations across abundant datasets with large number of variables. Here we analyzed proteomic and pharmacogenomic data in cancer tissues and cell lines using a global statistical model connecting protein pairs, genes and anti-cancer drugs. We estimated this model using direct coupling analysis (DCA), a powerful statistical inference method that has been successfully applied to protein sequence data to extract evolutionary signals that provide insights on protein structure, folding and interactions. We used Direct Information (DI) as a metric of connectivity between proteins as well as gene-drug pairs. We were able to infer important interactions observed in cancer-related pathways from proteomic data and predict potential connectivities in cancer networks. We also identified known and potential connections for anti-cancer drugs and gene mutations using DI in pharmacogenomic data. Our findings suggest that gene-drug connections predicted with direct couplings can be used as a reliable guide to cancer therapy and expand our understanding of the effects of gene alterations on drug efficacies.
format	article
author	Xian-Li Jiang Emmanuel Martinez-Ledesma Faruck Morcos
author_facet	Xian-Li Jiang Emmanuel Martinez-Ledesma Faruck Morcos
author_sort	Xian-Li Jiang
title	Revealing protein networks and gene-drug connectivity in cancer from direct information
title_short	Revealing protein networks and gene-drug connectivity in cancer from direct information
title_full	Revealing protein networks and gene-drug connectivity in cancer from direct information
title_fullStr	Revealing protein networks and gene-drug connectivity in cancer from direct information
title_full_unstemmed	Revealing protein networks and gene-drug connectivity in cancer from direct information
title_sort	revealing protein networks and gene-drug connectivity in cancer from direct information
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/4709e04e25cf472b984a02fd84ae8513
work_keys_str_mv	AT xianlijiang revealingproteinnetworksandgenedrugconnectivityincancerfromdirectinformation AT emmanuelmartinezledesma revealingproteinnetworksandgenedrugconnectivityincancerfromdirectinformation AT faruckmorcos revealingproteinnetworksandgenedrugconnectivityincancerfromdirectinformation
_version_	1718384957570678784

Revealing protein networks and gene-drug connectivity in cancer from direct information

Ejemplares similares