Personalized Integrated Network Modeling of the Cancer Proteome Atlas

Abstract Personalized (patient-specific) approaches have recently emerged with a precision medicine paradigm that acknowledges the fact that molecular pathway structures and activity might be considerably different within and across tumors. The functional cancer genome and proteome provide rich sour...

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Autores principales:	Min Jin Ha, Sayantan Banerjee, Rehan Akbani, Han Liang, Gordon B. Mills, Kim-Anh Do, Veerabhadran Baladandayuthapani
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2018
Materias:	Patient-specific Network General Bayesian Framework Kidney Renal Clear Cell Carcinoma (KIRC) Reverse Phase Protein Array (RPPA) Causal Structure Learning Medicine R Science Q
Acceso en línea:	https://doaj.org/article/43e7c4bc16ff4b8082b5fe42d8213ff3
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spelling	oai:doaj.org-article:43e7c4bc16ff4b8082b5fe42d8213ff32021-12-02T15:08:03ZPersonalized Integrated Network Modeling of the Cancer Proteome Atlas10.1038/s41598-018-32682-x2045-2322https://doaj.org/article/43e7c4bc16ff4b8082b5fe42d8213ff32018-10-01T00:00:00Zhttps://doi.org/10.1038/s41598-018-32682-xhttps://doaj.org/toc/2045-2322Abstract Personalized (patient-specific) approaches have recently emerged with a precision medicine paradigm that acknowledges the fact that molecular pathway structures and activity might be considerably different within and across tumors. The functional cancer genome and proteome provide rich sources of information to identify patient-specific variations in signaling pathways and activities within and across tumors; however, current analytic methods lack the ability to exploit the diverse and multi-layered architecture of these complex biological networks. We assessed pan-cancer pathway activities for >7700 patients across 32 tumor types from The Cancer Proteome Atlas by developing a personalized cancer-specific integrated network estimation (PRECISE) model. PRECISE is a general Bayesian framework for integrating existing interaction databases, data-driven de novo causal structures, and upstream molecular profiling data to estimate cancer-specific integrated networks, infer patient-specific networks and elicit interpretable pathway-level signatures. PRECISE-based pathway signatures, can delineate pan-cancer commonalities and differences in proteomic network biology within and across tumors, demonstrates robust tumor stratification that is both biologically and clinically informative and superior prognostic power compared to existing approaches. Towards establishing the translational relevance of the functional proteome in research and clinical settings, we provide an online, publicly available, comprehensive database and visualization repository of our findings (https://mjha.shinyapps.io/PRECISE/).Min Jin HaSayantan BanerjeeRehan AkbaniHan LiangGordon B. MillsKim-Anh DoVeerabhadran BaladandayuthapaniNature PortfolioarticlePatient-specific NetworkGeneral Bayesian FrameworkKidney Renal Clear Cell Carcinoma (KIRC)Reverse Phase Protein Array (RPPA)Causal Structure LearningMedicineRScienceQENScientific Reports, Vol 8, Iss 1, Pp 1-14 (2018)
institution	DOAJ
collection	DOAJ
language	EN
topic	Patient-specific Network General Bayesian Framework Kidney Renal Clear Cell Carcinoma (KIRC) Reverse Phase Protein Array (RPPA) Causal Structure Learning Medicine R Science Q
spellingShingle	Patient-specific Network General Bayesian Framework Kidney Renal Clear Cell Carcinoma (KIRC) Reverse Phase Protein Array (RPPA) Causal Structure Learning Medicine R Science Q Min Jin Ha Sayantan Banerjee Rehan Akbani Han Liang Gordon B. Mills Kim-Anh Do Veerabhadran Baladandayuthapani Personalized Integrated Network Modeling of the Cancer Proteome Atlas
description	Abstract Personalized (patient-specific) approaches have recently emerged with a precision medicine paradigm that acknowledges the fact that molecular pathway structures and activity might be considerably different within and across tumors. The functional cancer genome and proteome provide rich sources of information to identify patient-specific variations in signaling pathways and activities within and across tumors; however, current analytic methods lack the ability to exploit the diverse and multi-layered architecture of these complex biological networks. We assessed pan-cancer pathway activities for >7700 patients across 32 tumor types from The Cancer Proteome Atlas by developing a personalized cancer-specific integrated network estimation (PRECISE) model. PRECISE is a general Bayesian framework for integrating existing interaction databases, data-driven de novo causal structures, and upstream molecular profiling data to estimate cancer-specific integrated networks, infer patient-specific networks and elicit interpretable pathway-level signatures. PRECISE-based pathway signatures, can delineate pan-cancer commonalities and differences in proteomic network biology within and across tumors, demonstrates robust tumor stratification that is both biologically and clinically informative and superior prognostic power compared to existing approaches. Towards establishing the translational relevance of the functional proteome in research and clinical settings, we provide an online, publicly available, comprehensive database and visualization repository of our findings (https://mjha.shinyapps.io/PRECISE/).
format	article
author	Min Jin Ha Sayantan Banerjee Rehan Akbani Han Liang Gordon B. Mills Kim-Anh Do Veerabhadran Baladandayuthapani
author_facet	Min Jin Ha Sayantan Banerjee Rehan Akbani Han Liang Gordon B. Mills Kim-Anh Do Veerabhadran Baladandayuthapani
author_sort	Min Jin Ha
title	Personalized Integrated Network Modeling of the Cancer Proteome Atlas
title_short	Personalized Integrated Network Modeling of the Cancer Proteome Atlas
title_full	Personalized Integrated Network Modeling of the Cancer Proteome Atlas
title_fullStr	Personalized Integrated Network Modeling of the Cancer Proteome Atlas
title_full_unstemmed	Personalized Integrated Network Modeling of the Cancer Proteome Atlas
title_sort	personalized integrated network modeling of the cancer proteome atlas
publisher	Nature Portfolio
publishDate	2018
url	https://doaj.org/article/43e7c4bc16ff4b8082b5fe42d8213ff3
work_keys_str_mv	AT minjinha personalizedintegratednetworkmodelingofthecancerproteomeatlas AT sayantanbanerjee personalizedintegratednetworkmodelingofthecancerproteomeatlas AT rehanakbani personalizedintegratednetworkmodelingofthecancerproteomeatlas AT hanliang personalizedintegratednetworkmodelingofthecancerproteomeatlas AT gordonbmills personalizedintegratednetworkmodelingofthecancerproteomeatlas AT kimanhdo personalizedintegratednetworkmodelingofthecancerproteomeatlas AT veerabhadranbaladandayuthapani personalizedintegratednetworkmodelingofthecancerproteomeatlas
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Personalized Integrated Network Modeling of the Cancer Proteome Atlas

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