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Personalized Integrated Network Modeling of the Cancer Proteome Atlas
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 in...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6175854/ https://www.ncbi.nlm.nih.gov/pubmed/30297783 http://dx.doi.org/10.1038/s41598-018-32682-x |
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author | Ha, Min Jin Banerjee, Sayantan Akbani, Rehan Liang, Han Mills, Gordon B. Do, Kim-Anh Baladandayuthapani, Veerabhadran |
author_facet | Ha, Min Jin Banerjee, Sayantan Akbani, Rehan Liang, Han Mills, Gordon B. Do, Kim-Anh Baladandayuthapani, Veerabhadran |
author_sort | Ha, Min Jin |
collection | PubMed |
description | 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 | Online Article Text |
id | pubmed-6175854 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-61758542018-10-12 Personalized Integrated Network Modeling of the Cancer Proteome Atlas Ha, Min Jin Banerjee, Sayantan Akbani, Rehan Liang, Han Mills, Gordon B. Do, Kim-Anh Baladandayuthapani, Veerabhadran Sci Rep Article 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/). Nature Publishing Group UK 2018-10-08 /pmc/articles/PMC6175854/ /pubmed/30297783 http://dx.doi.org/10.1038/s41598-018-32682-x Text en © The Author(s) 2018 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Ha, Min Jin Banerjee, Sayantan Akbani, Rehan Liang, Han Mills, Gordon B. Do, Kim-Anh Baladandayuthapani, Veerabhadran Personalized Integrated Network Modeling of the Cancer Proteome Atlas |
title | 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_short | Personalized Integrated Network Modeling of the Cancer Proteome Atlas |
title_sort | personalized integrated network modeling of the cancer proteome atlas |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6175854/ https://www.ncbi.nlm.nih.gov/pubmed/30297783 http://dx.doi.org/10.1038/s41598-018-32682-x |
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