Network-Based Analysis to Identify Drivers of Metastatic Prostate Cancer Using GoNetic

Most known driver genes of metastatic prostate cancer are frequently mutated. To dig into the long tail of rarely mutated drivers, we performed network-based driver identification on the Hartwig Medical Foundation metastatic prostate cancer data set (HMF cohort). Hereto, we developed GoNetic, a meth...

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Autores principales:	Louise de Schaetzen van Brienen, Giles Miclotte, Maarten Larmuseau, Jimmy Van den Eynden, Kathleen Marchal
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	network-based cancer data analysis driver identification metastatic prostate cancer somatic mutations Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282
Acceso en línea:	https://doaj.org/article/d39ae633b1bc40268809f76d24f6dd91
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id	oai:doaj.org-article:d39ae633b1bc40268809f76d24f6dd91
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spelling	oai:doaj.org-article:d39ae633b1bc40268809f76d24f6dd912021-11-11T15:27:24ZNetwork-Based Analysis to Identify Drivers of Metastatic Prostate Cancer Using GoNetic10.3390/cancers132152912072-6694https://doaj.org/article/d39ae633b1bc40268809f76d24f6dd912021-10-01T00:00:00Zhttps://www.mdpi.com/2072-6694/13/21/5291https://doaj.org/toc/2072-6694Most known driver genes of metastatic prostate cancer are frequently mutated. To dig into the long tail of rarely mutated drivers, we performed network-based driver identification on the Hartwig Medical Foundation metastatic prostate cancer data set (HMF cohort). Hereto, we developed GoNetic, a method based on probabilistic pathfinding, to identify recurrently mutated subnetworks. In contrast to most state-of-the-art network-based methods, GoNetic can leverage sample-specific mutational information and the weights of the underlying prior network. When applied to the HMF cohort, GoNetic successfully recovered known primary and metastatic drivers of prostate cancer that are frequently mutated in the HMF cohort (<i>TP53</i>, <i>RB1,</i> and <i>CTNNB1</i>). In addition, the identified subnetworks contain frequently mutated genes, reflect processes related to metastatic prostate cancer, and contain rarely mutated driver candidates. To further validate these rarely mutated genes, we assessed whether the identified genes were more mutated in metastatic than in primary samples using an independent cohort. Then we evaluated their association with tumor evolution and with the lymph node status of the patients. This resulted in forwarding several novel putative driver genes for metastatic prostate cancer, some of which might be prognostic for disease evolution.Louise de Schaetzen van BrienenGiles MiclotteMaarten LarmuseauJimmy Van den EyndenKathleen MarchalMDPI AGarticlenetwork-based cancer data analysisdriver identificationmetastatic prostate cancersomatic mutationsNeoplasms. Tumors. Oncology. Including cancer and carcinogensRC254-282ENCancers, Vol 13, Iss 5291, p 5291 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	network-based cancer data analysis driver identification metastatic prostate cancer somatic mutations Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282
spellingShingle	network-based cancer data analysis driver identification metastatic prostate cancer somatic mutations Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282 Louise de Schaetzen van Brienen Giles Miclotte Maarten Larmuseau Jimmy Van den Eynden Kathleen Marchal Network-Based Analysis to Identify Drivers of Metastatic Prostate Cancer Using GoNetic
description	Most known driver genes of metastatic prostate cancer are frequently mutated. To dig into the long tail of rarely mutated drivers, we performed network-based driver identification on the Hartwig Medical Foundation metastatic prostate cancer data set (HMF cohort). Hereto, we developed GoNetic, a method based on probabilistic pathfinding, to identify recurrently mutated subnetworks. In contrast to most state-of-the-art network-based methods, GoNetic can leverage sample-specific mutational information and the weights of the underlying prior network. When applied to the HMF cohort, GoNetic successfully recovered known primary and metastatic drivers of prostate cancer that are frequently mutated in the HMF cohort (<i>TP53</i>, <i>RB1,</i> and <i>CTNNB1</i>). In addition, the identified subnetworks contain frequently mutated genes, reflect processes related to metastatic prostate cancer, and contain rarely mutated driver candidates. To further validate these rarely mutated genes, we assessed whether the identified genes were more mutated in metastatic than in primary samples using an independent cohort. Then we evaluated their association with tumor evolution and with the lymph node status of the patients. This resulted in forwarding several novel putative driver genes for metastatic prostate cancer, some of which might be prognostic for disease evolution.
format	article
author	Louise de Schaetzen van Brienen Giles Miclotte Maarten Larmuseau Jimmy Van den Eynden Kathleen Marchal
author_facet	Louise de Schaetzen van Brienen Giles Miclotte Maarten Larmuseau Jimmy Van den Eynden Kathleen Marchal
author_sort	Louise de Schaetzen van Brienen
title	Network-Based Analysis to Identify Drivers of Metastatic Prostate Cancer Using GoNetic
title_short	Network-Based Analysis to Identify Drivers of Metastatic Prostate Cancer Using GoNetic
title_full	Network-Based Analysis to Identify Drivers of Metastatic Prostate Cancer Using GoNetic
title_fullStr	Network-Based Analysis to Identify Drivers of Metastatic Prostate Cancer Using GoNetic
title_full_unstemmed	Network-Based Analysis to Identify Drivers of Metastatic Prostate Cancer Using GoNetic
title_sort	network-based analysis to identify drivers of metastatic prostate cancer using gonetic
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/d39ae633b1bc40268809f76d24f6dd91
work_keys_str_mv	AT louisedeschaetzenvanbrienen networkbasedanalysistoidentifydriversofmetastaticprostatecancerusinggonetic AT gilesmiclotte networkbasedanalysistoidentifydriversofmetastaticprostatecancerusinggonetic AT maartenlarmuseau networkbasedanalysistoidentifydriversofmetastaticprostatecancerusinggonetic AT jimmyvandeneynden networkbasedanalysistoidentifydriversofmetastaticprostatecancerusinggonetic AT kathleenmarchal networkbasedanalysistoidentifydriversofmetastaticprostatecancerusinggonetic
_version_	1718435271866843136

Network-Based Analysis to Identify Drivers of Metastatic Prostate Cancer Using GoNetic

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