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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-10-01
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| Series: | Cancers |
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| Online Access: | https://www.mdpi.com/2072-6694/13/21/5291 |
| _version_ | 1797512740131045376 |
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| 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 |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-10T06:05:57Z |
| format | Article |
| id | doaj.art-d39ae633b1bc40268809f76d24f6dd91 |
| institution | Directory Open Access Journal |
| issn | 2072-6694 |
| language | English |
| last_indexed | 2024-03-10T06:05:57Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Cancers |
| spelling | doaj.art-d39ae633b1bc40268809f76d24f6dd912023-11-22T20:33:04ZengMDPI AGCancers2072-66942021-10-011321529110.3390/cancers13215291Network-Based Analysis to Identify Drivers of Metastatic Prostate Cancer Using GoNeticLouise de Schaetzen van Brienen0Giles Miclotte1Maarten Larmuseau2Jimmy Van den Eynden3Kathleen Marchal4Department of Plant Biotechnology and Bioinformatics, Faculty of Sciences, Ghent University, 9052 Ghent, BelgiumDepartment of Plant Biotechnology and Bioinformatics, Faculty of Sciences, Ghent University, 9052 Ghent, BelgiumDepartment of Plant Biotechnology and Bioinformatics, Faculty of Sciences, Ghent University, 9052 Ghent, BelgiumDepartment of Human Structure and Repair, Faculty of Medicine and Health Sciences, Ghent University, 9000 Ghent, BelgiumDepartment of Plant Biotechnology and Bioinformatics, Faculty of Sciences, Ghent University, 9052 Ghent, BelgiumMost 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.https://www.mdpi.com/2072-6694/13/21/5291network-based cancer data analysisdriver identificationmetastatic prostate cancersomatic mutations |
| spellingShingle | 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 Cancers network-based cancer data analysis driver identification metastatic prostate cancer somatic mutations |
| title | 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_short | 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 |
| topic | network-based cancer data analysis driver identification metastatic prostate cancer somatic mutations |
| url | https://www.mdpi.com/2072-6694/13/21/5291 |
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