Cancers, Vol. 13, Pages 5291: Network-Based Analysis to Identify Drivers of Metastatic Prostate Cancer Using GoNetic

Cancers, Vol. 13, Pages 5291: Network-Based Analysis to Identify Drivers of Metastatic Prostate Cancer Using GoNetic Cancers doi: 10.3390/cancers13215291 Authors: Louise de Schaetzen van Brienen Giles Miclotte Maarten Larmuseau Jimmy Van den Eynden Kathleen Marchal 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 (TP53, RB1, and CTNNB1). 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 fo...
Source: Cancers - Category: Cancer & Oncology Authors: Tags: Article Source Type: research