Dataset Information

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

ABSTRACT: 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 forwarding several novel putative driver genes for metastatic prostate cancer, some of which might be prognostic for disease evolution.

SUBMITTER: de Schaetzen van Brienen L

PROVIDER: S-EPMC8582433 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

de Schaetzen van Brienen Louise L Miclotte Giles G Larmuseau Maarten M Van den Eynden Jimmy J Marchal Kathleen K

Cancers 20211021 21

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 informat ...[more]

PMID: 34771455

Similar Datasets

Project description:BackgroundMetastatic castration-resistant prostate cancer (mCRPC) is the lethal form of the disease. Several recent studies have identified genomic alterations in mCRPC, but the clinical implications of these genomic alterations have not been fully elucidated.ObjectiveTo use whole-genome sequencing (WGS) to assess the association between key driver gene alterations and overall survival (OS), and to use whole-transcriptome RNA sequencing to identify genomic drivers of enzalutamide resistance.Design, setting, and participantsWe performed survival analyses and gene set enrichment analysis (GSEA) on WGS and RNA sequencing results for a cohort of 101 mCRPC patients.Outcome measurements and statistical analysisOS was the clinical endpoint for all univariate and multivariable survival analyses. Candidate drivers of enzalutamide resistance were identified in an unbiased manner, and mutations of the top candidate were further assessed for enrichment among enzalutamide-resistant patients using Fisher's exact test.Results and limitationsHarboring two DNA alterations in RB1 was independently predictive of poor OS (median 14.1 vs 42.0mo; p=0.007) for men with mCRPC. GSEA identified the Wnt/β-catenin pathway as the top differentially modulated pathway among enzalutamide-resistant patients. Furthermore, β-catenin mutations were exclusive to enzalutamide-resistant patients (p=0.01) and independently predictive of poor OS (median 13.6 vs 41.7mo; p=0.025).ConclusionsThe presence of two RB1 DNA alterations identified in our WGS analysis was independently associated with poor OS among men with mCRPC. The Wnt/β-catenin pathway plays an important role in enzalutamide resistance, with differential pathway expression and enrichment of β-catenin mutations in enzalutamide-resistant patients. Moreover, β-catenin mutations were predictive of poor OS in our cohort.Patient summaryWe observed a correlation between genomic findings for biopsy samples from metastases from men with metastatic castration-resistant prostate cancer (mCRPC) and clinical outcomes. This work sheds new light on clinically relevant genomic alterations in mCRPC and provides a roadmap for the development of new personalized treatment regimens in mCRPC.

Dataset Information

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

Publications

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets