Cold Spring Harbor molecular case studies, volume 9, issue 4, pages mcs.a006298

Prostate Cancer Patient Stratification by Molecular Signatures in the Veterans Precision Oncology Data Commons

Kyle M. Hernandez 1
Aarti Venkat 1
John R. Bihn 3
Mary T. Brophy 4
Nhan Do 4
Jennifer La 5
Qiong Liu 1
Andrew S. Prokhorenkov 1
Noah Metoki-Shlubsky 1
Feng-Chi Sung 3
Nathanael R Fillmore 7
Robert L Grossman 1
Show full list: 14 authors
Publication typeJournal Article
Publication date2023-11-22
scimago Q2
SJR0.801
CiteScore3.2
Impact factor1.8
ISSN23732873, 23732865
General Medicine
Abstract

Veterans are at an increased risk for prostate cancer, a disease with extraordinary clinical and molecular heterogeneity, compared with the general population. However, little is known about the underlying molecular heterogeneity within the veteran population and its impact on patient management and treatment. Using clinical and targeted tumor sequencing data from the National Veterans Affairs health system, we conducted a retrospective cohort study on 45 patients with advanced prostate cancer in the Veterans Precision Oncology Data Commons (VPODC), most of whom were metastatic castration-resistant. We characterized the mutational burden in this cohort and conducted unsupervised clustering analysis to stratify patients by molecular alterations. Veterans with prostate cancer exhibited a mutational landscape broadly similar to prior studies, includingKMT2AandNOTCH1mutations associated with neuroendocrine prostate cancer phenotype, previously reported to be enriched in veterans. We also identified several potential novel mutations inPTEN,MSH6,VHL,SMO, andABL1. Hierarchical clustering analysis revealed two subgroups containing therapeutically targetable molecular features with novel mutational signatures distinct from those reported in the Catalogue of Somatic Mutations in Cancer database. The clustering approach presented in this study can potentially be used to clinically stratify patients based on their distinct mutational profiles and identify actionable somatic mutations for precision oncology.

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