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Development and Validation of a Genomic Tool to Predict Seminal Vesicle Invasion in Adenocarcinoma of the Prostate.

Authors :
Hall WA
Fishbane N
Liu Y
Xu MJ
Davicioni E
Mahal BA
Den RB
Dess RT
Jackson WC
Wong AC
Schaeffer EM
Karnes RJ
Carroll PR
Cooperberg MR
Bismar TA
Kim HL
Klein EA
Davis JW
Ross AE
Tosoian JJ
Morgan TM
Mehra R
Salami SS
Nguyen PL
Lawton CAF
Spratt DE
Feng F
Source :
JCO precision oncology [JCO Precis Oncol] 2020 Nov; Vol. 4, pp. 1228-1238.
Publication Year :
2020

Abstract

Purpose: Pretreatment estimates of seminal vesicle invasion (SVI) are challenging and significantly influence the management of prostate cancer. We sought to improve current models to predict SVI through the development of an SVI prediction genomic signature.<br />Patients and Methods: A total of 15,889 patients who underwent radical prostatectomy (RP) with available baseline clinical, pathology, and transcriptome data were retrieved from the GRID registry (ClinicalTrials.gov identifier: NCT02609269) and other retrospective cohorts. These data were divided into a training (n = 6,766), test (n = 3,363), and two validation (n = 5,062 and 698) cohorts. Multivariable logistic regression was performed to assess the predictive effect of the genomic SVI (gSVI) classifier in the presence of established nomograms (Partin Tables and Memorial Sloan Kettering Cancer Center [MSKCC]).<br />Results: In the training cohort, univariable filtering identified 2,132 genes that were differentially expressed between RP tumors with and without SVI. Model parameters were tuned to maximize the area under the curve (AUC) in the testing cohort, resulting in a logistic generalized linear model with 581 genes. The gSVI model scores range from 0 to 1. In the first validation set, gSVI showed superior discrimination of patients with and without SVI at RP compared with other prognostic signatures trained to predict distant metastasis or clinical recurrence. Of the 698 patients in the second validation set, gSVI combined with the MSKCC nomogram had a superior AUC (0.86) compared with either nomogram individually (0.81).<br />Conclusion: The gSVI represents a novel and validated expression signature to predict the presence of SVI before treatment with surgery. This genomic tool adds discriminatory power to existing clinical predictive nomograms and may help with pretreatment counseling and decision making.<br />Competing Interests: Conflicts of Interest Statement:The authors have declared that no competing interests exist.

Details

Language :
English
ISSN :
2473-4284
Volume :
4
Database :
MEDLINE
Journal :
JCO precision oncology
Publication Type :
Academic Journal
Accession number :
35050780
Full Text :
https://doi.org/10.1200/PO.20.00013