Validation of a Genomic Risk Classifier to Predict Prostate Cancer-specific Mortality in Men with Adverse Pathologic Features

Robert Jeffrey Karnes, Voleak Choeurng, Ashley E. Ross, Edward M. Schaeffer, Eric A. Klein, Stephen J. Freedland, Nicholas Erho, Kasra Yousefi, Mandeep Takhar, Elai Davicioni, Matthew R. Cooperberg, Bruce J. Trock

Research output: Contribution to journalArticle

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Abstract

Background: Risk of prostate cancer-specific mortality (PCSM) is highly variable for men with adverse pathologic features at radical prostatectomy (RP); a majority will die of other causes. Accurately stratifying PCSM risk can improve therapy decisions. Objective: Validate the 22 gene Decipher genomic classifier (GC) to predict PCSM in men with adverse pathologic features after RP. Design, setting, and participants: Men with adverse pathologic features: pT3, pN1, positive margins, or Gleason score >. 7 who underwent RP in 1987-2010 at Johns Hopkins, Cleveland Clinic, Mayo Clinic, and Durham Veteran's Affairs Hospital. We also analyzed subgroups at high risk (prostate-specific antigen >. 20 ng/ml, RP Gleason score 8-10, or stage >. pT3b), or very high risk of PCSM (biochemical recurrence in. <. 2 yr [BCR2], or men who developed metastasis after RP [MET]). Outcome measurements and statistical analysis: Logistic regression evaluated the association of GC with PCSM within 10 yr of RP (PCSM10), adjusted for the Cancer of the Prostate Risk Assessment Postsurgical Score (CAPRA-S). GC performance was evaluated with area under the receiver operating characteristic curve (AUC) and decision curves. Results and limitations: Five hundred and sixty-one men (112 with PCSM10), median follow-up 13.0 yr (patients without PCSM10). For high GC score (> 0.6) versus low-intermediate (≤ 0.6), the odds ratio for PCSM10 adjusted for CAPRA-S was 3.91 (95% confidence interval: 2.43-6.29), with AUC = 0.77, an increase of 0.04 compared with CAPRA-S. Subgroup odds ratios were 3.96, 3.06, and 1.95 for high risk, BCR2, or MET, respectively (all p <. 0.05), with AUCs 0.64-0.72. GC stratified cumulative PCSM10 incidence from 2.8% to 30%. Combined use of case-control and cohort data is a potential limitation. Conclusions: In a large cohort with the longest follow-up to date, Decipher GC demonstrated clinically important prediction of PCSM at 10 yr, independent of CAPRA-S, in men with adverse pathologic features, BCR2, or MET after RP. Patient summary: Decipher genomic classifier may improve treatment decision-making for men with adverse or high risk pathology after radical prostatectomy. In men with adverse pathology or early disease progression after prostatectomy, the Decipher 22 gene genomic classifier predicts risk of prostate cancer death. When combined with the Cancer of the Prostate Risk Assessment Postsurgical Score it further stratifies risk, which may be useful for decisions about postprostatectomy treatment.

Original languageEnglish (US)
JournalEuropean Urology
DOIs
StateAccepted/In press - 2017

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Prostatectomy
Prostatic Neoplasms
Mortality
Neoplasm Grading
Area Under Curve
Odds Ratio
Pathology
Veterans Hospitals
Prostate-Specific Antigen
Genes
Disease Progression
Decision Making
Therapeutics
Confidence Intervals
Recurrence
Incidence

Keywords

  • Adverse pathologic features
  • CAPRA-S
  • Genomic classifier
  • Prostate cancer-specific mortality
  • Radical prostatectomy

ASJC Scopus subject areas

  • Urology

Cite this

Validation of a Genomic Risk Classifier to Predict Prostate Cancer-specific Mortality in Men with Adverse Pathologic Features. / Karnes, Robert Jeffrey; Choeurng, Voleak; Ross, Ashley E.; Schaeffer, Edward M.; Klein, Eric A.; Freedland, Stephen J.; Erho, Nicholas; Yousefi, Kasra; Takhar, Mandeep; Davicioni, Elai; Cooperberg, Matthew R.; Trock, Bruce J.

In: European Urology, 2017.

Research output: Contribution to journalArticle

Karnes, RJ, Choeurng, V, Ross, AE, Schaeffer, EM, Klein, EA, Freedland, SJ, Erho, N, Yousefi, K, Takhar, M, Davicioni, E, Cooperberg, MR & Trock, BJ 2017, 'Validation of a Genomic Risk Classifier to Predict Prostate Cancer-specific Mortality in Men with Adverse Pathologic Features', European Urology. https://doi.org/10.1016/j.eururo.2017.03.036
Karnes, Robert Jeffrey ; Choeurng, Voleak ; Ross, Ashley E. ; Schaeffer, Edward M. ; Klein, Eric A. ; Freedland, Stephen J. ; Erho, Nicholas ; Yousefi, Kasra ; Takhar, Mandeep ; Davicioni, Elai ; Cooperberg, Matthew R. ; Trock, Bruce J. / Validation of a Genomic Risk Classifier to Predict Prostate Cancer-specific Mortality in Men with Adverse Pathologic Features. In: European Urology. 2017.
@article{f6eab04dcb5840cdb9b6e03c5f59d0d7,
title = "Validation of a Genomic Risk Classifier to Predict Prostate Cancer-specific Mortality in Men with Adverse Pathologic Features",
abstract = "Background: Risk of prostate cancer-specific mortality (PCSM) is highly variable for men with adverse pathologic features at radical prostatectomy (RP); a majority will die of other causes. Accurately stratifying PCSM risk can improve therapy decisions. Objective: Validate the 22 gene Decipher genomic classifier (GC) to predict PCSM in men with adverse pathologic features after RP. Design, setting, and participants: Men with adverse pathologic features: pT3, pN1, positive margins, or Gleason score >. 7 who underwent RP in 1987-2010 at Johns Hopkins, Cleveland Clinic, Mayo Clinic, and Durham Veteran's Affairs Hospital. We also analyzed subgroups at high risk (prostate-specific antigen >. 20 ng/ml, RP Gleason score 8-10, or stage >. pT3b), or very high risk of PCSM (biochemical recurrence in. <. 2 yr [BCR2], or men who developed metastasis after RP [MET]). Outcome measurements and statistical analysis: Logistic regression evaluated the association of GC with PCSM within 10 yr of RP (PCSM10), adjusted for the Cancer of the Prostate Risk Assessment Postsurgical Score (CAPRA-S). GC performance was evaluated with area under the receiver operating characteristic curve (AUC) and decision curves. Results and limitations: Five hundred and sixty-one men (112 with PCSM10), median follow-up 13.0 yr (patients without PCSM10). For high GC score (> 0.6) versus low-intermediate (≤ 0.6), the odds ratio for PCSM10 adjusted for CAPRA-S was 3.91 (95{\%} confidence interval: 2.43-6.29), with AUC = 0.77, an increase of 0.04 compared with CAPRA-S. Subgroup odds ratios were 3.96, 3.06, and 1.95 for high risk, BCR2, or MET, respectively (all p <. 0.05), with AUCs 0.64-0.72. GC stratified cumulative PCSM10 incidence from 2.8{\%} to 30{\%}. Combined use of case-control and cohort data is a potential limitation. Conclusions: In a large cohort with the longest follow-up to date, Decipher GC demonstrated clinically important prediction of PCSM at 10 yr, independent of CAPRA-S, in men with adverse pathologic features, BCR2, or MET after RP. Patient summary: Decipher genomic classifier may improve treatment decision-making for men with adverse or high risk pathology after radical prostatectomy. In men with adverse pathology or early disease progression after prostatectomy, the Decipher 22 gene genomic classifier predicts risk of prostate cancer death. When combined with the Cancer of the Prostate Risk Assessment Postsurgical Score it further stratifies risk, which may be useful for decisions about postprostatectomy treatment.",
keywords = "Adverse pathologic features, CAPRA-S, Genomic classifier, Prostate cancer-specific mortality, Radical prostatectomy",
author = "Karnes, {Robert Jeffrey} and Voleak Choeurng and Ross, {Ashley E.} and Schaeffer, {Edward M.} and Klein, {Eric A.} and Freedland, {Stephen J.} and Nicholas Erho and Kasra Yousefi and Mandeep Takhar and Elai Davicioni and Cooperberg, {Matthew R.} and Trock, {Bruce J.}",
year = "2017",
doi = "10.1016/j.eururo.2017.03.036",
language = "English (US)",
journal = "European Urology",
issn = "0302-2838",
publisher = "Elsevier",

}

TY - JOUR

T1 - Validation of a Genomic Risk Classifier to Predict Prostate Cancer-specific Mortality in Men with Adverse Pathologic Features

AU - Karnes, Robert Jeffrey

AU - Choeurng, Voleak

AU - Ross, Ashley E.

AU - Schaeffer, Edward M.

AU - Klein, Eric A.

AU - Freedland, Stephen J.

AU - Erho, Nicholas

AU - Yousefi, Kasra

AU - Takhar, Mandeep

AU - Davicioni, Elai

AU - Cooperberg, Matthew R.

AU - Trock, Bruce J.

PY - 2017

Y1 - 2017

N2 - Background: Risk of prostate cancer-specific mortality (PCSM) is highly variable for men with adverse pathologic features at radical prostatectomy (RP); a majority will die of other causes. Accurately stratifying PCSM risk can improve therapy decisions. Objective: Validate the 22 gene Decipher genomic classifier (GC) to predict PCSM in men with adverse pathologic features after RP. Design, setting, and participants: Men with adverse pathologic features: pT3, pN1, positive margins, or Gleason score >. 7 who underwent RP in 1987-2010 at Johns Hopkins, Cleveland Clinic, Mayo Clinic, and Durham Veteran's Affairs Hospital. We also analyzed subgroups at high risk (prostate-specific antigen >. 20 ng/ml, RP Gleason score 8-10, or stage >. pT3b), or very high risk of PCSM (biochemical recurrence in. <. 2 yr [BCR2], or men who developed metastasis after RP [MET]). Outcome measurements and statistical analysis: Logistic regression evaluated the association of GC with PCSM within 10 yr of RP (PCSM10), adjusted for the Cancer of the Prostate Risk Assessment Postsurgical Score (CAPRA-S). GC performance was evaluated with area under the receiver operating characteristic curve (AUC) and decision curves. Results and limitations: Five hundred and sixty-one men (112 with PCSM10), median follow-up 13.0 yr (patients without PCSM10). For high GC score (> 0.6) versus low-intermediate (≤ 0.6), the odds ratio for PCSM10 adjusted for CAPRA-S was 3.91 (95% confidence interval: 2.43-6.29), with AUC = 0.77, an increase of 0.04 compared with CAPRA-S. Subgroup odds ratios were 3.96, 3.06, and 1.95 for high risk, BCR2, or MET, respectively (all p <. 0.05), with AUCs 0.64-0.72. GC stratified cumulative PCSM10 incidence from 2.8% to 30%. Combined use of case-control and cohort data is a potential limitation. Conclusions: In a large cohort with the longest follow-up to date, Decipher GC demonstrated clinically important prediction of PCSM at 10 yr, independent of CAPRA-S, in men with adverse pathologic features, BCR2, or MET after RP. Patient summary: Decipher genomic classifier may improve treatment decision-making for men with adverse or high risk pathology after radical prostatectomy. In men with adverse pathology or early disease progression after prostatectomy, the Decipher 22 gene genomic classifier predicts risk of prostate cancer death. When combined with the Cancer of the Prostate Risk Assessment Postsurgical Score it further stratifies risk, which may be useful for decisions about postprostatectomy treatment.

AB - Background: Risk of prostate cancer-specific mortality (PCSM) is highly variable for men with adverse pathologic features at radical prostatectomy (RP); a majority will die of other causes. Accurately stratifying PCSM risk can improve therapy decisions. Objective: Validate the 22 gene Decipher genomic classifier (GC) to predict PCSM in men with adverse pathologic features after RP. Design, setting, and participants: Men with adverse pathologic features: pT3, pN1, positive margins, or Gleason score >. 7 who underwent RP in 1987-2010 at Johns Hopkins, Cleveland Clinic, Mayo Clinic, and Durham Veteran's Affairs Hospital. We also analyzed subgroups at high risk (prostate-specific antigen >. 20 ng/ml, RP Gleason score 8-10, or stage >. pT3b), or very high risk of PCSM (biochemical recurrence in. <. 2 yr [BCR2], or men who developed metastasis after RP [MET]). Outcome measurements and statistical analysis: Logistic regression evaluated the association of GC with PCSM within 10 yr of RP (PCSM10), adjusted for the Cancer of the Prostate Risk Assessment Postsurgical Score (CAPRA-S). GC performance was evaluated with area under the receiver operating characteristic curve (AUC) and decision curves. Results and limitations: Five hundred and sixty-one men (112 with PCSM10), median follow-up 13.0 yr (patients without PCSM10). For high GC score (> 0.6) versus low-intermediate (≤ 0.6), the odds ratio for PCSM10 adjusted for CAPRA-S was 3.91 (95% confidence interval: 2.43-6.29), with AUC = 0.77, an increase of 0.04 compared with CAPRA-S. Subgroup odds ratios were 3.96, 3.06, and 1.95 for high risk, BCR2, or MET, respectively (all p <. 0.05), with AUCs 0.64-0.72. GC stratified cumulative PCSM10 incidence from 2.8% to 30%. Combined use of case-control and cohort data is a potential limitation. Conclusions: In a large cohort with the longest follow-up to date, Decipher GC demonstrated clinically important prediction of PCSM at 10 yr, independent of CAPRA-S, in men with adverse pathologic features, BCR2, or MET after RP. Patient summary: Decipher genomic classifier may improve treatment decision-making for men with adverse or high risk pathology after radical prostatectomy. In men with adverse pathology or early disease progression after prostatectomy, the Decipher 22 gene genomic classifier predicts risk of prostate cancer death. When combined with the Cancer of the Prostate Risk Assessment Postsurgical Score it further stratifies risk, which may be useful for decisions about postprostatectomy treatment.

KW - Adverse pathologic features

KW - CAPRA-S

KW - Genomic classifier

KW - Prostate cancer-specific mortality

KW - Radical prostatectomy

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