The impact of genomic biomarkers on a clinical risk prediction model for upgrading/upstaging among men with favorable‐risk prostate cancer

Background The challenge of distinguishing indolent from aggressive prostate cancer (PCa) complicates decision‐making for men considering active surveillance (AS). Genomic classifiers (GCs) may improve risk stratification by predicting end points such as upgrading or upstaging (UG/US). The aim of th...

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Published in:Cancer 2024-05, Vol.130 (10), p.1766-1772
Main Authors: Braun, Avery E., Chan, June M., Neuhaus, John, Cowan, Janet E., Kenfield, Stacey A., Van Blarigan, Erin L., Tenggara, Imelda, Broering, Jeanette M., Simko, Jeffry P., Carroll, Peter R., Cooperberg, Matthew R.
Format: Article
Language:English
Subjects:
active surveillance
Aged
Antigens
Biomarkers
Biomarkers, Tumor - genetics
Biopsy
Cancer
Confidence intervals
Decision making
genomic classifiers
Genomics
Genomics - methods
Humans
Male
Middle Aged
Neoplasm Grading
Neoplasm Staging
prediction
Prediction models
Prostate cancer
Prostate-Specific Antigen - blood
Prostatectomy
Prostatic Neoplasms - blood
Prostatic Neoplasms - genetics
Prostatic Neoplasms - pathology
Risk Assessment
ROC Curve
Statistical analysis
Stratification
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Summary:Background The challenge of distinguishing indolent from aggressive prostate cancer (PCa) complicates decision‐making for men considering active surveillance (AS). Genomic classifiers (GCs) may improve risk stratification by predicting end points such as upgrading or upstaging (UG/US). The aim of this study was to assess the impact of GCs on UG/US risk prediction in a clinicopathologic model. Methods Participants had favorable‐risk PCa (cT1‐2, prostate‐specific antigen [PSA] ≤15 ng/mL, and Gleason grade group 1 [GG1]/low‐volume GG2). A prediction model was developed for 864 men at the University of California, San Francisco, with standard clinical variables (cohort 1), and the model was validated for 2267 participants from the Cancer of the Prostate Strategic Urologic Research Endeavor (CaPSURE) registry (cohort 2). Logistic regression was used to compute the area under the receiver operating characteristic curve (AUC) to develop a prediction model for UG/US at prostatectomy. A GC (Oncotype Dx Genomic Prostate Score [GPS] or Prolaris) was then assessed to improve risk prediction. Results The prediction model included biopsy GG1 versus GG2 (odds ratio [OR], 5.83; 95% confidence interval [CI], 3.73–9.10); PSA (OR, 1.10; 95% CI, 1.01–1.20; per 1 ng/mL), percent positive cores (OR, 1.01; 95% CI, 1.01–1.02; per 1%), prostate volume (OR, 0.98; 95% CI, 0.97–0.99; per mL), and age (OR, 1.05; 95% CI, 1.02–1.07; per year), with AUC 0.70 (cohort 1) and AUC 0.69 (cohort 2). GPS was associated with UG/US (OR, 1.03; 95% CI, 1.01–1.06; p 
ISSN:0008-543X
1097-0142
DOI:10.1002/cncr.35215