A Biopsy-based 17-gene Genomic Prostate Score as a Predictor of Metastases and Prostate Cancer Death in Surgically Treated Men with Clinically Localized Disease

Eur Urol. 2018 Jan;73(1):129-138. doi: 10.1016/j.eururo.2017.09.013. Epub 2017 Oct 6.

Abstract

Background: A 17-gene biopsy-based reverse transcription polymerase chain reaction assay, which provides a Genomic Prostate Score (GPS-scale 0-100), has been validated as an independent predictor of adverse pathology and biochemical recurrence after radical prostatectomy (RP) in men with low- and intermediate-risk prostate cancer (PCa).

Objective: To evaluate GPS as a predictor of PCa metastasis and PCa-specific death (PCD) in a large cohort of men with localized PCa and long-term follow-up.

Design, setting, and participants: A retrospective study using a stratified cohort sampling design was performed in a cohort of men treated with RP within Kaiser Permanente Northern California. RNA from archival diagnostic biopsies was assayed to generate GPS results.

Outcome measurements and statistical analysis: We assessed the association between GPS and time to metastasis and PCD in prespecified uni- and multivariable statistical analyses, based on Cox proportional hazard models accounting for sampling weights.

Results and limitations: The final study population consisted of 279 men with low-, intermediate-, and high-risk PCa between 1995 and 2010 (median follow-up 9.8 yr), and included 64 PCD and 79 metastases. Valid GPS results were obtained for 259 (93%). In univariable analysis, GPS was strongly associated with time to PCD, hazard ratio (HR)/20 GPS units=3.23 (95% confidence interval [CI] 1.84-5.65; p<0.001), and time to metastasis, HR/20 units=2.75 (95% CI 1.63-4.63; p<0.001). The association between GPS and both end points remained significant after adjusting for National Comprehensive Cancer Network, American Urological Association, and Cancer of the Prostate Risk Assessment (CAPRA) risks (p<0.001). No patient with low- or intermediate-risk disease and a GPS of<20 developed metastases or PCD (n=31). In receiver operating characteristic analysis of PCD at 10 yr, GPS improved the c-statistic from 0.78 (CAPRA alone) to 0.84 (GPS+CAPRA; p<0.001). A limitation of the study was that patients were treated during an era when definitive treatment was standard of care with little adoption of active surveillance.

Conclusions: GPS is a strong independent predictor of long-term outcomes in clinically localized PCa in men treated with RP and may improve risk stratification for men with newly diagnosed disease.

Patient summary: Many prostate cancers are slow growing and unlikely to spread or threaten a man's life, while others are more aggressive and require treatment. Increasingly, doctors are using new molecular tests, such as the17-gene Genomic Prostate Score (GPS), which can be performed at the time of initial diagnosis to help determine how aggressive a given patient's cancer may be. In this study, performed in a large community-based healthcare network, GPS was shown to be a strong predictor as to whether a man's prostate cancer will spread and threaten his life after surgery, providing information that may help patients and their doctors decide on the best course of management of their disease.

Keywords: Gene expression; Molecular diagnostic testing; Prostate cancer.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Aged
  • Biomarkers, Tumor / genetics*
  • Biopsy
  • California
  • Disease Progression
  • Gene Expression Profiling / methods*
  • Genomics / methods*
  • Humans
  • Male
  • Middle Aged
  • Multivariate Analysis
  • Neoplasm Metastasis
  • Predictive Value of Tests
  • Proportional Hazards Models
  • Prostatectomy* / adverse effects
  • Prostatectomy* / mortality
  • Prostatic Neoplasms / genetics*
  • Prostatic Neoplasms / mortality
  • Prostatic Neoplasms / pathology
  • Prostatic Neoplasms / surgery*
  • Registries
  • Retrospective Studies
  • Reverse Transcriptase Polymerase Chain Reaction
  • Risk Assessment
  • Risk Factors
  • Time Factors
  • Transcriptome*
  • Treatment Outcome

Substances

  • Biomarkers, Tumor