Clinical Utility of Gene Expression Classifiers in Men With Newly Diagnosed Prostate Cancer

Abstract

Purpose Tissue-based gene expression classifiers (GECs) may assist with management decisions in patients with newly diagnosed prostate cancer. We sought to assess the current use of GEC tests and determine how the test results are associated with primary disease management. Methods In this observational study, patients diagnosed with localized prostate cancer were tracked through the Michigan Urological Surgery Improvement Collaborative registry. The utilization and results of three GECs (Decipher Prostate Biopsy, Oncotype DX Prostate, and Prolaris) were prospectively collected. Practice patterns, predictors of GEC use, and effect of GEC results on disease management were investigated. Results Of 3,966 newly diagnosed patients, 747 (18.8%) underwent GEC testing. The rate of GEC use in individual practices ranged from 0% to 93%, and patients undergoing GEC testing were more likely to have a lower prostate-specific antigen level, lower Gleason score, lower clinical T stage, and fewer positive cores (all P < .05). Among patients with clinical favorable risk of cancer, the rate of active surveillance (AS) differed significantly among patients with a GEC result above the threshold (46.2%), those with a GEC result below the threshold (75.9%), and those who did not undergo GEC (57.9%; P < .001 for comparison of the three groups). This results in an estimate that, for every nine men with favorable risk of cancer who undergo GEC testing, one additional patient may have their disease initially managed with AS. On multivariable analysis, patients with favorable-risk prostate cancer who were classified as GEC low risk were more likely to be managed on AS than those without testing (odds ratio, 1.84; P = .006). Conclusion There is large variability in practice-level use and GEC tests ordered in patients with newly diagnosed, localized prostate cancer. In patients with clinical favorable risk of cancer, GEC testing significantly increased the use of AS. Additional follow-up will help determine whether incorporation of GEC testing into initial patient care favorably affects clinical outcomes.

Prostate Cancer, Version 2.2019, NCCN Clinical Practice Guidelines in Oncology

“…[131][132][133][134][135][136][137][138][139][140] A prospective, clinical utility study of 3,966 patients newly diagnosed with localized prostate cancer found that the rates of active surveillance increased with use of a tissue-based gene expression classifier. 141 Active surveillance rates were 46.2%, 75.9%, and 57.9% for those whose classifier results were above the specified threshold, below the threshold, and those who did not undergo genomic testing, respectively (P,.001). The authors estimate that 1 additional patient may chose active surveillance for every 9 men with favorable risk prostate cancer who undergo genomic testing.…”

New Prognostic Biomarkers in Metastatic Castration-Resistant Prostate Cancer

“…Traditionally, Gleason score, clinical staging and PSA at diagnosis have helped classifying localized PCs as being at low, intermediate, and high risk of relapse [ 88 ]. Recently several tissue-based multigene expression (Decipher, Oncotype Dx Prostate, Polaris) and a protein-based (ProMark) classifiers have been proposed to identify patients with biologically significant disease [ 89 , 90 , 91 , 92 ].…”

“…), these classifiers (Decipher, Oncotype Dx Prostate, Polaris) offer scores that, integrating other clinical–pathological features, might help identifying patients with localized disease but with worse outcomes. Potentially, these tools could help clinicians in deciding which patients would benefit from surveillance vs. active treatment, or a salvage approach after a radical treatment [ 89 , 90 , 91 , 92 ].…”

Incorporating Prognostic Biomarkers into Risk Assessment Models and TNM Staging for Prostate Cancer

“…However, there is no high-quality comparative data to determine which of the markers is the most accurate prognosticator. Longer term data is needed to determine whether their utility will impact patient quality of life and prostate cancer specific outcomes [22,29,30]. They may be useful when combined with known risk stratification systems in prostate cancer.…”