Performance of clinicopathologic models in men with high risk localized prostate cancer: impact of a 22-gene genomic classifier

Abstract

Background

Prostate cancer exhibits biological and clinical heterogeneity even within established clinico-pathologic risk groups. The Decipher genomic classifier (GC) is a validated method to further risk-stratify disease in patients with prostate cancer, but its performance solely within National Comprehensive Cancer Network (NCCN) high-risk disease has not been undertaken to date.

Methods

A multi-institutional retrospective study of 405 men with high-risk prostate cancer who underwent primary treatment with radical prostatectomy (RP) or radiation therapy (RT) with androgen-deprivation therapy (ADT) at 11 centers from 1995 to 2005 was performed. Cox proportional hazards models were used to determine the hazard ratios (HR) for the development of metastatic disease based on clinico-pathologic variables, risk groups, and GC score. The area under the receiver operating characteristic curve (AUC) was determined for regression models without and with the GC score.

Results

Over a median follow-up of 82 months, 104 patients (26%) developed metastatic disease. On univariable analysis, increasing GC score was significantly associated with metastatic disease ([HR]: 1.34 per 0.1 unit increase, 95% confidence interval [CI]: 1.19–1.50, p < 0.001), while age, serum PSA, biopsy GG, and clinical T-stage were not (all p > 0.05). On multivariable analysis, GC score (HR: 1.33 per 0.1 unit increase, 95% CI: 1.19–1.48, p < 0.001) and GC high-risk (vs low-risk, HR: 2.95, 95% CI: 1.79–4.87, p < 0.001) were significantly associated with metastasis. The addition of GC score to regression models based on NCCN risk group improved model AUC from 0.46 to 0.67, and CAPRA from 0.59 to 0.71.

Conclusions

Among men with high-risk prostate cancer, conventional clinico-pathologic data had poor discrimination to risk stratify development of metastatic disease. GC score was a significant and independent predictor of metastasis and may help identify men best suited for treatment intensification/de-escalation.

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Fig. 1: Prognostic effect of clinical and pathologic variables on the cumulative incidence of metastases in men with high risk prostate cancer.
Fig. 2: Prognostic effect of Decipher on the cumulative incidence of metastases for men with high risk prostate cancer.
Fig. 3: Discriminatory performance of clinicopathologic models and Decipher to predict metastatic outcome.

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Acknowledgements

We would like to thank the National Institutes of Health/National Cancer Institute Advanced Training in Urologic Oncology Grant (JJT, T32/CA180984), Prostate Cancer Foundation (BAM, DES), the Prostate Cancer NIH SPORE (DES, P50CA186786), and the Department of Defense (DES, PC151068).

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Correspondence to Daniel E. Spratt.

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Nonrelevant conflicts of interest will be disclosed in the ICMJE forms. JJT: Leadership role with equity interest: LynxDx, Inc. SJF: Research funding: Decipher Biosciences. SGZ: Travel/expenses and patent applications pending with Decipher Biosciences. TMM: Research funding: Myriad Genetics, GenomeDx. EMS: Consultant: Abbvie. DES: Advisory board: Blue Earth,Janssen, and AstraZenica. Funding: Janssen.

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Tosoian, J.J., Birer, S.R., Jeffrey Karnes, R. et al. Performance of clinicopathologic models in men with high risk localized prostate cancer: impact of a 22-gene genomic classifier. Prostate Cancer Prostatic Dis (2020). https://doi.org/10.1038/s41391-020-0226-2

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