PurposeClinicopathologic features and biochemical recurrence are sensitive, but not specific, predictors of metastatic disease and lethal prostate cancer. We hypothesize that a genomic expression signature detected in the primary tumor represents true biological potential of aggressive disease and provides improved prediction of early prostate cancer metastasis.MethodsA nested case-control design was used to select 639 patients from the Mayo Clinic tumor registry who underwent radical prostatectomy between 1987 and 2001. A genomic classifier (GC) was developed by modeling differential RNA expression using 1.4 million feature high-density expression arrays of men enriched for rising PSA after prostatectomy, including 213 who experienced early clinical metastasis after biochemical recurrence. A training set was used to develop a random forest classifier of 22 markers to predict for cases - men with early clinical metastasis after rising PSA. Performance of GC was compared to prognostic factors such as Gleason score and previous gene expression signatures in a withheld validation set.ResultsExpression profiles were generated from 545 unique patient samples, with median follow-up of 16.9 years. GC achieved an area under the receiver operating characteristic curve of 0.75 (0.67–0.83) in validation, outperforming clinical variables and gene signatures. GC was the only significant prognostic factor in multivariable analyses. Within Gleason score groups, cases with high GC scores experienced earlier death from prostate cancer and reduced overall survival. The markers in the classifier were found to be associated with a number of key biological processes in prostate cancer metastatic disease progression.ConclusionA genomic classifier was developed and validated in a large patient cohort enriched with prostate cancer metastasis patients and a rising PSA that went on to experience metastatic disease. This early metastasis prediction model based on genomic expression in the primary tumor may be useful for identification of aggressive prostate cancer.

Purpose Prostate cancer patients with locally advanced disease after radical prostatectomy (RP) are candidates for secondary therapy. However, this higher risk population is heterogeneous and many will not metastasize even when conservatively managed. Given the limited specificity of pathologic features to predict metastasis, newer risk-prediction models are needed. This represents a validation study of a genomic classifier (GC) that predicts post-RP metastasis in a high-risk population. Materials and Methods A case-cohort design was used to sample 1,010 post-RP patients at high risk of recurrence treated between 2000-2006. Patients had preoperative PSA >20 ng/mL, Gleason ≥8, pT3b or GPSM score ≥10. Patients with metastasis at diagnosis or any prior treatment for prostate cancer were excluded. 20% random sampling created a subcohort that included all cases with metastasis. 22-marker GC scores were generated for 219 patients with available genomic data. Receiver operating characteristic and decision curves, competing risk, and weighted regression models assessed GC performance. Results GC had area under the curve of 0.79 for predicting 5-year metastasis post-RP. Decision curves showed that net benefit of GC exceeded clinical-only models. GC was the predominant predictor of metastasis in multivariable analysis. Cumulative incidence of metastasis at 5 years post-RP was 2.4%, 6.0% and 22.5% for patients with low (60% of patients), intermediate (21% of patients), and high (19% of patients) GC scores, respectively (p<0.001). Conclusions These results indicate that genomic information from the primary tumor can identify patients with adverse pathology who are most at risk for metastasis and potentially lethal prostate cancer.

Luminal- and basal-like prostate cancers demonstrate divergent clinical behavior, and patients with luminal B tumors respond better to postoperative ADT than do patients with non–luminal B tumors. These findings contribute novel insight into prostate cancer biology, providing a potential clinical tool to personalize ADT treatment for prostate cancer by predicting which men may benefit from ADT after surgery.

No abstract

PURPOSE Germline testing (GT) is a central feature of prostate cancer (PCA) treatment, management, and hereditary cancer assessment. Critical needs include optimized multigene testing strategies that incorporate evolving genetic data, consistency in GT indications and management, and alternate genetic evaluation models that address the rising demand for genetic services. METHODS A multidisciplinary consensus conference that included experts, stakeholders, and national organization leaders was convened in response to current practice challenges and to develop a genetic implementation framework. Evidence review informed questions using the modified Delphi model. The final framework included criteria with strong (> 75%) agreement (Recommend) or moderate (50% to 74%) agreement (Consider). RESULTS Large germline panels and somatic testing were recommended for metastatic PCA. Reflex testing—initial testing of priority genes followed by expanded testing—was suggested for multiple scenarios. Metastatic disease or family history suggestive of hereditary PCA was recommended for GT. Additional family history and pathologic criteria garnered moderate consensus. Priority genes to test for metastatic disease treatment included BRCA2, BRCA1, and mismatch repair genes, with broader testing, such as ATM, for clinical trial eligibility. BRCA2 was recommended for active surveillance discussions. Screening starting at age 40 years or 10 years before the youngest PCA diagnosis in a family was recommended for BRCA2 carriers, with consideration in HOXB13, BRCA1, ATM, and mismatch repair carriers. Collaborative (point-of-care) evaluation models between health care and genetic providers was endorsed to address the genetic counseling shortage. The genetic evaluation framework included optimal pretest informed consent, post-test discussion, cascade testing, and technology-based approaches. CONCLUSION This multidisciplinary, consensus-driven PCA genetic implementation framework provides novel guidance to clinicians and patients tailored to the precision era. Multiple research, education, and policy needs remain of importance.