Personalized prostate cancer care: from screening to treatment

Combined analysis of CRMP4 methylation levels and CAPRA-S score predicts metastasis and outcomes in prostate cancer patients

The present study analyzed the predictive value of combined analysis of collapsin response mediator protein 4 (CRMP4) methylation levels and the Cancer of the Prostate Risk Assessment (CAPRA-S) Postsurgical score of patients who required adjuvant hormone therapy (AHT) after radical prostatectomy (RP). We retrospectively analyzed 305 patients with prostate cancer (PCa) who received RP and subsequent androgen deprivation therapy (ADT). Two hundred and thirty patients with clinically high-risk PCa underwent immediate ADT, and 75 patients with intermediate risk PCa underwent deferred ADT. CRMP4 methylation levels in biopsies were determined, and CAPRA-S scores were calculated. In the deferred ADT group, the values of the hazard ratios for tumor progression and cancer-specific mortality (CSM) in patients with ≥15% CRMP4 methylation were 6.81 (95% CI: 2.34–19.80) and 12.83 (95% CI: 2.16–26.10), respectively. Receiver-operating characteristic curve analysis indicated that CRMP4 methylation levels ≥15% served as a significant prognostic marker of tumor progression and CSM. In the immediate ADT group, CAPRA-S scores ≥6 and CRMP4 methylation levels ≥15% were independent predictors of these outcomes (uni- and multi-variable Cox regression analyses). The differences in the 5-year progression-free survival between each combination were statistically significant. Combining CAPRA-S score and CRMP4 methylation levels improved the area under the curve compared with the CRMP4 or CAPRA-S model. Therefore, CRMP4 methylation levels ≥15% were significantly associated with a poor prognosis and their combination with CAPRA-S score accurately predicted tumor progression and metastasis for patients requiring AHT after RP.

Germline Genetics of Prostate Cancer: Time to Incorporate Genetics into Early Detection Tools

BACKGROUND

Prostate cancer (PCa) remains the most common solid malignancy in men, and its prevalence makes understanding its heritability of paramount importance. To date, the most common factors used to estimate a man's risk of developing PCa are age, race, and family history. Despite recent advances in its utility in multiple malignancies (e.g., breast and colon cancer), genetic testing is still relatively underutilized in PCa.

CONTENT

Multiple highly penetrant genes (HPGs) and single-nucleotide polymorphisms (SNPs) have been show to increase a patient's risk of developing PCa. Mutations in the former, like DNA damage repair genes, can confer a 2- to 3-fold increased risk of developing PCa and can increase the risk of aggressive disease. Similarly, PCa-risk SNPs can be used to create risk scores (e.g., genetic or polygenic risk scores) that can be used to further stratify an individual's disease susceptibility. Specifically, these genetic risk scores can provide more specific estimates of a man's lifetime risk ranging up to >6-fold higher risk of PCa.

SUMMARY

It is becoming increasingly evident that in addition to the standard family history and race information, it is necessary to obtain genetic testing (including an assessment of HPG mutation status and genetic risk score) to provide a full risk assessment. The additional information derived thereby will improve current practices in PCa screening by risk-stratifying patients before initial prostate-specific antigen testing, determining a patient's frequency of visits, and even help identify potentially at-risk family members.

Reclassification of prostate cancer risk using sequentially identified SNPs: Results from the REDUCE trial

BACKGROUND

Although the clinical validity of risk-associated single nucleotide polymorphisms (SNPs) for assessment of disease susceptibility has been consistently established, risk reclassification from increasing numbers of implicated risk-associated SNPs raises concern that it is premature for clinical use. Our objective is to assess the degree and impact of risk reclassification with the increasing number of SNPs.

METHODS

A total of 3239 patients from the Reduction by Dutasteride of Prostate Cancer Events (REDUCE) trial were included. Four genetic risk scores (GRSs) were calculated based on sets of sequentially discovered prostate cancer (PCa) risk-associated SNPs (17, 34, 51, and 68 SNPs).

RESULTS

Pair-wise correlation coefficients between sets of GRSs increased as more SNPs were included in the GRS: 0.80, 0.86, and 0.95 for 17 versus 34 SNPs, 34 versus 51 SNPs, and 51 versus 68 SNPs, respectively. Using a GRS of 1.5 as a cutoff for higher versus lower risk, reclassification rates of PCa risk decreased: 14.11%, 12.04%, and 8.15% for 17 versus 34 SNPs, 34 versus 51 SNPs, and 51 versus 68 SNPs, respectively. Evolving GRSs, nevertheless, provide a tool for further refining risk assessment. When all four sequential GRSs were considered, the detection rates of PCa for men whose GRSs were consistently <1.5, reclassified, and consistently ≥1.5 were 20.8%, 29.67%, and 39.26%, respectively (Ptrend  = 1.12 × 10-8 ). In comparison, the detection rates of PCa in men with negative or positive family history were 23.75% and 31.78%, respectively.

CONCLUSIONS

Risk assessment using currently available SNPs is justified. Multiple GRS values from evolving sets of SNPs provide a valuable tool for better refining risk.

Translation of genomics and epigenomics in prostate cancer: progress and promising directions

During the last several years, exciting discoveries have been made in prostate cancer (PCa) as a result of significant advances in genomic technology and information. For example, using genome-wide association studies, more than 100 inherited genetic variants associated with PCa risk have been identified. Similarly, with the use of next-generation sequencing, various types of recurrent somatic DNA alterations in prostate tumors have been revealed. Some of these discoveries have potential clinical application to supplement existing tools for better decision-making regarding the need for screening, biopsy, and treatment of PCa. However, because of the complexity of these genomic findings and incomplete understanding of the genetics of this multifactorial disease, this potential has not yet been fully realized.