Prediction of clinically significant prostate cancer using polygenic risk models in Asians

Predictive value of polygenic risk score for prostate cancer incidence and prognosis in the Han Chinese

Although prostate cancer is a common occurrence among males, the relationship between existing risk prediction models remains unclear. The objective of this hospital-based retrospective study is to investigate the impact of polygenic risk scores (PRSs) on the incidence and prognosis of prostate cancer in the Han Chinese population. A total of 24,778 male participants including 903 patients with prostate cancer at Taichung Veterans General Hospital were enrolled in the study. PRS was calculated using 269 single nucleotide polymorphisms and their corresponding effect sizes from the polygenic score catalog. The association between PRS and the risk prostate cancer was evaluated using Cox proportional hazards regression model. Among the 24,778 participants, 903 were diagnosed with prostate cancer. The risk of prostate cancer was significantly higher in the highest quartile of PRS distribution compared to the lowest (hazard ratio = 4.770, 95% CI = 3.999-5.689, p < 0.0001), with statistical significance across all age groups. Patients in the highest quartile were diagnosed with prostate cancer at a younger age (66.8 ± 8.3 vs. 69.5 ± 8.8, p = 0.002). Subgroup analysis of patients with localized or stage 4 prostate cancer showed no significant differences in biochemical failure or overall survival. This hospital-based cohort study observed that a higher PRS was associated with increased susceptibility to prostate cancer and younger age of diagnosis. However, PRS was not found to be a significant predictor of disease stage and prognosis. These findings suggest that PRS could serve as a useful tool in prostate cancer risk assessment.

A review of disease risk prediction methods and applications in the omics era

Risk prediction and disease prevention are the innovative care challenges of the 21st century. Apart from freeing the individual from the pain of disease, it will lead to low medical costs for society. Until very recently, risk assessments have ushered in a new era with the emergence of omics technologies, including genomics, transcriptomics, epigenomics, proteomics, and so on, which potentially advance the ability of biomarkers to aid prediction models. While risk prediction has achieved great success, there are still some challenges and limitations. We reviewed the general process of omics-based disease risk model construction and the applications in four typical diseases. Meanwhile, we highlighted the problems in current studies and explored the potential opportunities and challenges for future clinical practice.

A Systematic Review of Family History, Race/Ethnicity, and Genetic Risk on Prostate Cancer Detection and Outcomes: Considerations in PSA-based Screening

AIM

To investigate the role of family history, race/ethnicity, and genetics in prostate cancer (PCa) screening.

METHODS

We conducted a systematic review of articles from January 2013 through September 2023 that focused on the association of race/ethnicity and genetic factors on PCa detection. Of 10,815 studies, we identified 43 that fulfilled our pre-determined PICO (Patient, Intervention, Comparison and Outcome) criteria.

RESULTS

Men with ≥1 first-degree relative(s) with PCa are at increased risk of PCa, even with negative imaging and/or benign prostate biopsy. Black men have higher PCa risk, while Asian men have lower risk. Most of the differences in risks are attributable to environmental and socioeconomic factors; however, genetic differences may play a role. Among numerous pathogenic variants that increase PCa risk, BRCA2, MSH2, and HOXB13 mutations confer the highest risk of PCa. Polygenic risk score (PRS) models identify men at higher PCa risk for a given age and PSA; these models improve when considering other clinical factors and when the model population matches the study population's ancestry.

CONCLUSIONS

Family history of PCa, race/ethnicity, pathogenic variants (particularly BRCA2, MSH2, and HOXB13), and PRS are associated with increased PCa risk and should be considered in shared decision-making to determine PCa screening regimens.

Germline DNA-Repair Genes and HOXB13 Mutations in Korean Men with Metastatic Prostate Cancer: Data from a Large Korean Cohort

PURPOSE

Germline mutations in DNA damage repair (DDR) genes such as BRCA2 have been associated with prostate cancer (PC) risk but has not been thoroughly evaluated for metastatic prostate cancer (mPC) in Asian men. This study attempts to evaluate frequency of DDR mutations in the largest cohort of Koreans.

MATERIALS AND METHODS

We recruited 340 patients with mPC unselected for family history of cancer and compared to 495 controls. Whole genome sequencing was applied to assess germline pathogenic/likely pathogenic variants (PV/LPVs) in 26 DDR genes and HOXB13, including 7 genes (ATM, BRCA1/2, CHEK2, BRIP1, PALB2, and NBN) associated with hereditary PC. Comparisons to published Caucasian and Japanese cohorts were performed.

RESULTS

Total of 28 PV/LPVs were identified in 30 (8.8%) patients; mutations were found in 13 genes, including BRCA2 (15 men [4.41%]), ATM (2 men [0.59%]), NBN (2 men [0.59%], and BRIP1 (2 men [0.59%]). Only one patient had HOXB13 mutation (0.29%). A lower rate of overall germline variant frequency was observed in Korean mPC compared to Caucasians (8.8% vs. 11.8%), but individual variants notably differed from Caucasian and geographically similar Japanese cohorts. PV/LPVs in DDR genes tended to increase gradually with higher Gleason scores (GS 7, 7.1%; GS 8, 7.5%; GS 9-10, 9.9%).

CONCLUSIONS

BRCA2 was the most frequently mutated gene common to different cohorts supporting its importance, but differences in variant distribution in Korean mPC underscore the need for ethnic-specific genetic models. Future ethnic-specific analyses are warranted to verify our findings.

Predicting the germline dependence of hematuria risk in prostate cancer radiotherapy patients

BACKGROUND AND PURPOSE

Late radiation-induced hematuria can develop in prostate cancer patients undergoing radiotherapy and can negatively impact the quality-of-life of survivors. If a genetic component of risk could be modeled, this could potentially be the basis for modifying treatment for high-risk patients. We therefore investigated whether a previously developed machine learning-based modeling method using genome-wide common single nucleotide polymorphisms (SNPs) can stratify patients in terms of the risk of radiation-induced hematuria.

MATERIALS AND METHODS

We applied a two-step machine learning algorithm that we previously developed for genome-wide association studies called pre-conditioned random forest regression (PRFR). PRFR includes a pre-conditioning step, producing adjusted outcomes, followed by random forest regression modeling. Data was from germline genome-wide SNPs for 668 prostate cancer patients treated with radiotherapy. The cohort was stratified only once, at the outset of the modeling process, into two groups: a training set (2/3 of samples) for modeling and a validation set (1/3 of samples). Post-modeling bioinformatics analysis was conducted to identify biological correlates plausibly associated with the risk of hematuria.

RESULTS

The PRFR method achieved significantly better predictive performance compared to other alternative methods (all p < 0.05). The odds ratio between the high and low risk groups, each of which consisted of 1/3 of samples in the validation set, was 2.87 (p = 0.029), implying a clinically useful level of discrimination. Bioinformatics analysis identified six key proteins encoded by CTNND2, GSK3B, KCNQ2, NEDD4L, PRKAA1, and TXNL1 genes as well as four statistically significant biological process networks previously shown to be associated with the bladder and urinary tract.

CONCLUSION

The risk of hematuria is significantly dependent on common genetic variants. The PRFR algorithm resulted in a stratification of prostate cancer patients at differential risk levels of post-radiotherapy hematuria. Bioinformatics analysis identified important biological processes involved in radiation-induced hematuria.

Variations in genomic regions encoding long non-coding RNA genes associated with increased prostate cancer risk

From a single restriction fragment length polymorphism analysis to next generation sequencing analysis that screens the entire human genome, testing for genomic variations provides a great and robust approach to cancer testing. Non-coding RNAs have been shown to have a major impact on the development and progression of human cancers, including prostate cancer. However, the low stability of these molecules under laboratory conditions has made their clinical utility challenging, as in the case of PCA3 long non-coding RNA. Since testing for variations in genomic regions encoding non-coding RNAs offers a promising approach for cancer testing, identification and interpretation of single nucleotide polymorphisms associated with prostate cancer susceptibility is of great interest. Accordingly, here, for the first time, we review and discuss current available knowledge about genomic variation of long non-coding RNA molecules in prostate cancer.

Polygenic risk score for tumor aggressiveness and early-onset prostate cancer in Asians

We attempted to assess the performance of an ethnic-specific polygenic risk score (PRS) designed from a Korean population to predict aggressive prostate cancer (PCa) and early-onset (age < 60). A PRS score comprised of 22 SNPs was computed in 3695 patients gathered from one of 4 tertiary centers in Korea. Males with biopsy or radical prostatectomy-proven PCa were included for analysis, collecting additional clinical parameters such as age, BMI, PSA, Gleason Group (GG), and staging. Patients were divided into 4 groups of PRS quartiles. Intergroup differences were assessed, as well as risk ratio and predictive performance based on GG using logistic regression analysis and AUC. No significant intergroup differences were observed for BMI, PSA, and rate of ≥ T3a tumors on pathology. Rate of GG ≥ 2, GG ≥ 3, and GG ≥ 4 showed a significant pattern of increase by PRS quartile (p < 0.001, < 0.001, and 0.039, respectively). With the lowest PRS quartile as reference, higher PRS groups showed sequentially escalating risk for GG ≥ 2 and GG ≥ 3 pathology, with a 4.6-fold rise in GG ≥ 2 (p < 0.001) and 2.0-fold rise in GG ≥ 3 (p < 0.001) for the highest PRS quartiles. Combining PRS with PSA improved prediction of early onset csPCa (AUC 0.759) compared to PRS (AUC 0.627) and PSA alone (AUC 0.736). To conclude, an ethnic-specific PRS was found to predict susceptibility of aggressive PCa in addition to improving detection of csPCa when combined with PSA in early onset populations. PRS may have a role as a risk-stratification model in actual practice. Large scale, multi-ethnic trials are required to validate our results.

A review on the role of PRNCR1 in human disorders with an especial focus on cancer

Prostate Cancer Associated Non-Coding RNA 1 (PRNCR1) is a long non-coding RNA (lncRNA) which is transcribed from chromosome 8, plus strand. This lncRNA has been reported to be an oncogenic transcript participating in the pathogenesis of several kinds of cancers. Some single nucleotide polymorphisms within this lncRNA affect cancer risk. Moreover, few studies have revealed its possible roles in some non-neoplastic conditions, such as cisplatin-induced acute kidney injury, osteolysis after hip replacement, preeclampsia and pulmonary disorders. In the present narrative review, we explain diverse roles of PRNCR1 in human disorders.