A Polygenic Risk Score Predicts Incident Prostate Cancer Risk in Older Men but Does Not Select for Clinically Significant Disease

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.

Clinical features of prostate cancer by polygenic risk score

Genome-wide association studies have identified more than 290 single nucleotide variants (SNVs) associated with prostate cancer. These SNVs can be combined to generate a Polygenic Risk Score (PRS), which estimates an individual's risk to develop prostate cancer. Identifying individuals at higher risk for prostate cancer using PRS could allow for personalized screening recommendations, improve current screening tools, and potentially result in improved survival rates, but more research is needed before incorporating them into clinical use. Our study aimed to investigate associations between PRS and clinical factors in affected individuals, including age of diagnosis, metastases, histology, International Society of Urological Pathology (ISUP) Grade Group (GG) and family history of prostate cancer, while taking into account germline genetic testing in known prostate cancer related genes. To evaluate the relationship between these clinical factors and PRS, a quantitative retrospective chart review of 250 individuals of European ancestry diagnosed with prostate cancer who received genetic counseling services at The Ohio State University's Genitourinary Cancer Genetics Clinic and a 72-SNV PRS through Ambry Genetics, was performed. We found significant associations between higher PRS and younger age of diagnosis (p = 0.002), lower frequency of metastases (p = 0.006), and having a first-degree relative diagnosed with prostate cancer (p = 0.024). We did not observe significant associations between PRS and ISUP GG, histology or a having a second-degree relative with prostate cancer. These findings provide insights into features associated with higher PRS, but larger multi-ancestral studies using PRS that are informative across populations are needed to understand its clinical utility.

Multi-organ imaging-derived polygenic indexes for brain and body health

The UK Biobank (UKB) imaging project is a crucial resource for biomedical research, but is limited to 100,000 participants due to cost and accessibility barriers. Here we used genetic data to predict heritable imaging-derived phenotypes (IDPs) for a larger cohort. We developed and evaluated 4,375 IDP genetic scores (IGS) derived from UKB brain and body images. When applied to UKB participants who were not imaged, IGS revealed links to numerous phenotypes and stratified participants at increased risk for both brain and somatic diseases. For example, IGS identified individuals at higher risk for Alzheimer's disease and multiple sclerosis, offering additional insights beyond traditional polygenic risk scores of these diseases. When applied to independent external cohorts, IGS also stratified those at high disease risk in the All of Us Research Program and the Alzheimer's Disease Neuroimaging Initiative study. Our results demonstrate that, while the UKB imaging cohort is largely healthy and may not be the most enriched for disease risk management, it holds immense potential for stratifying the risk of various brain and body diseases in broader external genetic cohorts.

Applying polygenic risk score methods to pharmacogenomics GWAS: challenges and opportunities

Polygenic risk scores (PRSs) have emerged as promising tools for the prediction of human diseases and complex traits in disease genome-wide association studies (GWAS). Applying PRSs to pharmacogenomics (PGx) studies has begun to show great potential for improving patient stratification and drug response prediction. However, there are unique challenges that arise when applying PRSs to PGx GWAS beyond those typically encountered in disease GWAS (e.g. Eurocentric or trans-ethnic bias). These challenges include: (i) the lack of knowledge about whether PGx or disease GWAS/variants should be used in the base cohort (BC); (ii) the small sample sizes in PGx GWAS with corresponding low power and (iii) the more complex PRS statistical modeling required for handling both prognostic and predictive effects simultaneously. To gain insights in this landscape about the general trends, challenges and possible solutions, we first conduct a systematic review of both PRS applications and PRS method development in PGx GWAS. To further address the challenges, we propose (i) a novel PRS application strategy by leveraging both PGx and disease GWAS summary statistics in the BC for PRS construction and (ii) a new Bayesian method (PRS-PGx-Bayesx) to reduce Eurocentric or cross-population PRS prediction bias. Extensive simulations are conducted to demonstrate their advantages over existing PRS methods applied in PGx GWAS. Our systematic review and methodology research work not only highlights current gaps and key considerations while applying PRS methods to PGx GWAS, but also provides possible solutions for better PGx PRS applications and future research.

A polygenic two-hit hypothesis for prostate cancer

Prostate cancer is one of the most heritable cancers. Hundreds of germline polymorphisms have been linked to prostate cancer diagnosis and prognosis. Polygenic risk scores can predict genetic risk of a prostate cancer diagnosis. Although these scores inform the probability of developing a tumor, it remains unknown how germline risk influences the tumor molecular evolution. We cultivated a cohort of 1250 localized European-descent patients with germline and somatic DNA profiling. Men of European descent with higher genetic risk were diagnosed earlier and had less genomic instability and fewer driver genes mutated. Higher genetic risk was associated with better outcome. These data imply a polygenic "two-hit" model where germline risk reduces the number of somatic alterations required for tumorigenesis. These findings support further clinical studies of polygenic risk scores as inexpensive and minimally invasive adjuncts to standard risk stratification. Further studies are required to interrogate generalizability to more ancestrally and clinically diverse populations.

A Polygenic Risk Score for Prostate Cancer Risk Prediction

This retrospective cohort study compares 2 risk calculator systems that compute the probabilities of finding high-grade or any cancer on biopsy results in men undergoing a first prostate biopsy.

Value of machine learning-based transrectal multimodal ultrasound combined with PSA-related indicators in the diagnosis of clinically significant prostate cancer

Objective

To investigate the effect of transrectal multimodal ultrasound combined with serum prostate-specific antigen (PSA)-related indicators and machine learning for the diagnosis of clinically significant prostate cancer.

Methods

Based on Gleason score of postoperative pathological results, the subjects were divided into clinically significant prostate cancer groups(GS>6)and non-clinically significant prostate cancer groups(GS ≤ 6). The independent risk factors were obtained by univariate logistic analysis. Artificial neural network (ANN), logistic regression (LR), support vector machine (SVM), decision tree (DT), random forest (RF), and K-nearest neighbor (KNN) machine learning models were combined with clinically significant prostate cancer risk factors to establish the machine learning model, calculate the model evaluation indicators, construct the receiver operating characteristic curve (ROC), and calculate the area under the curve (AUC).

Results

Independent risk factor items (P< 0.05) were entered into the machine learning model. A comparison of the evaluation indicators of the model and the area under the ROC curve showed the ANN model to be best at predicting clinically significant prostate cancer, with a sensitivity of 80%, specificity of 88.6%, F1 score of 0.897, and the AUC was 0.855.

Conclusion

Establishing a machine learning model by rectal multimodal ultrasound and combining it with PSA-related indicators has definite application value in predicting clinically significant prostate cancer.

Aspirin and the Risk of Colorectal Cancer According to Genetic Susceptibility among Older Individuals

Although aspirin has been considered a promising agent for prevention of colorectal cancer, recent data suggest a lack of benefit among older individuals. Whether some individuals with higher risk of colorectal cancer may benefit from aspirin remains unknown. We used a 95-variant colorectal cancer polygenic risk score (PRS) to explore the association between genetic susceptibility to colorectal cancer and aspirin use in a prospective study of 12,609 individuals of European descent ages ≥70 years, enrolled in the ASPirin in Reducing Events in the Elderly (ASPREE) double-blinded, placebo-controlled randomized trial (randomized controlled trial; RCT). Cox proportional hazards models were used to assess the association of aspirin use on colorectal cancer, as well as the interaction between the PRS and aspirin treatment on colorectal cancer. Over a median of 4.7 years follow-up, 143 participants were diagnosed with incident colorectal cancer. Aspirin assignment was not associated with incidence of colorectal cancer overall [HR = 0.94; 95% confidence interval (CI), 0.68-1.30] or within strata of PRS (P for interaction = 0.97). However, the PRS was associated with an increased risk of colorectal cancer (HR = 1.28 per SD; 95% CI, 1.09-1.51). Individuals in the top quintile of the PRS distribution had an 85% higher risk compared with individuals in the bottom quintile (HR = 1.85; 95% CI, 1.08-3.15). In a prospective RCT of older individuals, a PRS is associated with incident colorectal cancer risk, but aspirin use was not associated with a reduction of incident colorectal cancer, regardless of baseline genetic risk.

PREVENTION RELEVANCE

There is strong evidence to support prophylactic aspirin use for the prevention of colorectal cancer. However recent recommendations suggest the risk of bleeding in older individuals outweighs the benefit. We sought to determine whether some older individuals might still benefit from aspirin based on their genetic susceptibility.

The role of genetic testing in prostate cancer screening, diagnosis, and treatment

PURPOSE OF REVIEW

This review provides an overview of the current role of genetic testing in prostate cancer screening, diagnosis, and treatment.

RECENT FINDINGS

Recent studies have uncovered few but highly penetrant rare pathogenic mutations (RPMs), in genes, such as BRCA2, with strong prostate cancer risk and outcomes associations. Over 260 single nucleotide polymorphisms (SNPs) have also been identified, each associated with small incremental prostate cancer risk and when combined in a polygenic risk score (PRS), they provide strong prostate cancer risk prediction but do not seem to predict outcomes. Tumor tissue sequencing can also help identify actionable somatic mutations in many patients with advanced prostate cancer and inform on their risk of harboring a germline pathogenic mutation.

SUMMARY

RPM testing, PRS testing, and tumor sequencing all have current and/or potential future roles in personalized prostate cancer care.