Background: Prostate cancer is the most common malignancy among UK men, yet the lack of a national screening program creates disparities in early detection. PSA testing during primary care is inconsistent, limiting timely diagnosis. The Graham Fulford Charitable Trust (GFCT) and its associated support groups offer community-based PSA testing to help bridge this gap. Objectives: This study evaluates GFCT's historical testing records and a participant survey (2021-2024) to assess their community-based PSA testing program, which is widely used and offers shared decision support. Additionally, we examine the GFCT's alignment with emerging initiatives such as the UK TRANSFORM trial and other PSA screening developments across Europe and the USA. Methods: The GFCT systematically collects PSA testing data, conducting over 50,000 tests annually. The study assesses the performance of their traffic light scoring system and a previously defined combination algorithm, "Riskman", which incorporates PSA, PSAft%, and age to classify individuals into risk categories. Multivariable logistic regression was used to evaluate and compare the predictive performance of each approach. Results: Based on the self-reported data in the survey, the GFCT's traffic light system-using age-specific PSA thresholds to assign green, amber, or red risk-showed good predictive ability. Men in the red group had over 15-fold increased odds of clinically significant cancer (Grade Group ≥ 3) compared to those in the green group. While the Riskman score achieved a higher AUC (0.84 vs. 0.76), both tools were effective in identifying high-risk individuals. Conclusions: This study highlights the GFCT's role in improving access to PSA screening and integrating practical risk stratification. Its alignment with evolving screening initiatives demonstrates the value of community-based, data-driven approaches for earlier detection of aggressive prostate cancer.

Although structural and social determinants of health (SSDH) have been consistently associated with health disparities, percentage African genetic ancestry (AGA) has been suggested as a risk factor associated with common diseases in Black populations. Appropriate use and interpretation of percentage AGA in understanding health disparities has been complicated by the fact that percentage AGA is correlated with genetic and nongenetic factors.

To evaluate associations of SSDH with mortality in the context of percentage AGA and how percentage AGA is correlated with SSDH.

This cohort study investigated data from the Multiethnic Cohort (MEC) Study, in which participants were enrolled from 1993 through 1996 and followed up until death or censoring on December 31, 2019. Participant data were analyzed between March and June 2023. The population-based sample was predominantly from Los Angeles County, California, consisting of self-identified Black adults aged 45 to 75 years who enrolled into the MEC Study; completed a baseline demographic, clinical, and lifestyle questionnaire; and provided biospecimens.

The Index of Concentration at the Extremes (ICE), capturing social polarization based on income and racial composition, and a neighborhood socioeconomic status (NSES) index were computed from the 1990 Census, scaled to county-specific quintiles, and linked to residential census tracts at study enrollment. Percentage AGA was estimated using 21 431 single-nucleotide variations based on similarity with African continental referent data.

Multivariable hazard ratios (HRs) for all-cause mortality were estimated from Cox models. Correlation of percentage AGA with SSDH measures was described.

After exclusions, 9685 participants were included (mean [SD] age, 61.0 [8.9] years; 5593 female [57.7%]), with a mean (SD) percentage AGA of 75.0% (14.0%). There were 5504 deaths over 204 463 person-years of follow-up. Comparing the most with least advantaged quintile, income ICE (adjusted HR [aHR], 1.30; 95% CI, 1.16-1.45) and NSES (aHR, 1.37, 95% CI, 1.20-1.56) were associated with lower all-cause mortality. Minimal changes were observed after adjusting for percentage AGA; for example, comparing the most with least advantaged quintile, NSES (aHR, 1.36; 95% CI, 1.19-1.55) remained associated with lower all-cause mortality. There was no association between percentage AGA and mortality after adjustment (aHR per 10-percentage point change in percentage AGA, 1.01; 95% CI, 0.99-1.03).

In this study, associations of SSDH with mortality persisted with adjustment for percentage AGA. Findings support the hypothesis that SSDH should be the primary factors to consider for eliminating health disparities.

Background: Epidemiological studies suggest lung cancer results from the combined effects of smoking and genetic susceptibility. The clinical application of polygenic risk scores (PRSs), derived from combining the results from multiple germline genetic variants, have not yet been explored in a lung cancer screening cohort. Methods: This was a post hoc analysis of 9191 non-Hispanic white subjects from the National Lung Screening Trial (NLST), a sub-study of high-risk smokers randomised to annual computed tomography (CT) or chest X-ray (CXR) and followed for 6.4 years (mean). This study's primary aim was to examine the relationship between a composite polygenic risk score (PRS) calculated from 12 validated risk genotypes and developing or dying from lung cancer during screening. Validation was undertaken in the UK Biobank of unscreened ever-smokers (N = 167,796) followed for 10 years (median). Results: In this prospective study, we found our PRS correlated with lung cancer incidence (p < 0.0001) and mortality (p = 0.004). In an adjusted multivariable logistic regression analysis, PRS was independently associated with lung cancer death (p = 0.0027). Screening participants with intermediate and high PRS scores had a higher lung cancer mortality, relative to those with a low PRS score (rate ratios = 1.73 (95%CI 1.14-2.64, p = 0.010) and 1.89 (95%CI 1.28-2.78, p = 0.009), respectively). This was despite comparable baseline demographics (including lung function) and comparable lung cancer characteristics. The PRS's association with lung cancer mortality was validated in an unscreened cohort from the UK Biobank (p = 0.002). Conclusions: In this biomarker-based cohort study, an elevated PRS was independently associated with dying from lung cancer in both screening and non-screening cohorts.

Genetic risk prediction for non-European populations is hindered by limited Genome-Wide Association Study (GWAS) sample sizes and small tuning datasets. We propose JointPRS, a data-adaptive framework that leverages genetic correlations across multiple populations using GWAS summary statistics. It achieves accurate predictions without individual-level tuning data and remains effective in the presence of a small tuning set thanks to its data-adaptive approach. Through extensive simulations and real data applications to 22 quantitative and four binary traits in five continental populations evaluated using the UK Biobank (UKBB) and All of Us (AoU), JointPRS consistently outperforms six state-of-the-art methods across three data scenarios: no tuning data, same-cohort tuning and testing, and cross-cohort tuning and testing. Notably, in the Admixed American population, JointPRS improves lipid trait prediction in AoU by 6.46%-172.00% compared to the other existing methods.

Idiopathic epilepsy (IE) has a high prevalence and a severe clinical course in the Italian Spinone breed of dog. A genome-wide association study meta-analysis of 52 cases and 51 controls was conducted to identify genomic regions that may be involved with the development of IE. Subsequent to the meta-analysis, a set of 175 controls and an independent validation set of 23 cases and 23 controls were genotyped for SNPs showing suggestive association with IE to find variants exhibiting evidence of replicable association and to test the predictiveness of SNPs for IE status when combined in a weighted risk score. Although two regions showed statistically significant association with IE in the GWAS meta-analysis, and additional regions with suggestive association were identified, the findings were not emulated in the validation set. This is the first GWAS of IE in the Italian Spinone, and the findings suggest that IE in the breed is not monogenic and demonstrates the challenges when investigating a multigenic or complex inherited disease in a numerically small domesticated animal population.

To address the concern that polygenic hazard scores for prostate cancer (PCa) might not distinguish between indolent and aggressive disease, we performed case-only analyses using a 601-variant polygenic score (PHS601). We hypothesized that among men who eventually developed PCa, those with higher PHS were more likely to develop aggressive disease. We analyzed genetic and phenotypic data from a diverse, national cohort of men diagnosed with PCa (Million Veteran Program, n = 69,901, 6413 metastatic). We used Cox proportional hazards models to examine the association of PHS601 with both age at onset of metastatic PCa (birth-to-met) and time from localized to metastatic diagnosis (localized-to-met). We performed an external validation of the case-only birth-to-met analysis in two population-based cohorts from the PRACTICAL Consortium. PHS601 was associated with both birth-to-met and localized-to-met within MVP. The HRs for men in the highest quintile of PHS601 vs. those in the lowest quintile (HR 80/20 ) were 1.74 [1.65-1.84] and 1.41 [1.31-1.41] for birth-to-met and localized-to-met, respectively. These findings were validated in two external cohorts (COSM and ProtecT). PHS601 was also associated with earlier development of metastasis. Men with high PHS601 are diagnosed with prostate cancer at a younger age and are more likely to develop metastasis.

Prostate cancer (PCa) incidence, morbidity, and mortality rates are significantly impacted by racial disparities. Despite innovative therapeutic approaches and advancements in prevention, men of African American (AA) ancestry are at a higher risk of developing PCa and have a more aggressive and metastatic form of the disease at the time of initial PCa diagnosis than other races. Research on PCa has underlined the biological and molecular basis of racial disparity and emphasized the genetic aspect as the fundamental component of racial inequality. Furthermore, the lower enrollment rate, limited access to national-level cancer facilities, and deferred treatment of AA men and other minorities are hurdles in improving the outcomes of PCa patients. This review provides the most up-to-date information on various biological and molecular contributing factors, such as the single nucleotide polymorphisms (SNPs), mutational spectrum, altered chromosomal loci, differential gene expression, transcriptome analysis, epigenetic factors, tumor microenvironment (TME), and immune modulation of PCa racial disparities. This review also highlights future research avenues to explore the underlying biological factors contributing to PCa disparities, particularly in men of African ancestry.

Early mid-life is marked by accumulating risks for cardiometabolic illness linked to health-risk behaviors like nicotine use. Identifying polygenic indices (PGI) has enriched scientific understanding of the cumulative genetic contributions to behavioral and cardiometabolic health, though few studies have assessed these associations alongside socioeconomic (SES) and lifestyle factors.

Drawing on data from 2337 individuals from the United States participating in the National Longitudinal Study of Adolescent to Adult Health, the current study assesses the fraction of variance in five related outcomes-use of conventional and electronic cigarettes, body mass index (BMI), waist circumference, and glycosylated hemoglobin (A1c)-explained by PGI, SES, and lifestyle.

Regression models on African ancestry (AA) and European ancestry (EA) subsamples reveal that the fraction of variance explained by PGI ranges across outcomes. While adjusting for sex and age, PGI explained 3.5%, 2.2%, and 0% in the AA subsample of variability in BMI, waist circumference, and A1c, respectively (in the EA subsample these figures were 7.7%, 9.4%, and 1.3%). The proportion of variance explained by PGI in nicotine-use outcomes is also variable. Results further indicate that PGI and SES are generally complementary, accounting for more variance in the outcomes when modeled together versus separately.

PGI are gaining attention in population health surveillance, but polygenic variability might not align clearly with health differences in populations or surpass SES as a fundamental cause of health disparities. We discuss future steps in integrating PGI and SES to refine population health prediction rules.

Study findings point to the complementary relationship of PGI and socioeconomic indicators in explaining population variance in nicotine outcomes and cardiometabolic wellness. Population health surveillance and prediction rules would benefit from the combination of information from both polygenic and socioeconomic risks. Additionally, the risk for electronic cigarette use among users of conventional cigarettes may have a genetic component tied to the cumulative genetic propensity for heavy smoking. Further research on PGI for vaping is needed.

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.

This study aims to assess the impact of healthy lifestyle on prostate cancer (PCa) risk in a diverse population.

Data for 281,923 men from the Million Veteran Program (MVP), a nationwide, health system-based cohort study, were analyzed. Self-reported information at enrollment included smoking status, exercise, diet, family history of PCa, and race/ethnicity. Body mass index (BMI) was obtained from clinical records. Genetic risk was assessed via a validated polygenic score. Cox proportional hazards models were used to assess associations with PCa outcomes.

After accounting for ancestry, family history, and genetic risk, smoking was associated with an increased risk of metastatic PCa (hazard ratio [HR], 1.83; 95% confidence interval [CI], 1.64-2.02; p < 10-16) and fatal PCa (HR, 2.73; 95% CI, 2.36-3.25; p < 10-16). Exercise was associated with a reduced risk of fatal PCa (HR, 0.86; 95% CI, 0.76-0.98; p = .03). Higher BMI was associated with a slightly reduced risk of fatal PCa, and diet score was not independently associated with any end point. Association with exercise was strongest among those who had nonmetastatic PCa at MVP enrollment. Absolute reductions in the risk of fatal PCa via lifestyle factors were greatest among men of African ancestry (1.7% for nonsmokers vs. 6.1% for smokers) or high genetic risk (1.4% for nonsmokers vs. 4.3% for smokers).

Healthy lifestyle is minimally related to the overall risk of developing PCa but is associated with a substantially reduced risk of dying from PCa. In multivariable analyses, both exercise and not smoking remain independently associated with reduced metastatic and fatal PCa.

Low-dose computed tomography (LDCT) can help reducing lung cancer mortality. In Taiwan, the existing screening criteria revolve around smoking habits and family history of lung cancer. The role of genetic variation in non-small cell lung cancer (NSCLC) development is increasingly recognized. In this study, we aimed to investigate the potential benefits of polygenic risk scores (PRSs) in predicting NSCLC and enhancing the effectiveness of screening programs.

We conducted a retrospective cohort study that included participants without prior diagnosis of lung cancer and later received LDCT for lung cancer screening. Genetic data for these participants were obtained from the project of Taiwan Precision Medicine Initiative. We adopted the model of genome-wide association study-derived PRS calculation using 19 susceptibility loci associated with the risk of NSCLC as reported by Dai et al.

We studied a total of 2,287 participants (1,197 male, 1,090 female). More female participants developed NSCLC during the follow-up period (4.4% v 2.5%, P = .015). The only risk factor of NSCLC diagnosis among male participants was age. Among female participants, independent risk factors of NSCLC diagnosis were age (adjusted hazard ratio [aHR], 1.08 [95% CI, 1.04 to 1.11]), a family history of lung cancer (aHR, 3.21 [95% CI, 1.78 to 5.77]), and PRS fourth quartile (aHR, 2.97 [95% CI, 1.25 to 7.07]). We used the receiver operating characteristics to show an AUC value of 0.741 for the conventional model. With the further incorporation of PRS, the AUC rose to 0.778.

The evaluation of PRS for NSCLC prediction holds promise for enhancing the effectiveness of lung cancer screening in Taiwan especially in women. By incorporating genetic information, screening criteria can be tailored to identify individuals at higher risks of NSCLC.

Prostate cancer (PrCa) is a largely heritable and polygenic disease. It is the most common cancer in people with prostates (PwPs) in Europe and the USA, including in PwPs of African descent. In the UK in 2020, 52% of all cancers were diagnosed at stage I or II. The National Health Service (NHS) long-term plan is to increase this to 75% by 2028, to reduce absolute incidence of late-stage disease. In the absence of a UK PrCa screening programme, we should explore how to identify those at increased risk of clinically significant PrCa.Incorporating genomics into the PrCa screening, diagnostic and treatment pathway has huge potential for transforming patient care. Genomics can increase efficiency of PrCa screening by focusing on those with genetic predisposition to cancer-which when combined with risk factors such as age and ethnicity, can be used for risk stratification in risk-based screening (RBS) programmes. The goal of RBS is to facilitate early diagnosis of clinically significant PrCa and reduce overdiagnosis/overtreatment in those unlikely to experience PrCa-related symptoms in their lifetime. Genetic testing can guide PrCa management, by identifying those at risk of lethal PrCa and enabling access to novel targeted therapies.PrCa is curable if diagnosed below stage III when most people do not experience symptoms. RBS using genetic profiling could be key here if we could show better survival outcomes (or reduction in cancer-specific mortality accounting for lead-time bias), in addition to more cost efficiency than age-based screening alone. Furthermore, PrCa outcomes in underserved communities could be optimised if genetic testing was accessible, minimising health disparities.

Gastric cancer (GC) is a complex disease representing a significant global health concern. Advanced tools for the early diagnosis and prediction of adverse outcomes are crucial. In this context, artificial intelligence (AI) plays a fundamental role. The aim of this work was to develop a diagnostic and prognostic tool for GC, providing support to clinicians in critical decision-making and enabling personalised strategies.

Different machine learning and deep learning techniques were explored to build diagnostic and prognostic models, ensuring model interpretability and transparency through explainable AI methods. These models were developed and cross-validated using data from 590 Spanish Caucasian patients with primary GC and 633 cancer-free individuals. Up to 261 variables were analysed, including demographic, environmental, clinical, tumoral, and genetic data. Variables such as Helicobacter pylori infection, tobacco use, family history of GC, TNM staging, metastasis, tumour location, treatment received, gender, age, and genetic factors (single nucleotide polymorphisms) were selected as inputs due to their association with the risk and progression of the disease.

The XGBoost algorithm (version 1.7.4) achieved the best performance for diagnosis, with an AUC value of 0.68 using 5-fold cross-validation. As for prognosis, the Random Survival Forest algorithm achieved a C-index of 0.77. Of interest, the incorporation of genetic data into the clinical-demographics models significantly increased discriminatory ability in both diagnostic and prognostic models.

This article presents GastricAITool, a simple and intuitive decision support tool for the diagnosis and prognosis of GC.

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.

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.

Although the risk of prostate cancer (PCa) varies across different ages and genetic risks, it's unclear about the effects of genetic-specific and age-specific prostate-specific antigen (PSA) screening for PCa.

Weighed and unweighted polygenic risk scores (PRS) were constructed to classify the participants from the PLCO trial into low- or high-PRS groups. The age-specific and PRS-specific cut-off values of PSA for PCa screening were determined with time-dependent receiver-operating-characteristic curves and area-under-curves (tdAUCs). Improved screening strategies integrating PRS-specific and age-specific cut-off values of PSA were compared to traditional PSA screening on accuracy, detection rates of high-grade PCa (Gleason score ≥7), and false positive rate.

Weighted PRS with 80 SNPs significantly associated with PCa was determined as the optimal PRS, with an AUC of 0.631. After stratifying by PRS, the tdAUCs of PSA with a 10-year risk of PCa were 0.818 and 0.816 for low- and high-PRS groups, whereas the cut-off values were 1.42 and 1.62 ng/mL, respectively. After further stratifying by age, the age-specific cut-off values of PSA were relatively lower for low PRS (1.42, 1.65, 1.60, and 2.24 ng/mL for aged <60, 60-64, 65-69, and ≥70 years) than high PRS (1.48, 1.47, 1.89, and 2.72 ng/mL). Further analyses showed an obvious interaction of positive PSA and high PRS on PCa incidence and mortality. Very small difference in PCa risk were observed among subgroups with PSA (-) across different age and PRS, and PCa incidence and mortality with PSA (+) significantly increased as age and PRS, with highest risk for high-PRS/PSA (+) in participants aged ≥70 years [HRs (95%CI): 16.00 (12.62-20.29) and 19.48 (9.26-40.96)]. The recommended screening strategy reduced 12.8% of missed PCa, ensured high specificity, but not caused excessive false positives than traditional PSA screening.

Risk-adapted screening integrating PRS-specific and age-specific cut-off values of PSA would be more effective than traditional PSA screening.

Despite differences in prostate cancer risk across ancestry groups, relative performance of prostate cancer genetic risks scores (GRS) for positive biopsy prediction in different ancestry groups is unknown. This cross-sectional retrospective analysis examines the association between a polygenic hazard score (PHS290) and risk of prostate cancer diagnosis upon first biopsy in male veterans using 2-sided tests. Our analysis included 36 717 veterans (10 297 of African ancestry). Unadjusted rates of positive first prostate biopsy increased with higher genetic risk (low risk: 34%, high risk: 58%; P < .001). Among men of African ancestry, higher genetic risk was associated with increased prostate cancer detection on first biopsy (odds ratio = 2.18, 95% confidence interval = 1.93 to 2.47), but the effect was stronger among men of European descent (odds ratio = 3.89, 95% confidence interval = 3.62 to 4.18). These findings suggest that incorporating genetic risk into prediction models could better personalize biopsy decisions, although further study is needed to achieve equitable genetic risk stratification among ancestry groups.

Early-onset prostate cancer (EOPC, ≤ 55 years) has a unique clinical entity harboring high genetic risk, but the majority of EOPC patients still substantial opportunity to be early-detected thus suffering an unfavorable prognosis. A refined understanding of age-based polygenic risk score (PRS) for prostate cancer (PCa) would be essential for personalized risk stratification.

We included 167,517 male participants [4882 cases including 205 EOPC and 4677 late-onset PCa (LOPC)] from UK Biobank. A General-, an EOPC- and an LOPC-PRS were derived from age-specific genome-wide association studies. Weighted Cox proportional hazard models were applied to estimate the risk of PCa associated with PRSs. The discriminatory capability of PRSs were validated using time-dependent receiver operating characteristic (ROC) curves with additional 4238 males from PLCO and TCGA. Phenome-wide association studies underlying Mendelian Randomization were conducted to discover EOPC linking phenotypes.

The 269-PRS calculated via well-established risk variants was more strongly associated with risk of EOPC [hazard ratio (HR) = 2.35, 95% confidence interval (CI) 1.99-2.78] than LOPC (HR = 1.95, 95% CI 1.89-2.01; I2 = 79%). EOPC-PRS was dramatically related to EOPC risk (HR = 4.70, 95% CI 3.98-5.54) but not to LOPC (HR = 0.98, 95% CI 0.96-1.01), while LOPC-PRS had similar risk estimates for EOPC and LOPC (I2 = 0%). Particularly, EOPC-PRS performed optimal discriminatory capability for EOPC (area under the ROC = 0.613). Among the phenomic factors to PCa deposited in the platform of ProAP (Prostate cancer Age-based PheWAS; https://mulongdu.shinyapps.io/proap ), EOPC was preferentially associated with PCa family history while LOPC was prone to environmental and lifestyles exposures.

This study comprehensively profiled the distinct genetic and phenotypic architecture of EOPC. The EOPC-PRS may optimize risk estimate of PCa in young males, particularly those without family history, thus providing guidance for precision population stratification.

Protocol-based active surveillance (AS) biopsies have led to poor compliance. To move to risk-based protocols, more accurate imaging biomarkers are needed to predict upgrading on AS prostate biopsy. We compared restriction spectrum imaging (RSI-MRI) generated signal maps as a biomarker to other available non-invasive biomarkers to predict upgrading or reclassification on an AS biopsy.

We prospectively enrolled men on prostate cancer AS undergoing repeat biopsy from January 2016 to June 2019 to obtain an MRI and biomarkers to predict upgrading. Subjects underwent a prostate multiparametric MRI and a short duration, diffusion-weighted enhanced MRI called RSI to generate a restricted signal map along with evaluation of 30 biomarkers (14 clinico-epidemiologic features, 9 molecular biomarkers, and 7 radiologic-associated features). Our primary outcome was upgrading or reclassification on subsequent AS prostate biopsy. Statistical analysis included operating characteristic improvement using AUROC and AUPRC.

The individual biomarker with the highest area under the receiver operator characteristic curve (AUC) was RSI-MRI (AUC = 0.84; 95% CI: 0.71-0.96). The best non-imaging biomarker was prostate volume-corrected Prostate Health Index density (PHI, AUC = 0.68; 95% CI: 0.53-0.82). Non-imaging biomarkers had a negligible effect on predicting upgrading at the next biopsy but did improve predictions of overall time to progression in AS.

RSI-MRI, PIRADS, and PHI could improve the predictive ability to detect upgrading in AS. The strongest predictor of clinically significant prostate cancer on AS biopsy was RSI-MRI signal output.

The availability and cost of germline and somatic genetic testing have dramatically improved over the past two decades, enabling precision medicine approaches in oncology, with significant implications for prostate cancer (PCa) care. Roughly 12% of individuals with advanced disease are carriers of rare pathogenic germline variants that predispose to particularly aggressive and earlier-onset disease. Several of these variants are already established as clinically actionable by modern precision oncology therapeutics, while others may come to aid the selection of active surveillance, definitive local therapies, and systemic therapies. Concurrently, the number of common variants (ie, incorporated into polygenic risk scores) associated with PCa risk has continued to grow, but with several important considerations both at the intersection of race and ancestry and for early detection of aggressive disease. Family history has historically been used as a proxy for this inherited genetic risk of PCa, but recently emerging evidence examining this relation has shifted our understanding of how best to leverage this tool in PCa care. This review seeks to clarify and contextualize the existing and emerging precision oncology paradigms that use inherited genetic risk in PCa care, for both early detection and localized disease management.

This review systematically addresses the correlation between the microbiome and prostate cancer and explores its diagnostic and therapeutic implications. Recent research has indicated an association between the urinary and gut microbiome composition and prostate cancer incidence and progression. Specifically, the urinary microbiome is a potential non-invasive biomarker for early detection and risk evaluation, with altered microbial profiles in prostate cancer patients. This represents an advancement in non-invasive diagnostic approaches to prostate cancer. The role of the gut microbiome in the efficacy of various cancer therapies has recently gained attention. Gut microbiota variations can affect the metabolism and effectiveness of standard treatment modalities, including chemotherapy, immunotherapy, and hormone therapy. This review explores the potential of gut microbiome modification through dietary interventions, prebiotics, probiotics, and fecal microbiota transplantation for improving the treatment response and mitigating adverse effects. Moreover, this review discusses the potential of microbiome profiling for patient stratification and personalized treatment strategies. While the current research identifies the pivotal role of the microbiome in prostate cancer, it also highlights the necessity for further investigations to fully understand these complex interactions and their practical applications in improving patient outcomes in prostate cancer management.

The disparity in genetic risk prediction accuracy between European and non-European individuals highlights a critical challenge in health inequality. To bridge this gap, we introduce JointPRS, a novel method that models multiple populations jointly to improve genetic risk predictions for non-European individuals. JointPRS has three key features. First, it encompasses all diverse populations to improve prediction accuracy, rather than relying solely on the target population with a singular auxiliary European group. Second, it autonomously estimates and leverages chromosome-wise cross-population genetic correlations to infer the effect sizes of genetic variants. Lastly, it provides an auto version that has comparable performance to the tuning version to accommodate the situation with no validation dataset. Through extensive simulations and real data applications to 22 quantitative traits and four binary traits in East Asian populations, nine quantitative traits and one binary trait in African populations, and four quantitative traits in South Asian populations, we demonstrate that JointPRS outperforms state-of-art methods, improving the prediction accuracy for both quantitative and binary traits in non-European populations.

Chronic kidney disease (CKD) is determined by an interplay of monogenic, polygenic, and environmental risks. Autosomal dominant polycystic kidney disease (ADPKD) and COL4A-associated nephropathy (COL4A-AN) represent the most common forms of monogenic kidney diseases. These disorders have incomplete penetrance and variable expressivity, and we hypothesize that polygenic factors explain some of this variability. By combining SNP array, exome/genome sequence, and electronic health record data from the UK Biobank and All-of-Us cohorts, we demonstrate that the genome-wide polygenic score (GPS) significantly predicts CKD among ADPKD monogenic variant carriers. Compared to the middle tertile of the GPS for noncarriers, ADPKD variant carriers in the top tertile have a 54-fold increased risk of CKD, while ADPKD variant carriers in the bottom tertile have only a 3-fold increased risk of CKD. Similarly, the GPS significantly predicts CKD in COL4A-AN carriers. The carriers in the top tertile of the GPS have a 2.5-fold higher risk of CKD, while the risk for carriers in the bottom tertile is not different from the average population risk. These results suggest that accounting for polygenic risk improves risk stratification in monogenic kidney disease.

Elevated red cell distribution width (RDW) has been associated with a range of health outcomes. This study aims to examine prognostic and etiological roles of RDW levels, both phenotypic and genetic predisposition, in predicting cardiovascular outcomes, diabetes, chronic kidney disease (CKD) and mortality.

We studied 27,141 middle-aged adults from the Malmö Diet and Cancer study (MDCS) with a mean follow up of 21 years. RDW was measured with a hematology analyzer on whole blood samples. Polygenic scores for RDW (PGS-RDW) were constructed for each participant using genetic data in MDCS and published summary statistics from genome-wide association study of RDW (n = 408,112). Cox proportional hazards regression was used to assess associations between RDW, PGS-RDW and cardiovascular outcomes, diabetes, CKD and mortality, respectively.

PGS-RDW was significantly associated with RDW (Pearson's correlation coefficient = 0.133, p < 0.001). RDW was significantly associated with incidence of stroke (hazard ratio (HR) per 1 standard deviation = 1.06, 95% confidence interval (CI): 1.02-1.10, p = 0.003), atrial fibrillation (HR = 1.09, 95% CI: 1.06-1.12, p < 0.001), heart failure (HR = 1.13, 95% CI: 1.08-1.19, p < 0.001), venous thromboembolism (HR = 1.21, 95% CI: 1.15-1.28, p < 0.001), diabetes (HR = 0.87, 95% CI: 0.84-0.90, p < 0.001), CKD (HR = 1.08, 95% CI: 1.03-1.13, p = 0.004) and all-cause mortality (HR = 1.18, 95% CI: 1.16-1.20, p < 0.001). However, PGS-RDW was significantly associated with incidence of diabetes (HR = 0.96, 95% CI: 0.94-0.99, p = 0.01), but not with any other tested outcomes.

RDW is associated with mortality and incidence of cardiovascular diseases, but a significant association between genetically determined RDW and incident cardiovascular diseases were not observed. However, both RDW and PGS-RDW were inversely associated with incidence of diabetes, suggesting a putative causal relationship. The relationship with incidence of diabetes needs to be further studied.

Since their first appearance in the context of schizophrenia and bipolar disorder in 2009, polygenic risk scores (PRSs) have been described for a large number of common complex diseases. However, the clinical utility of PRSs in disease risk assessment or therapeutic decision making is likely limited because PRSs usually only account for the heritable component of a trait and ignore the etiological role of environment and lifestyle. We surveyed the current state of PRSs for various diseases, including breast cancer, diabetes, prostate cancer, coronary artery disease, and Parkinson disease, with an extra focus upon the potential improvement of clinical scores by their combination with PRSs. We observed that the diagnostic and prognostic performance of PRSs alone is consistently low, as expected. Moreover, combining a PRS with a clinical score at best led to moderate improvement of the power of either risk marker. Despite the large number of PRSs reported in the scientific literature, prospective studies of their clinical utility, particularly of the PRS-associated improvement of standard screening or therapeutic procedures, are still rare. In conclusion, the benefit to individual patients or the health care system in general of PRS-based extensions of existing diagnostic or treatment regimens is still difficult to judge.

Significant racial disparities in prostate cancer incidence and mortality have been reported between African American Men (AAM), who are at increased risk for prostate cancer, and European American Men (EAM). In most of the studies carried out on prostate cancer, this population is underrepresented. With the advancement of genome-wide association studies, several genetic predictor models of prostate cancer risk have been elaborated, as well as numerous studies that identify both germline and somatic mutations with clinical utility.

Despite significant advances, the AAM population continues to be underrepresented in genomic studies, which can limit generalizability and potentially widen disparities. Here we outline racial disparities in currently available genomic applications that are used to estimate the risk of individuals developing prostate cancer and to identify personalized oncology treatment strategies. While the incidence and mortality of prostate cancer are different between AAM and EAM, samples from AAM remain to be unrepresented in different studies.

This disparity impacts the available genomic data on prostate cancer. As a result, the disparity can limit the predictive utility of the genomic applications and may lead to the widening of the existing disparities. More studies with substantially higher recruitment and engagement of African American patients are necessary to overcome this disparity.

Better understanding of primary open-angle glaucoma (POAG) genetics could enable timely screening and promote individualized disease risk prognostication.

To evaluate phenotypic features across genetic burden for POAG.

This was a cross-sectional, population-based study conducted from 2006 to 2010. Included participants were individuals from the UK Biobank aged 40 to 69 years. Individuals with non-POAG forms of glaucoma were excluded from the analysis. Data were statistically analyzed from October 2022 to January 2023.

POAG prevalence based on structural coding, self-reports, and glaucoma-related traits.

Among 407 667 participants (mean [SD] age, 56.3 [8.1] years; 219 183 majority sex [53.8%]) were 14 171 POAG cases. Area under receiver operating characteristic curve for POAG detection was 0.748 in a model including polygenic risk score (PRS), age, sex, and ancestry. POAG prevalence in the highest decile of PRS was 7.4% (3005 of 40 644) vs 1.3% (544 of 40 795) in lowest decile (P < .001). A 1-SD increase in PRS was associated with 1.74 times higher odds of POAG (95% CI, 1.71-1.77), a 0.61-mm Hg increase in corneal-compensated intraocular pressure (IOP; 95% CI, 0.59-0.64), a -0.09-mm Hg decrease in corneal hysteresis (95% CI, -0.10 to -0.08), a 0.08-mm Hg increase in corneal resistance factor (95% CI, 0.06-0.09), and a -0.08-diopter decrease in spherical equivalent (95% CI, -0.11 to -0.07; P < .001 for all). A 1-SD increase in PRS was associated with a thinning of the macula-region retinal nerve fiber layer (mRNFL) of 0.14 μm and macular ganglion cell complex (GCC) of 0.26 μm (P < .001 for both). In the subset of individuals with fundus photographs, a 1-SD increase in PRS was associated with 1.42 times higher odds of suspicious optic disc features (95% CI, 1.19-1.69) and a 0.013 increase in cup-disc ratio (CDR; 95% CI, 0.012-0.014; P < .001 for both). A total of 22 of 5193 fundus photographs (0.4%) in decile 10 had disc hemorrhages, and 27 of 5257 (0.5%) had suspicious optic disc features compared with 9 of 5158 (0.2%) and 10 of 5219 (0.2%), respectively, in decile 1 (P < .001 for both). CDR in decile 10 was 0.46 compared with 0.41 in decile 1 (P < .001).

Results suggest that PRS identified a group of individuals at substantially higher risk for POAG. Higher genetic risk was associated with more advanced disease, namely higher CDR and corneal-compensated IOP, thinner mRNFL, and thinner GCC. Associations with POAG PRS and corneal hysteresis and greater prevalence of disc hemorrhages were identified. These results suggest that genetic risk is an increasingly important parameter for risk stratification to consider in clinical practice.

Statistical associations of numerous single nucleotide polymorphisms with breast cancer (BC) have been identified in genome-wide association studies (GWAS). Recent evidence suggests that a Polygenic Risk Score (PRS) can be a useful risk stratification instrument for a BC screening strategy, and a PRS test has been developed for clinical use. The performance of the PRS is yet unknown in the Norwegian population.

To evaluate the performance of PRS models for BC in a Norwegian dataset.

We investigated a sample of 1053 BC cases and 7094 controls from different regions of Norway. PRS values were calculated using four PRS models, and their performance was evaluated by the area under the curve (AUC) and the odds ratio (OR). The effect of the PRS on the age of onset of BC was determined by a Cox regression model, and the lifetime absolute risk of developing BC was calculated using the iCare tool.

The best performing PRS model included 3820 SNPs, which yielded an AUC = 0.625 and an OR = 1.567 per one standard deviation increase. The PRS values of the samples correlate with an increased risk of BC, with a hazard ratio of 1.494 per one standard deviation increase (95% confidence interval of 1.406-1.588). The individuals in the highest decile of the PRS have at least twice the risk of developing BC compared to the individuals with a median PRS. The results in this study with Norwegian samples are coherent with the findings in the study conducted using Estonian and UK Biobank samples.

The previously validated PRS models have a similar observed accuracy in the Norwegian data as in the UK and Estonian populations. A PRS provides a meaningful association with the age of onset of BC and lifetime risk. Therefore, as suggested in Estonia, a PRS may also be integrated into the screening strategy for BC in Norway.

Exposure to Agent Orange, a known carcinogen, might increase risk of prostate cancer (PCa). We sought to investigate the association of Agent Orange exposure and PCa risk when accounting for race/ethnicity, family history, and genetic risk in a diverse population of US Vietnam War veterans.

This study utilized the Million Veteran Program (MVP), a national, population-based cohort study of United States military veterans conducted 2011-2021 with 590,750 male participants available for analysis. Agent Orange exposure was obtained using records from the Department of Veterans Affairs (VA) using the US government definition of Agent Orange exposure: active service in Vietnam while Agent Orange was in use. Only veterans who were on active duty (anywhere in the world) during the Vietnam War were included in this analysis (211,180 participants). Genetic risk was assessed via a previously validated polygenic hazard score calculated from genotype data. Age at diagnosis of any PCa, diagnosis of metastatic PCa, and death from PCa were assessed via Cox proportional hazards models.

Exposure to Agent Orange was associated with increased PCa diagnosis (HR 1.04, 95% CI 1.01-1.06, p=0.003), primarily among Non-Hispanic White men (HR 1.09, 95% CI 1.06- 1.12, p<0.001). When accounting for race/ethnicity and family history, Agent Orange exposure remained an independent risk factor for PCa diagnosis (HR 1.06, 95% CI 1.04-1.09, p<0.05). Univariable associations of Agent Orange exposure with PCa metastasis (HR 1.08, 95% CI 0.99-1.17) and PCa death (HR 1.02, 95% CI 0.84-1.22) did not reach significance on multivariable analysis. Similar results were found when accounting for polygenic hazard score.

Among US Vietnam War veterans, Agent Orange exposure is an independent risk factor for PCa diagnosis, though associations with PCa metastasis or death are unclear when accounting for race/ethnicity, family history, and/or polygenic risk.

One in three high-risk prostate cancer patients treated with radiotherapy recur. Detection of lymph node metastasis and microscopic disease spread using conventional imaging is poor, and many patients are under-treated due to suboptimal seminal vesicle or lymph node irradiation. We use Image Based Data Mining (IBDM) to investigate association between dose distributions, and prognostic variables and biochemical recurrence (BCR) in prostate cancer patients treated with radiotherapy. We further test whether including dose information in risk-stratification models improves performance.

Planning CTs, dose distributions and clinical information were collected for 612 high-risk prostate cancer patients treated with conformal hypo-fractionated radiotherapy, intensity modulated radiotherapy (IMRT), or IMRT plus a single fraction high dose rate (HDR) brachytherapy boost. Dose distributions (including HDR boost) of all studied patients were mapped to a reference anatomy using the prostate delineations. Regions where dose distributions significantly differed between patients that did and did-not experience BCR were assessed voxel-wise using 1) a binary endpoint of BCR at four-years (dose only) and 2) Cox-IBDM (dose and prognostic variables). Regions where dose was associated with outcome were identified. Cox proportional-hazard models with and without region dose information were produced and the Akaike Information Criterion (AIC) was used to assess model performance.

No significant regions were observed for patients treated with hypo-fractionated radiotherapy or IMRT. Regions outside the target where higher dose was associated with lower BCR were observed for patients treated with brachytherapy boost. Cox-IBDM revealed that dose response was influenced by age and T-stage. A region at the seminal vesicle tips was identified in binary- and Cox-IBDM. Including the mean dose in this region in a risk-stratification model (hazard ratio=0.84, p=0.005) significantly reduced AIC values (p=0.019), indicating superior performance, compared with prognostic variables only. The region dose was lower in the brachytherapy boost patients compared with the external beam cohorts supporting the occurrence of marginal misses.

Association was identified between BCR and dose outside of the target region in high-risk prostate cancer patients treated with IMRT plus brachytherapy boost. We show, for the first-time, that the importance of irradiating this region is linked to prognostic variables.

A polygenic risk score (PRS) is derived from a genome-wide association study and represents an aggregate of thousands of single-nucleotide polymorphisms that provide a baseline estimate of an individual's genetic risk for a specific disease or trait at birth. However, it remains unclear how PRSs can be used in clinical practice. We provide an overview of the PRSs related to cardiometabolic disease and discuss the evidence supporting their clinical applications and limitations. The Preferred Reporting Items For Systematic Reviews and Meta-Analysis Extension for Scoping Reviews protocol was used to conduct a scoping review of the MEDLINE, EMBASE, and CENTRAL databases. Across the 4863 studies screened, 82 articles met the inclusion criteria. The most common PRS related to coronary artery disease, followed by hypertension and cerebrovascular disease. Limited ancestral diversity was observed in the study sample populations. Most studies included only individuals of European ancestry. The predictive performance of most PRSs was similar to or superior to traditional risk factors. More than half of the included studies reported an integrated risk model combining a derived PRS and clinical risk tools such as the Framingham Risk Score and Pooled Cohort Equations. The inclusion of a PRS into a clinical risk model tended to improve predictive accuracy consistently. This scoping review is the first of its kind and reports strong evidence for the clinical utility of PRSs in coronary artery disease, hypertension, cerebrovascular disease, and atrial fibrillation. However, most PRSs are generated in cohorts of European ancestry, which likely contributes to a lack of PRS transferability across different ancestral groups. Future prospective studies should focus on further establishing the clinical utility of PRSs and ensuring diversity is incorporated into genome-wide association study cohorts.

Chronic overlapping pain conditions (COPCs) are believed to share common etiological mechanisms involving central sensitization. Genetic and environmental factors putatively combine to influence susceptibility to central sensitization and COPCs. This study employed a genome-wide polygenic risk score approach to evaluate genetic influences on 8 common COPCs. COPCs were identified by International Classification of Disease codes in Vanderbilt's deidentified clinical biorepository (BioVU), with each COPC condition empirically weighted for the level of central sensitization based on prior work. A centralized pain score (CPS) was calculated for 55,340 individuals by summing the weighted number of COPCs. Overall, 12,502 individuals (22.6%) were diagnosed with at least 1 COPC, with females exhibiting nearly twice the mean CPS as males. To assess the genetic influence on centralized pain in COPCs, 6 pain polygenic risk scores (PRSs) were developed using UK Biobank data to predict 6 pain criteria (no pain, neck/shoulder, abdomen, hip, knee, low back pain). These PRSs were then deployed in the BioVU cohort to test for association with CPS. In regression models adjusted for age, sex, and BMI, all pain PRSs except hip pain were significantly associated with CPS. Our findings support a shared polygenic influence across COPCs potentially involving central sensitization mechanisms. PERSPECTIVE: This study used a polygenic risk score approach to investigate genetic influences on chronic overlapping pain conditions. Significant findings in this study provide evidence supporting previous hypotheses that a shared polygenic influence involving central sensitization may underly chronic overlapping pain conditions and can guide future biomarker and risk assessment research.

Prostate cancer is the most heritable cancer. There is a need to identify possible modifiable factors for men at an increased risk of prostate cancer due to genetic factors.

To examine whether men at an increased genetic risk of prostate cancer can offset their risk of disease or disease progression by adhering to a healthy lifestyle.

We prospectively followed 12 411 genotyped men in the Health Professionals Follow-up Study (1993-2019) and the Physicians' Health Study (1983-2010). Genetic risk of prostate cancer was quantified using a polygenic risk score (PRS). A healthy lifestyle was defined by healthy weight, vigorous physical activity, not smoking, and a healthy diet.

Overall and lethal prostate cancer events (metastatic disease/prostate cancer-specific death) were analyzed using time-to-event analyses estimating hazard ratios (HRs) and lifetime risks.

During 27 yr of follow-up, 3005 overall prostate cancer and 435 lethal prostate cancer events were observed. The PRS enabled risk stratification not only for overall prostate cancer, but also for lethal disease with a four-fold difference between men in the highest and lowest quartiles (HR, 4.32; 95% confidence interval [CI], 3.16-5.89). Among men in the highest PRS quartile, adhering to a healthy lifestyle was associated with a decreased rate of lethal prostate cancer (HR, 0.55; 95% CI, 0.36-0.86) compared with having an unhealthy lifestyle, translating to a lifetime risk of 1.6% (95% CI, 0.8-3.1%) among the healthy and 5.3% (95% CI, 3.6-7.8%) among the unhealthy. Adhering to a healthy lifestyle was not associated with a decreased risk of overall prostate cancer.

Our findings suggest that a genetic predisposition for prostate cancer is not deterministic for a poor cancer outcome. Maintaining a healthy lifestyle may provide a way to offset the genetic risk of lethal prostate cancer.

This study examined whether the genetic risk of prostate cancer can be attenuated by a healthy lifestyle including a healthy weight, regular exercise, not smoking, and a healthy diet. We observed that adherence to a healthy lifestyle reduced the risk of metastatic disease and prostate cancer death among men at the highest genetic risk. We conclude that men at a high genetic risk of prostate cancer may benefit from adhering to a healthy lifestyle.

Genetic variations are associated with individual susceptibility to gastric cancer. Recently, polygenic risk score (PRS) models have been established based on genetic variants to predict the risk of gastric cancer. To assess the accuracy of current PRS models in the risk prediction, a systematic review was conducted. A total of eight eligible studies consisted of 544842 participants were included for evaluation of the performance of PRS models. The overall accuracy was moderate with Area under the curve values ranging from 0.5600 to 0.7823. Incorporation of epidemiological factors or Helicobacter pylori (H. pylori) status increased the accuracy for risk prediction, while selection of single nucleotide polymorphism (SNP) and number of SNPs appeared to have little impact on the model performance. To further improve the accuracy of PRS models for risk prediction of gastric cancer, we summarized the association between gastric cancer risk and H. pylori genomic variations, cancer associated bacteria members in the gastric microbiome, discussed the potentials for performance improvement of PRS models with these microbial factors. Future studies on comprehensive PRS models established with human SNPs, epidemiological factors and microbial factors are indicated.

Genetic scores may provide an objective measure of prostate cancer risk and thus inform screening decisions. We evaluated whether a polygenic hazard score based on 290 genetic variants (PHS290) is associated with prostate cancer risk in a diverse population, including Black men, who have higher average risk of prostate cancer death but are often treated as a homogeneously high-risk group.

This was a retrospective analysis of the Million Veteran Program, a national, population-based cohort study of US military veterans conducted 2011-2021. Cox proportional hazards analyses tested for association of genetic and other risk factors (including self-reported race and ethnicity and family history) with age at death from prostate cancer, age at diagnosis of metastatic (nodal or distant) prostate cancer, and age at diagnosis of any prostate cancer.

A total of 590 750 male participants were included. Median age at last follow-up was 69 years. PHS290 was associated with fatal prostate cancer in the full cohort and for each racial and ethnic group (P < .001). Comparing men in the highest 20% of PHS290 with those in the lowest 20% (based on percentiles from an independent training cohort), the hazard ratio for fatal prostate cancer was 4.42 (95% confidence interval = 3.91 to 5.02). When accounting for guideline-recommended risk factors (family history, race, and ethnicity), PHS290 remained a strong independent predictor of any, metastatic, and fatal prostate cancer.

PHS290 stratified US veterans of diverse ancestry for lifetime risk of prostate cancer, including metastatic and fatal cancer. Predicting genetic risk of lethal prostate cancer with PHS290 might inform individualized decisions about prostate cancer screening.