Abstract Polygenic risk scores (PRSs) may enhance risk stratification for pancreatic ductal adenocarcinoma (PDAC), but existing models vary widely in design, predictive performance, and cross-ancestry transferability. We developed genome-wide PRSs using Bayesian methods (LDpred2 and PRS-CS) and p value thresholding (PRSice-2) and systematically evaluated these alongside 13 published PRSs to identify models with robust predictive performance across ancestries. Using GWAS summary statistics from 7531 cases and 10,631 controls, we derived the PRSs and tested associations in an independent sample of 4508 PDAC cases and 46,189 controls, with adjustment for well-established PDAC risk factors. Among all models, the genome-wide LDpred2-based PRS showed the strongest association with PDAC (OR = 1.57 per standard deviation increase; 95% CI: 1.51–1.62) and significantly improved discrimination beyond established risk factors alone (AUC = 0.74–0.76; p < 0.0001). Importantly, the genome-wide LDpred2 PRS demonstrated consistent associations across African, Admixed American, and European ancestry groups, whereas the best-performing published PRS was associated with PDAC risk only in individuals of European ancestry. These findings support genome-wide PRSs as a promising framework for multi-ancestry risk stratification for PDAC and to inform targeted early detection strategies.

Although genome-wide association studies (GWAS) now routinely reveal genetic associations and biological insights in millions of individuals, underrepresentation of global populations, such as those from Polynesia, continue to persist. These exclusions, often driven by logistical challenges and lack of data, prevent systematic identification of population-enriched associations, such as the association of the missense variant at the CREBRF locus to BMI and type 2 diabetes discovered commonly occurring in Polynesian populations due to its rarity in global populations. Armed with the recently updated TOPMed imputation panel that could benefit studies in diverse populations that previously had poorer imputation performance, we performed the first GWAS of Native Hawaiians and largest to date of Polynesian-ancestry populations (combined N up to 8,461) to identify population-enriched associations for 13 adiposity and cardiometabolic traits available across both cohorts: BMI, fasting glucose, fasting insulin, HDL, height, hip circumference, HOMA-IR, LDL, T2D, total cholesterol, triglycerides, waist circumference, and waist-hip ratio. We found 25 trait-loci associations that met genome-wide significance: 20 previously reported or known associations and 5 associations newly confirmed via meta-analysis. In particular, with improved statistical power, we were able to confirm the suspected association between the missense CREBRF variant with fasting glucose levels. The remaining 4 potentially novel loci-trait associations for BMI, LDL, and waist-hip ratio, however, were not replicated in multi-ethnic datasets from All-of-Us despite having reasonable power to replicate. The lack of Polynesian-enriched findings outside of the CREBRF locus informs the bounds of the effect sizes or frequency of any enriched variants, and suggests that further expansion of cohort sizes from this region of the world and improved imputation references specific to these populations are needed to identify more population-enriched associations.

Mosaic loss of the Y chromosome (mLOY) in blood is the most common acquired somatic genomic event in aging men and has been implicated in cancer susceptibility. Experimental studies suggest loss of Y-chromosome gene expression, such as KDM5D, may contribute to tumor aggressiveness and poorer treatment response. However, epidemiologic evidence on mLOY in relation to prostate cancer incidence and clinical subtypes remains limited. We studied 6,081 cancer-free men from the prospective Health Professionals Follow-up Study (HPFS) and the Physicians’ Health Study (PHS) with genome-wide array data from pre-diagnostic blood samples. mLOY was inferred using MoChA by identifying phased B-allele frequency (BAF) deviations in the pseudoautosomal region (PAR) consistent with mosaic Y-chromosome loss, supported by reduced chromosome-Y log R ratio. The clonal fraction of mLOY was derived from BAF deviation. Cox proportional hazards models using age as the time scale estimated hazard ratios (HRs) and 95% confidence intervals (CIs) for overall prostate cancer and by clinical subtype-specific prostate cancer (stage, Gleason score, PSA at diagnosis, and lethal disease defined by metastasis or prostate cancer-specific death), adjusting for body mass index, smoking, ancestry principal components, and array platform. Median age at blood draw was 61 years (interquartile range: 53-68) and mLOY was detected in 635 men (10%), with clonal fraction ranging from 0.6% to 93.3% with a median of 21%. Over a median of 17 years of follow-up, 2,733 incident prostate cancer cases were documented, including 378 lethal cases. mLOY was associated with a higher risk of total (HR 1.23; 95% CI 1.08-1.39) and lethal prostate cancer (HR 1.42; 95% CI 1.07-1.90). Associations were stronger for cases with PSA ≥20 ng/mL at diagnosis (HR 1.66; 95% CI 1.08-2.55) than for those with PSA <10 ng/mL (HR 1.03; 95% CI 0.86-1.23). Elevated risks were also observed for late-onset disease (age ≥75 years: HR 1.36; 95% CI 1.11-1.67), but not early-onset disease (age <65 years: HR 0.94; 95% CI 0.63-1.40). A dose-response relationship was observed, with a higher mLOY clonal fraction associated with increased risk of total (per 5% higher: HR 1.03; 1.01-1.05) and lethal prostate cancer (HR 1.06; 95% CI 1.00-1.11). No statistically significant associations were observed for Gleason score or stage-specific diseases (p>0.05). In two prospective U.S. cohorts, blood-derived mLOY including higher clonal fraction was associated with increased prostate cancer risk many years in the future, particularly with lethal disease. These findings highlight the importance of acquired genomic alterations in aging hematopoietic cells in prostate cancer development and may inform risk stratification strategies for screening and early detection. Anqi Wang, Rebecca Kelly, Constance Turman, Anna Plym, Konrad Stopsack, Mitchell Machiela, Massimo Loda, Christopher Haiman, Philip Kantoff, Lorelei Mucci. Mosaic loss of Y chromosome and risk of prostate cancer in two U.S. cohorts [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Innovations in Prostate Cancer Research and Treatment; 2026 Jan 20-22; Philadelphia PA. Philadelphia (PA): AACR; Cancer Res 2026;86(2_Suppl):Abstract nr PR025.

Abstract Background: Obesity is a major modifiable risk factor for postmenopausal breast cancer prognosis. Mechanisms underlying the association between obesity and post-menopausal breast cancer include higher levels of estradiol, cholesterol, and inflammation. Circulating cholesterol is metabolized into 27-hydroxychlolesterol (27HC) by the sterol 27-hydroxylase enzyme (CYP27A1). 27HC is catabolized by the oxysterol 7α-hydroxylase enzyme (CYP7B1). 27HC can regulate breast cancer pathobiology by functioning as an endogenous selective estrogen receptor modulator in breast tumors. In this study, we examined the associations of expression levels of CYP27A1 and CYP7B1 in tumor tissue with clinicopathological characteristics of breast cancer in a multiethnic population. Methods: Invasive breast tumor tissue from 510 postmenopausal females (62 African American, 114 Japanese American, 93 Latino, 135 Native Hawaiian, and 106 White) in the Multiethnic Cohort Study were used for targeted profiling of gene expression using the NanoString nCounter Breast Cancer 360™ (BC360) Panel, including 51 additional custom genes. Generalized odds logistic regression analysis was conducted to examine associations of gene expression levels for CYP27A1 and CYP7B1 with clinicopathological characteristics -- stage, PAM50 molecular subtype (Luminal A, B, HER2-enriched, Basal-like) and NanoString Risk of Recurrence (ROR) score. Results: CYP27A1 expression was associated with a lower likelihood of advanced versus localized stage at diagnosis (OR=0.87; 95% CI 0.74, 1.02). No association was observed for CYP7B1 with stage. CYP27A1 expression was associated with a lower likelihood of HER2-enciched (OR=0.71; 95% CI 0.55, 0.92) and Luminal B (OR=0.71; 95% CI 0.58, 0.89) subtypes in comparison to Luminal A subtypes. CYP7B1 expression was associated with Basal-like (OR=1.23; 95% CI 1.02, 1.49), HER2-enriched (OR=0.59; 95% CI 0.43, 0.80), and Luminal B (OR=0.43; 95% CI 0.32, 0.58) subtypes in comparison to Luminal A. No significant association was observed for CYP27A1 and ROR categories (low, intermediate, and high). In contrast, CYP7B1 expression was associated with lower risk of recurrence score (ROR intermediate vs. low: OR=0.77; 95% CI 0.62, 0.96; ROR high vs. low: OR=0.56; 95% CI 0.40, 0.79). Conclusion: This study identified that gene expression levels of 27HC metabolizing enzymes, CYP27A1 and CYP7B1, in breast tumors were associated stage, breast cancer subtype, and risk of recurrence among a multiethnic population of women. Citation Format: Lenora W. M. Loo, Yuqing Li, Kami K. White, Jose A. Aparicio, Veronica Wendy Setiawan, Brenda Y. Hernandez, Anna H. Wu, Christopher Haiman, Loic Le Marchand, Lynne R. Wilkens, Iona Cheng. Expression of 27-hydroxycholesterol metabolizing enzymes and breast cancer clinicopathological characteristics: The Multiethnic Cohort Study [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 2321.

BACKGROUND AND OBJECTIVES:Meta-analysis results, based largely among Whites, suggested that fine particulate matter (PM2.5) exposure increases the risk of clinical dementia. This study investigated the association of air pollution and incidence of Alzheimer's disease and related dementias (ADRD) by race and ethnicity. METHODS:We investigated incidence of AD (n = 4,010) and other dementia (n = 4,971) among 44,954 California Multiethnic Cohort (MEC) participants (28% African American, 14% Japanese American, 44% Latino, 14% White adults) who were enrolled in the fee-for-service component of Medicare (2001-2016). We used Cox proportional hazards regression to examine associations between exposure to PM, airport-related ultrafine particles (aUFP) and gaseous pollutants and incidence of AD, other dementia, and ADRD in a minimally- and fully-adjusted model, considering 12 established ADRD risk factors. We conducted stratified analyses to examine associations by sex, and race/ethnicity. RESULTS:ADRD incidence was associated with PM2.5 (per 2 µg/m3), airport-related UFP (aUFP, per 4400 particles/cm3) and nitrogen dioxide (NO2, per 10 µg/m3) with hazard ratios (HRs, 95%CI), respectively, of 1.04 (1.02-1.06), 1.03 (1.01-1.05) and 1.09 (1.06-1.12). The AD-associations with PM2.5 and NO2, were stronger than the corresponding associations with other dementia (Pheterogeneity ≤ 0.003). Similar patterns of results were observed by sex and across race and ethnicity. Statistically significant findings for ADRD with PM2.5, aUFP and NO2 were observed among African American (respective HRs 1.03, 1.04, 1.09), and Latino and White participants for NO2 (HR 1.10, 1.08). Results in all and African American participants remained statistically significant in fully-adjusted models. Although the effect of PM2.5 was diluted in a co-pollutant with NO2, both PM2.5 and aUFP were significantly associated with ADRD incidence in a co-pollutant model, and NO2 and aUFP (but not PM2.5) remained associated in a multipollutant model. We did not observe consistent modifying effects for any of the 12 established ADRD risk factors. CONCLUSIONS:In this multiethnic population, incidence of ADRD increased with exposures to PM2.5, aUFP, and NO2 in all subjects and this pattern was most prominent among African American adults. These results emphasize that ADRD prevention should include not only individual-level factors but also population-wide policies and regulation to curb air pollution.

ABSTRACT Background Several breast cancer (BC) risk prediction models have been developed to provide personal risk assessments. Though individually validated, their performance has not been systematically evaluated across a wide range of populations or ages. Methods We harmonized individual-level baseline questionnaire data and incident BC diagnoses from 21 cohorts from North America, Europe, and Australia participating in the Breast Cancer Risk Prediction Project. Five-year absolute risk of invasive BC was estimated for five established risk prediction models using classical risk factors only. Discrimination was evaluated by area under the curve (AUC). Calibration was assessed using average and risk-decile specific expected to observed (E/O) ratios. Performance metrics were meta-analyzed across cohorts and models. Metaregression tested associations between cohort characteristics and performance metrics. Results This analysis included 1,595,977 women aged 20-75 years, enrolled in studies between 1976-2015, with 19,062 (1.2%) invasive BC cases ascertained within 5 years from exposure assessment. Age-adjusted AUCs were similar across models and cohorts (pooled AUCs by model: 0.57-0.58), while E/O ratios varied substantially (pooled E/O ratios by model: 0.83-1.25). Overestimation was common among predicted high-risk individuals (>3%). No appreciable differences in model performance by cohort age, birth year, race, and variable missingness emerged. Calibration improved after assigning race-specific incidence rates. Conclusion Existing BC risk prediction models provided similar risk discrimination across multiple cohorts, although there was overestimation of risk for high-risk individuals. Performance variation across cohorts was not driven by specific characteristics, which supports development of a unified risk model for diverse populations that leverages appropriate incidence rates. Key messages When using classical risk factor components of existing risk prediction models, we found similar discriminatory ability of models across diverse cohorts. Aside from underlying cancer incidence rate, which heavily influenced calibration, no cohort-specific characteristics were consistently associated with model performance. Risk was underestimated at lower predicted risk deciles and overestimated at higher predicted risk deciles, indicating a need to improve model fit by integrating more complex risk-factor relationships.

BACKGROUND:The associations between different types of diabetes, characterized by distinct pathophysiology and genetic architecture, and pancreatic ductal adenocarcinoma (PDAC) risk are not understood. METHODS:We investigated associations of genetic susceptibility to type 2 diabetes (T2D), 8 T2D mechanistic clusters, type 1 diabetes (T1D), and maturity-onset diabetes of the young (MODY) with PDAC risk. We used genome-wide association study (GWAS) summary-level statistics for T2D (242 283 cases, 1 569 734 controls), T1D (18 942 cases, 501 638 controls), and PDAC (10 244 cases and 360 535 controls) in individuals of European ancestry. RESULTS:Two-sample Mendelian randomization (MR) using the Robust Adjusted Profile Score (MR-RAPS) method indicated that genetically predicted T2D was associated with PDAC risk (OR = 1.10; 95% CI = 1.05 to 1.15), particularly the T2D obesity (OR = 1.28; 95% CI = 1.15 to 1.42) and lipodystrophy (OR = 1.25; 95% CI = 1.03 to 1.51) clusters. No association was observed for T1D with PDAC risk (OR = 1.01; 95% CI = 0.99 to 1.02). Pathway/gene-set analysis using the summary-based Adaptive Rank Truncated Product (sARTP) method revealed a significant association between the MODY gene-sets and PDAC risk (P = 1.5 × 10-8), which remained after excluding 20 known PDAC GWAS loci (P = 7.6 × 10-4). HNF1A, FOXA3, and HNF4A were the top contributing genes after excluding the previously identified GWAS loci regions. CONCLUSIONS:Our results from this genetic association study support that T2D, particularly the obesity and lipodystrophy mechanistic clusters, and MODY genomic susceptibility regions play a role in the etiology of PDAC.

Melatonin regulates circadian rhythms, metabolism, and immunity. Its primary metabolite, 6-sulfatoxymelatonin (aMT6s), is a biomarker linked to cancer risk and metabolic disorders. However, genetic determinants of aMT6s remain poorly understood, with only one prior GWAS limited to an East Asian cohort. We conducted the first multi-ancestry genome-wide association meta-analysis of urinary aMT6s, integrating 11,744 participants from five cohorts: East Asians (Taiwan Biobank), European women (Nurses’ Health Studies), European men (MrOS), and multiethnic participants (MEC). aMT6s was measured from overnight or first-morning urine samples. Association analyses were conducted using both ancestry-aware meta-regression (MR-MEGA) and fixed-effects meta-analysis (METAL). Polygenic risk scores (PRS) were constructed with PRS-CSx and evaluated in phenome-wide analyses in the Mass General Brigham Biobank and UK Biobank. No genome-wide significant loci were identified, and previously reported East Asian signals were not replicated. At suggestive significance, 23 loci emerged, with eight supported by both MR-MEGA and METAL. Several loci showed ancestry-specific heterogeneity, suggesting that genetic associations with urinary aMT6s may vary by population context, although limited power and cohort heterogeneity may also contribute. PRS analyses identified associations with sleep duration and metabolic traits, including type 2 diabetes, but these findings require cautious interpretation. Overall, our results suggest that urinary aMT6s is influenced by a polygenic and potentially population-dependent genetic architecture. This study provides a multi-ancestry framework for investigating melatonin-related biomarkers and highlights the importance of careful interpretation across diverse populations.

Background: The Multiethnic Cohort Study (MEC) is a US prospective cohort of more than 215,000 participants, designed to investigate variation in risk factors and disease across diverse racial and ethnic groups. More than 74,000 participants contributed biospecimens for genetic studies. We describe this subcohort and demonstrate the types of analyses it enables.Methods: The MEC recruited adults aged 45 to 75 in California and Hawaii between 1993 and 1996. Cancer diagnoses were identified via state tumor registries. The MEC Genetics Database includes 73,139 participants with germline genotype data. We evaluated genetic similarity, its relationship with self-reported race/ethnicity, and baseline characteristics, including neighborhood socioeconomic status (nSES). Using breast, colorectal, and prostate cancer as examples, we conducted genome-wide association studies (GWAS), assessed nongenetic risk factors, and performed time-to-event analyses.Results: Participants included 10,962 African Americans, 24,234 Japanese Americans, 17,242 Latinos, 5,488 Native Hawaiians, 14,649 Whites, and 564 others. Principal component analysis showed substantial diversity. Multiethnic GWAS replicated known variants with effective control of population stratification. Polygenic risk score (PRS) effects varied across groups. Time-to-event models revealed associations between cancer incidence and nSES, population descriptors, and genetic similarity.Conclusions: The MEC Genetics Database enables multiancestry analyses of genetic and nongenetic cancer risk, supporting research on disparities, polygenic traits, and integrated risk prediction.Impact: Example analyses using these resources show the relationship between population descriptors, PRSs, and common cancer risk factors that require special consideration in genetic analyses.

Polygenic risk score (PRS) models effectively predict breast cancer (BC) risk in European-ancestry women but have limited accuracy for African-ancestry women, particularly for aggressive subtypes. We developed PRS models for overall BC, estrogen receptor (ER)-positive, ER-negative and triple-negative BC (TNBC) in African-ancestry women using data from the African Ancestry Breast Cancer Genetics consortium (17,391 cases and 18,800 controls). We applied several PRS methods and integrated information across ancestries and BC subtypes. The best models for overall, ER-positive, ER-negative and TNBC showed an area under the receiving operating curve of 0.612, 0.621, 0.611 and 0.639, respectively, and maintained predictive accuracy in external validation studies with area under the receiving operating curves of 0.612, 0.640, 0.605 and 0.652. We further introduce a parsimonious 162-variant PRS for TNBC with comparable accuracy (0.626). These findings demonstrate markedly improved PRS accuracy for BC risk prediction in African-ancestry women. Using these PRS models for screening will help promote more equitable cancer prevention efforts.

Bladder cancer is the ninth most common cancer worldwide, caused by genetic and environmental risk factors. Here, we report the findings of a multi-population meta-analysis of genome-wide association studies, including 32,470 individuals with and 1,753,462 without bladder cancer. We identify 70 independent risk loci, of which 43 are novel. Using a 70-marker polygenic risk score (HR = 1.63 per standard deviation), we increase the area under the curve from 0.71 (baseline risk model) to 0.75. Integrative analyses reveal the enrichment of the associated variants within accessible chromatin regions, and of the prioritized genes within pathways for xenobiotic metabolism and smoking behavior. Specifically, we show that the 15q25.1 variant rs71581744-ACCCC/A co-localizes with tissue-specific CHRNA3 expression, modulates mRNA stability, and associates with risk of muscle-invasive bladder cancer among current smokers. Together, these findings substantially expand the known genetic architecture of bladder cancer risk and highlight the germline regulation of smoking behavior as a mechanism driving bladder cancer susceptibility. This study integrates genetic data from diverse populations to identify 70 loci linked to bladder cancer risk, including 43 novel, and uses experimental approaches to uncover how inherited variation influences this risk in the context of smoking.

10598 Background: The relationship between adiposity and breast cancer risk is complex and incompletely understood, particularly among women of African ancestry. We investigated the association between genetically predicted and excess body mass index (BMI) and breast cancer risk in the African Ancestry Breast Cancer Genetic Consortium (AABCG) using complementary genetic and epidemiologic approaches. Methods: We conducted a genome-wide association study (GWAS) of BMI in AABCG. Independent BMI-associated variants were then used as instrumental variables in one-sample Mendelian randomization (MR) analyses, using two-stage residual inclusion with logistic regression to estimate the causal effect of BMI on breast cancer risk. To explore the joint influence of genetic and non-genetic contributions to BMI, we constructed a BMI polygenic risk score and defined the BMI polygenic-measured gap (BMI-PGM) as the difference between the percentile of observed BMI and the percentile of genetically predicted BMI. BMI-PGM was categorized into three groups based on quartile distribution: discordantly low (≤25 th percentile), concordant (25 th -75 th percentile), and discordantly high (≥75 th percentile). The association between BMI-PGM and breast cancer risk was evaluated. Results: We identified thirteen loci that were associated with BMI at the genome-wide significance level. Of these, seven loci had not been reported in prior GWAS of BMI. In the MR analyses (n = 31,522), genetically predicted BMI was inversely associated with overall breast cancer risk: odds ratio (OR) = 0.92 per 5 kg/m 2 increase, 95% confidence interval (CI) 0.86-0.99, p = 0.028. In stratified analyses, there was a stronger inverse relationship observed for estrogen receptor (ER) negative compared with ER positive breast cancer. Inverse associations with similar effect sizes were observed in analyses stratified by menopausal status. Compared with women whose observed BMI was discordantly low relative to their genetically predicted BMI, those with concordant BMI had a modestly increased risk of breast cancer (OR = 1.08, 95% CI 1.02-1.14, p = 0.0074), while women with discordantly high BMI had a 12% higher risk of breast cancer (OR = 1.12, 95% CI 1.05-1.19, p = 0.00049). Conclusions: Genetically predicted higher BMI was associated with lower breast cancer risk, whereas BMI in excess of genetic predisposition was associated with increased risk. These opposing associations highlight a distinction between genetically mediated body size versus excess weight that likely reflects adverse metabolic processes. Together, these results underscore the roles of both genetic susceptibility and modifiable influences on adiposity in breast cancer etiology and highlight the importance of maintaining a healthy weight to reduce breast cancer risk, despite variability in genetic predisposition to adiposity across the population.

Genome-wide association studies (GWAS) have identified over 200 genetic risk loci for breast cancer, yet the target genes in these loci remain largely unknown. To address this knowledge gap, we conducted a series of multi-ancestry transcriptome-wide association studies (TWAS) to discover potential breast cancer susceptibility genes. We developed and validated ancestry-specific genetic models to predict levels of gene expression, alternative splicing, and 3' UTR alternative polyadenylation, using genomic and transcriptomic data from normal breast tissue samples of 652 females of African, Asian, or European ancestry. These models were then applied to GWAS data of 178,534 breast cancer cases and 248,300 controls from these ancestry groups for association analyses. We identified 290 genes associated with breast cancer risk, including 103 previously unreported in TWAS and 46 located at least 500Kb away from any previously identified risk variants. Among them, 39 genes exhibited distinct associations with breast cancer risk by estrogen receptor status. The identified genes were enriched in pathways related to homologous recombination, apoptosis, p53, PI3K/AKT/mTOR, estrogen, and IL-2/STAT5 signaling. Single-cell RNA sequencing and in vitro experiment data provided additional functional evidence for 169 genes. Our study uncovered large numbers of candidate breast cancer susceptibility genes and contributed valuable insights into the genetics and biology of this common cancer.

Compared to European American women, African American women are more likely to be diagnosed with triple-negative breast cancer (TNBC). This difference may be partially due to genetic factors. This study aims to investigate associations of African ancestry and risk variants with TNBC among African American women. We used data from 2,335 TNBC cases, 8,159 estrogen receptor (ER)-positive cases, and 9,814 controls included in the African-ancestry Breast Cancer Genetics (AABCG) Consortium. The proportion of African ancestry (

Genomic testing is now embedded in contemporary prostate cancer care, yet the clinical meaning of different genomic platforms varies substantially by disease state and clinical context. In localized disease, tissue-based genomic classifiers primarily serve prognostic functions by refining risk estimates beyond clinicopathologic variables, whereas in advanced disease, germline and somatic testing identify predictive biomarkers linked to therapy selection. This distinction is clinically consequential because the supporting evidence, endpoints, and implementation challenges differ across assays and across points on the disease continuum. In this review, we position tissue-based assays, germline testing, somatic sequencing, circulating tumor DNA (ctDNA), and artificial intelligence-enabled biomarkers within a unified clinical framework spanning localized disease, biochemical recurrence, and metastatic progression. We critically compare commercially available genomic assays with respect to methodology, specimen type, intended use, validation cohorts, and clinically relevant outcomes. We distinguish prognostic classifiers from predictive biomarkers such as homologous recombination repair deficiency and mismatch repair deficiency, and we evaluate emerging approaches, including liquid biopsy, multimodal integration with imaging, and digital pathology-based algorithms. We further address implementation barriers that may limit real-world impact, including reimbursement uncertainty, disparities in access to next-generation sequencing, limited provider familiarity with genomic interpretation, and the need for patient-centered communication and navigation in genomics-informed care. A clinically useful framework for prostate cancer genomics must therefore move beyond cataloging tests and instead clarify when genomic results change management, where evidence remains immature, and how implementation strategies can improve equity and actionability.

OBJECTIVE:To evaluate the effects of ambient air pollution on rheumatoid arthritis (RA) incidence in a racially and ethnically diverse population. METHODS:This analysis included 42,152 California Multiethnic Cohort participants, aged ≥65 years (>70% African American and Latino adults) who were enrolled in the Fee For Service component of Medicare (2001-2018). We employed multivariable Cox proportional hazards regression to examine the associations of time-varying air pollutants based on spatiotemporal models with RA incidence (n = 2,027) after adjusting for demographics, neighborhood socioeconomic status, smoking, work, and other exposures. RESULTS:RA incidence increased with exposure to fine particulate matter (PM) with diameter ≤2.5 μm (PM2.5) (hazard ratio [HR] per 2 μg/m3 = 1.20 [95% confidence interval (CI) 1.16-1.23]) and nitrogen dioxide (NO2) (HR per 10 μg/m3 = 1.44 [95% CI 1.35-1.52]). Air pollutant levels and risk associations were higher in African American and Latino than in Japanese American and White adults (Pheterogeneity < 0.05). The RA-PM2.5 association was higher in men (HR 1.23 [95% CI 1.15-1.32]) than in women (HR 1.17 [95% CI 1.12-1.21]; Pheterogeneity = 0.06). RA associations with PM2.5 (and NO2) did not differ by demographics, smoking, or other lifestyle factors, but the HR associated with PM2.5 was higher among those with high-risk work (longest occupation in labor/craftsman work and exposed to ≥10 years in one or more of 13 industries; HR 1.29 [95% CI 1.18-1.41]) than those without high-risk work exposures (HR 1.16 [95% CI 1.12-1.21]; Pheterogeneity = 0.04). CONCLUSION:Exposure to PM and gaseous pollution associates with increased RA incidence after age 65 years, particularly among African American and Latino adults. Further characterization of air pollution's contribution to racial and ethnic disparities in RA risk is warranted.

Polygenic scores (PGSs) have promising clinical applications for risk stratification, disease screening, and personalized medicine. However, most PGSs are trained on predominantly European ancestry cohorts and have limited portability to external populations. While cross-population PGSs have demonstrated greater generalizability than single-ancestry PGSs, they fail to properly account for individuals with recent admixture between continental ancestry groups. GAUDI, a recently proposed PGS method, overcomes this gap by leveraging local ancestry to estimate ancestry-specific effects, penalizing but allowing ancestry-differential effects. However, the modified fused LASSO approach used by GAUDI is computationally expensive and does not readily accommodate more than two-way admixture. To address these limitations, we introduce HAUDI, an efficient LASSO framework for admixed PGS construction. HAUDI reparameterizes the GAUDI model as a standard LASSO problem, allowing for extension to multiway admixture settings and far superior computational speed than GAUDI. In extensive simulations, HAUDI compares favorably to GAUDI while dramatically reducing computation time. In real data applications, HAUDI uniformly outperforms GAUDI across 18 clinical phenotypes, including total triglycerides, C-reactive protein, and mean corpuscular hemoglobin concentration, and shows substantial benefits over ancestry-agnostic PGSs for white blood cell count and chronic kidney disease. It is also substantially faster and more accurate than the recently proposed SDPR_admix method.

Polygenic risk scores (PRS) hold prognostic value for identifying individuals at higher risk of type 2 diabetes (T2D). However, further characterization is needed to understand the generalizability of T2D PRS in diverse populations across various contexts. We characterized a multi-ancestry T2D PRS among 244,637 cases and 637,891 controls across eight populations from the Population Architecture Genomics and Epidemiology (PAGE) Study and 13 additional biobanks and cohorts. PRS performance was context dependent, with better performance in those who were younger, male, with a family history of T2D, without hypertension, and not obese or overweight. Additionally, the PRS was associated with various diabetes-related cardiometabolic traits and T2D complications, suggesting its utility for stratifying risk of complications and identifying shared genetic architecture between T2D and other diseases. These findings highlight the need to account for context when evaluating PRS as a tool for T2D risk prognostication and potentially generalizable associations of T2D PRS with diabetes-related traits despite differential performance in T2D prediction across diverse populations.