Why cancer risk varies so dramatically across human tissues remains a fundamental question in oncology. While this variation has been linked to differences in total stem cell divisions (TNSD), molecular tools for measuring tissue-specific proliferative history are lacking. Existing DNA methylation-based mitotic clocks conflate chronological aging with mitotic activity and apply pan-tissue assumptions that obscure tissue-level turnover dynamics. Here, we develop tissue-specific mitotic clocks for six major tissues by modeling global hypomethylation at solo-WCGW CpG sites within partially methylated domains (PMDs) — an experimentally validated, age-independent marker of cumulative cell division — using biologically normal tissue from the Genotype-Tissue Expression (GTEx) project. This design explicitly avoids confounding from chronological age and field cancerization. Our tissue-specific clocks demonstrate strong generalizability, replicate across independent cohorts, and outperform existing pan-tissue clocks in recapitulating Vogelstein and Tomasetti-derived (2017) TNSD estimates and SEER cancer incidence data. These results provide direct molecular evidence that cumulative cellular turnover underlies tissue cancer susceptibility, and establish a precision framework for quantifying proliferative burden in human malignancies — with implications for cancer risk stratification, prevention, and mechanistic dissection of replication-driven versus exposure-related carcinogenesis.

AIMS:Particulate matter ≤2.5μm (PM2.5) air pollution is a leading global environmental risk factor. We investigated the impact of PM2.5 on cardiovascular risk with lifetime genetic predisposition to low-density lipoprotein cholesterol (LDL-C) and systolic blood pressure (SBP). METHODS:We conducted a Mendelian Randomization (MR) study of participants from the UK Biobank study (n = 412,446) over 13.85 years. Polygenic Risk Scores (PRS) for LDL-C and SBP were used as instrument variables to estimate association and causal effects with major adverse cardiovascular events (MACE). The interaction between exogenous PM2.5 exposure with individual or combined LDL-C, SBP PRS, and clinical phenotypes was assessed using adjusted survival regression, causal inference, and machine learning models. RESULTS:A significant negative interaction was observed between PM2.5 and PRS on MACE incidence for both SBP and LDL-C. The association of 1-SD PRS increase for SBP (≈ 10 mm Hg) and LDL-C (≈38.6 mg/dl) with MACE was attenuated at higher PM2.5 levels (HR = 0.947, p = 0.039, HR = 0.943, p = 0.025, respectively). Analyses with phenotype showed a similar trend for SBP, further confirmed by causal effects interactions (HR = 0.9979, p < 0.001, HR = 0.9995, p = 0.093, respectively). Absolute risk analysis showed that predicted risk remained highest in the high-PRS groups across PM2.5 levels. CONCLUSION:Higher-genetic-risk groups for SBP and LDL-C carried the greatest absolute cardiovascular risk when exposed to air pollution, even though PM2.5 conferred lower relative risk, perhaps related to high baseline risk. These findings support a gene-environment interaction between air pollution exposure and genetically proxied cardiovascular risk factors.

Abstract Metastatic breast cancer (BC) remains one of the leading causes of female cancer-related mortality worldwide. BC disease progression is driven by complex oncogenic signaling networks that promote invasion, immune evasion, and therapeutic resistance. The integrin-binding adaptor protein oncogene Kindlin-2 (K2) is a critical regulator of BC metastasis and survival. While previous studies have characterized the transcriptomic changes associated with K2 activity, the role of K2 in shaping the tumor epigenome remains poorly defined. This study aims to identify methylation changes attributable to K2 knockout (K2-KO) and thus addressing a gap of therapeutic potential by exploring novel methylation-dependent targets and pathways. We conducted genome-wide DNA methylation profiling in both cultured 4T1 control cells, 4T1-derived tumors, K2-KO cells and K2-KO tumors (n=3 per group) in a syngeneic mouse model. We evaluated 280,190 CpG sites on Illumina Infinium Mouse Methylation BeadChip across 12 samples. Differential methylation gene (DMG) analysis (fold change |FC| > 2.5; FDR < 5%) was conducted by comparing K2-KO vs control in both cell lines and tumors. Gene set enrichment analysis (GSEA) was implemented for overlapping DMGs between K2-KO cells and tumors to identify significantly dysregulated pathways. Statistically significant DMGs were examined in TCGA BC cohort for clinical significance where a β-value cutoff of 0.5 defined methylation status (hyper- vs hypomethylated) and BC strata was grouped by early (I-II) and advanced (III) stage. Overall survival was assessed using Kaplan-Meier (KM) and log-rank test. Our analysis identified 10815 DMGs in K2-KO cells and 71 DMGs in K2-KO tumors, with 57 DMGs altered across both models. Among these significant associations, we identified DMGs previously linked to BC progression, including PDLIM2 and ZMIZ1. Notably, the novel transcriptional corepressor gene BCOR, an epigenetic regulator, exhibited divergent methylation states in our cell (hyper; p = 4.88 × 10-6, log2FC = 1.98) and tumor (hypo; p = 0.04, log2FC = -2.7) model, suggesting tumor-specific selective pressures may influence metastasis-related gene. GSEA identified 27 significant pathways, including canonical cancer signaling, cAMP/cGMP-PKG signaling, oxytocin signaling, adrenergic and dopaminergic synapses. KM analysis in TCGA indicated women with stage 3 BC and BCOR hypermethylation (β > 0.5) had poorer overall survival (p=0.002). Collectively, this study identifies K2 as likely playing a key role in epigenetic processes underlying metastatic BC and highlighting the potential of methylation-regulated pathways as therapeutic targets. Our findings identified several novel DMGs, including clinical significance of BCOR with overall BC survival, and highlighted previously unrecognized pathways. Future work will define the functional and clinical impact of these alterations. Citation Format: Yanning Wu, Khalid Sossey-Alaoui, Fredrick Schumacher. Kindlin-2 loss rewires breast cell and tumor epigenomes to reveal novel methylation-dependent drivers [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 3195.

Aims Air pollution caused by particulate matter of size <= 2.5 mu m (PM2.5) is a leading global environmental risk factor. We investigated the impact of PM2.5 on cardiovascular risk with lifetime genetic predisposition to low-density lipoprotein cholesterol (LDL-C) and systolic blood pressure (SBP). Methods We conducted a Mendelian randomization (MR) study of participants from the UK Biobank study (n = 412 446) over 13.85 years. Polygenic risk scores (PRSs) for LDL-C and SBP were used as instrument variables to estimate association and causal effects with major adverse cardiovascular events (MACEs). The interaction between exogenous PM2.5 exposure with individual or combined LDL-C, SBP PRS, and clinical phenotypes was assessed using adjusted survival regression, causal inference, and machine learning models. Results A significant negative interaction was observed between PM2.5 and PRS on MACE incidence for both SBP and LDL-C. The association of 1-SD PRS increase for SBP (approximate to 10 mm Hg) and LDL-C (approximate to 38.6 mg/dL) with MACE was attenuated at higher PM2.5 levels (HR = 0.947, P = 0.039, HR = 0.943, P = 0.025, respectively). Analyses with phenotype showed a similar trend for SBP, further confirmed by causal effects interactions (HR = 0.9979, P < 0.001, HR = 0.9995, P = 0.093, respectively). Absolute risk analysis showed that predicted risk remained highest in the high-PRS groups across PM2.5 levels. Conclusion Higher genetic-risk groups for SBP and LDL-C carried the greatest absolute cardiovascular risk when exposed to air pollution, even though PM2.5 conferred lower relative risk, perhaps related to high baseline risk. These findings support a gene-environment interaction between air pollution exposure and genetically proxied cardiovascular risk factors.

Kidney stone disease is a common and increasingly prevalent condition, with its incidence rising by 70

Pain is the initial symptom reported by nearly 75% of individuals diagnosed with head and neck squamous cell carcinoma (HNSCC), yet effective management remains elusive, with up to 80% of patients reporting inadequate pain management despite opioid use. Pretreatment pain is a key predictor of survival and recurrence, but the molecular mechanisms underlying pain in HNSCC are not well understood. This pilot study aims to explore the epigenetic mechanisms underlying pain perception in HNSCC patients by cancer subtype and identify potential biomarkers to improve pain management strategies. DNA methylation levels were generated for individuals newly diagnosed with HNSCC of oral cavity (OCSCC; n=18) or oropharynx (OPSCC; n=8) prior to definitive treatment using the Illumina Infinium MethylationEPIC v2.0 array. Perceived worst pain score was measured using the Brief Pain Inventory-Worst Pain item prior to treatment, categorized according to NCCN guidelines, with patients reporting no pain serving as the reference group. After quality control, M-values for 26 samples and 913, 962 methylation probes were evaluated. Differentially methylated positions (DMPs) associated with pain severity were identified combining subtypes, with hypo- (fold change (FC) < -2) and hypermethylated (FC > 2) sites determined after adjusting for age. Stratified analyses were performed by cancer type. All analyses set a significance level with a false discovery rate < 5%. Pain phenotype and blood collection occurred on the same day, after diagnosis but prior to definitive treatment. The cohort had a mean age of 65.1 ± 11.3 years, 42.3% of those reporting any level of pain were male. Pre-treatment pain was reported by 94.4% (N=17) of OCSCC and 25% (N=2) of OPSCC patients. DMP analysis revealed probe cg08573204_TC21 in LMO1 gene was hypermethylated in OCSCC patients with mild pain versus no pain (p=0.02, Log2FC=1.11). While LMO1 has not been previously linked to pain, its role in adrenergic cell identity suggests potential involvement in pain pathways. In patients with moderate/severe pain, cg18566479_BC21 located in CHDH gene was hypermethylated in OCSCC (p=1.09E-04, Log2FC=1.98). CHDH, associated with metabolic signatures in HNSCC and neuroinflammatory activation in neuropathic pain, showed significant hypermethylation, suggesting its potential as a biomarker for pain management in HNSCC. Differential DNA methylation may be associated and predictive of the development and severity of pain in HNSCC. With future work in larger samples, this research may provide insight into the biological underpinnings of pain and guide cancer symptom management by using DNA methylation profiles to identify patients at risk for pain who may benefit from preventative measures. Monica A. Wagner, Yanning Wu, Naji Ayyash, Fredrick R. Schumacher, Quintin Pan. A discovery of novel methylation profiles in head and neck squamous cell carcinoma in relation to pretreatment pain by cancer subtype [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 4990.

The development of analytical methods for Genome-wide Association Studies (GWAS) has outpaced the evolution of simulation techniques and pipelines. This disparity underscores the importance of innovative simulation methods that can keep pace with the rapidly increasing scale of GWAS. The median sample size of GWAS over the past ten years has exceeded 50,000 individuals, a trend that emphasizes the need for simulation tools capable of generating data on a similar or larger scale. This paper introduces a novel method, the small-group originating (SGO) model, utilizing the SLiM software for simulating individual-level GWAS data. Our standardized protocol facilitates the generation of tens of thousands of pseudo-individuals with millions of variants from small (30~90) open-access datasets.SGO stands out, especially when compared to the widely-used resampling method in HapGen, showcasing superior simulation efficiency for large sample sizes (> 13,000) of unrelated individuals. This capability is particularly relevant given the current trajectory towards larger GWAS, necessitating tools that can simulate datasets reflective of this growth. Additionally, SGO provides customization options and can model dynamic life cycles and mating across generations, positioning it as a highly promising alternative for GWAS simulations.In a case study, sensitivity analyses of chromosome-level principal component analysis and kinship coefficient estimation were conducted. The results highlighted the poor robustness of chromosome-level quality control (QC) indexes and the uneven distribution of population structure across chromosomes and ancestries, advocating for the caution against relying solely on chromosome-level QC statistics.With its flexible and efficient approach to generating pseudo GWAS data, our standardized SGO protocol emerges as a crucial asset for method development, power analysis, and benchmarking in GWAS research. It is especially vital in the context of accommodating the demands for large-scale simulations, aligning with the current and future scale of GWAS.

Monogenic causes account for up to 20% of nephrolithiasis instances and are crucial for developing targeted treatments. Whole-exome sequencing, genome-wide association, candidate gene, and in vitro and animal functional studies are crucial to identify these mutations. Therapies targeting monogenic variants, such as RNA-interference-based treatments, have been successfully used to treat monogenic disorders.

Importance Prostate-specific antigen (PSA) screening for prostate cancer is controversial but may be associated with benefit for certain high-risk groups. Objectives To evaluate associations of county-level PSA screening prevalence with prostate cancer outcomes, as well as variation by sociodemographic and clinical factors. Design, Setting, and Participants This cohort study used data from cancer registries based in 8 US states on Hispanic, non-Hispanic Black, and non-Hispanic White men aged 40 to 99 years who received a diagnosis of prostate cancer between January 1, 2000, and December 31, 2015. Participants were followed up until death or censored after 10 years or December 31, 2018, whichever end point came first. Data were analyzed between September 2023 and January 2024. Exposure County-level PSA screening prevalence was estimated using the Behavior Risk Factor Surveillance System survey data from 2004, 2006, 2008, 2010, and 2012 and weighted by population characteristics. Main Outcomes and Measures Multivariable logistic, Cox proportional hazards regression, and competing risks models were fit to estimate adjusted odds ratios (AOR) and adjusted hazard ratios (AHR) for associations of county-level PSA screening prevalence at diagnosis with advanced stage (regional or distant), as well as all-cause and prostate cancer–specific survival. Results Of 814 987 men with prostate cancer, the mean (SD) age was 67.3 (9.8) years, 7.8% were Hispanic, 12.2% were non-Hispanic Black, and 80.0% were non-Hispanic White; 17.0% had advanced disease. There were 247 570 deaths over 5 716 703 person-years of follow-up. Men in the highest compared with lowest quintile of county-level PSA screening prevalence at diagnosis had lower odds of advanced vs localized stage (AOR, 0.86; 95% CI, 0.85-0.88), lower all-cause mortality (AHR, 0.86; 95% CI, 0.85-0.87), and lower prostate cancer–specific mortality (AHR, 0.83; 95% CI, 0.81-0.85). Inverse associations between PSA screening prevalence and advanced cancer were strongest among men of Hispanic ethnicity vs other ethnicities (AOR, 0.82; 95% CI, 0.78-0.87), older vs younger men (aged ≥70 years: AOR, 0.77; 95% CI, 0.75-0.79), and those in the Northeast vs other US Census regions (AOR, 0.81; 95% CI, 0.79-0.84). Inverse associations with all-cause mortality were strongest among men of Hispanic ethnicity vs other ethnicities (AHR, 0.82; 95% CI, 0.78-0.85), younger vs older men (AHR, 0.81; 95% CI, 0.77-0.85), those with advanced vs localized disease (AHR, 0.80; 95% CI, 0.78-0.82), and those in the West vs other US Census regions (AHR, 0.89; 95% CI, 0.87-0.90). Conclusions and Relevance This population-based cohort study of men with prostate cancer suggests that higher county-level prevalence of PSA screening was associated with lower odds of advanced disease, all-cause mortality, and prostate cancer–specific mortality. Associations varied by age, race and ethnicity, and US Census region.

BACKGROUND:Breast cancer (BrCa) is the most common cancer for women globally. BrCa incidence varies by age and differs between racial groups, with Black women having an earlier age of onset and higher mortality compared to White women. The underlying biological mechanisms of this disparity remain uncertain. Here, we address this knowledge gap by examining the association between overall epigenetic age acceleration and BrCa initiation as well as the mediating role of race. RESULTS:We measured whole-genome methylation (866,238 CpGs) using the Illumina EPIC array in blood DNA extracted from 209 women recruited from University Hospitals Cleveland Medical Center. Overall and intrinsic epigenetic age acceleration was calculated-accounting for the estimated white blood cell distribution-using the second-generation biological clock GrimAge. After quality control, 149 BrCa patients and 42 disease-free controls remained. The overall chronological mean age at BrCa diagnosis was 57.4 ± 11.4 years and nearly one-third of BrCa cases were self-reported Black women (29.5%). When comparing BrCa cases to disease-free controls, GrimAge acceleration was 2.48 years greater (p-value = 0.0056), while intrinsic epigenetic age acceleration was 1.72 years higher (p-value = 0.026) for cases compared to controls. After adjusting for known BrCa risk factors, we observed BrCa risk increased by 14% [odds ratio (OR) = 1.14; 95% CI: 1.05, 1.25] for a one-year increase in GrimAge acceleration. The stratified analysis by self-reported race revealed differing ORs for GrimAge acceleration: White women (OR = 1.17; 95% CI: 1.03, 1.36), and Black women (OR = 1.08; 95% CI: 0.96, 1.23). However, our limited sample size failed to detect a statistically significant interaction for self-reported race (p-value >0.05) when examining GrimAge acceleration with BrCa risk. CONCLUSIONS:Our study demonstrated that epigenetic age acceleration is associated with BrCa risk, and the association suggests variation by self-reported race. Although our sample size is limited, these results highlight a potential biological mechanism for BrCa risk and identifies a novel research area of BrCa health disparities requiring further inquiry.