Genkinger, Jeanine M.

Abstract Pancreatic cancer disproportionately affects Black individuals in the United States, but they have limited representation in genetic studies of pancreatic ductal adenocarcinoma (PDAC). To address this gap, we performed admixture mapping and genome-wide association analysis (GWAS) in genetically inferred African ancestry individuals (1,030 cases and 889 controls). Admixture mapping identified three regions with a significantly higher proportion of African ancestry in cases compared to controls (5q33.3, 10p1, 22q12.3). GWAS identified a genome-wide significant association at 5p15.33 ( CLPTM1L , rs383009:T>C, T Allele Frequency=0.51, OR:1.45, P value=1.24×10 -8 ), a locus previously associated with PDAC. Known loci at 5p15.33, 7q32.3, 8q24.21 and 7q25.1 also replicated (P value <0.01). Multi-ancestral fine-mapping identified two potential causal SNPs (rs3830069 and rs2735940) at 5p15.33. Collectively these findings identified novel PDAC risk loci and expanded our understanding of this deadly cancer in underrepresented populations, emphasizing the multifactorial nature of PDAC risk including inherited genetic and non-genetic factors. Statement of Significance To understand how genetic variation contributes to PDAC risk in Black people in North American, we studied individuals of genetically-inferred African ancestry. We identified novel risk loci and differences in the contribution of known loci. This demonstrates that ancestry-informed genetic analyses improve our understanding of PDAC risk and enhances discovery.

Background and Aims: Cholangiocarcinoma (CCA) is a rare but aggressive malignancy with poorly understood genetic susceptibility. To date, genome-wide association studies (GWAS) investigating germline variants associated with CCA risk remain limited. We aimed to identify genetic risk loci for CCA and its clinical subtypes through comprehensive GWAS and post-GWAS analyses. Approach and Results: We conducted a GWAS of 2366 CCA cases and 11,750 controls of European ancestry. Genome-wide significant loci ( p <5×10 −8 ) were identified and further examined through fine-mapping, functional annotation, and HLA imputation. Subgroup analyses were conducted by CCA subtypes and primary sclerosing cholangitis (PSC) status. Cross-trait linkage disequilibrium score regression and Mendelian randomization were employed to investigate the shared genetic architecture and potential causal relationships with a diverse range of traits. We identified 1 new genome-wide significant variant, rs535777 (OR=1.44), near HLA-DRB1/DQA1 associated with CCA, and 2 variants associated with extrahepatic CCA: rs116224263 (OR=0.17) in LINC02506 at 4p15.1 and rs6914950 (OR=1.63) near HLA-DRB1/DQB1 . Stratified analyses revealed rs2395184 (OR=3.51) near HLA-DRA/DRB5 associated with PSC-related CCA, and rs142674434 (OR=2.98) in THSD7A at 7p21.3 associated with non-PSC-related CCA. HLA imputation uncovered new amino acid residues associated with disease risk. Cross-trait analyses identified shared genetic signals between CCA and anthropometric, lipidemic, lifestyle, and medical traits. Mendelian randomization supported putative causal associations for 12 traits with CCA or its subtypes. Conclusions: Our large-scale GWAS highlights new genetic variants and HLA-linked mechanisms underlying CCA susceptibility. Integrating multi-step post-GWAS approaches enhances understanding of CCA pathogenesis and may facilitate the development of risk biomarkers for early detection and precision prevention strategies.

ABSTRACT Aims To investigate whether the association between the nitrite‐generating capacity of the subgingival microbiome and early cardiometabolic risk biomarkers varies by dietary nitrate intake. Materials and Methods Cross‐sectional data from 668 participants (mean age 31 ± 9 years, 73% women) were analysed. Dietary nitrate intake was calculated from food frequency questionnaires. Subgingival 16S rRNA sequencing (Illumina, MiSeq) and PICRUSt2 estimated microbial genes. The Microbiome‐Induced Nitric Oxide Enrichment Score (MINES) was calculated as a ratio of microbial gene abundances representing enhanced net capacity for NO generation. Adjusted multivariable linear models regressed cardiometabolic risk biomarkers (HbA1c, glucose, insulin, insulin resistance (HOMA‐IR), blood pressure) on nitrate intake and MINES together with a MINES × nitrate intake interaction term. Results Mean nitrate intake was 190 ± 171 mg/day. Significant interactions of MINES and nitrate intake were observed for insulin and HOMA‐IR ( p < 0.05). Among participants with a low MINES, higher nitrate intake was associated with lower HOMA‐IR (1.2 [1.1–1.4] vs. 1.5 [1.3–1.6]; p = 0.002), but levels were similar in those with high MINES ( p = 0.84). Conclusions A biomarker of higher microbial NO‐generating capacity in subgingival plaque is associated with lower insulin and insulin resistance among individuals with lower dietary nitrate intake. Future trials evaluating the cardiometabolic benefits of nitrate‐rich diets should incorporate measures of the entire oral microbiome.

Background: Breast cancer polygenic risk scores (PRS) and traditional risk models (e.g., the Gail model [Gail]) are known to contribute largely independent information, but it is unclear how the overlap varies by ancestry, age, disease type (invasive breast cancer, DCIS), and risk threshold. Methods: In a retrospective case–control study, we evaluated risk prediction performance in 180,398 women (161,849 of European ancestry; 18,549 of Asian ancestry). Odds ratios (ORs) from logistic regression models and the area under the receiver operating characteristic curve (AUC) were estimated. Results: PRS for invasive disease showed a stronger association in younger (<50 years) women (OR = 2.51, AUC = 0.622) than in women ≥ 50 years (OR = 2.06, AUC = 0.653) of European ancestry. PRS performance in Asians was lower (OR range = 1.62–1.64, AUC = 0.551–0.600). Gail performance was modest across groups and poor in younger Asian women (OR = 0.94–0.99, AUC = 0.523–0.533). Age interactions were observed for both PRS (p < 0.001) and Gail (p < 0.001) in Europeans, whereas in Asians, age interaction was observed only for Gail (invasive: p < 0.001; DCIS: p = 0.002). PRS identified more high-risk individuals than Gail in Asian populations, especially ≥50 years, while Gail identified more in Europeans. Overlap between PRS, Gail, and family history was limited at higher thresholds. Calibration analysis, comparing empirical and model-based ROC curves, showed divergence for both PRS and Gail (p < 0.001), which indicates miscalibration. In Europeans, family history and prior biopsies drove Gail discrimination. In younger Asians, age at first live birth was influential. Conclusions: PRS adds value to risk stratification beyond traditional tools, especially in younger women and Asian ancestry populations.

Abstract Breast cancer risk assessment tools are widely used in clinical practice to guide decisions regarding screening timing and modality, lifestyle interventions, genetic testing, preventive therapy, and risk-reducing surgery. Although a number of tools designed for the general population are used in practice, they face a series of challenges including: (i) modest discriminatory ability due to lack of a unified model that flexibly incorporates a comprehensive set of risk factors; (ii) inability to produce subtype-specific risk estimates, relevant for tailoring screening and/or preventive strategies (e.g. prophylactic endocrine therapy that is effective only for hormone receptor positive tumors); (iii) lack of data from diverse populations to build models that work well for all women; and, (iv) scarcity of opportunities for comparative validation of different models. The Breast Cancer Risk Prediction Project (BCRPP), a collaboration currently including 20 prospective cohorts from the National Cancer Institute’s Cohort Consortium, is addressing these challenges by harmonizing, sharing, and analyzing data on over 1.8 million women, over 115, 000 of whom have developed breast cancer. The BCRPP is developing a comprehensive tool that will predict breast cancer risk, overall and by subtypes, across major racial and ethnic groups in the United States. This tool incorporates known and emerging risk factors (e.g. reproductive history, anthropometry, smoking, alcohol intake, medication use, mammography, family history, and measured genotypes). In parallel to model building and validation efforts, BCRPP investigators are developing privacy-preserving web and mobile interfaces to deliver the risk tools to patients and clinicians. Here we present descriptive data on participating cohorts (e.g. dates of enrollment, ages at enrollment, length of follow up, numbers of cases by subtype), summarize missing data patterns and our approach to model development and validation that accounts for heterogeneity in missingness patterns across cohorts, and describe data governance procedures and data sharing infrastructure (including pooled and federated analyses, to account for policy limitations on sharing individual data). Descriptive information and procedures for requesting and accessing data are available via the BCRPP data platform (https://epidataplatforms.cancer.gov/bcrpp/). Although originally assembled for risk model development, these data on a large and diverse sample of women with extensive harmonized risk factors and covariates can be a resource to address other outstanding questions in breast cancer epidemiology across time, the lifecourse, and diverse populations. Citation Format: Peter Kraft, Laura Beane-Freeman, Julie Palmer, Ellen O'Meara, Jeanine Genkinger, James Lacey, Thomas Rohan, Lauren Teras, Marian L. Neuhouser, Kala Visvanathan, Celine Vachon, Roger Milne, Christopher Haiman, Yu Chen, Heather Eliassen, Cari Kitahara, Katie O'Brien, Emily White, Garnet Anderson, I-Min Lee, Archie Campbell, Renee Fortner, Ylva Lagerros, Sven Sandin, Mia Gaudet, Montserrat Garcia-Closas, Nilanjan Chatterjee. The Breast Cancer Risk Prediction Project: a resource of 1.8 million women with diverse backgrounds from 20 prospective cohorts to improve breast cancer risk prediction and advance breast cancer epidemiology [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 3583.

ABSTRACTObjectiveTo study the association between dietary patterns and subgingival microbiota.MethodsParticipants (n = 651) who were enrolled in the Oral Infections, Glucose Intolerance, and Insulin Resistance Study (ORIGINS) with subgingival plaque sampling (n = 890 plaques) and a dietary assessment were included. 16S rRNA gene amplicon sequences of subgingival plaque from sites with either probing depth <4 or ≥4 mm were processed separately and used to obtain α‐diversity metrics (Faith, Shannon, Simpson, Observed) and taxa ratios (Red Complex to Corynebacterium [RCLR], Treponema to Corynebacterium [TCLR], and Treponema to Neisseria [TNLR]). Food frequency questionnaires (FFQs) were processed to calculate Alternate Healthy Eating Index (AHEI) and A Priori Diet Quality Score (APDQS) scores. Mixed regression models examined the mean levels of microbial metrics across quartiles of diet quality. Means ± standard errors are reported along with p‐values.ResultsIn multivariable models assessing the association between diet scores and α‐diversity metrics, higher AHEI values were significantly associated with lower Faith (p‐value = 0.01) and Observed (p‐value = 0.04) diversity values; similar findings were observed for APDQS (p‐value = 0.01, p‐value = 0.04). In multivariable models assessing the association between diet scores (AHEI and APDQS) and taxa ratios (RCLR, TCLR and TNLR), as the AHEI quartile increased, all taxa ratios decreased significantly as follows: −1.06 ± 0.093 in Q1 to −1.34 ± 0.099 in Q4 (RCLR), −0.43 ± 0.077 in Q1 to −0.64 ± 0.083 in Q4 (TCLR) and −0.09 ± 0.083 in Q1 to −0.38 ± 0.089 in Q4 (TNLR), respectively. In contrast, as the APDQS quartiles increased, only TNLR decreased significantly from −0.08 ± 0.085 in Q1 to −0.34 ± 0.091 in Q4.ConclusionDiets rich in fruits, vegetables, whole grains and other nutritionally rich plant foods are associated with lower oral microbial diversity and favourable ratios of pathogenic to commensal microbiota.

INTRODUCTION: Reflux bile acids are believed to promote esophageal adenocarcinoma (EAC), but the role of systemic bile acids is unknown. This study aimed to assess associations between systemic bile acids and stages of Barrett's esophagus (BE) progression. METHODS: Subjects with and without BE were enrolled in this multicenter cross-sectional study. Targeted serum bile acid profiling was performed, and a subset of subjects completed a validated food frequency questionnaire. RNA sequencing was performed on BE or gastric cardia tissue to assess bile acid associations with gene expression. RESULTS: A total of 141 subjects were enrolled with serum bile acids profiled (49 non-BE; 92 BE: 44 no dysplasia, 25 indefinite/low grade dysplasia, 23 high-grade dysplasia/EAC). Lower Healthy Eating Index score, older age, higher body mass index, and no proton pump inhibitor use were associated with increased levels of multiple bile acids. Global bile acid pools were distinct between non-BE and stages of BE neoplasia ( P = 0.004). Increasing cholic acid was associated with high-grade dysplasia/EAC compared with non-BE, even after adjusting for EAC risk factors (adjusted odds ratio 2.03, 95% confidence interval 1.11–3.71) as was the combination of unconjugated primary bile acids (adjusted odds ratio 1.81, 95% confidence interval 1.04–3.13). High cholic acid levels were associated with tissue gene expression changes including increased DNA replication and reduced lymphocyte differentiation genes. DISCUSSION: Alterations in serum bile acids are independently associated with advanced neoplasia in BE and may contribute to neoplastic progression. Future studies should explore associated gut microbiome changes, proneoplastic effects of bile acids, and whether these bile acids, particularly cholic acid, represent potential biomarkers or viable therapeutic targets for advanced neoplasia in BE.

Background: Dietary nitrate consumption is associated with cardiometabolic health in many but not all studies. The enterosalivary nitrate-nitrite-nitric oxide (NO 3 - NO 2 - NO) pathway requires the oral microbiome for NO production (a signaling molecule beneficial to cardiometabolic health) following nitrate intake. We hypothesize that the oral microbiome modifies the cardiometabolic benefits of nitrate intake. Methods: We analyzed cross-sectional data from 657 ORGINS participants. Dietary nitrate intakes and alternative healthy eating index (AHEI) scores were calculated from food frequency questionnaire responses and a US food database. Subgingival 16S rRNA microbial genes (Illumina, MiSeq) were sequenced, and PICRUSt2 estimated metagenomic content. The Microbiome Induced Nitric oxide Enrichment Score (MINES) was calculated as a microbial gene abundance ratio representing enhanced net capacity for NO generation. Cardiometabolic biomarker (systolic and diastolic blood pressure, HbA1C, glucose, insulin, and HOMA-IR) Z-scores were averaged to form a cardiometabolic Z-score (CMZ). Adjusted multivariable linear models regressed CMZ and individual Z-scores on nitrate intake tertiles. Results: Participants were mean age 31±9, 72% female, 31% White, 14% Black, and 28% Hispanic. Mean nitrate intake was 189±171 mg/day. Higher nitrate intake was significantly associated with lower CMZ, glucose, insulin, and HOMA-IR. Mean CMZ(SE) across dietary nitrate tertiles were 0.53(0.30), 0.35(0.31), and -0.11(0.30), p for trend=0.04; findings were attenuated after adjustment for AHEI (p for trend=0.12). In subgroup analysis by MINES level, higher nitrate intake was only associated with lower cardiometabolic biomarker levels among participants with a <median MINES even after AHEI adjustment (Table). Conclusion: Higher dietary nitrate intake was associated with lower cardiometabolic risk biomarker levels only among those with reduced potential for oral microbiome-dependent NO generation.

Background Polygenic risk score (PRS), calculated based on genome-wide association studies (GWASs), can improve breast cancer (BC) risk assessment. To date, most BC GWASs have been performed in individuals of European (EUR) ancestry, and the generalisation of EUR-based PRS to other populations is a major challenge. In this study, we examined the performance of EUR-based BC PRS models in Ashkenazi Jewish (AJ) women. Methods We generated PRSs based on data on EUR women from the Breast Cancer Association Consortium (BCAC). We tested the performance of the PRSs in a cohort of 2161 AJ women from Israel (1437 cases and 724 controls) from BCAC (BCAC cohort from Israel (BCAC-IL)). In addition, we tested the performance of these EUR-based BC PRSs, as well as the established 313-SNP EUR BC PRS, in an independent cohort of 181 AJ women from Hadassah Medical Center (HMC) in Israel. Results In the BCAC-IL cohort, the highest OR per 1 SD was 1.56 (±0.09). The OR for AJ women at the top 10% of the PRS distribution compared with the middle quintile was 2.10 (±0.24). In the HMC cohort, the OR per 1 SD of the EUR-based PRS that performed best in the BCAC-IL cohort was 1.58±0.27. The OR per 1 SD of the commonly used 313-SNP BC PRS was 1.64 (±0.28). Conclusions Extant EUR GWAS data can be used for generating PRSs that identify AJ women with markedly elevated risk of BC and therefore hold promise for improving BC risk assessment in AJ women.

Abstract Background Breast cancer (BC) patients with a germline CHEK2 c.1100delC variant have an increased risk of contralateral BC (CBC) and worse BC‐specific survival (BCSS) compared to non‐carriers. Aim To assessed the associations of CHEK2 c.1100delC, radiotherapy, and systemic treatment with CBC risk and BCSS. Methods Analyses were based on 82,701 women diagnosed with a first primary invasive BC including 963 CHEK2 c.1100delC carriers; median follow‐up was 9.1 years. Differential associations with treatment by CHEK2 c.1100delC status were tested by including interaction terms in a multivariable Cox regression model. A multi‐state model was used for further insight into the relation between CHEK2 c.1100delC status, treatment, CBC risk and death. Results There was no evidence for differential associations of therapy with CBC risk by CHEK2 c.1100delC status. The strongest association with reduced CBC risk was observed for the combination of chemotherapy and endocrine therapy [HR (95% CI): 0.66 (0.55–0.78)]. No association was observed with radiotherapy. Results from the multi‐state model showed shorter BCSS for CHEK2 c.1100delC carriers versus non‐carriers also after accounting for CBC occurrence [HR (95% CI): 1.30 (1.09–1.56)]. Conclusion Systemic therapy was associated with reduced CBC risk irrespective of CHEK2 c.1100delC status. Moreover, CHEK2 c.1100delC carriers had shorter BCSS, which appears not to be fully explained by their CBC risk.