MacInnis, Robert J.

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.

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 Menopausal users of hormone replacement therapy (HRT) are at increased breast cancer risk and decreased colorectal cancer (CRC) risk compared with individuals who have never used HRT, but these opposing associations may differ by familial risk of breast cancer and CRC. We harmonized data from 3 cohorts and generated separate breast cancer and CRC familial risk scores based on cancer family history. We defined moderate or strong family history as a risk score of 0.4 or higher, where 0.4 was equivalent to a 50-year-old woman with 1 parent diagnosed with either breast cancer or CRC at 55 years of age. Of 24 486 women assessed, 1243 and 405 were diagnosed with incident breast cancer and CRC, respectively. For breast cancer, menopausal HRT ever use versus never use hazard ratios were 1.27 (95% CI = 1.11 to 1.45) for a breast cancer familial risk score below 0.4 and 1.01 (95% CI = 0.82 to 1.25) for a breast cancer familial risk score of 0.4 or higher (Pdifference = .08). For CRC, menopausal HRT hazard ratios were 0.63 (95% CI = 0.50 to 0.78) for a CRC familial risk score below 0.4 and 1.21 (95% CI = 0.73 to 2.00) for a CRC familial risk score of 0.4 or higher (Pdifference = .03). Associations with menopausal HRT use that apply to the general population may not hold for women at moderate or strong familial risk of these cancers.

Importance Germline gene panel testing is recommended for men with advanced prostate cancer (PCa) or a family history of cancer. While evidence is limited for some genes currently included in panel testing, gene panels are also likely to be incomplete and missing genes that influence PCa risk and aggressive disease. Objective To identify genes associated with aggressive PCa. Design, Setting, and Participants A 2-stage exome sequencing case-only genetic association study was conducted including men of European ancestry from 18 international studies. Data analysis was performed from January 2021 to March 2023. Participants were 9185 men with aggressive PCa (including 6033 who died of PCa and 2397 with confirmed metastasis) and 8361 men with nonaggressive PCa. Exposure Sequencing data were evaluated exome-wide and in a focused investigation of 29 DNA repair pathway and cancer susceptibility genes, many of which are included on gene panels. Main Outcomes and Measures The primary study outcomes were aggressive (category T4 or both T3 and Gleason score ≥8 tumors, metastatic PCa, or PCa death) vs nonaggressive PCa (category T1 or T2 and Gleason score ≤6 tumors without known recurrence), and metastatic vs nonaggressive PCa. Results A total of 17 546 men of European ancestry were included in the analyses; mean (SD) age at diagnosis was 65.1 (9.2) years in patients with aggressive PCa and 63.7 (8.0) years in those with nonaggressive disease. The strongest evidence of association with aggressive or metastatic PCa was noted for rare deleterious variants in known PCa risk genes BRCA2 and ATM ( P ≤ 1.9 × 10 −6 ), followed by NBN ( P = 1.7 × 10 −4 ). This study found nominal evidence ( P &amp;lt; .05) of association with rare deleterious variants in MSH2 , XRCC2 , and MRE11A . Five other genes had evidence of greater risk (OR≥2) but carrier frequency differences between aggressive and nonaggressive PCa were not statistically significant: TP53 , RAD51D , BARD1 , GEN1 , and SLX4 . Deleterious variants in these 11 candidate genes were carried by 2.3% of patients with nonaggressive, 5.6% with aggressive, and 7.0% with metastatic PCa. Conclusions and Relevance The findings of this study provide further support for DNA repair and cancer susceptibility genes to better inform disease management in men with PCa and for extending testing to men with nonaggressive disease, as men carrying deleterious alleles in these genes are likely to develop more advanced disease.

Abstract Background: DEPendency of association on the number of Top Hits (DEPTH) is an approach to identify candidate susceptibility regions by considering the risk signals from overlapping groups of sequential variants across the genome. Methods: We applied a DEPTH analysis using a sliding window of 200 SNPs to colorectal cancer data from the Colon Cancer Family Registry (CCFR; 5,735 cases and 3,688 controls), and Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO; 8,865 cases and 10,285 controls) studies. A DEPTH score &gt; 1 was used to identify candidate susceptibility regions common to both analyses. We compared DEPTH results against those from conventional genome-wide association study (GWAS) analyses of these two studies as well as against 132 published susceptibility regions. Results: Initial DEPTH analysis revealed 2,622 (CCFR) and 3,686 (GECCO) candidate susceptibility regions, of which 569 were common to both studies. Bootstrapping revealed 40 and 49 candidate susceptibility regions in the CCFR and GECCO data sets, respectively. Notably, DEPTH identified at least 82 regions that would not be detected using conventional GWAS methods, nor had they been identified by previous colorectal cancer GWASs. We found four reproducible candidate susceptibility regions (2q22.2, 2q33.1, 6p21.32, 13q14.3). The highest DEPTH scores were in the human leukocyte antigen locus at 6p21 where the strongest associated SNPs were rs762216297, rs149490268, rs114741460, and rs199707618 for the CCFR data, and rs9270761 for the GECCO data. Conclusions: DEPTH can identify candidate susceptibility regions for colorectal cancer not identified using conventional analyses of larger datasets. Impact: DEPTH has potential as a powerful complementary tool to conventional GWAS analyses for discovering susceptibility regions within the genome.

Abstract Background: Use of menopausal hormone therapy (MHT) is associated with increased risk for breast cancer. However, the relevant mechanisms and its interaction with genetic variants are not fully understood. Methods: We conducted a genome-wide interaction analysis between MHT use and genetic variants for breast cancer risk in 27,585 cases and 34,785 controls from 26 observational studies. All women were post-menopausal and of European ancestry. Multivariable logistic regression models were used to test for multiplicative interactions between genetic variants and current MHT use. We considered interaction p-values<5x10-8 as genome-wide significant, and p-values<1x10-5 as suggestive. Linkage disequilibrium (LD)-based clumping was performed to identify independent candidate variants.Results: None of the 9.7 million genetic variants tested for interactions with MHT use reached genome-wide significance. Only 213 variants, representing 18 independent loci, had p-values<1x105. The strongest evidence was found for rs4674019 (p-value=2.27x10-7), which showed genome-wide significant interaction (p-value=3.8x10-8) with current MHT use when analysis was restricted to population-based studies only. Limiting the analyses to combined estrogen-progesterone MHT use only or to estrogen receptor (ER) positive cases did not identify any genome-wide significant evidence of interactions. Conclusions: In this large genome-wide SNP-MHT interaction study of breast cancer, we found no strong support for common genetic variants modifying the effect of MHT on breast cancer risk. These results suggest that common genetic variation has limited impact on the observed MHT–breast cancer risk association.

Abstract Background: Use of menopausal hormone therapy (MHT) is associated with increased risk for breast cancer. However, the relevant mechanisms and its interaction with genetic variants are not fully understood. Methods: We conducted a genome-wide interaction analysis between MHT use and genetic variants for breast cancer risk in 27,585 cases and 34,785 controls from 26 observational studies. All women were post-menopausal and of European ancestry. Multivariable logistic regression models were used to test for multiplicative interactions between genetic variants and current MHT use. We considered interaction p-values<5x10-8 as genome-wide significant, and p-values<1x10-5 as suggestive. Linkage disequilibrium (LD)-based clumping was performed to identify independent candidate variants.Results: None of the 9.7 million genetic variants tested for interactions with MHT use reached genome-wide significance. Only 213 variants, representing 18 independent loci, had p-values<1x105. The strongest evidence was found for rs4674019 (p-value=2.27x10-7), which showed genome-wide significant interaction (p-value=3.8x10-8) with current MHT use when analysis was restricted to population-based studies only. Limiting the analyses to combined estrogen-progesterone MHT use only or to estrogen receptor (ER) positive cases did not identify any genome-wide significant evidence of interactions. Conclusions: In this large genome-wide SNP-MHT interaction study of breast cancer, we found no strong support for common genetic variants modifying the effect of MHT on breast cancer risk. These results suggest that common genetic variation has limited impact on the observed MHT–breast cancer risk association.

Abstract We evaluated the joint associations between a new 313-variant PRS (PRS313) and questionnaire-based breast cancer risk factors for women of European ancestry, using 72 284 cases and 80 354 controls from the Breast Cancer Association Consortium. Interactions were evaluated using standard logistic regression and a newly developed case-only method for breast cancer risk overall and by estrogen receptor status. After accounting for multiple testing, we did not find evidence that per-standard deviation PRS313 odds ratio differed across strata defined by individual risk factors. Goodness-of-fit tests did not reject the assumption of a multiplicative model between PRS313 and each risk factor. Variation in projected absolute lifetime risk of breast cancer associated with classical risk factors was greater for women with higher genetic risk (PRS313 and family history) and, on average, 17.5% higher in the highest vs lowest deciles of genetic risk. These findings have implications for risk prevention for women at increased risk of breast cancer.

Abstract Purpose: Reducing colorectal cancer incidence and mortality through early detection would improve efficacy if targeted. We developed a colorectal cancer risk prediction model incorporating personal, family, genetic, and environmental risk factors to enhance prevention. Methods: A familial risk profile (FRP) was calculated to summarize individuals' risk based on detailed cancer family history (FH), family structure, probabilities of mutation in major colorectal cancer susceptibility genes, and a polygenic component. We developed risk models, including individuals' FRP or binary colorectal cancer FH, and colorectal cancer risk factors collected at enrollment using population-based colorectal cancer cases (N = 4,445) and controls (N = 3,967) recruited by the Colon Cancer Family Registry Cohort (CCFRC). Model validation used CCFRC follow-up data for population-based (N = 12,052) and clinic-based (N = 5,584) relatives with no cancer history at recruitment to assess model calibration [expected/observed rate ratio (E/O)] and discrimination [area under the receiver-operating-characteristic curve (AUC)]. Results: The E/O [95% confidence interval (CI)] for FRP models for population-based relatives were 1.04 (0.74–1.45) for men and 0.86 (0.64–1.20) for women, and for clinic-based relatives were 1.15 (0.87–1.58) for men and 1.04 (0.76–1.45) for women. The age-adjusted AUCs (95% CI) for FRP models for population-based relatives were 0.69 (0.60–0.78) for men and 0.70 (0.62–0.77) for women, and for clinic-based relatives were 0.77 (0.69–0.84) for men and 0.68 (0.60–0.76) for women. The incremental values of AUC for FRP over FH models for population-based relatives were 0.08 (0.01–0.15) for men and 0.10 (0.04–0.16) for women, and for clinic-based relatives were 0.11 (0.05–0.17) for men and 0.11 (0.06–0.17) for women. Conclusions: Both models calibrated well. The FRP-based model provided better risk stratification and risk discrimination than the FH-based model. Impact: Our findings suggest detailed FH may be useful for targeted risk-based screening and clinical management.

Abstract Aim: We aimed to develop a comprehensive Colorectal cancer Risk Prediction Tool (CRiPT). To achieve this, it is necessary to incorporate germline mutations in the DNA mismatch repair genes and MUTYH to account for a proportion of the familial aggregation of colorectal cancer. Population prevalence of these mutations and the genetic and environmental causes of the remaining familial aggregation, however, are not known. Methods: We studied the families of 5,744 colorectal cancer cases (probands) recruited from population cancer registries in the USA, Canada and Australia, and screened probands for mutations in the mismatch repair genes MLH1, MSH2, MSH6, and PMS2, and MUTYH. We fitted modified segregation analysis models to the cancer history of first-degree relatives, conditional on the age at diagnosis of the proband, using the software MENDEL. We determined the genetic model that best explained the familial aggregation of colorectal cancer by estimating the prevalence of mutations in the known susceptibility genes, the prevalence of and hazard ratio for unmeasured high-risk gene mutations, and the variance of the unmeasured polygenic component, using a χ2 goodness-of-fit test. Results: The best fitting model was a mixed dominant model with the polygenic standard deviation varying by age. Under that model, we estimated 1 in 279 of the population carry mutations in the mismatch repair genes (MLH = 1 in 1946, MSH2 = 1 in 2841, MSH6 = 1 in 758, PMS2 = 1 in 714), 1 in 45 carry mutations in MUTYH, and 1 in 504 carry mutations in unknown major gene(s) which are associated with on average a 31-fold increased risk of colorectal cancer. The estimated variance of the polygenic component decreased from 1.8 for age &lt;40 years to 0.7 for age ≥70 years (equivalent to a sibling relative risk of 5.1 and 1.3, respectively). There was good internal consistency in the best fitting model; i.e., predicted and observed numbers were close. Conclusion: CRiPT is a comprehensive prediction model that incorporates both known and unknown major genes and polygenes. CRiPT can provide the probabilities of having a mutation in a DNA mismatch repair gene or MUTYH as well as estimate future risk (e.g., 5-year risk) of developing colorectal cancer. This model is similar to the Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) that calculates for women the probabilities of carrying a BRCA1 or BRCA2 mutation and their future risk of developing breast and ovarian cancer based on their family history. Further work will include measured environmental factors and genetic variants to CRiPT, and it will be useful for genetic counselling and targeted colorectal cancer screening in clinical practices. This abstract is also being presented as Poster B04. Citation Format: Aung Ko Win, Mark A. Jenkins, James G. Dowty, Antonis C. Antoniou, Andrew Lee, Yingye Zheng, Noralane M. Lindor, Polly A. Newcomb, John L. Hopper, Robert J. MacInnis. Development of a comprehensive colorectal cancer risk prediction tool (CRiPT) incorporating known and unknown major genes and polygenes. [abstract]. In: Proceedings of the AACR Special Conference: Improving Cancer Risk Prediction for Prevention and Early Detection; Nov 16-19, 2016; Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2017;26(5 Suppl):Abstract nr PR10.