Kraft, Peter

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 Rigorous evaluation of the calibration and discrimination of breast-cancer risk-prediction models in prospective cohorts is critical for applications under clinical guidelines. We comprehensively evaluated an integrated model incorporating classical risk factors and a 313-variant polygenic risk score (PRS) to predict breast-cancer risk. Methods Fifteen prospective cohorts from six countries with 239 340 women (7646 incident breast-cancer cases) of European ancestry aged 19–75 years were included. Calibration of 5-year risk was assessed by comparing expected and observed proportions of cases overall and within risk categories. Risk stratification for women of European ancestry aged 50–70 years in those countries was evaluated by the proportion of women and future cases crossing clinically relevant risk thresholds. Results Among women &lt;50 years old, the median (range) expected-to-observed ratio for the integrated model across 15 cohorts was 0.9 (0.7–1.0) overall and 0.9 (0.7–1.4) at the highest-risk decile; among women ≥50 years old, these were 1.0 (0.7–1.3) and 1.2 (0.7–1.6), respectively. The proportion of women identified above a 3% 5-year risk threshold (used for recommending risk-reducing medications in the USA) ranged from 7.0% in Germany (∼841 000 of 12 million) to 17.7% in the USA (∼5.3 of 30 million). At this threshold, 14.7% of US women were reclassified by adding the PRS to classical risk factors, with identification of 12.2% of additional future cases. Conclusion Integrating a 313-variant PRS with classical risk factors can improve the identification of European-ancestry women at elevated risk who could benefit from targeted risk-reducing strategies under current clinical guidelines.

Abstract Background: It is not known whether modifiable lifestyle factors that predict survival after invasive breast cancer differ by subtype. Methods: We analyzed data for 121,435 women diagnosed with breast cancer from 67 studies in the Breast Cancer Association Consortium with 16,890 deaths (8,554 breast cancer specific) over 10 years. Cox regression was used to estimate associations between risk factors and 10-year all-cause mortality and breast cancer–specific mortality overall, by estrogen receptor (ER) status, and by intrinsic-like subtype. Results: There was no evidence of heterogeneous associations between risk factors and mortality by subtype (Padj &gt; 0.30). The strongest associations were between all-cause mortality and BMI ≥30 versus 18.5–25 kg/m2 [HR (95% confidence interval (CI), 1.19 (1.06–1.34)]; current versus never smoking [1.37 (1.27–1.47)], high versus low physical activity [0.43 (0.21–0.86)], age ≥30 years versus &lt;20 years at first pregnancy [0.79 (0.72–0.86)]; &gt;0–&lt;5 years versus ≥10 years since last full-term birth [1.31 (1.11–1.55)]; ever versus never use of oral contraceptives [0.91 (0.87–0.96)]; ever versus never use of menopausal hormone therapy, including current estrogen–progestin therapy [0.61 (0.54–0.69)]. Similar associations with breast cancer mortality were weaker; for example, 1.11 (1.02–1.21) for current versus never smoking. Conclusions: We confirm associations between modifiable lifestyle factors and 10-year all-cause mortality. There was no strong evidence that associations differed by ER status or intrinsic-like subtype. Impact: Given the large dataset and lack of evidence that associations between modifiable risk factors and 10-year mortality differed by subtype, these associations could be cautiously used in prognostication models to inform patient-centered care.

Abstract Background: Clinical use of breast cancer risk prediction requires simplified models. We evaluate a simplified version of the validated Rosner–Colditz model and add percent mammographic density (MD) and polygenic risk score (PRS), to assess performance from ages 45–74. We validate using the Mayo Mammography Health Study (MMHS). Methods: We derived the model in the Nurses' Health Study (NHS) based on: MD, 77 SNP PRS and a questionnaire score (QS; lifestyle and reproductive factors). A total of 2,799 invasive breast cancer cases were diagnosed from 1990–2000. MD (using Cumulus software) and PRS were assessed in a nested case–control study. We assess model performance using this case–control dataset and evaluate 10-year absolute breast cancer risk. The prospective MMHS validation dataset includes 21.8% of women age &lt;50, and 434 incident cases identified over 10 years of follow-up. Results: In the NHS, MD has the highest odds ratio (OR) for 10-year risk prediction: ORper SD = 1.48 [95% confidence interval (CI): 1.31–1.68], followed by PRS, ORper SD = 1.37 (95% CI: 1.21–1.55) and QS, ORper SD = 1.25 (95% CI: 1.11–1.41). In MMHS, the AUC adjusted for age + MD + QS 0.650; for age + MD + QS + PRS 0.687, and the NRI was 6% in cases and 16% in controls. Conclusion: A simplified assessment of QS, MD, and PRS performs consistently to discriminate those at high 10-year breast cancer risk. Impact: This simplified model provides accurate estimation of 10-year risk of invasive breast cancer that can be used in a clinical setting to identify women who may benefit from chemopreventive intervention. See related commentary by Tehranifar et al., p. 587

Abstract Background: Accumulating evidence suggests a relationship between endometrial cancer and ovarian cancer. Independent genome-wide association studies (GWAS) for endometrial cancer and ovarian cancer have identified 16 and 27 risk regions, respectively, four of which overlap between the two cancers. We aimed to identify joint endometrial and ovarian cancer risk loci by performing a meta-analysis of GWAS summary statistics from these two cancers. Methods: Using LDScore regression, we explored the genetic correlation between endometrial cancer and ovarian cancer. To identify loci associated with the risk of both cancers, we implemented a pipeline of statistical genetic analyses (i.e., inverse-variance meta-analysis, colocalization, and M-values) and performed analyses stratified by subtype. Candidate target genes were then prioritized using functional genomic data. Results: Genetic correlation analysis revealed significant genetic correlation between the two cancers (rG = 0.43, P = 2.66 × 10−5). We found seven loci associated with risk for both cancers (PBonferroni &lt; 2.4 × 10−9). In addition, four novel subgenome-wide regions at 7p22.2, 7q22.1, 9p12, and 11q13.3 were identified (P &lt; 5 × 10−7). Promoter-associated HiChIP chromatin loops from immortalized endometrium and ovarian cell lines and expression quantitative trait loci data highlighted candidate target genes for further investigation. Conclusions: Using cross-cancer GWAS meta-analysis, we have identified several joint endometrial and ovarian cancer risk loci and candidate target genes for future functional analysis. Impact: Our research highlights the shared genetic relationship between endometrial cancer and ovarian cancer. Further studies in larger sample sets are required to confirm our findings.

Abstract Background: Whether circulating polyunsaturated fatty acid (PUFA) levels are associated with pancreatic cancer risk is uncertain. Mendelian randomization (MR) represents a study design using genetic instruments to better characterize the relationship between exposure and outcome. Methods: We utilized data from genome-wide association studies within the Pancreatic Cancer Cohort Consortium and Pancreatic Cancer Case–Control Consortium, involving approximately 9,269 cases and 12,530 controls of European descent, to evaluate associations between pancreatic cancer risk and genetically predicted plasma n-6 PUFA levels. Conventional MR analyses were performed using individual-level and summary-level data. Results: Using genetic instruments, we did not find evidence of associations between genetically predicted plasma n-6 PUFA levels and pancreatic cancer risk [estimates per one SD increase in each PUFA-specific weighted genetic score using summary statistics: linoleic acid odds ratio (OR) = 1.00, 95% confidence interval (CI) = 0.98–1.02; arachidonic acid OR = 1.00, 95% CI = 0.99–1.01; and dihomo-gamma-linolenic acid OR = 0.95, 95% CI = 0.87–1.02]. The OR estimates remained virtually unchanged after adjustment for covariates, using individual-level data or summary statistics, or stratification by age and sex. Conclusions: Our results suggest that variations of genetically determined plasma n-6 PUFA levels are not associated with pancreatic cancer risk. Impact: These results suggest that modifying n-6 PUFA levels through food sources or supplementation may not influence risk of pancreatic cancer.

Abstract Introduction: Pancreatic cancer is the seventh leading cause of cancer death worldwide with pancreatic ductal adenocarcinoma (PDAC) being the most common subtype (&gt;90%). Inherited genetic changes and cigarette smoking are established independent risk factors of PDAC. Methods: We conducted a genome-wide gene by smoking interaction analysis of PDAC risk among 7,937 PDAC cases and 12,389 controls of European ancestry from the Pancreatic Cancer Cohort (PanScan) and Pancreatic Cancer Case-Control Consortia (PanC4) with complete genotyping and smoking data. Over 6,769,447 SNPs met our inclusion criteria of imputation score &gt;0.5 and MAF &gt;0.05 after imputation to phase 3 1000G and were included in our analysis. We evaluated the gene-environment interactions using the empirical Bayes method which allows data-adaptive relaxation of the gene-environment independence assumption. The logistic regression model was adjusted for age, sex, top principal components for each phase, study and study region (PanScan only). The analysis was conducted separately for the PanScan and PanC4 GWAS studies, using a score test to efficiently incorporate expected SNP dosages. The estimates were then combined using a fixed-effect meta-analysis. We examined expression quantitative trait locus using data from the NIH Genotype-Tissue Expression (GTEx) database. We considered a P-value &lt; 5.0 × 10−8 statistically significant. Results: We found a genome-wide significant qualitative interaction with the SNP rs1818613 by smoking status (P-value &lt;5 × 10−9) where, compared to the G allele, the T allele was associated with a reduced risk PDAC in never smokers (per T allele OR, 0.87; 95% CI, 0.82-0.93), had no association in former smokers (OR, 1.00; 95% CI, 0.91-1.07) and was associated with an increased risk in current smokers (OR, 1.25; 95% CI, 1.12-1.40). This SNP is located on chromosome 2q21.3 in intron 5 of the transmembrane protein 163 (TMEM163) gene and upstream of the cyclin T2 (CCNT2) gene. More than 40 additional SNPs within 100Kbp and in high LD (r2 &gt; 0.8) with rs1818613 provided significant evidence of interaction by smoking status for PDAC risk. rs1818613 was associated with differential expression of TMEM163 in several tissues: for T allele compared with the G allele there was increased expression in the heart, pituitary, whole blood, and esophagus-muscularis and decreased expression in the testis tissues (P-value &lt; 3 × 10−11). It was also associated with decreased CCNT2 expression in the tibial nerve tissue (P-value = 1.9 × 10−9). Conclusions: Our work supports that the genetic variation in the region, in conjunction with cigarette smoking, influences PDAC risk. Future studies are needed to explore this association in other smoking-related cancers and to understand the biological mechanism of such association. Citation Format: Prosenjit Kundu, Evelina Mocci, William Wheeler, Laufey T. Amundadottir, Donghui Li, Eric J. Jacobs, Gloria M. Petersen, Brian M. Wolpin, Harvey A. Risch, Peter Kraft, Nilanjan Chatterjee, Alison P. Klein, Rachael Z. Stolzenberg-Solomon, The PanScan and Panc4 consortia. Genome-wide interaction scan identifies gene by smoking interaction at 2q21.3 for pancreatic cancer risk [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 1213.

Abstract Blood lipids have been associated with the development of a range of cancers, including breast, lung and colorectal cancer. For endometrial cancer, observational studies have reported inconsistent associations between blood lipids and cancer risk. To reduce biases from unmeasured confounding, we performed a bidirectional, two‐sample Mendelian randomization analysis to investigate the relationship between levels of three blood lipids (low‐density lipoprotein [LDL] and high‐density lipoprotein [HDL] cholesterol, and triglycerides) and endometrial cancer risk. Genetic variants associated with each of these blood lipid levels ( P < 5 × 10 −8 ) were identified as instrumental variables, and assessed using genome‐wide association study data from the Endometrial Cancer Association Consortium (12 906 cases and 108 979 controls) and the Global Lipids Genetic Consortium (n = 188 578). Mendelian randomization analyses found genetically raised LDL cholesterol levels to be associated with lower risks of endometrial cancer of all histologies combined, and of endometrioid and non‐endometrioid subtypes. Conversely, higher genetically predicted HDL cholesterol levels were associated with increased risk of non‐endometrioid endometrial cancer. After accounting for the potential confounding role of obesity (as measured by genetic variants associated with body mass index), the association between genetically predicted increased LDL cholesterol levels and lower endometrial cancer risk remained significant, especially for non‐endometrioid endometrial cancer. There was no evidence to support a role for triglycerides in endometrial cancer development. Our study supports a role for LDL and HDL cholesterol in the development of non‐endometrioid endometrial cancer. Further studies are required to understand the mechanisms underlying these findings.

Abstract Background: Obesity and diabetes are major modifiable risk factors for pancreatic cancer. Interactions between genetic variants and diabetes/obesity have not previously been comprehensively investigated in pancreatic cancer at the genome-wide level. Methods: We conducted a gene–environment interaction (GxE) analysis including 8,255 cases and 11,900 controls from four pancreatic cancer genome-wide association study (GWAS) datasets (Pancreatic Cancer Cohort Consortium I–III and Pancreatic Cancer Case Control Consortium). Obesity (body mass index ≥30 kg/m2) and diabetes (duration ≥3 years) were the environmental variables of interest. Approximately 870,000 SNPs (minor allele frequency ≥0.005, genotyped in at least one dataset) were analyzed. Case–control (CC), case-only (CO), and joint-effect test methods were used for SNP-level GxE analysis. As a complementary approach, gene-based GxE analysis was also performed. Age, sex, study site, and principal components accounting for population substructure were included as covariates. Meta-analysis was applied to combine individual GWAS summary statistics. Results: No genome-wide significant interactions (departures from a log-additive odds model) with diabetes or obesity were detected at the SNP level by the CC or CO approaches. The joint-effect test detected numerous genome-wide significant GxE signals in the GWAS main effects top hit regions, but the significance diminished after adjusting for the GWAS top hits. In the gene-based analysis, a significant interaction of diabetes with variants in the FAM63A (family with sequence similarity 63 member A) gene (significance threshold P &lt; 1.25 × 10−6) was observed in the meta-analysis (PGxE = 1.2 ×10−6, PJoint = 4.2 ×10−7). Conclusions: This analysis did not find significant GxE interactions at the SNP level but found one significant interaction with diabetes at the gene level. A larger sample size might unveil additional genetic factors via GxE scans. Impact: This study may contribute to discovering the mechanism of diabetes-associated pancreatic cancer.