Cai, Qiuyin

Abstract Background Lung adenocarcinoma in never-smokers is a major public health burden, especially among East Asian women. Polygenic risk scores (PRSs) are promising for risk stratification but are primarily developed in European-ancestry populations. We aimed to develop and validate single- and multi-ancestry PRSs for East Asian never-smokers to improve lung adenocarcinoma risk prediction. Methods PRSs were developed using genome-wide association study summary statistics from East Asian (8002 cases; 20 782 controls) and European (2058 cases; 5575 controls) populations. Single-ancestry models included a PRS with 25 single nucleotide variants (PRS-25), PRS clumping and thresholding (CT), and LDpred2; multi-ancestry models included LDpred2 plus European PRS-128, PRS-CSx, and clumping and thresholding, super learning, empirical Bayes (CT-SLEB). Performance was evaluated in independent East Asian data from the Female Lung Cancer Consortium and externally validated in the Nanjing Lung Cancer Cohort. We assessed predictive accuracy via area under the curve operating procedure (AUC), with 10-year and (ages 30-80 years) absolute risks estimates. Results The best multi-ancestry PRS, using East Asian and European data via CT-SLEB, outperformed the best East Asian-only (LDpred2; AUC = 0.629, 95% confidence interval [CI] = 0.618 to 0.641), achieving an AUC of 0.640 (95% CI = 0.629 to 0.653) and odds ratio of 1.71 (95% CI = 1.61 to 1.82) per standard deviation increase. Nanjing Lung Cancer Cohort validation confirmed robust performance (AUC = 0.649, 95% CI = 0.623 to 0.676). The top 20% PRS group had a 3.92-fold higher lung adenocarcinoma risk than the bottom 20%. Further, the top 5% PRS group reached a 6.69% lifetime absolute risk. Notably, this group reached the average population 10-year lung adenocarcinoma risk at age 50 years (0.42%) by age 41 years, 9 years earlier. Conclusions Multi-ancestry PRS approaches enhance lung adenocarcinoma risk stratification in East Asian never-smokers, with consistent external validation, suggesting future clinical utility.

Abstract Background: The etiology of colorectal cancer is not fully understood. Methods: Using genetic variants and metabolomics data including 217 metabolites from the Framingham Heart Study (n = 1,357), we built genetic prediction models for circulating metabolites. Models with prediction R2 > 0.01 (Nmetabolite = 58) were applied to predict levels of metabolites in two large consortia with a combined sample size of approximately 46,300 cases and 59,200 controls of European and approximately 21,700 cases and 47,400 controls of East Asian (EA) descent. Genetically predicted levels of metabolites were evaluated for their associations with colorectal cancer risk in logistic regressions within each racial group, after which the results were combined by meta-analysis. Results: Of the 58 metabolites tested, 24 metabolites were significantly associated with colorectal cancer risk [Benjamini–Hochberg FDR (BH-FDR) < 0.05] in the European population (ORs ranged from 0.91 to 1.06; P values ranged from 0.02 to 6.4 × 10−8). Twenty one of the 24 associations were replicated in the EA population (ORs ranged from 0.26 to 1.69, BH-FDR < 0.05). In addition, the genetically predicted levels of C16:0 cholesteryl ester was significantly associated with colorectal cancer risk in the EA population only (OREA: 1.94, 95% CI, 1.60−2.36, P = 2.6 × 10−11; OREUR: 1.01, 95% CI, 0.99−1.04, P = 0.3). Nineteen of the 25 metabolites were glycerophospholipids and triacylglycerols (TAG). Eighteen associations exhibited significant heterogeneity between the two racial groups (PEUR-EA-Het < 0.005), which were more strongly associated in the EA population. This integrative study suggested a potential role of lipids, especially certain glycerophospholipids and TAGs, in the etiology of colorectal cancer. Conclusions: This study identified potential novel risk biomarkers for colorectal cancer by integrating genetics and circulating metabolomics data. Impact: The identified metabolites could be developed into new tools for risk assessment of colorectal cancer in both European and EA populations.

AbstractBackground:The role of methylation in pancreatic cancer risk remains unclear. We integrated genome and methylome data to identify CpG sites (CpG) with the genetically predicted methylation to be associated with pancreatic cancer risk. We also studied gene expression to understand the identified associations.Methods:Using genetic data and white blood cell methylation data from 1,595 subjects of European descent, we built genetic models to predict DNA methylation levels. After internal and external validation, we applied prediction models with satisfactory performance to the genetic data of 8,280 pancreatic cancer cases and 6,728 controls of European ancestry to investigate the associations of predicted methylation with pancreatic cancer risk. For associated CpGs, we compared their measured levels in pancreatic tumor versus benign tissue.Results:We identified 45 CpGs at nine loci showing an association with pancreatic cancer risk, including 15 CpGs showing an association independent from identified risk variants. We observed significant correlations between predicted methylation of 16 of the 45 CpGs and predicted expression of eight adjacent genes, of which six genes showed associations with pancreatic cancer risk. Of the 45 CpGs, we were able to compare measured methylation of 16 in pancreatic tumor versus benign pancreatic tissue. Of them, six showed differentiated methylation.Conclusions:We identified methylation biomarker candidates associated with pancreatic cancer using genetic instruments and added additional insights into the role of methylation in regulating gene expression in pancreatic cancer development.Impact:A comprehensive study using genetic instruments identifies 45 CpG sites at nine genomic loci for pancreatic cancer risk.

Abstract Pancreatic ductal adenocarcinoma (PDAC) is one of most lethal malignancies with few known risk factors and biomarkers. Identification of disease biomarkers is critical for understanding the pathogenesis of this cancer and identifying high risk individuals for close surveillance. Several blood protein biomarkers have been linked to PDAC in previous studies, but these studies have assessed only a limited number of biomarkers usually in small samples. To identify novel circulating protein biomarkers of PDAC, we studied 8,280 cases and 6,728 controls of European descent from the Pancreatic Cancer Cohort Consortium and the Pancreatic Cancer Case-Control Consortium, by using genetic instruments. Protein quantitative trait loci (pQTLs) for 1,226 plasma proteins identified in a large INTERVAL study of 3,301 healthy European descendants were used as instruments to evaluate associations between genetically predicted protein levels and PDAC. For proteins showing a significant association, we further conducted conditional analysis with adjustments for previously identified risk variants to assess whether the observed associations between genetically predicted protein concentrations and PDAC risk were independent of the risk variants identified in genome-wide association studies (GWAS). Furthermore, for the proteins that were associated with PDAC risk, we performed an enrichment analysis of the genes encoding these proteins to examine whether they are enriched in specific pathways, functions or networks. We observed associations between predicted concentrations of 38 proteins and PDAC risk at a false discovery rate of < 0.05, including those of 23 proteins that showed a significant association even after Bonferroni correction (4.08 × 10−5). These include Histo-blood group ABO system transferase encoded by ABO, which has been previously implicated as a potential target gene of PDAC risk variant identified in GWAS. Eight of the identified proteins (Beta-crystallin B2, Dedicator of cytokinesis protein 9, VIP36-like protein, Erythrocyte band 7 integral membrane protein, Tensin-2, Transmembrane protease serine 11D, Alcohol dehydrogenase 1B, and C-X-C motif chemokine 10) were associated with PDAC risk after conditioning on previously reported pancreatic cancer risk variants (odds ratios ranged from 0.79 to 1.52, P-values from 1.28 × 10−3 to 6.47 × 10−4). Pathway enrichment analysis showed that the encoding genes for the implicated proteins were significantly enriched in cancer-related pathways, such as STAT3 and IL-15 production. In conclusion, we identified 38 protein biomarker candidates for PDAC risk, which if validated by additional studies, may contribute to the etiological understanding of PDAC tumor development. Citation Format: Jingjing Zhu, Xiang Shu, Xingyi Guo, Duo Liu, Jiandong bao, Roger Milne, Graham G Giles, Chong Wu, Mengmeng Du, Emily White, Harvey A Risch, Nuria Malats, Eric J. Duell, Phyllis J. Goodman, Donghui Li, Paige Bracci, Verena Katzke, Rachel E Neale, Steven Gallinger, Stephen Van Den Eeden, Alan Arslan, Federico Canzian, Charles Kooperberg, Brian Wolpin, Laura Beane-Freeman, Ghislaine Scelo, Kala Visvanatha, Christopher A. Haiman, Loïc Le Marchand, Herbert Yu, Gloria M Petersen, Rachael Stolzenberg-Solomon, Alison P Klein, Laufey T Amundadottir, Qiuyin Cai, Jirong Long, Xiao-Ou Shu, Wei Zheng, Lang Wu. Associations between genetically predicted blood protein biomarkers and pancreatic ductal adenocarcinoma 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 1200.

Abstract Background: Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal malignancies, with few known risk factors and biomarkers. Several blood protein biomarkers have been linked to PDAC in previous studies, but these studies have assessed only a limited number of biomarkers, usually in small samples. In this study, we evaluated associations of circulating protein levels and PDAC risk using genetic instruments. Methods: To identify novel circulating protein biomarkers of PDAC, we studied 8,280 cases and 6,728 controls of European descent from the Pancreatic Cancer Cohort Consortium and the Pancreatic Cancer Case-Control Consortium, using genetic instruments of protein quantitative trait loci. Results: We observed associations between predicted concentrations of 38 proteins and PDAC risk at an FDR of < 0.05, including 23 of those proteins that showed an association even after Bonferroni correction. These include the protein encoded by ABO, which has been implicated as a potential target gene of PDAC risk variant. Eight of the identified proteins (LMA2L, TM11D, IP-10, ADH1B, STOM, TENC1, DOCK9, and CRBB2) were associated with PDAC risk after adjusting for previously reported PDAC risk variants (OR ranged from 0.79 to 1.52). Pathway enrichment analysis showed that the encoding genes for implicated proteins were significantly enriched in cancer-related pathways, such as STAT3 and IL15 production. Conclusions: We identified 38 candidates of protein biomarkers for PDAC risk. Impact: This study identifies novel protein biomarker candidates for PDAC, which if validated by additional studies, may contribute to the etiologic understanding of PDAC development.

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.

AbstractPrevious transcriptome‐wide association studies (TWAS) have identified breast cancer risk genes by integrating data from expression quantitative loci and genome‐wide association studies (GWAS), but analyses of breast cancer subtype‐specific associations have been limited. In this study, we conducted a TWAS using gene expression data from GTEx and summary statistics from the hitherto largest GWAS meta‐analysis conducted for breast cancer overall, and by estrogen receptor subtypes (ER+ and ER−). We further compared associations with ER+ and ER− subtypes, using a case‐only TWAS approach. We also conducted multigene conditional analyses in regions with multiple TWAS associations. Two genes, STXBP4 and HIST2H2BA, were specifically associated with ER+ but not with ER– breast cancer. We further identified 30 TWAS‐significant genes associated with overall breast cancer risk, including four that were not identified in previous studies. Conditional analyses identified single independent breast‐cancer gene in three of six regions harboring multiple TWAS‐significant genes. Our study provides new information on breast cancer genetics and biology, particularly about genomic differences between ER+ and ER− breast cancer.

Abstract Background: Results from epidemiologic studies examining polyunsaturated fatty acids (PUFA) and colorectal cancer risk are inconsistent. Mendelian randomization may strengthen causal inference from observational studies. Given their shared metabolic pathway, examining the combined effects of aspirin/NSAID use with PUFAs could help elucidate an association between PUFAs and colorectal cancer risk. Methods: Information was leveraged from genome-wide association studies (GWAS) regarding PUFA-associated SNPs to create weighted genetic scores (wGS) representing genetically predicted circulating blood PUFAs for 11,016 non-Hispanic white colorectal cancer cases and 13,732 controls in the Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO). Associations per SD increase in the wGS were estimated using unconditional logistic regression. Interactions between PUFA wGSs and aspirin/NSAID use on colorectal cancer risk were also examined. Results: Modest colorectal cancer risk reductions were observed per SD increase in circulating linoleic acid [ORLA = 0.96; 95% confidence interval (CI) = 0.93–0.98; P = 5.2 × 10−4] and α-linolenic acid (ORALA = 0.95; 95% CI = 0.92–0.97; P = 5.4 × 10−5), whereas modest increased risks were observed for arachidonic (ORAA = 1.06; 95% CI = 1.03–1.08; P = 3.3 × 10−5), eicosapentaenoic (OREPA = 1.04; 95% CI = 1.01–1.07; P = 2.5 × 10−3), and docosapentaenoic acids (ORDPA = 1.03; 95% CI = 1.01–1.06; P = 1.2 × 10−2). Each of these effects was stronger among aspirin/NSAID nonusers in the stratified analyses. Conclusions: Our study suggests that higher circulating shorter-chain PUFAs (i.e., LA and ALA) were associated with reduced colorectal cancer risk, whereas longer-chain PUFAs (i.e., AA, EPA, and DPA) were associated with an increased colorectal cancer risk. Impact: The interaction of PUFAs with aspirin/NSAID use indicates a shared colorectal cancer inflammatory pathway. Future research should continue to improve PUFA genetic instruments to elucidate the independent effects of PUFAs on colorectal cancer.

Abstract Epithelial ovarian cancer (EOC) is the most common gynecological cancer, and ranks fifth in cancer deaths among women in the United States. Previous studies have identified several circulating proteins biomarkers for EOC risk, such as CA125 antigen and insulin-like growth factor-1 (IGF-1). However, previous studies on protein biomarkers for EOC were limited with small sample sizes, and only a few proteins investigated. In this study, we integrated genomics and proteomics data to search for novel circulating protein biomarkers for EOC risk. We constructed study instruments using genetic variants for protein quantitative trait loci (pQTL) for over 1,400 circulating proteins. Beta coefficients and standard errors of the selected pQTL variants in relation to EOC risk were obtained from 25,509 EOC cases and 40,941 controls from the Ovarian Cancer Association Consortium (OCAC). An inverse-variance weighted method was used to evaluate the association of these genetically predicted protein levels and risk of overall and serous EOC. We identified 35 protein biomarkers, for which their genetically predicted levels were significantly associated with the risk of either overall or serous EOC (false discovery rate <0.05). There are 25 proteins showing significant associations with both overall and serous EOC; most of the associations were stronger for serous EOC than overall EOC. The association for 31 proteins could be attributed, in part, to variants at ABO 9q34.2, a known ovarian cancer susceptibility locus. Five ABO locus-related proteins are in the PI3K-Akt signaling pathway, including insulin-like growth factor 1 receptor, hepatocyte growth factor receptor, interleukin-3 receptor subunit alpha, insulin receptor, and vascular endothelial growth factor receptor 2. Four of these proteins are also in the Rap1 and Ras signaling pathway. These five proteins are all inversely associated with the risk of overall EOC, with odds ratios ranging from 0.64 to 0.93 per unit increase in genetic risk scores (p values: 7.55×10-7 to 5.66×10-2). Four EOC associated proteins which were not influenced by variants in 9q34.2 are microfibrillar-associated protein 2, protein delta homolog 1, semenogelin-2, and netrin-G1, all of which showed significant associations with overall EOC, but not the serous subtype. Our study identifies potential novel biomarkers for ovarian cancer and suggests that genetic variants at locus 9q34.2 may play a role in the relation between levels of these proteins and risk of ovarian cancer. Citation Format: Guochong Jia, Xiang Shu, Xiao-Ou Shu, Jirong Long, Qiuyin Cai, Kirsten B. Moysich, Ellen L. Goode, Thomas A. Sellers, Wei Zheng. Genetically predicted concentration of circulating proteins and epithelial ovarian cancer risk [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 598.

Abstract DNA methylation is instrumental for gene regulation. Global changes in the epigenetic landscape have been recognized as a hallmark of cancer. However, the role of DNA methylation in epithelial ovarian cancer (EOC) remains unclear. In this study, high-density genetic and DNA methylation data in white blood cells from the Framingham Heart Study (N = 1,595) were used to build genetic models to predict DNA methylation levels. These prediction models were then applied to the summary statistics of a genome-wide association study (GWAS) of ovarian cancer including 22,406 EOC cases and 40,941 controls to investigate genetically predicted DNA methylation levels in association with EOC risk. Among 62,938 CpG sites investigated, genetically predicted methylation levels at 89 CpG were significantly associated with EOC risk at a Bonferroni-corrected threshold of P < 7.94 × 10−7. Of them, 87 were located at GWAS-identified EOC susceptibility regions and two resided in a genomic region not previously reported to be associated with EOC risk. Integrative analyses of genetic, methylation, and gene expression data identified consistent directions of associations across 12 CpG, five genes, and EOC risk, suggesting that methylation at these 12 CpG may influence EOC risk by regulating expression of these five genes, namely MAPT, HOXB3, ABHD8, ARHGAP27, and SKAP1. We identified novel DNA methylation markers associated with EOC risk and propose that methylation at multiple CpG may affect EOC risk via regulation of gene expression. Significance: Identification of novel DNA methylation markers associated with EOC risk suggests that methylation at multiple CpG may affect EOC risk through regulation of gene expression.