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Moore, Steven C

0000-0002-8169-1661

Mayo Clinic

Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD, United States.

Citations 3

Different diabetes types and pancreatic ductal adenocarcinoma: a Mendelian randomization and pathway/gene-set analysis
Zhang, Ting , Mohindroo, Chirayu , Wang, Xiaoyu , Dutta, Diptavo , Liu, Jia , Katta, Shilpa, Li, Shengchao A. , Wang, Jiahui , Antwi, Samuel O. , Bracci, Paige M. , Du, Mengmeng, Gallinger, Steven , Goodman, Phyllis J. , Katzke, Verena, Patel, Alpa V. , Visvanathan, Kala , Andreotti, Gabriella , Brennan, Paul , Buring, Julie E. , Chanock, Stephen J., Chen, Yu , Giovannucci, Edward L. , Haiman, Christopher A. , Hassan, Manal M. , Holly, Elizabeth A., Jones, Miranda R. , Kraft, Peter, Kurtz, Robert C. , Ng, Kimmie , Oberg, Ann L. , Peters, Ulrike , Rabe, Kari G. , Rothman, Nathaniel , Sesso, Howard D. , Yuan, Chen, Wolpin, Brian M. , Yu, Kai , Stolzenberg‐Solomon, Rachael Z. , Mohindroo, Chirayu , Wang, Xiaoyu , Dutta, Diptavo , Liu, Jia , Li, Shengchao A , Antwi, Samuel O , Arslan, Alan A , Beane Freeman, Laura E , Canzian, Federico , Du, Mengmeng , Goodman, Phyllis J , Katzke, Verena , Kooperberg, Charles , Marchand, Loïc Le , Neale, Rachel E , Perdomo, Sandra , Shu, Xiao-Ou , Visvanathan, Kala , Van Den Eeden, Stephen K , White, Emily E. , Zheng, Wei , Albanes, Demetrius , Andreotti, Gabriella , Brennan, Paul , Buring, Julie E , Chanock, Stephen Jacob , Chen, Yu , Darst, Burcu F , Ferrari, Pietro , Giovannucci, Edward L , Goggins, Michael G , Haiman, Christopher A , Hassan, Manal M , Hung, Rayjean J , Jones, Miranda R , Kraft, Peter , Malats, Nuria , Moore, Steven C , Ng, Kimmie , Oberg, Ann L , Orlow, Irene , Peters, Ulrike , Porta, Miquel , Chaffee, Kari G. , Sánchez, Maria-Jose , Sesso, Howard D , Southey, Melissa C , Um, Caroline Y , Yarmolinsky, James , Yu, Herbert , Yuan, Chen , Zhong, Jun , Wolpin, Brian M , Risch, Harvey A , Amundadottir, Laufey , Klein, Alison P , Yu, Kai , Zhang, Haoyu , Stolzenberg-Solomon, Rachael , Zhang, Ting , Hua, Xing , Arslan, Alan A. , Freeman, Laura E. Beane , Canzian, Federico , Kooperberg, Charles , Marchand, Loı̈c Le , Neale, Rachel Ε. , Zheng, Wei , Albanes, Demetrius , Bamlet, William R. , Darst, Burcu F. , Goggins, Michael , Hung, Rayjean J. , Malats, Núria , Moore, Steven C. , Orlow, Irene , Porta, Miquel , Sánchez, María‐José , Silverman, Debra T. , Southey, Melissa C. , Um, Caroline Y. , Yu, Kai , Risch, Harvey A. , Ámundadóttir, Laufey T. , Klein, Alison P.

JNCI: Journal of the National Cancer Institute | November 8, 2025 | Volume: 118 | Issue: 3 | Pages: 437-447

10.1093/jnci/djaf308 Get It

Abstract Background The associations between different types of diabetes, characterized by distinct pathophysiology and genetic architecture, and pancreatic ductal adenocarcinoma (PDAC) risk are not understood. Methods We investigated associations of genetic susceptibility to type 2 diabetes (T2D), 8 T2D mechanistic clusters, type 1 diabetes (T1D), and maturity-onset diabetes of the young (MODY) with PDAC risk. We used genome-wide association study (GWAS) summary-level statistics for T2D (242 283 cases, 1 569 734 controls), T1D (18 942 cases, 501 638 controls), and PDAC (10 244 cases and 360 535 controls) in individuals of European ancestry. Results Two-sample Mendelian randomization (MR) using the Robust Adjusted Profile Score (MR-RAPS) method indicated that genetically predicted T2D was associated with PDAC risk (OR = 1.10; 95% CI = 1.05 to 1.15), particularly the T2D obesity (OR = 1.28; 95% CI = 1.15 to 1.42) and lipodystrophy (OR = 1.25; 95% CI = 1.03 to 1.51) clusters. No association was observed for T1D with PDAC risk (OR = 1.01; 95% CI = 0.99 to 1.02). Pathway/gene-set analysis using the summary-based Adaptive Rank Truncated Product (sARTP) method revealed a significant association between the MODY gene-sets and PDAC risk (P = 1.5 × 10−8), which remained after excluding 20 known PDAC GWAS loci (P = 7.6 × 10−4). HNF1A, FOXA3, and HNF4A were the top contributing genes after excluding the previously identified GWAS loci regions. Conclusions Our results from this genetic association study support that T2D, particularly the obesity and lipodystrophy mechanistic clusters, and MODY genomic susceptibility regions play a role in the etiology of PDAC.
Genome-Wide Analysis to Assess if Heavy Alcohol Consumption Modifies the Association between SNPs and Pancreatic Cancer Risk
Ni, Zhanmo , Kundu, Prosenjit , McKean, David , Wheeler, William , Albanes, Demetrius , Andreotti, Gabriella , Antwi, Samuel O. , Arslan, Alan A. , Bamlet, William R. , Freeman, Laura E. Beane , Berndt, Sonja I. , Bracci, Paige M. , Brennan, Paul , Buring, Julie E. , Gallinger, Steven , Gaziano, J. Michael , Giovannucci, Edward L. , Goodman, Phyllis J. , Haiman, Christopher A. , Hassan, Manal M. , Holly, Elizabeth A. , Li, Donghui , Oberg, Ann L. , Patel, Alpa V. , Peters, Ulrike , Visvanathan, Kala , White, Emily , Wolpin, Brian M. , Zhong, Jun, McKean, David F , Beane Freeman, Laura E , Chanock, Stephen Jacob , Gallinger, Steven , Gaziano, J Michael , Giles, Graham G , Giovannucci, Edward L , Goggins, Michael G , Goodman, Phyllis J , Haiman, Christopher A , Hassan, Manal M , Holly, Elizabeth A , Hung, Rayjean J , Katzke, Verena , Kooperberg, Charles , Kraft, Peter , Marchand, Loïc Le , Li, Donghui , McCullough, Marjorie L , Milne, Roger L , Moore, Steven C , Neale, Rachel E , Oberg, Ann L , Patel, Alpa V , Peters, Ulrike , Chaffee, Kari G. , Risch, Harvey A , Shu, Xiao-Ou , Smith-Byrne, Karl , Visvanathan, Kala , Wactawski-Wende, Jean , White, Emily , Wolpin, Brian M , Yu, Herbert , Zeleniuch-Jacquotte, Anne , Zheng, Wei , Zhong, Jun , Amundadottir, Laufey , Stolzenberg-Solomon, Rachael , Klein, Alison P , Antwi, Samuel O , Arslan, Alan A , Bamlet, William R , Albanes, Demetrius , Giles, Graham G. , Goggins, Michael , Hung, Rayjean J. , Kooperberg, Charles , Kraft, Peter , Marchand, Loı̈c Le , McCullough, Marjorie L. , Milne, Roger L. , Moore, Steven C. , Neale, Rachel Ε. , Rabe, Kari G. , Risch, Harvey A. , Shu, Xiao‐Ou , Wactawski‐Wende, Jean , Yu, Kai , Zeleniuch‐Jacquotte, Anne , Zheng, Wei , Ámundadóttir, Laufey T. , Stolzenberg‐Solomon, Rachael Z. , Klein, Alison P.

Cancer Epidemiology, Biomarkers & Prevention | June 13, 2024 | Volume: 33 | Issue: 9 | Pages: 1229-1239

10.1158/1055-9965.epi-24-0096 Get It

Abstract Background: Pancreatic cancer is a leading cause of cancer-related death globally. Risk factors for pancreatic cancer include common genetic variants and potentially heavy alcohol consumption. We assessed if genetic variants modify the association between heavy alcohol consumption and pancreatic cancer risk. Methods: We conducted a genome-wide interaction analysis of single-nucleotide polymorphisms (SNP) by heavy alcohol consumption (more than three drinks per day) for pancreatic cancer in European ancestry populations from genome-wide association studies. Our analysis included 3,707 cases and 4,167 controls from case–control studies and 1,098 cases and 1,162 controls from cohort studies. Fixed-effect meta-analyses were conducted. Results: A potential novel region of association on 10p11.22, lead SNP rs7898449 (interaction P value (Pinteraction) = 5.1 × 10−8 in the meta-analysis; Pinteraction = 2.1 × 10−9 in the case–control studies; Pinteraction = 0.91 in the cohort studies), was identified. An SNP correlated with this lead SNP is an expression quantitative trait locus for the neuropilin 1 gene. Of the 17 genomic regions with genome-wide significant evidence of association with pancreatic cancer in prior studies, we observed suggestive evidence that heavy alcohol consumption modified the association for one SNP near LINC00673, rs11655237 on 17q25.1 (Pinteraction = 0.004). Conclusions: We identified a novel genomic region that may be associated with pancreatic cancer risk in conjunction with heavy alcohol consumption located near an expression quantitative trait locus for neuropilin 1, a protein that plays an important role in the development and progression of pancreatic cancer. Impact: This work can provide insights into the etiology of pancreatic cancer, particularly in heavy drinkers.
Comparison of Collection Methods for Fecal Samples for Discovery Metabolomics in Epidemiologic Studies
Loftfield, Erikka , Vogtmann, Emily , Moore, Steven C. , Nelson, Heidi , Knight, Rob , Chia, Nicholas , Sampson, Joshua N. , Chia, Nicholas , Sinha, Rashmi R. , Moore, Steven C

Cancer Epidemiology, Biomarkers & Prevention | August 19, 2016 | Volume: 25 | Issue: 11 | Pages: 1483-1490

10.1158/1055-9965.epi-16-0409 Get It

Abstract Background: The gut metabolome may be associated with the incidence and progression of numerous diseases. The composition of the gut metabolome can be captured by measuring metabolite levels in the feces. However, there are little data describing the effect of fecal sample collection methods on metabolomic measures. Methods: We collected fecal samples from 18 volunteers using four methods: no solution, 95% ethanol, fecal occult blood test (FOBT) cards, and fecal immunochemical test (FIT). One set of samples was frozen after collection (day 0), and for 95% ethanol, FOBT, and FIT, a second set was frozen after 96 hours at room temperature. We evaluated (i) technical reproducibility within sample replicates, (ii) stability after 96 hours at room temperature for 95% ethanol, FOBT, and FIT, and (iii) concordance of metabolite measures with the putative “gold standard,” day 0 samples without solution. Results: Intraclass correlation coefficients (ICC) estimating technical reproducibility were high for replicate samples for each collection method. ICCs estimating stability at room temperature were high for 95% ethanol and FOBT (median ICC > 0.87) but not FIT (median ICC = 0.52). Similarly, Spearman correlation coefficients (rs) estimating metabolite concordance with the “gold standard” were higher for 95% ethanol (median rs = 0.82) and FOBT (median rs = 0.70) than for FIT (median rs = 0.40). Conclusions: Metabolomic measurements appear reproducible and stable in fecal samples collected with 95% ethanol or FOBT. Concordance with the “gold standard” is highest with 95% ethanol and acceptable with FOBT. Impact: Future epidemiologic studies should collect feces using 95% ethanol or FOBT if interested in studying fecal metabolomics. Cancer Epidemiol Biomarkers Prev; 25(11); 1483–90. ©2016 AACR.