Objectives We adapted previously developed decision rules from the New England Bladder Cancer Study (NEBCS) to assign occupational exposure to straight, soluble, and synthetic metalworking fluids (MWFs) to participants of the Spanish Bladder Cancer Study (SBCS).
Methods The SBCS and NEBCS are case–control studies that used the same lifetime occupational history and job module questionnaires. We adapted published decision rules from the NEBCS that linked questionnaire responses to estimates of the probability (<5, ≥5 to <50, ≥50 to <100, and 100%), frequency (in h week−1), and intensity (in mg m−3) of exposure to each of the three broad classes of MWFs to assign exposure to 10 182 reported jobs in the SBCS. The decision rules used the participant's module responses to MWF questions wherever possible. We then used these SBCS module responses to calculate job-, industry-, and time-specific patterns in the prevalence and frequency of MWF exposure. These estimates replaced the NEBCS-specific estimates in decision rules applied to jobs without MWF module responses. Intensity estimates were predicted using a previously developed statistical model that used the decade, industry (three categories), operation (grinding versus machining), and MWF type extracted from the SBCS questionnaire responses. We also developed new decision rules to assess mineral oil exposure from non-machining sources (possibly exposed versus not exposed). The decision rules for MWF and mineral oil identified questionnaire response patterns that required job-by-job expert review.
Results To assign MWF exposure, we applied decision rules that incorporated participant's responses and job group patterns for 99% of the jobs and conducted expert review of the remaining 1% (145) jobs. Overall, 14% of the jobs were assessed as having ≥5% probability of exposure to at least one of the three MWFs. Probability of exposure of ≥50% to soluble, straight, and synthetic MWFs was identified in 2.5, 1.7, and 0.5% of the jobs, respectively. To assign mineral oil from non-machining sources, we used module responses for 49% of jobs, a job-exposure matrix for 41% of jobs, and expert review for the remaining 10%. We identified 24% of jobs as possibly exposed to mineral oil from non-machining sources.
Conclusions We demonstrated that we could adapt existing decision rules to assess exposure in a new population by deriving population-specific job group patterns.
Pathway analysis of genome-wide association studies (GWAS) offer a unique opportunity to collectively evaluate genetic variants with effects that are too small to be detected individually. We applied a pathway analysis to a bladder cancer GWAS containing data from 3,532 cases and 5,120 controls of European background (n = 5 studies). Thirteen hundred and ninety-nine pathways were drawn from five publicly available resources (Biocarta, Kegg, NCI-PID, HumanCyc, and Reactome), and we constructed 22 additional candidate pathways previously hypothesized to be related to bladder cancer. In total, 1421 pathways, 5647 genes and 90,000 SNPs were included in our study. Logistic regression model adjusting for age, sex, study, DNA source, and smoking status was used to assess the marginal trend effect of SNPs on bladder cancer risk. Two complementary pathway-based methods (gene-set enrichment analysis [GSEA], and adapted rank-truncated product [ARTP]) were used to assess the enrichment of association signals within each pathway. Eighteen pathways were detected by either GSEA or ARTP at P≤0.01. To minimize false positives, we used the I2 statistic to identify SNPs displaying heterogeneous effects across the five studies. After removing these SNPs, seven pathways ('Aromatic amine metabolism' [PGSEA = 0.0100, PARTP = 0.0020], 'NAD biosynthesis' [PGSEA = 0.0018, PARTP = 0.0086], 'NAD salvage' [PARTP = 0.0068], 'Clathrin derived vesicle budding' [PARTP = 0.0018], 'Lysosome vesicle biogenesis' [PGSEA = 0.0023, PARTP<0.00012], 'Retrograde neurotrophin signaling' [PGSEA = 0.00840], and 'Mitotic metaphase/anaphase transition' [PGSEA = 0.0040]) remained. These pathways seem to belong to three fundamental cellular processes (metabolic detoxification, mitosis, and clathrin-mediated vesicles). Identification of the aromatic amine metabolism pathway provides support for the ability of this approach to identify pathways with established relevance to bladder ; This project has been funded in part with federal funds from the Natio nal Cancer Institute, National Institutes of Health, under Contract No. HHSN261200800001E. The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations imply endorsement by the U.S. Government. Support for individual studies that participated in the effort is as follows: SBCS (Dr. Silverman) - Intramural Research Program of the National Institutes of Health, National Cancer Institute, Division of Cancer Epidemiology and Genetics and intramural contract number NCI N02-CP-11015. FIS/Spain 98/1274, FIS/Spain 00/0745, PI061614, and G03/174 Fundació Marató TV3, Red Temática Investigación Cooperativa en Cáncer (RTICC), Consolider ONCOBIO, EU-FP7-201663; and RO1- CA089715 and CA34627. NEBCS (Dr. Silverman) - Intramural research program of the National Institutes of Health, National Cancer Institute, Division of Cancer Epidemiology and Genetics and intramural contract number NCI N02-CP-01037 PLCO (Dr. Purdue) - The National Institutes of Health (NIH) Genes, Environment and Health Initiative(GEI) partly funded DNA extraction and statistical analyses (HG-06-033-NCI-01 and RO1HL091172-01), genotyping at the Johns Hopkins University Center for Inherited Disease Research (U01HG004438 and NIH HHSN268200782096C), and study coordination at the GENEVA (Dr. Caporaso)- The NIH Genes, Environment and Health Initiative [GEI] partly funded DNA extraction and statistical analyses (HG-06-033-NCI-01 and RO1HL091172-01), genotyping at the Johns Hopkins University Center for Inherited Disease Research (U01HG004438 and NIH HHSN268200782096C) and study coordination at the GENEVA Coordination Center (U01HG004446) for EAGLE and part of PLCO studies. Genotyping for the remaining part of PLCO and all ATBC and CPS-II samples were supported by the Intramural Research Program of the National Institutes of Health, NCI, Division of Cancer Epidemiology and Genetics. The PLCO is supported by the Intramural Research Program of the Division of Cancer Epidemiology and Genetics and supported by contracts from the Division of Cancer Prevention, National Cancer Institute, National Institutes of Health. ATBC (Dr. Albanes) - This research was supported in part by the Intramural Research Program of the NIH and the National Cancer Institute. Additionally, this research was supported by U.S. Public Health Service contracts N01-CN-45165, N01-RC-45035, and N01-RC-37004 from the National Cancer Institute, Department of Health and Human Services
OBJECTIVES: Previous studies established a causal relationship between occupational benzene exposure and acute myeloid leukemia (AML). However, mixed results have been reported for associations between benzene exposure and other myeloid and lymphoid malignancies. Our work examined whether occupational benzene exposure is associated with increased mortality from overall lymphohaematopoietic (LH) cancer and major subtypes. METHODS: Mortality records were linked to a Swiss census-based cohort from two national censuses in 1990 and 2000. Cases were defined as having any LH cancers registered in death certificates. We assessed occupational exposure by applying a quantitative benzene job-exposure matrix (BEN-JEM) to census-reported occupations. Exposure was calculated as the products of exposure proportions and levels (P × L). Cox proportional hazards models were used to calculate LH cancer death hazard ratios (HR) and 95% confidence intervals (CI) associated with benzene exposure, continuously and in ordinal categories. RESULTS: Our study included approximately 2.97 million persons and 13 415 LH cancer cases, including 3055 cases with benzene exposure. We observed increased mortality risks per unit (P × L) increase in continuous benzene exposure for AML (HR 1.03, 95% CI 1.00–1.06) and diffuse large B-cell lymphoma (HR 1.09, 95% CI 1.04–1.14). When exposure was assessed categorically, increasing trends in risks were observed with increasing benzene exposure for AML (P=0.04), diffuse large B-cell lymphoma (P=0.02), and follicular lymphoma (P=0.05). CONCLUSION: In a national cohort from Switzerland, we found that occupational exposure to benzene is associated with elevated mortality risks for AML, diffuse large B-cell lymphoma, and possibly follicular lymphoma.
AbstractObjectivesOccupational exposures in population-based case–control studies are increasingly being assessed using decision rules that link participants' responses to occupational questionnaires to exposure estimates. We used a hierarchical process that incorporated decision rules and job-by-job expert review to assign occupational benzene exposure estimates in a US population-based case–control study of non-Hodgkin lymphoma.MethodsWe conducted a literature review to identify scenarios in which occupational benzene exposure has occurred, which we grouped into 12 categories of benzene exposure sources. For each source category, we then developed decision rules for assessing probability (ordinal scale based on the likelihood of exposure > 0.02 ppm), frequency (proportion of work time exposed), and intensity of exposure (in ppm). The rules used the participants' occupational history responses and, for a subset of jobs, responses to job- and industry-specific modules. For probability and frequency, we used a hierarchical assignment procedure that prioritized subject-specific module information when available. Next, we derived job-group medians from the module responses to assign estimates to jobs with only occupational history responses. Last, we used job-by-job expert review to assign estimates when job-group medians were not available or when the decision rules identified possible heterogeneous or rare exposure scenarios. For intensity, we developed separate estimates for each benzene source category that were based on published measurement data whenever possible. Frequency and intensity annual source-specific estimates were assigned only for those jobs assigned ≥75% probability of exposure. Annual source-specific concentrations (intensity × frequency) were summed to obtain a total annual benzene concentration for each job.ResultsOf the 8827 jobs reported by participants, 8% required expert review for one or more source categories. Overall, 287 (3.3%) jobs were assigned ≥75% probability of exposure from any benzene source category. The source categories most commonly assigned ≥75% probability of exposure were gasoline and degreasing. The median total annual benzene concentration among jobs assigned ≥75% probability was 0.11 ppm (interquartile range: 0.06–0.55). The highest source-specific median annual concentrations were observed for ink and printing (2.3 and 1.2 ppm, respectively).ConclusionsThe applied framework captures some subject-specific variability in work tasks, provides transparency to the exposure decision process, and facilitates future sensitivity analyses. The developed decision rules can be used as a starting point by other researchers to assess occupational benzene exposure in future population-based studies.
Background: Generic job-exposure matrices (JEMs) are often used in population-based epidemiologic studies to assess occupational risk factors when only the job and industry information of each subject is available. JEM ratings are often based on professional judgment, are usually ordinal or semi-quantitative, and often do not account for changes in exposure over time. We present an empirical Bayesian framework that combines ordinal subjective JEM ratings with benzene measurements. Our aim was to better discriminate between job, industry, and time differences in exposure levels compared to using a JEM alone.
Methods: We combined 63 221 short-term area air measurements of benzene exposure (1954–2000) collected during routine health and safety inspections in Shanghai, China, with independently developed JEM intensity ratings for each job and industry using a mixed-effects model. The fixed-effects terms included the JEM intensity ratings for job and industry (both ordinal, 0–3) and a time trend that we incorporated as a b-spline. The random-effects terms included job (n = 33) and industry nested within job (n = 399). We predicted the benzene concentration in two ways: (i) a calibrated JEM estimate was calculated using the fixed-effects model parameters for calendar year and JEM intensity ratings; (ii) a job-/industry-specific estimate was calculated using the fixed-effects model parameters and the best linear unbiased predictors from the random effects for job and industry using an empirical Bayes estimation procedure. Finally, we applied the predicted benzene exposures to a prospective population-based cohort of women in Shanghai, China (n = 74 942).
Results: Exposure levels were 13 times higher in 1965 than in 2000 and declined at a rate that varied from 4 to 15% per year from 1965 to 1985, followed by a small peak in the mid-1990s. The job-/industry-specific estimates had greater differences between exposure levels than the calibrated JEM estimates (97.5th percentile/2.5th percentile exposure level, BGR95B: 20.4 versus 3.0, respectively). The calibrated JEM and job-/industry-specific estimates were moderately correlated in any given year (Pearson correlation, rp = 0.58). We classified only those jobs and industries with a job or industry JEM exposure probability rating of 3 (>50% of workers exposed) as exposed. As a result, 14.8% of the subjects and 8.7% of the employed person-years in the study population were classified as benzene exposed. The cumulative exposure metrics based on the calibrated JEM and job-/industry-specific estimates were highly correlated (rp = 0.88).
Conclusions: We provide a useful framework for combining quantitative exposure data with expert-based exposure ratings in population-based studies that maximized the information from both sources. Our framework calibrated the ratings to a concentration scale between ratings and across time and provided a mechanism to estimate exposure when a job/industry group reported by a subject was not represented in the exposure database. It also allowed the job/industry groups' exposure levels to deviate from the pooled average for their respective JEM intensity ratings.
AbstractObjectivesAutomatic job coding tools were developed to reduce the laborious task of manually assigning job codes based on free-text job descriptions in census and survey data sources, including large occupational health studies. The objective of this study is to provide a case study of comparative performance of job coding and JEM (Job-Exposure Matrix)-assigned exposures agreement using existing coding tools.MethodsWe compared three automatic job coding tools [AUTONOC, CASCOT (Computer-Assisted Structured Coding Tool), and LabourR], which were selected based on availability, coding of English free-text into coding systems closely related to the 1988 version of the International Standard Classification of Occupations (ISCO-88), and capability to perform batch coding. We used manually coded job histories from the AsiaLymph case-control study that were translated into English prior to auto-coding to assess their performance. We applied two general population JEMs to assess agreement at exposure level. Percent agreement and PABAK (Prevalence-Adjusted Bias-Adjusted Kappa) were used to compare the agreement of results from manual coders and automatic coding tools.ResultsThe coding per cent agreement among the three tools ranged from 17.7 to 26.0% for exact matches at the most detailed 4-digit ISCO-88 level. The agreement was better at a more general level of job coding (e.g. 43.8–58.1% in 1-digit ISCO-88), and in exposure assignments (median values of PABAK coefficient ranging 0.69–0.78 across 12 JEM-assigned exposures). Based on our testing data, CASCOT was found to outperform others in terms of better agreement in both job coding (26% 4-digit agreement) and exposure assignment (median kappa 0.61).ConclusionsIn this study, we observed that agreement on job coding was generally low for the three tools but noted a higher degree of agreement in assigned exposures. The results indicate the need for study-specific evaluations prior to their automatic use in general population studies, as well as improvements in the evaluated automatic coding tools.
5 pages, 1 figure, 2 tables.-- PMID: 16556748 [PubMed]. ; [Objetives] To evaluate lifetime exposure to trihalomethanes (THM) through ingestion, inhalation, and dermal absorption in a hospital based case‐control study of bladder cancer conducted between 1998 and 2001 in five areas of Spain. The study base was comprised of subjects living in the catchment areas of the participating hospitals. ; [Methods] Individual information on water related habits was obtained from personal interviews of 1219 cases and 1271 controls: residential and occupational history, drinking water source at each residence and job, amount of water consumption, frequency and duration of showering, bathing, and swimming pool attendance. THM levels, water source history, and year when chlorination started in study areas were ascertained through measurements in drinking water samples and questionnaires to water companies and local authorities. Estimates of THM levels covered 79% of the subjects' person‐years of exposure. ; [Result] Current and historical average THM levels in water were correlated. Control subjects reported that drinking water source in the last residence was municipal for 63%, bottled for 22%, private well for 2%, and other sources for 13%. For the time window between age 15 and the time of interview, average residential THM level was 32.2 μg/l. THM exposure through ingestion was 23.7 μg/day on average, and was correlated with the ingestion THM level in the workplace. Overall, 79% usually took showers, 16% usually took baths, and 13% had ever attended a swimming pool. Between 21% and 45% of controls unexposed to THM through ingestion were evaluated as moderately or highly exposed through showering or bathing, and 5–10% were exposed through swimming in pools. ; [Conclusion] The importance of evaluating different routes is underscored by findings from experimental studies showing substantial differences in THM uptake and internal distribution by route. ; This project was funded by the Spanish Ministry of Health (FIS 2001–2002), the EPICUR-red (ISIII-GO3/174), the Intramural Research Program of the NIH, National Cancer Institute, Division of Cancer Epidemiology and Genetics (NCI Contract No. NO2-CP-11015), and the European Union (Environment and genetic factors in bladder cancer: a multicentric case-control study in Europe. BIOMED. 1998–2001). ; Peer reviewed
DNA methylation changes contribute to bladder carcinogenesis. Trihalomethanes (THM), a class of disinfection by-products, are associated with increased urothelial bladder cancer (UBC) risk. THM exposure in animal models produces DNA hypomethylation. We evaluated the relationship of LINE-1 5-methylcytosine levels (LINE-1%5mC) as outcome of long-term THM exposure among controls and as an effect modifier in the association between THM exposure and UBC risk. We used a case-control study of UBC conducted in Spain. We obtained personal lifetime residential THM levels and measured LINE-1%5mC by pyrosequencing in granulocyte DNA from blood samples in 548 incident cases and 559 hospital controls. Two LINE-1%5mC clusters (above and below 64%) were identified through unsupervised hierarchical cluster analysis. The association between THM levels and LINE-1%5mC was evaluated with β regression analyses and logistic regression was used to estimate odds ratios (OR) adjusting for covariables. LINE-1%5mC change between percentiles 75(th) and 25(th) of THM levels was 1.8% (95% confidence interval (CI): 0.1, 3.4%) among controls. THM levels above vs. below the median (26 μg/L) were associated with increased UBC risk, OR = 1.86 (95% CI: 1.25, 2.75), overall and among subjects with low levels of LINE-1%5mC (n = 975), OR = 2.14 (95% CI: 1.39, 3.30), but not associated with UBC risk among subjects' high levels of LINE-1%5mC (n = 162), interaction P = 0.03. Results suggest a positive association between LINE-1%5mC and THM levels among controls, and LINE-1%5mC status may modify the association between UBC risk and THM exposure. Because reverse causation and chance cannot be ruled out, confirmation studies are warranted. ; This study was partially supported by the Intramural Research Program of the National Institutes of Health, National Cancer Institute, Division of Cancer Epidemiology and Genetics (Contract NCI NO2-CP-11015); the Spanish Health Ministry (Fondo de Investigaciones Sanitarias–FIS, Instituto de Salud Carlos III, Spain 00/0745, ISIII-GO3/174, PI080533, PI051436, PI061614, PI09–02102, and PI11/00226) and the European Union (BMH4–98–3243); Red Temática de Investigación Cooperativa en Cáncer- RD12/0036/0050-RTICC; USA-NIH-RO1-CA089715; a postdoctoral fellowship awarded to AFSA from the Fundación Científica de la AECC; Fundació Marató TV3. The work was partially supported by the Association for International Cancer Research (AICR, #09–0780, including a PhD scholarship awarded to S.M.T.). The current analyses were supported by a Colciencias PhD Scholarship, Colombia (Grant: 529/2011 to L.A.S.). This work was also supported by grants from the Instituto de Salud Carlos III FEDER, (PI11/00226)
In 2020, 146,063 deaths due to pancreatic cancer are estimated to occur in Europe and the United States combined. To identify common susceptibility alleles, we performed the largest pancreatic cancer GWAS to date, including 9040 patients and 12,496 controls of European ancestry from the Pancreatic Cancer Cohort Consortium (PanScan) and the Pancreatic Cancer Case-Control Consortium (PanC4). Here, we find significant evidence of a novel association at rs78417682 (7p12/TNS3, P = 4.35 × 10-8). Replication of 10 promising signals in up to 2737 patients and 4752 controls from the PANcreatic Disease ReseArch (PANDoRA) consortium yields new genome-wide significant loci: rs13303010 at 1p36.33 (NOC2L, P = 8.36 × 10-14), rs2941471 at 8q21.11 (HNF4G, P = 6.60 × 10-10), rs4795218 at 17q12 (HNF1B, P = 1.32 × 10-8), and rs1517037 at 18q21.32 (GRP, P = 3.28 × 10-8). rs78417682 is not statistically significantly associated with pancreatic cancer in PANDoRA. Expression quantitative trait locus analysis in three independent pancreatic data sets provides molecular support of NOC2L as a pancreatic cancer susceptibility gene. ; This work was supported by RO1 CA154823, the Lustgarten Foundation, and federal funds from the NCI, US NIH under contract number HHSN261200800001E. The content of this publication does not necessarily reflect the views or policies of the US Department of Health and Human Services, and mention of trade names, commercial products, or organizations does not imply endorsement by the US government. Geno-typing Services were provided by the CIDR and the NCIs CGR. CIDR is fully funded through a federal contract from the NIH to the Johns Hopkins University, contract number HHSN268201100011I. The IARC/Central Europe study was supported by a grant from the US NCI at the NIH (R03 CA123546-02) and grants from the Ministry of Health of the Czech Republic (NR 9029-4/2006, NR9422-3, NR9998-3, and MH CZ- DRO-MMCI 00209805). The work at Johns Hopkins University was supported by the NCI Grants P50CA062924 and R01CA97075. Additional support was provided by, Susan Wojcicki, and Dennis Troper, and the Sol Goldman Pancreas Cancer Research Center. The Mayo Clinic Biospecimen Resource for Pancreas Research study is supported by the Mayo Clinic SPORE in Pancreatic Cancer (P50 CA102701). The Memorial Sloan Ket- tering Cancer Center Pancreatic Tumor Registry is supported by P30CA008748, the Geoffrey Beene Foundation, the Arnold and Arlene Goldstein Family, Foundation, and the Society of MSKCC. The PACIFIC Study was supported by RO1CA102765, Kaiser Permanente, and Group Health Cooperative. The Queensland Pancreatic Cancer Study was supported by a grant from the National Health and Medical Research Council of Australia (NHMRC; Grant number 442302). R.E.N. is supported by a NHMRC Senior Research Fellowship (#1060183). The UCSF pancreas study was supported by NIH-NCI grants (R01CA1009767, R01CA109767-S1, and R0CA059706) and the Joan Rombauer Pancreatic Cancer Fund. Collection of cancer incidence data was supported by the California Department of Public Health as part of the statewide cancer reporting pro- gram; the NCIs SEER Program under contract HSN261201000140C awarded to CPIC; and the CDCs National Program of Cancer Registries, under agreement #U58DP003862-01 awarded to the California Department of Public Health. The Yale (CT) pancreas cancer study is supported by NCI at the U.S. NIH, grant 5R01CA098870. The cooperation of 30 Connecticut hospitals, including Stamford Hospital, in allowing patient access is gratefully acknowledged. The Connecticut Pancreas Cancer Study was approved by the State of Connecticut Department of Public Health Human Investigation Committee. Certain data used in that study were obtained from the Connecticut Tumor Registry in the Connecticut Department of Public Health. The authors assume full responsibility for analyses and interpretation of these data. Studies included in PAN- DoRA were partly funded by the Czech Science Foundation (No. P301/12/1734), the Internal Grant Agency of the Czech Ministry of Health (IGA NT 13 263); the Baden- Württemberg State Ministry of Research, Science and Arts (Professor H. Brenner), the Heidelberger EPZ-Pancobank (Professor M.W. Büchler and team: Professor T. Hackert, Dr. N. A. Giese, Dr. Ch. Tjaden, E. Soyka, M. Meinhardt; Heidelberger. Stiftung Chir- urgie and BMBF grant 01GS08114), the BMBH (Professor P. Schirmacher; BMBF grant 01EY1101), the " 5 × 1000 " voluntary contribution of the Italian Government, the Italian Ministry of Health (RC1203GA57, RC1303GA53, RC1303GA54, and RC1303GA50), the Italian Association for Research on Cancer (Professor A. Scarpa; AIRC n. 12182), the Italian Ministry of Research (Professor A. Scarpa; FIRB - RBAP10AHJB), the Italian FIMP-Ministry of Health (Professor A. Scarpa; 12 CUP_J33G13000210001), and by the National Institute for Health Research Liverpool Pancreas Biomedical Research Unit, UK. We would like to acknowledge the contribution of Dr. Frederike Dijk and Professor Oliver Busch (Academic Medical Center, Amsterdam, the Netherlands). Assistance with genotype data quality control was provided by Cecelia Laurie and Cathy Laurie at the University of Washington Genetic Analysis Center. The American Cancer Society (ACS) funds the creation, maintenance, and updating of the Cancer Prevention Study II cohort. Cancer incidence data for CLUE were provided by the Maryland Cancer Registry, Center for Cancer Surveillance and Control, Department of Health and Mental Hygiene, 201 W. Preston Street, Room 400, Baltimore, MD 21201, http://phpa.dhmh.maryland.gov/ cancer , 410-767-4055. We acknowledge the State of Maryland, the Maryland Cigarette Restitution Fund, and the National Program of Cancer Registries of the Centers for Disease Control and Prevention for the funds that support the collection and availability of the cancer registry data. We thank all the CLUE participants. The Melbourne Col- laborative Cohort Study (MCCS) recruitment was funded by VicHealth and Cancer Council Victoria. The MCCS was further supported by Australian NHMRC grants 209057 and 396414 and by the infrastructure provided by Cancer Council Victoria. Cases and their vital status were ascertained through the Victorian Cancer Registry and the Australian Institute of Health and Welfare, including the National Death Index and the Australian Cancer Database. The NYU study (AZJ and AAA) was funded by NIH R01 CA098661, UM1 CA182934 and center grants P30 CA016087 and P30 ES000260. The PANKRAS II Study in Spain was supported by research grants from Instituto de Salud Carlos III-FEDER, Spain: Fondo de Investigaciones Sanitarias (FIS; #PI13/00082 and #PI15/01573) and Red Temática de Investigación Cooperativa en Cáncer, Spain (#RD12/ 0036/0050); and European Cooperation in Science and Technology (COST Action #BM1204: EU_Pancreas), Ministerio de Ciencia y Tecnología (CICYT SAF 2000-0097), Fondo de Investigación Sanitaria (95/0017), Madrid, Spain; Generalitat de Catalunya(CIRIT—SGR);"Red temática de investigación cooperativa de centros en Cáncer (C03/10),"Red temática de investigación cooperativa de centros en Epidemiología y salud pública(C03/09), and CIBER de Epidemiología (CIBERESP), Madrid. The Physicians 'Health Study was supported by research grants CA-097193, CA-34944, CA-40360, HL- 26490, and HL-34595 from the NIH, Bethesda, MD, USA. The Womens Health Study was supported by research grants CA-047988, HL-043851, HL-080467, and HL-099355 from the NIH, Bethesda, MD, USA. Health Professionals Follow-up Study is supported by NIH grant UM1 CA167552 from the NCI, Bethesda, MD, USA. Nurses ' Health Study is supported by NIH grants UM1 CA186107, P01 CA87969, and R01 CA49449 from the NCI, Bethesda, MD, USA. Additional support from the Hale Center for Pancreatic Cancer Research, U01 CA21017 from the NCI, Bethesda, MD, USA, and the United States Department of Defense CA130288, Lustgarten Foundation, Pancreatic Cancer Action Network, Noble Effort Fund, Peter R. Leavitt Family Fund, Wexler Family Fund, and Promises for Purple to B.M. Wolpin is acknowledged. The WHI program is funded by the National Heart, Lung, and Blood Institute, NIH, U.S. Department of Health and Human Services through contracts HHSN268201600018C, HHSN268201600001C, HHSN268201600002C, HHSN268201600003C, and HHSN268201600004C. The authors thank the WHI investigators and staff for their dedication, and the study participants for making the program possible. A full listing of WHI investigators can be found at http://www.whi.org/researchers/Documents%20%20Write%20a%20Paper/WHI%20Investigator%20Long%20List.pdf . We thank Laurie Burdett, Aurelie Vogt, BelyndaHicks, Amy Hutchinson, Meredith Yeager, and other staff at the NCI's Division ofEpidemiology and Genetics (DECG) CGR for GWAS genotyping. We also thank Bao Tran, Jyoti Shetty, and other members of the NCI Center for Cancer Research (CCR) Sequencing Facility for sequencing RNA from histologically normal pancreatic tissue samples (LTG samples). This study utilized the high-performance computational cap- abilities of the Biowulf Linux cluster at the NIH, Bethesda, MD, USA (http://biowulf.nih.gov). The Genotype-Tissue Expression (GTEx) Project was supported by the Common Fund of the Office of the Director of the NIH, and by NCI, NHGRI, NHLBI, NIDA, NIMH, and NINDS. The data used for the analyses described in this manuscript were obtained from the pancreatic tissue data from the GTEx Portal on 05/04/17. The results published here are in part based upon data generated by The Cancer Genome Atlas (TCGA) managed by the NCI and NHGRI. Information about TCGA can be found at http://cancergenome.nih.gov/. We acknowledge the clinical contributors that provided PDAC samples and the data producers of RNA-seq and GWAS genotype data from TCGA Research Network. The data set used for the analyses described in this manuscript was obtained by formal permission through the TCGA Data Access Committee (DAC) ; Sí
