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Original Investigation |

Association of Aspirin and NSAID Use With Risk of Colorectal Cancer According to Genetic Variants FREE

Hongmei Nan, MD, PhD1,2; Carolyn M. Hutter, MS, PhD3; Yi Lin, MS4; Eric J. Jacobs, PhD, MS5; Cornelia M. Ulrich, PhD4,6; Emily White, PhD4,7; John A. Baron, MD8; Sonja I. Berndt, PharmD, PhD9; Hermann Brenner, MD, MPH10,11; Katja Butterbach, PhD10; Bette J. Caan, DrPH12; Peter T. Campbell, PhD5; Christopher S. Carlson, PhD4; Graham Casey, PhD13; Jenny Chang-Claude, PhD14; Stephen J. Chanock, MD9; Michelle Cotterchio, PhD15; David Duggan, PhD16; Jane C. Figueiredo, PhD13; Charles S. Fuchs, MD, MPH17; Edward L. Giovannucci, MD18; Jian Gong, PhD4; Robert W. Haile, DrPH13; Tabitha A. Harrison, MPH4; Richard B. Hayes, DDS, PhD19; Michael Hoffmeister, PhD10; John L. Hopper, PhD20; Thomas J. Hudson, MD21,22; Mark A. Jenkins, PhD20; Shuo Jiao, PhD4; Noralane M. Lindor, MD23; Mathieu Lemire, PhD22; Loic Le Marchand, MD, PhD24; Polly A. Newcomb, PhD, MPH4; Shuji Ogino, MD, PhD17,25,26; Bethann M. Pflugeisen, PhD4; John D. Potter, MD, PhD4,27; Conghui Qu, MS4; Stephanie A. Rosse, PhD4; Anja Rudolph, PhD14; Robert E. Schoen, MD, MPH28; Fredrick R. Schumacher, PhD13; Daniela Seminara, PhD, MPH3; Martha L. Slattery, PhD29; Stephen N. Thibodeau, PhD30,31; Fridtjof Thomas, PhD32; Mark Thornquist, PhD4; Greg S. Warnick4; Brent W. Zanke, MD, PhD, FRCPC33; W. James Gauderman, PhD13; Ulrike Peters, PhD, MPH4,7; Li Hsu, PhD4,34; Andrew T. Chan, MD, MPH18,35 ; for the CCFR and GECCO
[+] Author Affiliations
1Department of Epidemiology, Richard M. Fairbanks School of Public Health, Indiana University, Indianapolis
2Indiana University Melvin and Bren Simon Cancer Center, Indianapolis
3National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland
4Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
5Epidemiology Research Program, American Cancer Society, Atlanta, Georgia
6Huntsman Cancer Institute, University of Utah, Salt Lake City
7Department of Epidemiology, University of Washington School of Public Health, Seattle
8Division of Gastroenterology and Hepatology, University of North Carolina School of Medicine, Chapel Hill
9Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
10Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
11German Cancer Consortium (DKTK), Heidelberg, Germany
12Division of Research, Kaiser Permanente Medical Care Program of Northern California, Oakland
13Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles
14Division of Cancer Epidemiology, German Cancer Research Center, Heidelberg, Germany
15Prevention and Cancer Control, Cancer Care Ontario, Toronto, Ontario, Canada
16Genetic Basis of Human Disease Division, Translational Genomics Research Institute (TGen), Phoenix, Arizona
17Department of Medical Oncology, Dana Farber Cancer Institute, Boston, Massachusetts
18Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massacusetts
19Division of Epidemiology, Department of Population Health, New York University School of Medicine, New York, New York
20Melbourne School of Population Health, University of Melbourne, Victoria, Australia
21Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
22Ontario Institute for Cancer Research, Toronto, Ontario, Canada
23Department of Health Sciences Research, Mayo Clinic, Scottsdale, Arizona
24Epidemiology Program, University of Hawaii Cancer Center, Honolulu
25Department of Pathology, Brigham and Women’s Hospital, Boston, Massachusetts
26Department of Epidemiology, Harvard School of Public Health, Boston, Masschusetts
27Centre for Public Health Research, Massey University, Wellington, New Zealand
28Department of Medicine and Epidemiology, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania
29Department of Internal Medicine, University of Utah Health Sciences Center, Salt Lake City
30Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota
31Department of Medical Genetics, Mayo Clinic, Rochester, Minnesota
32Division of Biostatistics and Epidemiology, Department of Preventive Medicine, University of Tennessee Health Science Center, Memphis
33Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada
34Department of Biostatistics, University of Washington, Seattle
35Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
JAMA. 2015;313(11):1133-1142. doi:10.1001/jama.2015.1815.
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Importance  Use of aspirin and other nonsteroidal anti-inflammatory drugs (NSAIDs) is associated with lower risk of colorectal cancer.

Objective  To identify common genetic markers that may confer differential benefit from aspirin or NSAID chemoprevention, we tested gene × environment interactions between regular use of aspirin and/or NSAIDs and single-nucleotide polymorphisms (SNPs) in relation to risk of colorectal cancer.

Design, Setting, and Participants  Case-control study using data from 5 case-control and 5 cohort studies initiated between 1976 and 2003 across the United States, Canada, Australia, and Germany and including colorectal cancer cases (n=8634) and matched controls (n=8553) ascertained between 1976 and 2011. Participants were all of European descent.

Exposures  Genome-wide SNP data and information on regular use of aspirin and/or NSAIDs and other risk factors.

Main Outcomes and Measures  Colorectal cancer.

Results  Regular use of aspirin and/or NSAIDs was associated with lower risk of colorectal cancer (prevalence, 28% vs 38%; odds ratio [OR], 0.69 [95% CI, 0.64-0.74]; P = 6.2 × 10−28) compared with nonregular use. In the conventional logistic regression analysis, the SNP rs2965667 at chromosome 12p12.3 near the MGST1 gene showed a genome-wide significant interaction with aspirin and/or NSAID use (P = 4.6 × 10−9 for interaction). Aspirin and/or NSAID use was associated with a lower risk of colorectal cancer among individuals with rs2965667-TT genotype (prevalence, 28% vs 38%; OR, 0.66 [95% CI, 0.61-0.70]; P = 7.7 × 10−33) but with a higher risk among those with rare (4%) TA or AA genotypes (prevalence, 35% vs 29%; OR, 1.89 [95% CI, 1.27-2.81]; P = .002). In case-only interaction analysis, the SNP rs16973225 at chromosome 15q25.2 near the IL16 gene showed a genome-wide significant interaction with use of aspirin and/or NSAIDs (P = 8.2 × 10−9 for interaction). Regular use was associated with a lower risk of colorectal cancer among individuals with rs16973225-AA genotype (prevalence, 28% vs 38%; OR, 0.66 [95% CI, 0.62-0.71]; P = 1.9 × 10−30) but was not associated with risk of colorectal cancer among those with less common (9%) AC or CC genotypes (prevalence, 36% vs 39%; OR, 0.97 [95% CI, 0.78-1.20]; P = .76).

Conclusions and Relevance  In this genome-wide investigation of gene × environment interactions, use of aspirin and/or NSAIDs was associated with lower risk of colorectal cancer, and this association differed according to genetic variation at 2 SNPs at chromosomes 12 and 15. Validation of these findings in additional populations may facilitate targeted colorectal cancer prevention strategies.

Figures in this Article

Considerable evidence demonstrates that use of aspirin and other nonsteroidal anti-inflammatory drugs (NSAIDs) is associated with lower risk of colorectal neoplasms.15 However, the mechanisms behind this association are not well understood. Routine use of aspirin, NSAIDs, or both for chemoprevention of cancer is not currently recommended because of uncertainty about risk-benefit profile. Hence, understanding the interrelationship between genetic markers and use of aspirin and NSAIDs, also known as gene × environment interactions, can help to identify population subgroups defined by genetic background that may preferentially benefit from chemopreventive use of these agents and offer novel insights into underlying mechanisms of carcinogenesis.

Previous genetic studies have examined the association of aspirin, NSAIDs, or both with colorectal cancer according to a limited number of candidate genes or pathways.610 Thus, to comprehensively identify common genetic markers that characterize individuals who may obtain differential benefit from aspirin and NSAIDs, we conducted a discovery-based, genome-wide analysis of gene × environment interactions between regular use of aspirin, NSAIDs, or both and single-nucleotide polymorphisms (SNPs) in relation to risk of colorectal cancer.

Study Population and Harmonization of Environmental Data

We included individual-level data pooled from a case-control study from the Colon Cancer Family Registry (CCFR) and 9 studies from the Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO) that were initiated between 1976 and 2003 and that enrolled cases of colorectal cancer diagnosed between 1976 and 2011 and matched controls across the United States, Canada, Australia, and Germany (Table 1). The cohorts are described in the eAppendix in the Supplement. All cases were defined as invasive colorectal adenocarcinoma and confirmed by medical record, pathology report, or death certificate. For prospective cohorts, nested case-control sets were constructed by fixing the cohort at a point at which risk set sampling was used to select cases and controls. For other case-control studies, population-based controls were used. For all studies, controls were matched on age, sex, and race/ethnicity; for some studies, controls were also matched on additional factors, such as enrollment date and trial group.

Table Graphic Jump LocationTable 1.  Descriptive Characteristics of Study Populations

Study-specific eligibility and our multistep data harmonization procedure are described in the eAppendix in the Supplement. Briefly, within each study, all exposure information, including use of aspirin, NSAIDs, or both, was collected by in-person interviews, structured questionnaires, or both with the reference time for cohort studies as the time of enrollment (Women’s Health Initiative [WHI]; Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial [PLCO]; and Vitamins and Lifestyle [VITAL]) or blood draw (Health Professionals Follow-up Study [HPFS] and Nurses’ Health Study [NHS]). Individuals with missing data on use of aspirin and NSAIDs were excluded. The precise definition of regular use of aspirin, NSAIDs, or both, which was determined individually by each study cohort, is provided in Table 1.

All participants provided written or oral informed consent, and studies were reviewed and approved by their respective institutional review boards or ethics committees.

Statistical Methods

A detailed description for genotyping, quality assurance and quality control, and imputation is provided in the Supplement. Mean sample and SNP call rates, and concordance rates for blinded duplicates, are listed in eTable 1 in the Supplement. In brief, genotyped SNPs were excluded based on call rate (<98%), lack of Hardy-Weinberg equilibrium (HWE) in controls (P < 1 × 10−4), and minor allele frequency (MAF) (MAF <5% for WHI Set 1, Diet, Activity and Lifestyle Study [DALS] Set 1, and Ontario Familial Colorectal Cancer Registry [OFCCR]; MAF <5/No. of samples for each other study). Because imputation of genotypes is standard practice in genetic association analysis, all autosomal SNPs of each study were imputed to the CEPH collection (CEU) population in HapMap II using IMPUTE (CCFR), BEAGLE (OFCCR), and MACH (all other studies).

After imputation and quality-control analyses, a total of approximately 2.7 million SNPs were used in the analysis. To reduce heterogeneity, all analyses were restricted to samples self-reported as of European descent and clustering with Utah residents with Northern/Western European ancestry from the CEU population in principal component analysis, including the HapMap II populations as reference.

Statistical analyses were conducted centrally on individual-level data. We adjusted for age at reference time, sex, center, and racial composition using the first 3 principal components from EIGENSTRAT to account for population substructure. Each directly genotyped SNP was coded as 0, 1, or 2 copies of the variant allele. For imputed SNPs, we used the expected number of copies of the variant allele, which provides unbiased test statistics.11 Both genotyped and imputed SNPs were examined as continuous variables (ie, assuming log-additive effects).

We analyzed each study separately using logistic regression models and combined study-specific results using fixed effect to obtain summary odds ratios (ORs) and 95% CIs. We calculated P values for heterogeneity using the Cochran Q test.12 Fixed-effect meta-analysis is routinely used in genome-wide association studies (GWAS) because it is the most powerful approach for identifying disease-associated variants.13,14 Furthermore, in our study fixed effect was more appropriate than random effects, since the Q-Q plots and the P value distributions indicated minimal heterogeneity across studies. Moreover, the effects may not fit a Gaussian distribution as required by the random-effects model, and the limited number of included studies may lead to an imprecise estimate of heterogeneity.15

To test for gene × environment interactions between SNPs and the regular use of aspirin, NSAIDs, or both (including use of aspirin only, NSAIDs only, or both aspirin and NSAIDs) or the regular use of aspirin only, we used conventional case-control logistic regression and case-only interaction analyses. Equations for the models used in the interaction analyses are provided in the eAppendix in the Supplement. We examined genome-wide correlations between SNPs and use of aspirin, NSAIDs, or both using linear regression analysis and did not observe deviation from independence. For all genome-wide gene × environment interaction analyses, P < 5.0 × 10−8 (2-sided), which yields a genome-wide significance level of .05, was considered statistically significant.

As described in the eAppendix in the Supplement, for each SNP showing gene × environment interaction with use of aspirin, NSAIDs, or both, we estimated the association of such use with colorectal cancer risk stratified by SNP genotypes, as well as associations in strata defined by SNP and use of aspirin, NSAIDs, or both with 1 common reference group. We also estimated absolute risks associated with use of aspirin, NSAIDs, or both among individuals defined by specific genotypes based on Surveillance, Epidemiology, and End Results age-adjusted colorectal cancer incidence rates (eAppendix in the Supplement).

All analyses were conducted using R 3.1.2 (R Foundation for Statistical Computing [http://www.r-project.org]).

The characteristics of the 8634 colorectal cancer cases and 8553 controls of European descent within each cohort from the CCFR and GECCO are provided in Table 1. As shown in the Figure, compared with nonregular use, regular use of aspirin, NSAIDs, or both (prevalence, 28% vs 38%; OR, 0.69 [95% CI, 0.64-0.74]; P = 6.2 × 10−28; P = .02 for heterogeneity) or aspirin only (prevalence, 24% vs 31%; OR, 0.71 [95% CI, 0.66-0.77]; P = 5.0 × 10−19; P = .01 for heterogeneity) was associated with lower risk of colorectal cancer.

Place holder to copy figure label and caption
Figure.
Main Associations of Regular Use of Aspirin, NSAIDs, or Both and Aspirin Only With the Risk of Colorectal Cancer

The size of the data markers is proportional to the precision of the estimate, which is the inverse of the variance. NSAID indicates nonsteroidal anti-inflammatory drug. For expansions of study names, see Table 1.

Graphic Jump Location

For the conventional logistic regression interaction analysis between each SNP and aspirin and/or NSAID use, the P values are shown in the Manhattan plot and Q-Q plot (eFigure 1 in the Supplement). At chromosome 12p12.3, we observed SNP rs2965667 (MAF = 1.7%) showing a genome-wide significant interaction with regular use of aspirin, NSAIDs, or both (P = 4.6 × 10−9 for interaction). The SNP rs10505806 (MAF = 3.8%), which had the second-lowest P value, was also found in the same locus, but it did not reach genome-wide significant interaction (P = 5.5 × 10−8 for interaction). These 2 top SNPs (rs2965667 and rs10505806) were highly correlated (D′ = 1.0 and r2 = 0.74 in HapMap CEU). In stratified analysis, compared with nonregular use, regular use of aspirin, NSAIDs, or both was statistically significantly associated with lower risk of colorectal cancer among individuals with rs2965667-TT genotype (prevalence, 28% vs 38%; OR, 0.66 [95% CI, 0.61-0.70]; P = 7.7 × 10−33), which comprised 96% (n = 16 465) of the population. In contrast, a higher risk was observed among the 4% (n = 722) of the population with TA or AA genotypes (prevalence, 35% vs 29%; OR, 1.89 [95% CI, 1.27-2.81]; P = .002).

As expected, stratified results for the highly correlated rs10505806 were similar to those for rs2965667. Compared with nonregular use, regular use of aspirin, NSAIDs, or both was statistically significantly associated with lower risk of colorectal cancer among individuals with rs10505806-AA genotype (prevalence, 28% vs 38%; OR, 0.66 [95% CI, 0.61-0.70]; P = 8.7 × 10−33), which comprised 95% (n = 16 328) of the population. In contrast, a higher risk was observed among the 5% (n = 859) of the population with AT or TT genotypes (prevalence, 35% vs 31%; OR, 1.56 [95% CI, 1.12-2.16]; P = .008) (Table 2 and eFigure 2 in the Supplement). The SNP rs2965667 also appeared as the SNP with the lowest P value in the exploratory analyses of aspirin only, but it did not reach genome-wide significant interaction (P = 8.0 × 10−7 for interaction; P = .35 for heterogeneity) (eTable 2 in the Supplement).

Table Graphic Jump LocationTable 2.  Risk for Colorectal Cancer According to Regular Use of Aspirin and/or NSAIDs, Stratified by the Genotypes of rs2965667, rs10505806, and rs16973225a

Both of these 2 highly correlated SNPs (rs2965667 and rs10505806) were imputed across all studies (100% of study samples), with a mean imputation R2 of 0.7 for rs2965667 and 0.8 for rs10505806 (eTable 3 in the Supplement). To further validate accuracy of imputation, we conducted direct genotyping of rs10505806 in participants enrolled in the NHS (553 cases and 955 controls) and the HPFS (403 cases and 401 controls).

The overall concordance of the SNP rs10505806 between imputed vs genotyped data was high (Pearson correlation coefficient r of 0.89). Among the total 956 cases and 1356 controls within NHS and HPFS whom we also directly genotyped rs10505806, we compared the gene × environment interaction statistical effect using direct genotype data with the imputed data. We confirmed no material difference in interaction estimates (P = .50 for heterogeneity) between imputed data (OR, 2.57 [95% CI, 1.02-6.43]; P = .045 for interaction) and directly genotyped data (OR, 2.19 [95% CI, 1.04-4.59]; P = .04 for interaction).

In case-only interaction analysis, SNP rs16973225 at chromosome 15q25.2 showed a genome-wide significant interaction with regular use of aspirin, NSAIDs, or both (P = 8.2 × 10−9 for interaction). In the stratified analysis, compared with nonregular use, regular use of aspirin, NSAIDs, or both was statistically significantly associated with lower risk of colorectal cancer among individuals with rs16973225-AA genotype (prevalence, 28% vs 38%; OR, 0.66 [95% CI, 0.62-0.71]; P = 1.9 × 10−30), which comprised 91% (n = 15 616) of the population, but was not associated with risk of colorectal cancer among those with AC or CC genotypes (prevalence, 36% vs 39%; OR, 0.97 [95% CI, 0.78-1.20]; P = .76) (Table 2 and eFigure 2 in the Supplement), which comprised 9% (n = 1568) of the population.

The SNP rs16973225 was directly genotyped in 9 of 15 study sets and was imputed with high quality (R2 = 0.9) in the remaining 6 study sets (38% of study samples) (eTable 3 in the Supplement). To validate imputation of rs16973225, we compared the gene × environment interaction statistical effect with colorectal cancer between imputed vs genotyped study sets in case-only interaction analysis. We found that the interaction statistical effect size was not different (P = .73 for heterogeneity) within cohorts based on imputed data (OR, 1.68 [95% CI, 1.30-2.17]; P = 4.7 × 10−5 for interaction) compared with cohorts based on directly genotyped data (OR, 1.59 [95% CI, 1.28-1.97]; P = 4.2 × 10−5 for interaction). In the case-only analysis of aspirin only, we did not observe genome-wide significant interactions.

The SNP rs2965667 showing a genome-wide significant interaction with use of aspirin, NSAIDs, or both in conventional logistic regression case-control analysis also appeared as a notable variant in case-only interaction analysis, although it did not achieve a genome-wide significance level (P = 7.5 × 10−8 for interaction). Similarly, the SNP rs16973225 reaching a genome-wide significant interaction with use of aspirin, NSAIDs, or both in case-only interaction analysis also showed evidence for gene × environment interaction in conventional logistic regression analysis (P = 2.2 × 10−4 for interaction).

The results for the 3 SNPs showing gene × environment interaction (rs2965667, rs10505806, and rs16973225) did not materially change after adjusting for additional colorectal cancer risk factors, including smoking status, body mass index, alcohol consumption, and red meat consumption (Table 2 and eTable 4 in the Supplement). For these 3 SNPs, we report the ORs for use of aspirin, NSAIDs, or both across genotypes corresponding to 0, 1, or 2 copies of the variant allele (eTable 5 in the Supplement) and the ORs for each SNP by strata of use of aspirin, NSAIDs, or both with 1 common reference group (eTable 6 in the Supplement), to fully describe the interaction.

We estimated absolute risks associated with use of aspirin, NSAIDs, or both among individuals with specific genotypes defined by each of these 3 SNPs. Compared with nonuse of aspirin, NSAIDs, or both, regular use was associated with 16.6 fewer colorectal cancer cases per 100 000 individuals with the rs2965667-TT genotype per year; 16.7 fewer colorectal cancer cases per 100 000 individuals with the rs10505806-AA genotype per year; and 16.8 fewer colorectal cancer cases per 100 000 individuals with the rs16973225-AA genotype per year. In contrast, regular use of aspirin, NSAIDs, or both was associated with 34.7 additional colorectal cancer cases per 100 000 individuals with rs2965667-TA or -AA genotypes per year; 21.1 additional colorectal cancer cases per 100 000 individuals with rs10505806-AT or -TT genotypes per year; and only 1.5 fewer colorectal cancer cases per 100 000 with rs16973225-AC or -CC genotypes per year.

Consistent with the preponderance of experimental, epidemiologic, and clinical trial evidence, we found that use of aspirin, NSAIDs, or both was associated with overall lower risk of colorectal cancer in this large genome-wide investigation of gene × environment interaction, which included 8634 colorectal cancer cases and 8553 controls. However, we identified that use of aspirin, NSAIDs, or both was differentially associated with colorectal cancer risk according to genetic variation at 2 highly correlated SNPs at chromosome 12p12.3 (rs2965667 and rs10505806) using a conventional logistic regression analysis.

These SNPs are 927 kb to 971 kb downstream from microsomal glutathione S-transferase 1 (MGST1 [NCBI Entrez Gene 4257]) (eFigure 3 in the Supplement), a member of the superfamily of membrane-associated proteins in eicosanoid and glutathione metabolism (MAPEG). MGST1 has high sequence homology to prostaglandin E synthase (MGST1L1 [NCBI Entrez Gene 9536]), another homologue of the MAPEG family that shares 38% of its DNA sequences with MGST1.16 MGST1 and MGST1L1 are up-regulated in several cancers, including colorectal cancer.17,18 MGST1L1 is coexpressed and functionally coupled to prostaglandin-endoperoxide synthase 2 (PTGS2; also known as cyclooxygenase 2 [COX-2]), and the combined activity of MGST1L1 and COX-2 increases production of proinflammatory prostaglandin E2 (PGE2), which promotes carcinogenesis through several mechanisms, including stimulation of WNT signaling, an essential oncogenic pathway of colorectal cancer.1922 An in vitro experiment has demonstrated that NSAIDs can inhibit expression of MGST1L1 and COX-2, thereby blocking COX-2–mediated synthesis of PGE2 in human colon carcinoma cells.23

Taken together, both MGST1L1 and the closely related gene MGST1 may influence NSAID-mediated inhibition of colorectal carcinogenesis partially through involvement in the PGE2-induced WNT signaling pathway. This finding is consistent with strong biological evidence linking genes in WNT signaling; use of aspirin, NSAIDs, or both; and colorectal cancer.24,25

Another candidate gene in this region is LIM domain only 3 (LMO3 [NCBI Entrez Gene 55885), a known oncogene located about 686 kb upstream from rs2965667 (eFigure 3 in the Supplement). Altered expression of LMO3 may contribute to the development of several cancers, such as neuroblastoma and lung cancer.26,27

The SNP rs2965667 is also located about 970 kb upstream from phosphatidylinositol-4-phosphate 3-kinase, catalytic subunit type 2 gamma (PIK3C2G [NCBI Entrez Gene 5288]) (eFigure 3 in the Supplement). The protein encoded by the PIK3C2G gene belongs to the phosphatidylinositol-4,5-bisphosphonate 3-kinase (PI3K) family, which plays a critical role in cancer.28 Experimental evidence suggests that activation of PI3K signaling enhances production of COX-2 and PGE2, which results in inhibition of apoptosis in colon cancer cell lines that can be restored with NSAID-mediated blockade of PI3K.29

Moreover, our previous study found that regular use of aspirin after diagnosis was associated with longer survival among the 15% to 30% of patients with colorectal cancer and with a mutation in phosphatidylinositol-4,5-bisphosphate 3-kinase, catalytic subunit alpha (PIK3CA [NCBI Entrez Gene 5290]), one of the PI3K family genes.30 Markedly improved survival associated with aspirin according to PIK3CA status was also found in an analysis within a separate clinical trial cohort.31 Further investigations for the joint effect of these genes would be helpful to better understand the underlying molecular mechanisms of aspirin, NSAIDs, and colorectal cancer.

In the case-only interaction analysis, another SNP, rs16973225 at chromosome 15q25.2, was identified with genome-wide significant association. This SNP is about 625 kb upstream of interleukin 16 (IL16 [NCBI Entrez Gene 3603) (eFigure 4 in the Supplement). As a multifunctional cytokine, IL16 plays a critical role in proinflammatory processes, including inflammatory bowel disease, Clostridium difficile–associated colitis, and many cancers, including colorectal.3234 Moreover, IL16 may stimulate monocyte induction of proinflammatory cytokines associated with tumorigenesis, including IL6 and tumor necrosis factor α,35,36 induction of COX-2 expression, and activation of WNT signaling.36 This evidence suggests the possibility that polymorphisms in or near the IL16 gene may regulate the production of inflammatory cytokines that modify the chemopreventive effect of aspirin or NSAIDs on colorectal cancer. It is plausible that those GWAS-identified promising loci outside of known coding regions affect more distant genes rather than the closest gene, since GWAS loci may be enhancers that can influence gene expression over a span of several hundred kilobases.37

Our study has several strengths. First, our large sample size facilitated detection of genome-wide gene × environment interactions, even using a conventional logistic regression or case-only interaction analysis and accounting for the stringent threshold for statistical significance. Second, we identified variants near genes possessing high functional plausibility given their critical roles in inflammation and prostaglandin synthesis, which have been mechanistically linked to use of aspirin or NSAIDs and colorectal carcinogenesis.

We acknowledge some limitations. First, heterogeneity exists in the definition of regular use of aspirin, NSAIDs, or both and the range of exposure periods encompassed by each study. However, we used a standardized harmonization process on a range of environmental variables, including use of aspirin, NSAIDs, or both across 10 cohort and case-control studies. The forest plots (Figure) show the consistency of the association between use of aspirin, NSAIDs, or both and colorectal cancer on a per-study level, and the pooled risk estimate (ie, OR) is remarkably similar to those from prior studies.38 Thus, bias attributable to heterogeneity in the definition and period of exposure is likely to be minimal.

Second, we acknowledge that SNP rs2965667 and the highly correlated rs10505806 are relatively rare and imputed in all studies. However, we directly genotyped rs10505806 in cases and controls within 2 cohorts included in our study population. The high overall concordance (r = 0.89) between imputed and directly genotyped data and the consistent gene × environment interaction statistical effect using either imputed or directly genotyped data support our assumption that our results are not greatly affected by the amount of imputed data.

Although prior GWAS-based studies have traditionally examined promising findings within a replication cohort, we did not split our data into discovery and replication sets because the most powerful analytical approach is a combined analysis across all studies.39 This approach is increasingly used as more individual-level GWAS data are becoming available.40 Moreover, the consistency of our findings and lack of heterogeneity across distinct study cohorts supports the validity of the results.

In this genome-wide investigation of gene × environment interactions, use of aspirin, NSAIDs, or both was associated with lower risk of colorectal cancer, and the association of these medications with colorectal cancer risk differed according to genetic variation at 2 SNPs at chromosomes 12 and 15. Validation of these findings in additional populations may facilitate targeted colorectal cancer prevention strategies.

Corresponding Authors: Li Hsu, PhD, Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue North, M2-B500, Seattle, WA 98109 (lih@fredhutch.org); Andrew T. Chan, MD, MPH, Division of Gastroenterology, Massachusetts General Hospital, GRJ-825C, Boston, MA 02114 (achan@mgh.harvard.edu).

Author Contributions: Ms Lin and Dr Peters had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Drs Peters, Hsu, and Chan share senior authorship.

Study concept and design: Nan, Hutter, Ulrich, Brenner, Campbell, Chang-Claude, Fuchs, Hopper, Seminara, Slattery, Thibodeau, Peters, Hsu, Chan.

Acquisition, analysis, or interpretation of data: Nan, Hutter, Lin, Jacobs, Ulrich, White, Baron, Berndt, Brenner, Butterbach, Caan, Campbell, Carlson, Casey, Chanock, Cotterchio, Duggan, Figueiredo, Fuchs, Giovannucci, Gong, Haile, Harrison, Hayes, Hoffmeister, Hopper, Hudson, Jenkins, Jiao, Lindor, Lemire, Le Marchand, Newcomb, Ogino, Pflugeisen, Potter, Qu, Rosse, Rudolph, Schoen, Schumacher, Slattery, Thomas, Thornquist, Warnick, Zanke, Gauderman, Peters, Hsu, Chan.

Drafting of the manuscript: Nan, Lin, Campbell, Chang-Claude, Figueiredo, Fuchs, Warnick, Peters, Hsu, Chan.

Critical revision of the manuscript for important intellectual content: Nan, Hutter, Jacobs, Ulrich, White, Baron, Berndt, Brenner, Butterbach, Caan, Carlson, Casey, Chanock, Cotterchio, Duggan, Fuchs, Giovannucci, Gong, Haile, Harrison, Hayes, Hoffmeister, Hopper, Hudson, Jenkins, Jiao, Lindor, Lemire, Le Marchand, Newcomb, Ogino, Pflugeisen, Potter, Qu, Rosse, Rudolph, Schoen, Schumacher, Seminara, Slattery, Thibodeau, Thomas, Thornquist, Zanke, Gauderman, Peters, Hsu, Chan.

Statistical analysis: Nan, Hutter, Lin, Campbell, Chang-Claude, Fuchs, Jiao, Lemire, Pflugeisen, Qu, Gauderman, Hsu, Chan.

Obtained funding: Brenner, Caan, Chanock, Fuchs, Giovannucci, Haile, Hopper, Hudson, Jenkins, Lindor, Newcomb, Schoen, Slattery, Peters, Chan.

Administrative, technical, or material support: White, Brenner, Campbell, Duggan, Gong, Harrison, Hayes, Hoffmeister, Hopper, Newcomb, Potter, Rudolph, Schumacher, Thibodeau, Thornquist, Warnick, Zanke, Chan.

Study supervision: Nan, Brenner, Campbell, Chanock, Fuchs, Harrison, Hudson, Potter, Peters, Hsu, Chan.

Conflict of Interest Disclosures: All authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Dr Baron reported holding a use patent for aspirin as a colorectal chemopreventive agent. Dr Zanke reported holding a patent licensed to Arctic Dx. Dr Chan reported receiving personal fees from Bayer Healthcare, Pozen, and Pfizer. No other authors reported disclosures.

Funding/Support: GECCO (Genetics and Epidemiology of Colorectal Cancer Consortium) was supported by the National Cancer Institute (NCI), National Institutes of Health (NIH), US Department of Health and Human Services (U01 CA137088; R01 CA059045). CCFR (Colon Cancer Family Registry) was supported by the NIH (RFA CA-95-011) and through cooperative agreements with members of the Colon Cancer Family Registry and prinicipal investigators. This genome wide scan was supported by the NCI, NIH, by U01 CA122839. The following Colon CFR centers contributed data to this manuscript and were supported by NIH: Australasian Colorectal Cancer Family Registry (U01 CA097735), Ontario Registry for Studies of Familial Colorectal Cancer (U01 CA074783), and Seattle Colorectal Cancer Family Registry (U01 CA074794). DACHS (Darmkrebs: Chancen der Verhütung durch Screening Study) is supported by the German Research Council (Deutsche Forschungsgemeinschaft, BR 1704/6-1, BR 1704/6-3, BR 1704/6-4 and CH 117/1-1) and the German Federal Ministry of Education and Research (01KH0404 and 01ER0814). DALS (Diet, Activity and Lifestyle Study) is supported by the NIH (R01 CA48998 to Dr Slattery). HPFS (Health Professionals Follow-up Study) is supported by the NIH (P01 CA 055075, UM1 CA167552, R01 137178, R01 CA151993, and P50 CA 127003) and the NHS (Nurses’ Health Study) by the NIH (R01 CA137178, P01 CA 087969, R01 CA151993, and P50 CA 127003). Dr Chan is also supported by K24 DK098311 and is a Damon Runyon Clinical Investigator. OFCCR (Ontario Familial Colorectal Cancer Registry) is supported by the NIH through funding allocated to the Ontario Registry for Studies of Familial Colorectal Cancer (U01 CA074783) (see CCFR section above). As subset of ARCTIC, OFCCR is supported by a GL2 grant from the Ontario Research Fund, the Canadian Institutes of Health Research, and the Cancer Risk Evaluation (CaRE) Program grant from the Canadian Cancer Society Research Institute. Dr Hudson and Dr Zanke are recipients of Senior Investigator Awards from the Ontario Institute for Cancer Research, through generous support from the Ontario Ministry of Research and Innovation. PLCO (Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial) is supported by the Intramural Research Program of the Division of Cancer Epidemiology and Genetics and by contracts from the Division of Cancer Prevention, NCI, NIH, Department of Health and Human Services. PMH-CCFR (Postmenopausal Hormone Study–Colon Cancer Family Registry) is supported by the NIH (R01 CA076366 to Dr Newcomb). VITAL (VITamins And Lifestyle) is supported by the NIH (K05 CA154337). WHI (Women’s Health Initiative) is supported by the National Heart, Lung, and Blood Institute, NIH, US Department of Health and Human Services, through contracts HHSN268201100046C, HHSN268201100001C, HHSN268201100002C, HHSN268201100003C, HHSN268201100004C, and HHSN271201100004C.

Role of the Funders/Sponsors: The funders and sponsors had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Disclaimer: The content of this manuscript does not necessarily reflect the views or policies of the NCI or any of the collaborating centers in the CFRs, nor does mention of trade names, commercial products, or organizations imply endorsement by the US government or the CFR.

Additional Contributions: We thank all study participants for making this work possible. We appreciate the efforts of the GECCO Coordinating Center ensuring the success of this collaboration. For DACHS, we thank all participants and cooperating clinicians, and Ute Handte-Daub, Renate Hettler-Jensen, Utz Benscheid, Muhabbet Celik, and Ursula Eilber for excellent technical assistance. For NHS and HPFS, we acknowledge all participants and staff; Patrice Soule and Hardeep Ranu of the Dana Farber Harvard Cancer Center High-Throughput Polymorphism Core, who assisted in the genotyping under the supervision of Dr Immaculata Devivo and Dr David Hunter; Qun Guo, who assisted in programming. We also thank the cancer registries of Alabama, Arizona, Arkansas, California, Colorado, Connecticut, Delaware, Florida, Georgia, Idaho, Illinois, Indiana, Iowa, Kentucky, Louisiana, Maine, Maryland, Massachusetts, Michigan, Nebraska, New Hampshire, New Jersey, New York, North Carolina, North Dakota, Ohio, Oklahoma, Oregon, Pennsylvania, Rhode Island, South Carolina, Tennessee, Texas, Virginia, Washington, and Wyoming. For PLCO, we thank Drs Christine Berg and Philip Prorok, Division of Cancer Prevention, National Cancer Institute; the Screening Center investigators and staff for the PLCO Cancer Screening Trial; Tom Riley and staff, Information Management Services Inc; Barbara O’Brien and staff, Westat Inc; and Drs Bill Kopp, Wen Shao, and staff, SAIC-Frederick. For PMH, we thank the staff of the Hormones and Colon Cancer study. For WHI, we 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 on the WHI website. Last, we acknowledge COMPASS (Comprehensive Center for the Advancement of Scientific Strategies) at the Fred Hutchinson Cancer Research Center for its work harmonizing the GECCO epidemiologic data set.

Correction: This article was corrected online on March 25, 2015, to add a note regarding shared authorship, correct an author degree, and correct a typographical error in the Acknowledgment section.

Chan  AT, Giovannucci  EL, Meyerhardt  JA, Schernhammer  ES, Curhan  GC, Fuchs  CS.  Long-term use of aspirin and nonsteroidal anti-inflammatory drugs and risk of colorectal cancer. JAMA. 2005;294(8):914-923.
PubMed   |  Link to Article
Flossmann  E, Rothwell  PM; British Doctors Aspirin Trial and the UK-TIA Aspirin Trial.  Effect of aspirin on long-term risk of colorectal cancer: consistent evidence from randomised and observational studies. Lancet. 2007;369(9573):1603-1613.
PubMed   |  Link to Article
Friis  S, Poulsen  AH, Sørensen  HT,  et al.  Aspirin and other non-steroidal anti-inflammatory drugs and risk of colorectal cancer: a Danish cohort study. Cancer Causes Control. 2009;20(5):731-740.
PubMed   |  Link to Article
García Rodríguez  LA, Huerta-Alvarez  C.  Reduced incidence of colorectal adenoma among long-term users of nonsteroidal antiinflammatory drugs: a pooled analysis of published studies and a new population-based study. Epidemiology. 2000;11(4):376-381.
PubMed   |  Link to Article
Rothwell  PM, Wilson  M, Elwin  CE,  et al.  Long-term effect of aspirin on colorectal cancer incidence and mortality: 20-year follow-up of five randomised trials. Lancet. 2010;376(9754):1741-1750.
PubMed   |  Link to Article
Hutter  CM, Chang-Claude  J, Slattery  ML,  et al.  Characterization of gene-environment interactions for colorectal cancer susceptibility loci. Cancer Res. 2012;72(8):2036-2044.
PubMed   |  Link to Article
Hutter  CM, Slattery  ML, Duggan  DJ,  et al.  Characterization of the association between 8q24 and colon cancer: gene-environment exploration and meta-analysis. BMC Cancer. 2010;10:670.
PubMed   |  Link to Article
Makar  KW, Poole  EM, Resler  AJ,  et al.  COX-1 (PTGS1) and COX-2 (PTGS2) polymorphisms, NSAID interactions, and risk of colon and rectal cancers in two independent populations. Cancer Causes Control. 2013;24(12):2059-2075.
PubMed   |  Link to Article
Nan  H, Morikawa  T, Suuriniemi  M,  et al.  Aspirin use, 8q24 single nucleotide polymorphism rs6983267, and colorectal cancer according to CTNNB1 alterations. J Natl Cancer Inst. 2013;105(24):1852-1861.
PubMed   |  Link to Article
Seufert  BL, Poole  EM, Whitton  J,  et al.  IκBKβ and NFκB1, NSAID use and risk of colorectal cancer in the Colon Cancer Family Registry. Carcinogenesis. 2013;34(1):79-85.
PubMed   |  Link to Article
Jiao  S, Hsu  L, Hutter  CM, Peters  U.  The use of imputed values in the meta-analysis of genome-wide association studies. Genet Epidemiol. 2011;35(7):597-605.
PubMed   |  Link to Article
Cochran  WG.  The combination of estimates from different experiments. Biometrics. 1954;10:101-129.
Link to Article
Pfeiffer  RM, Gail  MH, Pee  D.  On combining data from genome-wide association studies to discover disease-associated SNPs. Stat Sci. 2009;24(4):547-560.
Link to Article
Evangelou  E, Ioannidis  JP.  Meta-analysis methods for genome-wide association studies and beyond. Nat Rev Genet. 2013;14(6):379-389.
PubMed   |  Link to Article
Begum  F, Ghosh  D, Tseng  GC, Feingold  E.  Comprehensive literature review and statistical considerations for GWAS meta-analysis. Nucleic Acids Res. 2012;40(9):3777-3784.
PubMed   |  Link to Article
Prage  EB, Pawelzik  SC, Busenlehner  LS,  et al.  Location of inhibitor binding sites in the human inducible prostaglandin E synthase, MPGES1. Biochemistry. 2011;50(35):7684-7693.
PubMed   |  Link to Article
Morgenstern  R, Zhang  J, Johansson  K.  Microsomal glutathione transferase 1: mechanism and functional roles. Drug Metab Rev. 2011;43(2):300-306.
PubMed   |  Link to Article
Nakanishi  M, Gokhale  V, Meuillet  EJ, Rosenberg  DW.  mPGES-1 as a target for cancer suppression: a comprehensive invited review “Phospholipase A2 and lipid mediators.” Biochimie. 2010;92(6):660-664.
PubMed   |  Link to Article
Castellone  MD, Teramoto  H, Gutkind  JS.  Cyclooxygenase-2 and colorectal cancer chemoprevention: the beta-catenin connection. Cancer Res. 2006;66(23):11085-11088.
PubMed   |  Link to Article
Castellone  MD, Teramoto  H, Williams  BO, Druey  KM, Gutkind  JS.  Prostaglandin E2 promotes colon cancer cell growth through a Gs-axin-beta-catenin signaling axis. Science. 2005;310(5753):1504-1510.
PubMed   |  Link to Article
Kamei  D, Murakami  M, Nakatani  Y, Ishikawa  Y, Ishii  T, Kudo  I.  Potential role of microsomal prostaglandin E synthase-1 in tumorigenesis. J Biol Chem. 2003;278(21):19396-19405.
PubMed   |  Link to Article
Murakami  M, Naraba  H, Tanioka  T,  et al.  Regulation of prostaglandin E2 biosynthesis by inducible membrane-associated prostaglandin E2 synthase that acts in concert with cyclooxygenase-2. J Biol Chem. 2000;275(42):32783-32792.
PubMed   |  Link to Article
Sherratt  PJ, McLellan  LI, Hayes  JD.  Positive and negative regulation of prostaglandin E2 biosynthesis in human colorectal carcinoma cells by cancer chemopreventive agents. Biochem Pharmacol. 2003;66(1):51-61.
PubMed   |  Link to Article
Dihlmann  S, Siermann  A, von Knebel Doeberitz  M.  The nonsteroidal anti-inflammatory drugs aspirin and indomethacin attenuate beta-catenin/TCF-4 signaling. Oncogene. 2001;20(5):645-653.
PubMed   |  Link to Article
Gala  MK, Chan  AT.  Molecular pathways: aspirin and Wnt signaling—a molecularly targeted approach to cancer prevention and treatment [published online December 11, 2014]. Clin Cancer Res. doi:10.1158/1078-0432.CCR-14-0877.
Kwon  YJ, Lee  SJ, Koh  JS,  et al.  Genome-wide analysis of DNA methylation and the gene expression change in lung cancer. J Thorac Oncol. 2012;7(1):20-33.
PubMed   |  Link to Article
Aoyama  M, Ozaki  T, Inuzuka  H,  et al.  LMO3 interacts with neuronal transcription factor, HEN2, and acts as an oncogene in neuroblastoma. Cancer Res. 2005;65(11):4587-4597.
PubMed   |  Link to Article
Fresno Vara  JA, Casado  E, de Castro  J, Cejas  P, Belda-Iniesta  C, González-Barón  M.  PI3K/Akt signalling pathway and cancer. Cancer Treat Rev. 2004;30(2):193-204.
PubMed   |  Link to Article
Kaur  J, Sanyal  SN.  PI3-kinase/Wnt association mediates COX-2/PGE(2) pathway to inhibit apoptosis in early stages of colon carcinogenesis: chemoprevention by diclofenac. Tumour Biol. 2010;31(6):623-631.
PubMed   |  Link to Article
Liao  X, Lochhead  P, Nishihara  R,  et al.  Aspirin use, tumor PIK3CA mutation, and colorectal-cancer survival. N Engl J Med. 2012;367(17):1596-1606.
PubMed   |  Link to Article
Domingo  E, Church  DN, Sieber  O,  et al.  Evaluation of PIK3CA mutation as a predictor of benefit from nonsteroidal anti-inflammatory drug therapy in colorectal cancer. J Clin Oncol. 2013;31(34):4297-4305.
PubMed   |  Link to Article
Azimzadeh  P, Romani  S, Mohebbi  SR,  et al.  Interleukin-16 (IL-16) gene polymorphisms in Iranian patients with colorectal cancer. J Gastrointestin Liver Dis. 2011;20(4):371-376.
PubMed
Cruikshank  WW, Kornfeld  H, Center  DM.  Interleukin-16. J Leukoc Biol. 2000;67(6):757-766.
PubMed
Gerhard  R, Queisser  S, Tatge  H,  et al.  Down-regulation of interleukin-16 in human mast cells HMC-1 by Clostridium difficile toxins A and B. Naunyn Schmiedebergs Arch Pharmacol. 2011;383(3):285-295.
PubMed   |  Link to Article
Grivennikov  SI, Karin  M.  Inflammatory cytokines in cancer: tumour necrosis factor and interleukin 6 take the stage. Ann Rheum Dis. 2011;70(suppl 1):i104-i108.
PubMed   |  Link to Article
Klampfer  L.  Cytokines, inflammation and colon cancer. Curr Cancer Drug Targets. 2011;11(4):451-464.
PubMed   |  Link to Article
Symmons  O, Uslu  VV, Tsujimura  T,  et al.  Functional and topological characteristics of mammalian regulatory domains. Genome Res. 2014;24(3):390-400.
PubMed   |  Link to Article
Chan  AT, Arber  N, Burn  J,  et al.  Aspirin in the chemoprevention of colorectal neoplasia: an overview. Cancer Prev Res (Phila). 2012;5(2):164-178.
PubMed   |  Link to Article
Skol  AD, Scott  LJ, Abecasis  GR, Boehnke  M.  Joint analysis is more efficient than replication-based analysis for two-stage genome-wide association studies. Nat Genet. 2006;38(2):209-213.
PubMed   |  Link to Article
Pearce  CL, Rossing  MA, Lee  AW,  et al; Australian Cancer Study (Ovarian Cancer); Australian Ovarian Cancer Study Group; Ovarian Cancer Association Consortium.  Combined and interactive effects of environmental and GWAS-identified risk factors in ovarian cancer. Cancer Epidemiol Biomarkers Prev. 2013;22(5):880-890.
PubMed   |  Link to Article

Figures

Place holder to copy figure label and caption
Figure.
Main Associations of Regular Use of Aspirin, NSAIDs, or Both and Aspirin Only With the Risk of Colorectal Cancer

The size of the data markers is proportional to the precision of the estimate, which is the inverse of the variance. NSAID indicates nonsteroidal anti-inflammatory drug. For expansions of study names, see Table 1.

Graphic Jump Location

Tables

Table Graphic Jump LocationTable 1.  Descriptive Characteristics of Study Populations
Table Graphic Jump LocationTable 2.  Risk for Colorectal Cancer According to Regular Use of Aspirin and/or NSAIDs, Stratified by the Genotypes of rs2965667, rs10505806, and rs16973225a

References

Chan  AT, Giovannucci  EL, Meyerhardt  JA, Schernhammer  ES, Curhan  GC, Fuchs  CS.  Long-term use of aspirin and nonsteroidal anti-inflammatory drugs and risk of colorectal cancer. JAMA. 2005;294(8):914-923.
PubMed   |  Link to Article
Flossmann  E, Rothwell  PM; British Doctors Aspirin Trial and the UK-TIA Aspirin Trial.  Effect of aspirin on long-term risk of colorectal cancer: consistent evidence from randomised and observational studies. Lancet. 2007;369(9573):1603-1613.
PubMed   |  Link to Article
Friis  S, Poulsen  AH, Sørensen  HT,  et al.  Aspirin and other non-steroidal anti-inflammatory drugs and risk of colorectal cancer: a Danish cohort study. Cancer Causes Control. 2009;20(5):731-740.
PubMed   |  Link to Article
García Rodríguez  LA, Huerta-Alvarez  C.  Reduced incidence of colorectal adenoma among long-term users of nonsteroidal antiinflammatory drugs: a pooled analysis of published studies and a new population-based study. Epidemiology. 2000;11(4):376-381.
PubMed   |  Link to Article
Rothwell  PM, Wilson  M, Elwin  CE,  et al.  Long-term effect of aspirin on colorectal cancer incidence and mortality: 20-year follow-up of five randomised trials. Lancet. 2010;376(9754):1741-1750.
PubMed   |  Link to Article
Hutter  CM, Chang-Claude  J, Slattery  ML,  et al.  Characterization of gene-environment interactions for colorectal cancer susceptibility loci. Cancer Res. 2012;72(8):2036-2044.
PubMed   |  Link to Article
Hutter  CM, Slattery  ML, Duggan  DJ,  et al.  Characterization of the association between 8q24 and colon cancer: gene-environment exploration and meta-analysis. BMC Cancer. 2010;10:670.
PubMed   |  Link to Article
Makar  KW, Poole  EM, Resler  AJ,  et al.  COX-1 (PTGS1) and COX-2 (PTGS2) polymorphisms, NSAID interactions, and risk of colon and rectal cancers in two independent populations. Cancer Causes Control. 2013;24(12):2059-2075.
PubMed   |  Link to Article
Nan  H, Morikawa  T, Suuriniemi  M,  et al.  Aspirin use, 8q24 single nucleotide polymorphism rs6983267, and colorectal cancer according to CTNNB1 alterations. J Natl Cancer Inst. 2013;105(24):1852-1861.
PubMed   |  Link to Article
Seufert  BL, Poole  EM, Whitton  J,  et al.  IκBKβ and NFκB1, NSAID use and risk of colorectal cancer in the Colon Cancer Family Registry. Carcinogenesis. 2013;34(1):79-85.
PubMed   |  Link to Article
Jiao  S, Hsu  L, Hutter  CM, Peters  U.  The use of imputed values in the meta-analysis of genome-wide association studies. Genet Epidemiol. 2011;35(7):597-605.
PubMed   |  Link to Article
Cochran  WG.  The combination of estimates from different experiments. Biometrics. 1954;10:101-129.
Link to Article
Pfeiffer  RM, Gail  MH, Pee  D.  On combining data from genome-wide association studies to discover disease-associated SNPs. Stat Sci. 2009;24(4):547-560.
Link to Article
Evangelou  E, Ioannidis  JP.  Meta-analysis methods for genome-wide association studies and beyond. Nat Rev Genet. 2013;14(6):379-389.
PubMed   |  Link to Article
Begum  F, Ghosh  D, Tseng  GC, Feingold  E.  Comprehensive literature review and statistical considerations for GWAS meta-analysis. Nucleic Acids Res. 2012;40(9):3777-3784.
PubMed   |  Link to Article
Prage  EB, Pawelzik  SC, Busenlehner  LS,  et al.  Location of inhibitor binding sites in the human inducible prostaglandin E synthase, MPGES1. Biochemistry. 2011;50(35):7684-7693.
PubMed   |  Link to Article
Morgenstern  R, Zhang  J, Johansson  K.  Microsomal glutathione transferase 1: mechanism and functional roles. Drug Metab Rev. 2011;43(2):300-306.
PubMed   |  Link to Article
Nakanishi  M, Gokhale  V, Meuillet  EJ, Rosenberg  DW.  mPGES-1 as a target for cancer suppression: a comprehensive invited review “Phospholipase A2 and lipid mediators.” Biochimie. 2010;92(6):660-664.
PubMed   |  Link to Article
Castellone  MD, Teramoto  H, Gutkind  JS.  Cyclooxygenase-2 and colorectal cancer chemoprevention: the beta-catenin connection. Cancer Res. 2006;66(23):11085-11088.
PubMed   |  Link to Article
Castellone  MD, Teramoto  H, Williams  BO, Druey  KM, Gutkind  JS.  Prostaglandin E2 promotes colon cancer cell growth through a Gs-axin-beta-catenin signaling axis. Science. 2005;310(5753):1504-1510.
PubMed   |  Link to Article
Kamei  D, Murakami  M, Nakatani  Y, Ishikawa  Y, Ishii  T, Kudo  I.  Potential role of microsomal prostaglandin E synthase-1 in tumorigenesis. J Biol Chem. 2003;278(21):19396-19405.
PubMed   |  Link to Article
Murakami  M, Naraba  H, Tanioka  T,  et al.  Regulation of prostaglandin E2 biosynthesis by inducible membrane-associated prostaglandin E2 synthase that acts in concert with cyclooxygenase-2. J Biol Chem. 2000;275(42):32783-32792.
PubMed   |  Link to Article
Sherratt  PJ, McLellan  LI, Hayes  JD.  Positive and negative regulation of prostaglandin E2 biosynthesis in human colorectal carcinoma cells by cancer chemopreventive agents. Biochem Pharmacol. 2003;66(1):51-61.
PubMed   |  Link to Article
Dihlmann  S, Siermann  A, von Knebel Doeberitz  M.  The nonsteroidal anti-inflammatory drugs aspirin and indomethacin attenuate beta-catenin/TCF-4 signaling. Oncogene. 2001;20(5):645-653.
PubMed   |  Link to Article
Gala  MK, Chan  AT.  Molecular pathways: aspirin and Wnt signaling—a molecularly targeted approach to cancer prevention and treatment [published online December 11, 2014]. Clin Cancer Res. doi:10.1158/1078-0432.CCR-14-0877.
Kwon  YJ, Lee  SJ, Koh  JS,  et al.  Genome-wide analysis of DNA methylation and the gene expression change in lung cancer. J Thorac Oncol. 2012;7(1):20-33.
PubMed   |  Link to Article
Aoyama  M, Ozaki  T, Inuzuka  H,  et al.  LMO3 interacts with neuronal transcription factor, HEN2, and acts as an oncogene in neuroblastoma. Cancer Res. 2005;65(11):4587-4597.
PubMed   |  Link to Article
Fresno Vara  JA, Casado  E, de Castro  J, Cejas  P, Belda-Iniesta  C, González-Barón  M.  PI3K/Akt signalling pathway and cancer. Cancer Treat Rev. 2004;30(2):193-204.
PubMed   |  Link to Article
Kaur  J, Sanyal  SN.  PI3-kinase/Wnt association mediates COX-2/PGE(2) pathway to inhibit apoptosis in early stages of colon carcinogenesis: chemoprevention by diclofenac. Tumour Biol. 2010;31(6):623-631.
PubMed   |  Link to Article
Liao  X, Lochhead  P, Nishihara  R,  et al.  Aspirin use, tumor PIK3CA mutation, and colorectal-cancer survival. N Engl J Med. 2012;367(17):1596-1606.
PubMed   |  Link to Article
Domingo  E, Church  DN, Sieber  O,  et al.  Evaluation of PIK3CA mutation as a predictor of benefit from nonsteroidal anti-inflammatory drug therapy in colorectal cancer. J Clin Oncol. 2013;31(34):4297-4305.
PubMed   |  Link to Article
Azimzadeh  P, Romani  S, Mohebbi  SR,  et al.  Interleukin-16 (IL-16) gene polymorphisms in Iranian patients with colorectal cancer. J Gastrointestin Liver Dis. 2011;20(4):371-376.
PubMed
Cruikshank  WW, Kornfeld  H, Center  DM.  Interleukin-16. J Leukoc Biol. 2000;67(6):757-766.
PubMed
Gerhard  R, Queisser  S, Tatge  H,  et al.  Down-regulation of interleukin-16 in human mast cells HMC-1 by Clostridium difficile toxins A and B. Naunyn Schmiedebergs Arch Pharmacol. 2011;383(3):285-295.
PubMed   |  Link to Article
Grivennikov  SI, Karin  M.  Inflammatory cytokines in cancer: tumour necrosis factor and interleukin 6 take the stage. Ann Rheum Dis. 2011;70(suppl 1):i104-i108.
PubMed   |  Link to Article
Klampfer  L.  Cytokines, inflammation and colon cancer. Curr Cancer Drug Targets. 2011;11(4):451-464.
PubMed   |  Link to Article
Symmons  O, Uslu  VV, Tsujimura  T,  et al.  Functional and topological characteristics of mammalian regulatory domains. Genome Res. 2014;24(3):390-400.
PubMed   |  Link to Article
Chan  AT, Arber  N, Burn  J,  et al.  Aspirin in the chemoprevention of colorectal neoplasia: an overview. Cancer Prev Res (Phila). 2012;5(2):164-178.
PubMed   |  Link to Article
Skol  AD, Scott  LJ, Abecasis  GR, Boehnke  M.  Joint analysis is more efficient than replication-based analysis for two-stage genome-wide association studies. Nat Genet. 2006;38(2):209-213.
PubMed   |  Link to Article
Pearce  CL, Rossing  MA, Lee  AW,  et al; Australian Cancer Study (Ovarian Cancer); Australian Ovarian Cancer Study Group; Ovarian Cancer Association Consortium.  Combined and interactive effects of environmental and GWAS-identified risk factors in ovarian cancer. Cancer Epidemiol Biomarkers Prev. 2013;22(5):880-890.
PubMed   |  Link to Article
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The American Medical Association is accredited by the Accreditation Council for Continuing Medical Education to provide continuing medical education for physicians. The AMA designates this journal-based CME activity for a maximum of 1 AMA PRA Category 1 CreditTM per course. Physicians should claim only the credit commensurate with the extent of their participation in the activity. Physicians who complete the CME course and score at least 80% correct on the quiz are eligible for AMA PRA Category 1 CreditTM.
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Supplement.

eAppendix. Description of Study Populations

eReferences

eFigure 1. Manhattan Plot and Q-Q Plot for the Interaction Results With Aspirin and/or NSAIDs (Meta-analysis) From Conventional Logistic Regression Analysis

eFigure 2. Risk for Colorectal Cancer According to Regular Use of Aspirin and/or NSAIDs, Stratified by the Genotypes of rs2965667, rs10505806, and rs16973225

eFigure 3. Regional Association Plot of 1000 kb for the Interaction Between Regular Use of Aspirin and/or NSAIDs and rs2965667, as Well as Surrounding SNPs

eFigure 4. Regional Association Plot of 1000 kb for the Interaction Between Regular Use of Aspirin and/or NSAIDs and rs16973225, as Well as Surrounding SNPs

eTable 1. Details on Genotyping Platform and Quality Assurance and Quality Control (QA/QC Measurements)a

eTable 2. Interaction Between Regular Use of Aspirin Only and rs2965667 on the Risk of Colorectal Cancer

eTable 3. Imputation Quality for Three SNPs (rs2965667, rs10505806 and rs16973225) Identified in this Study

eTable 4. Additional Descriptive Characteristics of Study Populations

eTable 5. Risk for Colorectal Cancer According to Regular Use of Aspirin and/or NSAIDs, Stratified by the Genotypes of rs2965667, rs10505806, and rs16973225

eTable 6. Interactions Between Regular Use of Aspirin and/or NSAIDs and Genotypes of rs2965667, rs10505806, and rs16973225 on the Risk of Colorectal Cancer

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