Genome-wide Interaction Study with Smoking for Colorectal Cancer Risk Identifies Novel Genetic Loci Related to Tumor Suppression, Inflammation, and Immune Response

Colorectal cancer is one of the most commonly diagnosed malignancies globally, being the second and third most frequent cancer among women and men, respectively (1).

Germline genetics play an important role in the etiology of this complex disease. Genome-wide association studies (GWAS) to date have identified approximately 150 independent loci associated with colorectal cancer risk (2–6). However, the identified GWAS risk loci explain less than 20% of the heritability of colorectal cancer, leaving a large fraction unexplained (7). Gene–environment (GxE) interactions have been postulated to identify novel genetic risk loci and to explain some of this missing heritability (8).

Among modifiable cancer risk factors, tobacco smoking is the single strongest cause of cancer worldwide. It has been observed that between 8% to 35% of cancer deaths are attributable to smoking in the United States (9); while recent cohort and literature-based studies of wide-world populations estimated that around 10% of colorectal cancer cases can be attributable to cigarette smoking (10, 11). There is sufficient evidence to consider cigarette smoking as a causal risk factor for colorectal cancer, as reported in the last Monograph of the International Agency for Research on Cancer (IARC; ref. 12). Ever smokers showed a risk increase of 18% to 20% for colorectal cancer as compared with never smokers. This risk was higher for rectal cancer, and for colorectal cancer tumors with CpG island methylator phenotype (CIMP), microsatellite instability (MSI), and presence of BRAF somatic mutations, features often seen in tumors derived through the serrated pathway (13, 14). In addition, colorectal cancer risk increases with smoking intensity and duration in a dose-dependent manner (14, 15). However, a recent study using an instrumental approach found no causal association between ever having smoked regularly and colorectal cancer risk; while positive association between lifetime amount of smoking and colorectal cancer risk was observed (16). The dichotomization of the continuous phenotype (amount of smoking) to the binary exposure (ever having smoked) was done at 100 cigarettes over the course of life. In an instrumental setting, the causal estimates for a binary exposure assume that the causal effect is a stepwise function at the point of dichotomization (17), which could not reflect the relationship between smoking and colorectal cancer risk.

Tobacco use has been observed to interact with germline genetics, providing novel risk loci for some cancers, such as lung (18), pancreatic (19), and bladder cancers (20). Furthermore, our group estimated that a proportion of colorectal cancer risk heritability is explained by interactions between smoking and common genetic variants (21). However, our previous genome-wide interaction study of smoking and colorectal cancer risk did not identify any interaction that reached genome-wide significance (22). At that time, the study included 11,219 cases and 11,382 controls, and the null results were possibly due to the limited sample size.

The standard interaction test generally needs a sample size at least four times larger than that required to detect a main effect of comparable magnitude (23). However, novel statistical approaches enable an increase in power to detect GxE interactions. For instance, joint tests can detect novel susceptibility loci by accounting for both main and interaction effects, while two-step methods prioritize SNPs to decrease multiple testing burden by applying a filtering step prior to GxE testing (24).

In this study, we applied a set of interaction tests in a combined large dataset from three colorectal cancer consortia to identify novel genetic risk loci among: (i) nearly 35,000 cases and 45,000 controls with smoking status data, and (ii) over 13,000 cases and 16,000 controls among current or former smokers with smoking intensity and duration data. Significant results were stratified by colon subsite (proximal colon, distal colon, and rectum) and, in a subset of cases, by tumor molecular markers including CIMP, MSI, and BRAF and KRAS mutations. In addition, significant lead and correlated genetic variants were evaluated for chromatin accessibility and associations with gene expression of nearby genes. For the identified genes, genetic models predicting expression were developed, and interaction between predicted gene expression and smoking was tested on the colorectal cancer consortia datasets.

A total of 34 studies (cohort and case–control) comprising individuals of European ancestry were included in this study from three colorectal cancer genetic consortia: the Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO), the Colorectal Cancer Transdisciplinary Study (CORECT), and the Colon Cancer Family Registry (CCFR; ref. 2). For cohort studies, nested case–control sets were assembled via risk-set sampling, while population-based controls were used for case–control studies. Controls were matched on age, sex, race, and enrollment date/trial group, when applicable. Cases were defined as colorectal adenocarcinoma or advanced adenomas, and were confirmed by medical records, pathologic reports, or death certificate information. For the small subset of advanced adenoma cases, matched controls displayed polyp-free sigmoidoscopy or colonoscopy at the time of adenoma selection. All participants gave written informed consent and studies were approved by their respective Institutional Review Boards.

Lifestyle and environmental risk factors were collected by telephone or in-person interviews and structured self-administrated questionnaires. Harmonized quality-control checks were performed in each study following similar criteria (25, 26). Participants with any available present or past smoking habits comprised a total of 33,756 colorectal cancer cases (14,975 never vs. 18,781 ever cigarette smokers) and 44,346 controls (21,976 never vs. 22,370 ever cigarette smokers). Among ever smokers, 13,320 cases and 16,176 controls also had indicators of smoking intensity (cigarettes smoked per day), and 12,531 cases and 15,271 controls had data on duration of cigarette smoking (packs of cigarettes smoked in years of habit; pack-years; Supplementary Table S1). For the analysis of ever versus never smoking variable, we excluded 184 participants from the Alpha-Tocopherol, Beta Carotene Cancer Prevention Study (ATBC) because study participants only included ever smokers. Never smokers were excluded from analyses of smoking intensity and duration. To improve the interpretation of the results, smoking intensity and duration parameters were rescaled to units of 10 cigarettes smoked per day and 20 pack-years, respectively, to approximate one unit of the rescaled measures to one SD of the raw measures.

Details on quality control and genotyping were published previously (2, 27), and the genotyping arrays used are summarized in Supplementary Table S1. Briefly, exclusion criteria included: SNPs with missing call rate >2%–5%, departure from Hardy-Weinberg equilibrium (P < 1 × 10⁻⁴), inconsistencies between self-reported and genotypic sex, and discordant genotype calls within duplicate samples. Genotypes were imputed to the Haplotype Reference Consortium panel (ref. 28; 39.1 million variants) using the University of Michigan Imputation Server (29), and converted into a binary format for data management and analyses using the BinaryDosage R package (https://cran.r-project.org/web/packages/BinaryDosage). Imputed SNPs were restricted on the basis of a pooled minor allele frequency (MAF) ≥1% and imputation accuracy (R² > 0.8). After imputation and quality control, a total of over 7.2 million SNPs were selected for subsequent analyses. Principal component analysis for population stratification assessment was performed using PLINK1.9 on 30,000 randomly sampled imputed SNPs with MAF > 5% and R² > 0.99 of imputation quality score. We observed that the first three principal components (PC) explained the 72% of the variation and that the additional PCs explained a small and decreasing amount of the variation.

The association of smoking exposure variables with colorectal cancer risk was assessed by meta-analysis of study-specific estimates, adjusted by sex, age, study, and three principal genetic components to account for population stratification. Between-study heterogeneity was investigated using the χ² test, and inconsistency was measured using the I² statistic, which represents the proportion of total variation attributable to between-study variance. No outlier studies were identified, by estimating the posterior probability (>0.99) of having outliers based on mixture random effects using the “outlierProbs” function of metaplus R package. We also assessed smoking habits with colorectal cancer risk stratified by sex and by tumor site. Between-sex and between-site heterogeneity were investigated using the χ² test, and inconsistency was measured using the I² statistic.

To identify novel colorectal cancer interaction loci, we performed genome-wide scans using the GxEScanR R package (https://cran.r-project.org/web/packages/GxEScanR), which implements several interaction testing methods. Imputed allelic dosages were modeled as continuous variables. All the analyses were adjusted for sex, age, study, and three principal genetic components to account for population stratification. These covariates did not lead to any missing values. No single GxE test is universally most powerful and with that in mind, we applied three different testing procedures to maximize the chance of discovering novel loci related to colorectal cancer. These include the standard one-degree-of-freedom (1df) test of GxE interaction, a 3df joint test, and an efficient two-step test.

All three of the abovementioned tests utilize a logistic regression model of the form: |${\rm{logit}}({\rm{Pr}}(D\ = \ 1|G))\ = \ {\beta }_0\ + \ {\beta }_GG\ +{\beta }_EE\ + \ {\beta }_{GxE}GxE\ + \ {\beta }_CC$|⁠, where D is colorectal cancer, G is a particular SNP, E is smoking, and C are the adjustment covariates. The standard 1df GxE test is based on a likelihood ratio test of the null hypothesis |$H0\!:{\beta }_{GxE}\ = \ 0$|⁠. The 3df test (30) extends the well-known 2df joint test (31) of |$H0\!:{\beta }_G\ = \ {\beta }_{GxE}\ = \ 0$| to test |$H0\!:{\beta }_G\ = \ {\beta }_{GxE}\ = \ {\delta }_G\ = \ 0$|⁠, where |${\delta }_G$| represents the association between G and E in the combined case–control sample (32). The component sources of information for the 3df test have been shown to be statistically independent (33), thus guaranteeing that the overall type I error rate for this test is preserved. The two-step procedure weights GxE interaction tests (step 2, based on |${\beta }_{GxE}$|⁠) using ranks of a filtering statistic (step 1, based on |${\beta }_G\ $|and |${\delta }_G$|⁠; ref. 34) under the weighted hypothesis testing framework (Supplementary Fig. S1; refs. 35, 36). The two-step procedure can decrease multiple testing burden and improve power to detect interaction loci. A full description of this two-step methodology can be found in Supplementary Data.

Only SNPs not previously associated with colorectal cancer risk or smoking phenotypes, and not in linkage disequilibrium (LD) with variants associated with these phenotypes were considered. The likelihood of confounding is substantially smaller when testing for interactions as a confounder needs to be correlated with both the main effects and the interaction (37). However, because it has been observed that obesity is causally influencing smoking habits using an instrumental setting (38), SNPs that reached genome-wide significance (5 × 10⁻⁸) in the scans were further adjusted by body mass index. In addition, significant SNPs were further assessed by stratifying by sex, and cases by tumor location (proximal colon, distal colon, and rectum) or tumor molecular markers, when available, including CIMP, MSI or microsatellite stable (MSS), and BRAF and KRAS somatic mutation status.

Regional plots for all significant findings were generated, which enables inspection of strength of association, the extent of association signal and LD, and position of findings relative to genes in the region. Plots were generated using the software LocusZoom v1.3 (39). Measures of LD were estimated using European populations of the 1000 Genomes Project.

The putative functional role of these SNPs and those in LD (R² > 0.5) at 500 kb flanking regions were investigated with relation to their potential contribution to regulate gene expression in two ways: first, by their physical location in regions of chromatin accessibility or histone modifications, and second, through their direct association with expression of nearby genes (expression quantitative trait loci: eQTL). To evaluate the physical location, we obtained regions containing active enhancer elements in tissue from healthy colon and from tumor colon tissue samples from previously analyzed Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq), DNaseI hypersensitivity (DHS) sequencing, and H3K27ac histone chromatin immunoprecipitation sequencing datasets (40). To identify eQTLs, we used the following databases: colon transverse tissue sample from GTEx v8 dataset (41), and Colon Transcriptome Explorer (CoTrEx 2.0; https://barcuvaseq.org/cotrex/, accessed on May 2021) of the University of Barcelona and University of Virginia genotyping and RNA sequencing (BarcUVa-seq) project dataset, which is comprised of 445 epithelium-enriched healthy colon biopsies from ascending, transverse, and descending colon (42). We reported on all genes for which expression was associated with the lead and correlated SNPs. In addition, for loci at which gene expression was associated with lead and correlated SNPs, eQTL models predicting gene expression were developed through an elastic net regularized regression (43) using SNPs with MAF > 0.01 and imputation R² > 0.7 of the BarcUVa-seq project dataset, and validated in the colon transverse tissue sample from GTEx v8 dataset (34). These predicted gene expression levels were converted into quartiles and tested for interaction with smoking on colorectal cancer risk using the previously described logistic regression models.

The summary statistics data that support the findings of this study are available on request from the corresponding authors.

Ever smoking was more prevalent among colorectal cancer cases (56%) than controls (50%, P < 0.001; Supplementary Table S2). Among ever smokers, colorectal cancer cases were heavier smokers than controls (18.6 ± 11.5 vs. 18.0 ± 11.4 cigarettes smoked per day P < 0.001; and 26.8 ± 22.4 vs. 24.6 ± 21.6 pack-years, P < 0.001; Supplementary Table S1).

We observed that ever being a cigarette smoker was associated with increased colorectal cancer risk [OR, 1.25; 95% confidence interval (CI), 1.20–1.30; P_het = 0.07; I² = 23%] (Fig. 1). Sensitivity analyses showed a slightly lower estimate for cohort-based studies (OR, 1.22; 95% CI, 1.16–1.29) compared with case–control studies (OR, 1.28; 95% CI, 1.19–1.37). No evidence for heterogeneity was observed among cohort-based studies (P_het = 0.33; I² = 9%), while results among case–control studies showed moderate heterogeneity (P_het = 0.05; I² = 34%; Supplementary Table S3). Analyses stratified by sex showed higher risk for men (OR, 1.32; 95% CI, 1.24–1.40) than for women (OR, 1.19; 95% CI, 1.12–1.26; P_{sex difference} = 0.01; Fig. 1). The association between smoking and colorectal cancer varied by tumor site, with the strongest association observed in rectum (OR, 1.34; 95% CI, 1.24–1.45), compared with distal colon (OR, 1.23; 95% CI, 1.15–1.31) and proximal colon (OR, 1.18; 95% CI, 1.13–1.24; P_{site difference} = 0.03; Fig. 1). Among smokers, smoking intensity and duration parameters also showed a positive association with colorectal cancer risk (OR, 1.06; 95% CI, 1.03–1.09, per an increase in 10 cigarettes smoked per day; and OR, 1.11; 95% CI, 1.08–1.15, per an increase in 20 pack-years). Similar patterns for smoking intensity and duration were observed in analyses stratified by sex and tumor site (Fig. 1). Given that these results showed colorectal cancer–smoking associations overall and across all subsets, we focused our genome-wide GxE testing on the overall study population to maximize power. However, to explore variation in GxE effect estimates, we also conducted stratified analyses for novel findings.

Genomic control inflation and quantile-quantile plots for the SNP–smoking interactions for risk of colorectal cancer at the genome-wide level did not show evidence for residual population stratification (Supplementary Fig. S2).

The initial conventional test did not identify any genome-wide significant loci interacting with smoking status on the risk of colorectal cancer (Fig. 2). However, based on the 3df test, we identified a novel susceptibility locus at chromosome 3p12.1 (P ≤ 4.58 × 10⁻⁸; Table 1). The most significant SNP in this locus is rs9880919, located in an intron of cell adhesion molecule 2 (CADM2, 3p12.1) gene (Supplementary Table S4; Supplementary Fig. S3). This SNP showed evidence of an interaction with smoking status on colorectal cancer risk, as well as direct associations with both colorectal cancer and smoking status (Table 1). When stratified by genotype for this SNP, smoking status showed higher risk in those carrying the GG genotype (OR, 1.25; 95% CI, 1.20–1.30; P = 4.80 × 10⁻²⁷) compared with those carrying the GA genotype (OR, 1.17; 95% CI, 1.11–1.23; P = 2.40 × 10⁻⁹), and the AA genotype (OR, 1.12; 95% CI, 0.99–1.27; P = 0.08; Fig. 3; Supplementary Table S5). We observed similar interaction effects when analyses were stratified for study type, sex, or tumor site (Supplementary Table S4). The two-step approach did not identify any significant interactions (Supplementary Fig. S4).

Among smokers, we identified two genome-wide significant loci interacting with smoking intensity (cigarettes smoked per day) on the risk of colorectal cancer (Fig. 2). The first locus was on chromosome 6p21.33, with rs4151657 being the most significant interacting SNP (1df GxE P = 1.72 × 10⁻⁸, 3df test P = 3.52 × 10⁻⁸; Table 1) located in the intron of complement factor B (CFB) gene (Supplementary Table S4; Supplementary Fig. S5). When risk for colorectal cancer of smoking intensity was stratified by rs4151657 genotype, we observed that the association between smoking intensity and colorectal cancer risk was stronger in those carrying the TT genotype (OR, 1.12; 95% CI, 1.09–1.16; P = 1.30 × 10⁻¹², per an increase in 10 cigarettes smoked per day) compared with those carrying the TC genotype (OR, 1.06; 95% CI, 1.03–1.10; P = 1.10 × 10⁻⁴) or the CC genotype (OR, 0.94; 95% CI, 0.89–0.99; P = 0.03; Fig. 3; Supplementary Table S6). The second locus was on chromosome 8q24.23 with rs7005722 being the most significant interaction SNP (1df GxE P = 2.88 × 10⁻⁸; Table 1) located in an intergenic region between an uncharacterized noncoding RNA LOC101927915 and family with sequence similarity 135 member B (FAM135B) gene (Supplementary Table S4; Supplementary Fig. S5). When stratified by genotype, we observed that smoking intensity was only associated with colorectal cancer risk in those with genotypes carrying the A allele (OR, 1.17; 95% CI, 1.07–1.28; P = 6.70 × 10⁻⁵; and OR, 1.13; 95% CI, 1.10–1.16; P = 3.40 × 10⁻¹³; among individuals with AA and AC genotypes, respectively, for each 10 extra cigarettes smoked per day) but not in those carrying CC genotype (OR, 1.01; 95% CI, 0.98–1.04; P = 0.48; Fig. 3; Supplementary Table S6). Similar interaction patterns were observed when analyses were stratified by study type, sex, or tumor colon site for both rs4151657 and rs7005722 (Supplementary Table S4). We did not identify novel susceptibility loci interacting with cigarettes per day using 3df joint test or two-step test (Supplementary Fig. S4) and did not observe interactions with pack-years by any method (Fig. 2; Supplementary Fig. S4).

Further adjustment of interaction scans with body mass index did not modify observed interactions between genetics and smoking parameters on colorectal cancer risk (Supplementary Table S7).

To investigate whether the above observed interactions differed by tumor subtypes defined by CIMP, MSI, BRAF, and KRAS mutation status, we estimated the interaction in a subset with available data on these molecular characteristics (Supplementary Table S8). Similar to results using the whole dataset, the identified SNPs showed some evidence of interaction in all tumor marker subtypes, with the exception of the SNP in 3p12.1. However, the strength and significance of the interaction were different across the subtypes (Supplementary Table S9). For both rs4151657 (6p21.33) and rs7005722 (8q24.23), the interaction was observed for CIMP-, MSS, BRAF nonmutated, and KRAS both mutated and nonmutated tumors (Supplementary Table S9).

Regarding functional annotation, SNPs correlated with rs9880919 (3p12.1) and rs4151657 (6p21.33) were located in accessible chromatin regions, and H3K27ac and DHS peaks in nonmalignant colon samples, and in H3K27ac peaks in colon tumor samples (Table 2). This suggests a potential regulatory role of these genetic regions on transcription of nearby genes in nonmalignant and tumor samples. The SNP rs7005722 (8q24.23) was not correlated with other SNPs, nor was it identified as a variant enhancer locus or eQTL of nearby genes (Table 2). Lead and correlated SNPs in the 3p12.1 region were associated with expression levels of CADM2, in both BarcUVa-seq and GTEx colon transverse datasets (Table 2). For the SNPs in the 6p21.33 region, the long LD extension of this region provided association with 22 genes; of which activating transcription factor 6 beta (ATF6B) and EGF like domain multiple 8 (EGFL8) genes were associated in both datasets (BarcUVa-seq and GTEx colon transverse; Table 2).

Genetic models predicting gene expression were developed for CADM2 (3p12.1), and ATF6B and EGFL8 (6p21.33) genes (Supplementary Table S10). The lead SNPs for loci 3p12.1 (rs9880919), and 6p21.33 (rs4151657) were not selected in the genetic models, but they were strongly associated with predicted gene expression levels of corresponding nearby genes in the full case–control sample (P < 1 × 10⁻¹⁶). In turn, a statistical interaction was observed between expression levels of CADM2 with smoking status on risk for colorectal cancer (P = 0.034; Supplementary Table S11). When stratified by quartiles of CADM2 expression levels, smoking status showed higher risk in those individuals in the first quartile (OR, 1.28; 95% CI, 1.20–1.36; P = 3.67 × 10⁻¹⁴) compared with those in upper quartiles (fourth quartile, OR, 1.16; 95% CI, 1.09–1.23; P = 3.00 × 10⁻⁶; Fig. 4). In addition, an interaction was observed between expression levels of ATF6B with smoking intensity (cigarettes smoked per day) on risk for colorectal cancer (P = 5 × 10⁻⁵; Supplementary Table S10). When stratified by quartiles of ATF6B expression levels, we observed that the association between smoking intensity and colorectal cancer risk was stronger in those in the first quartile (OR, 1.11; 95% CI, 1.06–1.16; P = 2.65 × 10⁻⁶, per an increase in 10 cigarettes smoked per day) compared with those in upper quartiles (fourth quartile, OR, 0.99; 95% CI, 0.95–1.03; P = 0.5; Fig. 4). Finally, the interaction between expression of EGFL8 and smoking intensity on colorectal cancer risk was not significant (P = 0.052; Supplementary Table S11).

Combining genome-wide genetic data with harmonized smoking habit data across 34 studies enabled us to conduct the largest genome-wide exploration of GxSmoking interactions for colorectal cancer risk to date. We discovered one novel colorectal cancer locus in our genome-wide GxE scan of smoking status (3p12.1), and two SNPs in our scan of smoking intensity among smokers. None of the three genetic loci we discovered have been associated with colorectal cancer or smoking previously. In addition, two of the identified GxE interactions (3p12.1 and 6p21.33) showed differences in strength by colorectal cancer tumor marker subtypes as defined on the basis of MSI status, CIMP, and BRAF and KRAS mutation status. In addition, for these two loci, functional annotations indicated that they are located in enhancer regions and are linked to differential gene expression, and the putative gene for each was identified by demonstrating interactions between smoking and predicted expression affecting colorectal cancer risk.

Consistent with previous studies, we observed a strong positive association between smoking habits with risk of colorectal cancer with similar magnitudes to those previously observed. As expected, results showed higher risk estimates in men compared with women, and in rectal compared with colon cancer. The different associations across colon subsites have been related to tumor molecular markers of the serrated pathway associated with cigarette smoking and with colon subsites in the same fashion (13).

An enhancer locus in the CADM2 gene was found interacting with smoking status on colorectal cancer risk. In addition, genetically predicted CADM2 gene expression was observed to decrease colorectal cancer risk among ever smokers, but not among never smokers. This is the first time that this locus and CADM2 expression have been associated with colorectal cancer risk. However, CADM2 expression has been reported to reduce risk in prostate cancer, ovarian cancer, liver cancer, kidney cancer, lymphoma, melanoma, and glioma (44–50). These functional data could indicate a tumor suppression role of CADM2 gene. Other functional roles could be related to this gene. This locus has been associated with several risk-taking traits, where smoking was included among other phenotypes; however, risk-taking proneness was found mediating these associations (51, 52). CADM2 risk-taking risk variants were associated with increased CADM2 expression levels in brain tissues (53). These reported results could explain both the marginal evidence of association with smoking status in this study, and its interaction with smoking status on colorectal cancer risk as tumor suppressor gene.

For smoking intensity, we observed an interaction with rs4151657 at 6p21.33 located in the human leukocyte antigen (HLA) superlocus that encodes for many genes key for immune regulation and cellular processes. Genetic variants in this gene-rich region have been linked to several different diseases including colorectal cancer. The SNP rs4151657 falls within an enhancer region in the intron of the CFB gene. The complement system plays a pivotal role in the innate immune response to pathogens. It also has an important homeostatic role in recognizing damaged or altered “self” components (such as apoptotic and necrotic cells) to promote their elimination. Complement factor B amplifies the turnover of C3, the most abundant complement protein, activating the terminal pathway (54, 55). Insufficient or excessive complement activation can promote infections, immune-related disease, or tissue damage. The C allele of rs4151657 SNP (which homozygous genotype cancels out the risk effect of smoking intensity on colorectal cancer) has been reported to be associated to increased risk for ulcerative colitis (56, 57) and IgA nephropathy (58). We did not observe an association between SNPs in this locus and expression of CFB. When we expanded our eQTL analysis to nearby genes we observed associations with expression of ATF6B and EGFL8. In addition, predicted ATF6B gene expression was observed to increase colorectal cancer risk among light smokers, but not among heavy smokers. The protein encoded by ATF6B is a transcription factor in the unfolded protein response pathway during endoplasmic reticulum stress (59), and genetic variants in this gene region have been associated with levels of complement C4 protein (60) as well as with inflammatory lung diseases (61, 62). Downregulation of EGFL8 expression has been observed in colon tumor tissue (63). It has also been associated with a less favorable immune component in tumor tissue and with poor prognosis for patients with colorectal cancer, indicating that EGFL8 may act as a tumor suppressor (64). In summary, the observed interaction between smoking and genetic variants in the HLA region point to as strong candidate risk factor the differential expression of ATF6B gene linked to inflammation and immune function. Interestingly, smoking promotes cellular damage (65, 66) and bacterial colorectal cancer–related infections (67, 68), and cigarette smoking has been associated with higher colorectal cancer risk particularly in tumor samples with lower number of infiltrated T cells and macrophages (69, 70). Therefore, the identified germline genetic variants related to inflammation and immune response, may modify or reduce the risk of the effects of tobacco smoking on colorectal cancer.

Finally, we observed an interaction between smoking intensity and the intergenic region at 8q24.23 upstream of LOC101927915 and downstream of FAM135B. In contrast with the other identified regions, genetic markers of this region did not appear associated with enhancing activity or gene expression of nearby genes. FAM135B has been reported to be associated with risk for esophageal squamous cell carcinoma (71). Accordingly, the functional support for this genetic locus interacting with smoking on colorectal cancer risk is limited. In our analysis of GxE interaction incorporating gene expression data from the BarcUVa sample, tested genes (CADM2, ATF6B, and EGFL8) did not include the respective lead SNP discovered in the primary genome-wide GxE scan in the predictive model. However, each lead SNP was strongly associated with predicted gene expression of corresponding nearby genes in our full case–control sample. This suggests that these lead SNPs are likely not causal variants themselves, but rather that they are pointing to genes for which expression is modifying the effect of smoking on colorectal cancer risk.

This study generated results using a large dataset from three colorectal cancer consortia for gene–cigarette smoking interaction on colorectal cancer risk, including analyses stratified by tumor molecular markers, and functional annotations related to gene regulation. However, molecular marker analysis was limited on sample size, and we could not provide robust results on the pathways where the identified potential mechanisms are involved. In the findings for interaction with smoking intensity, genetic variants seem to contribute to colorectal cancer risk in the tumor molecular subtypes’ characteristic of the adenoma pathway. Thus, cigarette smoking would be related to colorectal cancer risk mainly in the cases derived from the serrated pathway, but interacting with genetic variants to increase colorectal cancer risk in the cases derived from the adenoma pathway. Further analyses are needed to evaluate this dual relationship between cigarette smoking and colorectal cancer risk.

Finally, the map of common genetic variants interacting with smoking habits and associated with colorectal cancer risk is not still saturated. Novel functional genetic regions can be predicted integrating strong functional data, for instance genomic single-cell data, and thereby reducing the multiple comparisons. In addition, further efforts are needed to enlarge the sample size of the analysis including wide-world populations, because most common germline genetic risk loci are have been replicated across racial and ethnic groups (72, 73), reducing inequalities in research, and to provide independent datasets to estimate the heritability contribution.

In conclusion, we identified novel genetic loci that modulate the association between cigarette smoking status and intensity and colorectal cancer risk. The potential mechanism behind these associations could be linked, in part, to tumor suppression, inflammation and immune response to the effects of tobacco smoking. These findings can guide potential prevention treatments in addition to quitting smoking. Additional functional studies are needed to verify the role of the identified loci interacting with smoking for colorectal cancer risk.

The Editor-in-Chief of Cancer Epidemiology, Biomarkers & Prevention is an author on this article. In keeping with AACR editorial policy, a senior member of the Cancer Epidemiology, Biomarkers & Prevention editorial team managed the consideration process for this submission and independently rendered the final decision concerning acceptability. No disclosures were reported by the other authors.

Where authors are identified as personnel of the International Agency for Research on Cancer/World Health Organization, the authors alone are responsible for the views expressed in this article and they do not necessarily represent the decisions, policy, or views of the International Agency for Research on Cancer/World Health Organization.

R. Carreras-Torres: Writing–original draft. A.E. Kim: Data curation, formal analysis, writing–review and editing. Y. Lin: Data curation, formal analysis, writing–review and editing. V. Díez-Obrero: Formal analysis, writing–review and editing. S.A. Bien: Data curation, project administration, writing–review and editing. C. Qu: Data curation, formal analysis, writing–review and editing. J. Wang: Data curation, formal analysis, writing–review and editing. N. Dimou: Formal analysis, writing–review and editing. E. Aglago: Data curation, writing–review and editing. D. Albanes: Resources, funding acquisition, writing–review and editing. V. Arndt: Resources, funding acquisition, writing–review and editing. J.W. Baurley: Resources, funding acquisition, writing–review and editing. S.I. Berndt: Funding acquisition, writing–review and editing. S. Bézieau: Resources, funding acquisition, writing–review and editing. D.T. Bishop: Resources, funding acquisition, writing–review and editing. E. Bouras: Resources, writing–review and editing. H. Brenner: Resources, writing–review and editing. A. Budiarto: Resources, funding acquisition, writing–review and editing. P.T. Campbell: Conceptualization, resources, funding acquisition, methodology, writing–review and editing. G. Casey: Resources, funding acquisition, writing–review and editing. A.T. Chan: Resources, funding acquisition, writing–review and editing. J. Chang-Claude: Resources, funding acquisition, writing–review and editing. X. Chen: Resources, funding acquisition, writing–review and editing. D.V. Conti: Writing–review and editing. C.H. Dampier: Writing–review and editing. M.A.M. Devall: Writing–review and editing. D.A. Drew: Writing–review and editing. J.C. Figueiredo: Resources, funding acquisition, writing–review and editing. S. Gallinger: Funding acquisition, writing–review and editing. G.G. Giles: Funding acquisition, writing–review and editing. S.B. Gruber: Resources, funding acquisition, writing–review and editing. A. Gsur: Writing–review and editing. M.J. Gunter: Resources, funding acquisition, writing–review and editing. T.A. Harrison: Writing–review and editing. A. Hidaka: Funding acquisition, writing–review and editing. M. Hoffmeister: Funding acquisition, writing–review and editing. J.R. Huyghe: Data curation, writing–review and editing. M.A. Jenkins: Funding acquisition, writing–review and editing. K.M. Jordahl: Data curation, writing–review and editing. E. Kawaguchi: Formal analysis, writing–review and editing. T.O. Keku: Funding acquisition, writing–review and editing. A. Kundaje: Data curation, funding acquisition, methodology, writing–review and editing. L. Le Marchand: Resources, funding acquisition, writing–review and editing. J. Lewinger: Methodology, writing–review and editing. L. Li: Funding acquisition, writing–review and editing. B. Mahesworo: Funding acquisition, writing–review and editing. J.L. Morrison: Funding acquisition, writing–review and editing. N. Murphy: Writing–review and editing. H. Nan: Writing–review and editing. R. Nassir: Funding acquisition, writing–review and editing. P.A. Newcomb: Writing–review and editing. M. Obón-Santacana: Writing–review and editing. S. Ogino: Funding acquisition, writing–review and editing. J. Ose: Funding acquisition, writing–review and editing. R.K. Pai: Funding acquisition, writing–review and editing. J.R. Palmer: Funding acquisition, writing–review and editing. N. Papadimitriou: Writing–review and editing. B. Pardamean: Funding acquisition, writing–review and editing. A.R. Peoples: Writing–review and editing. P.D.P. Pharoah: Funding acquisition, writing–review and editing. E.A. Platz: Funding acquisition, writing–review and editing. G. Rennert: Funding acquisition, writing–review and editing. E. Ruiz-Narvaez: Writing–review and editing. L.C. Sakoda: Funding acquisition, writing–review and editing. P.C. Scacheri: Conceptualization, resources, funding acquisition, writing–review and editing. S.L. Schmit: Funding acquisition, writing–review and editing. R.E. Schoen: Funding acquisition, writing–review and editing. A. Shcherbina: Funding acquisition, writing–review and editing. M.L. Slattery: Resources, funding acquisition, writing–review and editing. M.C. Stern: Resources, writing–review and editing. Y.-R. Su: Resources, writing–review and editing. C.M. Tangen: Resources, writing–review and editing. D.C. Thomas: Resources, funding acquisition, writing–review and editing. Y. Tian: Resources, funding acquisition, writing–review and editing. K.K. Tsilidis: Funding acquisition, writing–review and editing. C.M. Ulrich: Resources, funding acquisition, writing–review and editing. F.J.B. van Duijnhoven: Resources, funding acquisition, writing–review and editing. B. Van Guelpen: Resources, funding acquisition, writing–review and editing. K. Visvanathan: Funding acquisition, project administration. P. Vodicka: Funding acquisition, writing–review and editing. T.W. Cenggoro: Resources, funding acquisition, writing–review and editing. S.J. Weinstein: Resources, writing–review and editing. E. White: Resources, writing–review and editing. A. Wolk: Resources, writing–review and editing. M.O. Woods: Resources, writing–review and editing. L. Hsu: Conceptualization, resources, supervision, funding acquisition, methodology, writing–review and editing. U. Peters: Conceptualization, resources, supervision, funding acquisition, methodology, project administration, writing–review and editing. V. Moreno: Conceptualization, resources, supervision, funding acquisition, writing–review and editing. W.J. Gauderman: Conceptualization, resources, software, supervision, funding acquisition, methodology, writing–review and editing.

GECCO: This research and all authors were funded through the NCI, NIH, U.S. Department of Health and Human Services (U01 CA137088, R01 CA059045, U01 CA164930, R21 CA191312, R01201407, R01CA488857). Genotyping/Sequencing services were provided by the Center for Inherited Disease Research (CIDR) contract nos. HHSN268201700006I and HHSN268201200008I. This research and all authors were funded in part through the NIH/NCI Cancer Center Support Grant P30 CA015704. Scientific Computing Infrastructure at Fred Hutch funded by ORIP grant S10OD028685.

ASTERISK: This research and all authors were supported by a Hospital Clinical Research Program (PHRC-BRD09/C) from the University Hospital Center of Nantes (CHU de Nantes) and by the Regional Council of Pays de la Loire, the Groupement des Entreprises Françaises dans la Lutte contre le Cancer (GEFLUC), the Association Anne de Bretagne Génétique and the Ligue Régionale Contre le Cancer (LRCC). We are very grateful to Dr. Bruno Buecher without whom this project would not have existed. We also thank all those who agreed to participate in this study, including the patients and the healthy control persons, as well as all the physicians, technicians, and students.

The ATBC Study and all authors were supported by the Intramural Research Program of the U.S. NCI, NIH, Department of Health and Human Services.

The CCFR (www.coloncfr.org) and all authors were supported in part by funding from the NCI, NIH (award U01 CA167551). Support for case ascertainment was provided in part from the Surveillance, Epidemiology, and End Results (SEER) Program and the following U.S. state cancer registries: AZ, CO, MN, NC, NH; and by the Victoria Cancer Registry (Australia) and Ontario Cancer Registry (Canada). The CCFR Set-1 (Illumina 1M/1M-Duo) and Set-2 (Illumina Omni1-Quad) scans were supported by NIH awards U01 CA122839 and R01 CA143247 (to G. Casey). The CCFR Set-3 (Affymetrix Axiom CORECT Set array) was supported by NIH award U19 CA148107 and R01 CA81488 (to S.B. Gruber). The CCFR Set-4 (Illumina OncoArray 600K SNP array) was supported by NIH award U19 CA148107 (to S.B. Gruber) and by the Center for Inherited Disease Research (CIDR), which is funded by the NIH to the Johns Hopkins University, contract number HHSN268201200008I. Additional funding for the OFCCR/ARCTIC was through award GL201-043 from the Ontario Research Fund (to B.W. Zanke), award 112746 from the Canadian Institutes of Health Research (to T.J. Hudson), through a Cancer Risk Evaluation (CaRE) Program grant from the Canadian Cancer Society (to S. Gallinger), and through generous support from the Ontario Ministry of Research and Innovation. The SFCCR Illumina HumanCytoSNP array was supported in part through NCI/NIH awards U01/U24 CA074794 and R01 CA076366 (to P.A. Newcomb). The content of this article does not necessarily reflect the views or policies of the NCI, NIH or any of the collaborating centers in the CCFR, nor does mention of trade names, commercial products, or organizations imply endorsement by the U.S. Government, any cancer registry, or the CCFR. The Colon CFR graciously thanks the generous contributions of their study participants, dedication of study staff, and the financial support from the U.S. NCI, without which this important registry would not exist. The authors would like to thank the study participants and staff of the Seattle CCFR and the Hormones and Colon Cancer study (CORE Studies).

CLUE II: This study and all authors were funded by the NCI (U01 CA86308, Early Detection Research Network; P30 CA006973), National Institute on Aging (U01 AG18033), and the American Institute for Cancer Research. 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. Maryland Cancer Registry Cancer data were provided by the Maryland Cancer Registry, Center for Cancer Prevention and Control, Maryland Department of Health, with funding from the State of Maryland and the Maryland Cigarette Restitution Fund. The collection and availability of cancer registry data are also supported by the Cooperative Agreement NU58DP006333, funded by the Centers for Disease Control and Prevention. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the Centers for Disease Control and Prevention or the Department of Health and Human Services. We thank the participants of Clue II and appreciate the continued efforts of the staff at the Johns Hopkins George W. Comstock Center for Public Health Research and Prevention in the conduct of the Clue II Cohort Study.

COLO2&3: This study and all authors were funded by the NIH (R01 CA60987).

Colorectal Cancer Transdisciplinary (CORECT) Study: The CORECT Study and all authors were supported by the NCI/NIH, U.S. Department of Health and Human Services (grant nos. U19 CA148107, R01 CA81488, P30 CA014089, R01 CA197350; P01 CA196569; R01 CA201407) and National Institutes of Environmental Health Sciences, NIH (grant no. T32 ES013678).

CORSA: The CORSA study was funded by Austrian Research Funding Agency (FFG) BRIDGE (grant 829675, to A. Gsur), the “Herzfelder'sche Familienstiftung” (grant to A. Gsur) and was supported by COST Action BM1206. We kindly thank all individuals who agreed to participate in the CORSA study. Furthermore, we thank all cooperating physicians and students and the Biobank Graz of the Medical University of Graz.

CPS-II: The American Cancer Society funds the creation, maintenance, and updating of the Cancer Prevention Study-II (CPS-II) cohort. This study was conducted with Institutional Review Board approval. The authors thank the CPS-II participants and Study Management Group for their invaluable contributions to this research. The authors would also like to acknowledge the contribution to this study from central cancer registries supported through the Centers for Disease Control and Prevention National Program of Cancer Registries, and cancer registries supported by the NCI Surveillance Epidemiology and End Results program.

CRCGEN: Colorectal Cancer Genetics & Genomics, Spanish study and all authors were supported by Instituto de Salud Carlos III, co-funded by FEDER funds—a way to build Europe—(grants PI14-613 and PI09-1286), Agency for Management of University and Research Grants (AGAUR) of the Catalan Government (grant 2017SGR723), and Junta de Castilla y León (grant LE22A10-2). Sample collection of this work was supported by the Xarxa de Bancs de Tumors de Catalunya sponsored by Pla Director d'Oncología de Catalunya (XBTC), Plataforma Biobancos PT13/0010/0013 and ICOBIOBANC, sponsored by the Catalan Institute of Oncology. Spanish Association Against Cancer (AECC) Scientific Foundation grant GCTRA18022MORE.

Czech Republic CCS: This work and all authors were supported by the Grant Agency of the Czech Republic (21-27902S, 20-03997S), by the Grant Agency of the Ministry of Health of the Czech Republic (grants AZV NV18/03/00199 and AZV NV19-09-00237), and Charles University grants Unce/Med/006 and Progress Q28/LF1. We are thankful to all clinicians in major hospitals in the Czech Republic, without whom the study would not be practicable. We are also sincerely grateful to all patients participating in this study.

DACHS: This work and all authors were supported by the German Research Council (BR 1704/6-1, BR 1704/6-3, BR 1704/6-4, CH 117/1-1, HO 5117/2-1, HE 5998/2-1, KL 2354/3-1, RO 2270/8-1, and BR 1704/17-1), the Interdisciplinary Research Program of the National Center for Tumor Diseases (NCT), Germany, and the German Federal Ministry of Education and Research (01KH0404, 01ER0814, 01ER0815, 01ER1505A, and 01ER1505B). We thank all participants and cooperating clinicians, and everyone who provided excellent technical assistance.

DALS: This study was supported by the NIH (R01 CA48998 to M.L. Slattery).

EDRN: This work and all authors were funded and supported by the NCI, EDRN Grant (U01 CA 84968-06). We acknowledge all contributors to the development of the resource at University of Pittsburgh School of Medicine, Department of Gastroenterology, Department of Pathology, Hepatology and Nutrition and Biomedical Informatics.

EPIC: The coordination of EPIC is financially supported by IARC and also by the Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London which has additional infrastructure support provided by the NIHR Imperial Biomedical Research Centre (BRC). The national cohorts and all authors are supported by: Danish Cancer Society (Denmark); Ligue Contre le Cancer, Institut Gustave Roussy, Mutuelle Générale de l'Education Nationale, Institut National de la Santé et de la Recherche Médicale (INSERM) (France); German Cancer Aid, German Cancer Research Center (DKFZ), German Institute of Human Nutrition Potsdam- Rehbruecke (DIfE), Federal Ministry of Education and Research (BMBF; Germany); Associazione Italiana per la Ricerca sul Cancro-AIRC-Italy, Compagnia di SanPaolo and National Research Council (Italy); Dutch Ministry of Public Health, Welfare and Sports (VWS), Netherlands Cancer Registry (NKR), LK Research Funds, Dutch Prevention Funds, Dutch ZON (Zorg Onderzoek Nederland), World Cancer Research Fund (WCRF), Statistics Netherlands (The Netherlands); Health Research Fund (FIS) - Instituto de Salud Carlos III (ISCIII), Regional Governments of Andalucía, Asturias, Basque Country, Murcia and Navarra, and the Catalan Institute of Oncology - ICO (Spain); Swedish Cancer Society, Swedish Research Council and Regions of Skåne and Västerbotten (Sweden); Cancer Research UK (14136 to EPIC-Norfolk; C8221/A29017 to EPIC-Oxford), Medical Research Council (1000143 to EPIC-Norfolk; MR/M012190/1 to EPIC-Oxford). (United Kingdom). Where authors are identified as personnel of the International Agency for Research on Cancer/World Health Organization, the authors alone are responsible for the views expressed in this article and they do not necessarily represent the decisions, policy, or views of the International Agency for Research on Cancer/World Health Organization.

ESTHER/VERDI. This work and all authors were supported by grants from the Baden-Württemberg Ministry of Science, Research and Arts and the German Cancer Aid.

Harvard cohorts (HPFS, NHS, PHS): HPFS and all authors are supported by the NIH (P01 CA055075, UM1 CA167552, U01 CA167552, R01 CA137178, R01 CA151993, and R35 CA197735), NHS by the NIH (R01 CA137178, P01 CA087969, UM1 CA186107, R01 CA151993, and R35 CA197735), and PHS by the NIH (R01 CA042182). The study protocol was approved by the Institutional Review Boards of the Brigham and Women's Hospital and Harvard T.H. Chan School of Public Health, and those of participating registries as required. We acknowledge Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital as home of the NHS. We would like to thank the participants and staff of the HPFS, NHS and PHS for their valuable contributions as well as the following state cancer registries for their help: AL, AZ, AR, CA, CO, CT, DE, FL, GA, ID, IL, IN, IA, KY, LA, ME, MD, MA, MI, NE, NH, NJ, NY, NC, ND, OH, OK, OR, PA, RI, SC, TN, TX, VA, WA, WY. The authors assume full responsibility for analyses and interpretation of these data.

Hawaii Adenoma Study: This study was supported by the NCI grants R01 CA072520 (to L. Le Marchand).

Kentucky: This work and all authors were supported by the following grant support: Clinical Investigator Award from Damon Runyon Cancer Research Foundation (CI-8); NCI R01CA136726. We would like to acknowledge the staff at the Kentucky Cancer Registry.

LCCS: The Leeds Colorectal Cancer Study and all authors were funded by the Food Standards Agency and Cancer Research UK Programme Award (C588/A19167). We acknowledge the contributions of Jennifer Barrett, Robin Waxman, Gillian Smith, and Emma Northwood in conducting this study.

Melbourne Collaborative Cohort Study (MCCS) cohort recruitment was funded by VicHealth and Cancer Council Victoria. The MCCS and all authors were further augmented by Australian National Health and Medical Research Council grants 209057, 396414, and 1074383 and by 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 DatabaseMEC: NIH (R37 CA054281, P01 CA033619, and R01 CA063464).

NCCCS I & II: We acknowledge funding support for this project and to all authors from the NIH, R01 CA66635 and P30 DK034987. We would like to thank the study participants, and the NC Colorectal Cancer Study staff.

NFCCR: This work was supported by an Interdisciplinary Health Research Team award from the Canadian Institutes of Health Research (CRT 43821); the NIH, U.S. Department of Health and Human Services (U01 CA74783); and NCI of Canada grants (18223 and 18226). The authors wish to acknowledge the contribution of Alexandre Belisle and the genotyping team of the McGill University and Génome Québec Innovation Centre, Montréal, Canada, for genotyping the Sequenom panel in the NFCCR samples. Funding was provided to M.O. Woods by the Canadian Cancer Society Research Institute.

PLCO: Intramural Research Program of the Division of Cancer Epidemiology and Genetics and supported by contracts from the Division of Cancer Prevention, National Cancer Institute, NIH, DHHS. Funding was provided to the study and all authors by NIH, Genes, Environment and Health Initiative (GEI) Z01 CP 010200, NIH U01 HG004446, and NIH GEI U01 HG 004438. The authors thank the PLCO Cancer Screening Trial screening center investigators and the staff from Information Management Services Inc and Westat Inc. Most importantly, we thank the study participants for their contributions that made this study possible. Cancer incidence data have been provided by the District of Columbia Cancer Registry, Georgia Cancer Registry, Hawaii Cancer Registry, Minnesota Cancer Surveillance System, Missouri Cancer Registry, Nevada Central Cancer Registry, Pennsylvania Cancer Registry, Texas Cancer Registry, Virginia Cancer Registry, and Wisconsin Cancer Reporting System. All are supported in part by funds from the Center for Disease Control and Prevention, National Program for Central Registries, local states or by the NCI, SEER program. The results reported here and the conclusions derived are the sole responsibility of the authors.

SEARCH: The University of Cambridge has received salary support in respect of PDPP from the NHS in the East of England through the Clinical Academic Reserve. Cancer Research UK (C490/A16561); the UK National Institute for Health Research Biomedical Research Centres at the University of Cambridge. We thank the SEARCH team.

SELECT: Research reported in this publication was supported in part by the NCI of the NIH under award numbers U10 CA37429 (to C.D. Blanke), and UM1 CA182883 (to C.M. Tangen and I.M. Thompson). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. We thank the research and clinical staff at the sites that participated on SELECT study, without whom the trial would not have been successful. We are also grateful to the 35,533 dedicated men who participated in SELECT.

SMS and REACH: This work was supported by the NCI (grant P01 CA074184, to J.D. Potter and P.A. Newcomb, grants R01 CA097325, R03 CA153323, and K05 CA152715, to P.A. Newcomb, and the National Center for Advancing Translational Sciences at the National Institutes of Health (grant KL2 TR000421, to A.N. Burnett-Hartman).

Swedish Mammography Cohort and Cohort of Swedish Men: This work is supported by the Swedish Research Council/Infrastructure grant, the Swedish Cancer Foundation, and the Karolinska Institute's Distinguished Professor award to A. Wolk.

UK Biobank: This research has been conducted using the UK Biobank Resource under application number 8614.

VITAL: This study was funded by the NIH (K05 CA154337, to E. White).

WHI: The WHI program and all authors are funded by the National Heart, Lung, and Blood Institute, NIH, U.S. Department of Health and Human Services through contracts HHSN268201100046C, HHSN268201100001C, HHSN268201100002C, HHSN268201100003C, HHSN268201100004C, and HHSN271201100004C. 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%20Short%20List.pdf.

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