Common Genetic Polymorphisms Modify the Effect of Smoking on Absolute Risk of Bladder Cancer

Bladder cancer is a smoking-related disease that occurs most frequently in males living in industrialized countries (1). According to GLOBOCAN (http://globocan.iarc.fr/), an estimated 382,660 new cases of bladder cancer were diagnosed worldwide in 2008, and 68,812 of those cases were diagnosed in the United States. Bladder cancer has high morbidity and represents an important public health problem as most cancers present as “superficial” tumors that recur frequently and require regular follow-up screening and intervention (2). Thus, effective bladder cancer prevention strategies could have an important public health impact.

Well-characterized polymorphisms in 2 carcinogen-metabolizing genes, NAT2 and GSTM1, are associated with bladder cancer risk (3, 4). More recently, genome-wide association studies (GWAS) have identified additional common genetic susceptibility variants (5–11), which provide important clues into underlying biologic pathways. For instance, a recent GWAS identified the UGT1A region as a bladder cancer susceptibility locus (6), and further fine mapping and functional work identified a functional variant in UGT1A6, a gene involved in the detoxification of bladder carcinogens (12).

Studies of gene–environment interactions can provide insights into biologic mechanisms of disease and could have public health implications (13, 14). However, there are very few examples of established gene–environment interactions in cancer. A notable example is the interaction between NAT2 acetylation and smoking in bladder cancer, by which subjects with the slow NAT2 acetylation genotype have a higher relative risk from smoking than those with the rapid/intermediate acetylation genotypes (3). In contrast, other susceptibility loci do not seem to modify the relative risk of smoking associated with bladder cancer (3, 6, 15, 16).

Although assessment of gene–environment interactions is usually done on a multiplicative scale (i.e., evaluating whether the relative risk for smoking varies across levels of genetic risk), assessment of interactions on an additive scale (i.e., evaluating whether the risk difference for smoking varies across levels of genetic risk) is more relevant for assessing public heath effects, such as evaluating if the number of cancers that could be prevented by an intervention differs for subjects at different levels of genetic risk. In this report, we used data from 7 studies in the National Cancer Institute (NCI)-GWAS and a new approach to study additive gene–environment interactions that could be useful to evaluate the potential implications for targeted prevention strategies, as well as to provide additional biologic insights.

Analyses are based on data from 7 studies participating in the NCI-GWAS (6, 9). These included 2 case–control studies [Spanish Bladder Cancer Study (SBCS) and New England Bladder Cancer Study (NEBCS)], and 5 prospective cohorts [Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial (PLCO), The American Cancer Society Cancer Prevention Study II Nutrition Cohort (CPS II), Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study (ATBC), Nurse's Health Study (NHS), and Health Professionals Follow-up Study (HPFS)]. Cases were defined as histologically confirmed primary carcinoma of the urinary bladder including carcinoma in situ [International Classification of Diseases (ICD)-0-2 topography codes C67.0–C67.9 or ICD-9 codes 188.0–188.9 and 2337]. Cases and controls were of European background as determined by population substructure analyses with STRUCTURE (17) and principal component analysis for subjects with scan data or by self-report otherwise.

Age and smoking information was obtained from risk factor questionnaires administered at the time of enrollment into the studies. For case–control studies, this corresponded to the time of diagnosis for cases and the time of selection for controls (18, 19). However, the time between questionnaire and diagnosis/selection varied in prospective cohorts depending on when subjects were diagnosed/selected after enrollment. Smoking habits were characterized as smoking status (never/ever and subcategories of ever smokers for current/former/occasional), average number of cigarettes smoked, and total duration of smoking, as previously described for SBCS (18) and NEBCS (19). These 2 studies used the same smoking questions, and smoking variables derived from other studies were harmonized on the basis of the information reported in their questionnaires. Smoking status was missing from a total of 21 subjects.

Each participating study obtained informed consent from study participants and approval from its Institutional Review Board for this study.

In this article, we define absolute risk as the probability of developing bladder cancer, relative risk as the ratio of the absolute risk in smokers over the absolute risk in nonsmokers, and risk difference as the difference between the absolute risk in smokers minus the absolute risk in nonsmokers.

This report includes data on 12 single-nucleotide polymorphisms (SNP) previously identified as susceptibility variants for bladder cancer (3, 4, 5–11), and genotyped in the NCI Core Genotyping Facility. Samples from 5 studies included in stage I of the NCI-GWAS [SBCS, the Maine and Vermont components the NEBCS (NEBCS-ME, VT), PLCO, CPS II, ATBC] had been scanned with Illumina Infinium Arrays as previously described (6). Quality control filtering included completion rates less than 94% to 96%, heterozygosity of less than 22% and of more than 35%, and discordant gender information (6). TaqMan custom genotyping assays (ABI) for rs9642880, rs710521, rs2294008, and rs401681 were used to genotype samples from the other 3 studies [the New Hampshire component of the NEBCS (NEBCS-NH), NHS, HPFS; ref. 6], and to genotype rs17863783 (ref. 20; 369 controls from PLCO not genotyped for rs17863783). GSTM1 was genotyped in samples from SBCS, NEBCS (NEBCS-ME, VT), NEBCS (NEBCS-NH), PLCO, CPSII, and ATBC. For PLCO and CPSII, we genotyped GSTM1 in samples from all cases and a subset of controls (638 from PLCO and 486 from CSPII) using a previously described assay (3).

Each SNP was coded as the number of risk alleles a subject carried. To assess gene–smoking interactions, SNPs were coded as dichotomous variables (see explanation under the heading “Test for gene–environment interactions” below). The rs1495741 AA genotype (corresponding to the NAT2 slow acetylation phenotype) was compared with the combined GG and AG genotypes (corresponding to the NAT2 rapid/intermediate acetylation phenotype; ref. 21); and the GSTM1 null (−/−) genotype was compared with the combined (+/−) and (+/+) genotypes (GSTM1 “present”).

Analyses were based on pooled individual data for subjects with complete smoking and SNP information. Subjects classified as occasional smokers in SBCS (N = 131), NEBCS (N = 53), and CPSII (N = 26) were considered ever smokers in the analyses of ever/never smoking, but excluded from analyses comparing never/former/current smokers. Smoking status was considered the primary analysis, whereas analyses for smoking dose and duration were secondary. Logistic regression models adjusted for study, age (5-year categories), and gender were used to estimate OR and 95% confidence intervals (95% CI) for genetic and smoking variables. ORs were used as an approximate measure of relative risk.

PLCO, HPFS, and NHS controls were sampled by smoking status (see Supplementary Table S1 for details) and therefore, these 2 studies did not contribute to the estimates of smoking main effects. To account for stratified sampling of controls by smoking status in the analysis, models with smoking variables included 3 interaction terms for smoking status and indicator variables for PLCO, HPFS, and NHS. All participants in ATBC were smoker males (Supplementary Table S1) and therefore data from this study also did not contribute to estimate smoking main effects. Differences in the estimates of smoking ORs by study design (case–control vs. cohort) were assessed by including an interaction term for smoking and study design in a logistic regression model.

To conduct test for gene–environment interaction on relative risk scale (multiplicative interaction), we used an Empirical Bayes (EB) model fitting procedure that can gain power by exploiting the assumption of gene–environment independence in the underlying population and yet is immune to bias when the independence assumption is violated (22, 23). For gene–environment interaction testing on the additive scale (i.e., risk difference), we conducted a likelihood ratio (LRT) test comparing an unconstrained and constrained model for joint effects using logistic regression models (24). Under the null hypothesis of additive model, the OR for the joint effect of a given SNP and smoking status is constrained so that the risk difference associated with one exposure (e.g., smoking) is constant across levels of other exposure (e.g., SNP), and vice versa. All tests for gene–environment interactions were conducted using categorical variables (each SNP was coded as a dichotomous variable indicating the presence of any risk allele) to avoid complex numerical issues in the additive test and to make the additive and multiplicative tests comparable. The limitations of the additive test are related to nonstandard model fitting procedures when using continuous variables such as log-additive effect of SNP alleles.

We present empirical estimates of joint ORs and compare them with those obtained under multiplicative and additive models. Expected ORs under the multiplicative model were calculated as the product of the observed main effects for smoking and SNP and expected ORs under the additive model were calculated as the sum of the main effects minus 1 (25).

As a parsimonious way of summarizing the effects of genetic variants across all loci for each subject, we constructed a “polygenic risk score” (PRS) variable as the weighted sum of the risk allele counts across all loci, where the weight for each individual SNP is determined by the log OR of its association with bladder cancer risk, adjusted for study, age, gender, and smoking status. PRS was estimated for a subset of 3,211 cases and 3,424 controls from SBCS, NEBCS-ME, VT, CPSII, PLCO, and ATBC with complete genotype data for all 12 SNPs. NEBCS (NEBCS-NH), NHS, and HPFS were excluded from PRS analyses because not all 12 SNPs were genotyped (see Materials and Methods). We derived quartiles of the PRS variable based on its distribution in the control sample. The score was calculated for all the SNPs as well as for the subset of SNPs showing significant additive interactions with smoking status for comparison. Tests for heterogeneity of PRS by tumor grade and stage were conducted using logistic regression analyses in cases with tumor subtype as the outcome variable.

The population attributable risks (PAR) can be interpreted as the proportion of bladder cancer cases occurring in a population that is attributable to a particular exposure or combination of exposures. We estimated PARs of smoking, PRS, and the combination of both factors for white males in the United States using data from subjects with these characteristics and complete smoking and PRS data (i.e., 1,385 cases and 1,347 controls from NEBCS-ME, VT, CPSII, and PLCO). The combined PARs for smoking and PRS were computed from estimates of the main and joint ORs from logistic regression models and the joint distribution of smoking status and PRS in cases from these studies: PAR = 1− ∑(ρ_j/OR_j) for j mutually exclusive strata formed by the cross classification of smoking and the PRS, where ρ_j is the proportion of all cases in stratum j and OR_j is the OR in stratum j compared with the reference stratum (26). The reference categories were never smokers and the lowest quintile of the PRS. It should be noted that this formula uses frequency distributions from cases only and therefore is not affected by selection factors in the control populations.

To compute absolute risks of bladder cancer for a given starting and end age, we used the formula from Petracci and colleagues (27). This formula calculates the absolute risk for a combination of risk factors based on the estimates of ORs for the combination of risk factors in relation to subjects without these factors, estimates of attributable risk, 5-year age-specific incidence rates for bladder cancer (source: SEER17 urinary bladder cancer age-specific incidence rates from 2005–2007 in white males), and 5-year age-specific mortality rates from causes other than bladder cancer (source: U.S. Mortality 2000–2007, with Kaposi Sarcoma and Mesothelioma in white males). Estimates of attributable risk did not vary substantially with age and therefore a single overall estimate was used in calculations of absolute risk. 95% CIs for estimates of attributable risk and risk differences were obtained from a nonparametric bootstrap (28) with 1,000 bootstrap replications. Each bootstrap sample was drawn with replacement separately for cases and controls within each study, with the original number of cases and controls in each replication. For each bootstrap replication, we fitted logistic regression models to obtain estimates of relative risk, attributable risk, and risk difference. The 95% CIs were calculated on the basis of the bootstrap distribution of 1,000 estimates obtained from each bootstrap sample.

Data analysis and management was conducted with R, and STATA S.E. v.11.1.

A total of 3,942 cases and 5,680 controls from 7 bladder cancer studies were available for analyses. Characteristics of the study populations are shown in Table 1 and Supplementary Table S1.

The observed joint relative risk (OR) for the combined effect of ever smoking and the risk allele for each individual SNP were larger than expected under additive effects for 11 of 12 SNPs (Table 2). The test for additive interaction was significant at P < 0.05 for 6 of the 12 SNPs, whereas the test of multiplicative interaction was significant only for rs149574 in NAT2 (P = 0.029). A similar pattern was seen when comparing current and former smokers with never smokers, but weaker evidence for interactions was found when comparing current with former smokers (Supplementary Tables S2A, S2B, and S2C). Of note, SLC14A1 showed stronger evidence for additive interactions for current versus never (P = 0.002) and former versus never (P = 0.015), than for ever versus never (P = 0.053). We observed no significant multiplicative or additive interactions when comparing former and current smokers, except for a multiplicative interaction for GSTM1 (P = 0.027). Evaluation of multiplicative interactions with smoking dose (average cigarettes per day) and duration (total number of years smoked) for each of the SNPs showed a significant interaction only for rs149574 in NAT2 and smoking dose (EB P_interaction = 0.006).

To summarize the genetic risk associated with multiple SNPs, we constructed a PRS based on the 12 SNPs associated with bladder cancer risk. For white males in the U.S. studies, those in the top quartile of the 12 SNP PRS had a 2.94-fold risk of bladder cancer (95% CI, 2.32%–3.73%) compared with subjects in the bottom quartile (Table 3). The OR for smoking did not vary significantly with quartiles of the PRS, although there was a suggestion of larger ORs for those in the highest quartiles of the PRS (Table 3). The estimated relative risks for the PRS did not vary significantly by stage or grade of the tumor (data not shown).

On the basis of data from white males in the U.S. studies, the estimated PAR for ever smoking was 50% (95% CI, 41%–58%), the PAR for the upper 3 quartiles of the PRS was 42% (95% CI, 33%–51%), whereas the combined PAR for smoking and PRS was 67% (95% CI 51%–78%). The 30-year cumulative risks of bladder cancer for a white male 50 years of age by increasing quartiles of the PRS were 1.3%, 2.5%, 2.9%, and 3.8%. Figure 1 shows the 30-year cumulative risk of bladder cancer by smoking status and the 12 SNP PRS. Estimates of 30-year absolute risk associated with smoking status ranged between 0.9% and 2.9% for subjects in the lowest quartile of the PRS and between 1.7% and 9.9% for subjects in the highest quartile of the PRS. Similarly, the absolute risk associated with quartiles of the PRS ranged between 0.9% and 1.7% for never smokers and between 2.9% and 9.9% for current smokers.

Table 4 shows the estimates of risk differences associated with smoking status by quartiles of the PRS. Tests for additive interactions indicate that there are strongly significant differences in risk differences for smoking status across levels of the PRS (Table 4). The P value for additive interaction testing for differences in smoking risk differences between the top and bottom quartiles of the risk score was 1.2 × 10⁻⁷ when smoking status was defined as ever/never smoking (1 degree of freedom (df) test), and it was 3.3 × 10⁻⁷ when smoking status was defined as never/former/current smoking (2 df test). The risk difference for current versus never smokers was 4 times larger for subjects in the top quartile (8.2% with 95% CI 4.7%–13.1%) compared with the bottom quartiles (2.0% with 95% CI 0.8%–3.8%; P = 1.1 × 10⁻⁴). Estimates of absolute risk and risk differences based on a PRS using the 7 SNPs with significant additive interactions (Table 2) yielded similar results as the 12 SNP PRS (data not shown).

Our analyses provide strong evidence for additive gene–environment interactions between smoking and known susceptibility loci for bladder cancer. The degree of risk stratification obtained from the combined effects of smoking and the PRS could be of significance for the development of targeted risk reduction strategies in the population. Specifically, our results suggest that the projected number of cases that could be avoided by smoking prevention efforts is larger for subjects at higher than lower genetic risk. For instance, according to our estimates of smoking risk difference by PRS, if elimination of smoking occurred in 100,000 men in the lower PRS quartile, 2,000 cases would be prevented, and if elimination of smoking occurred among 100,000 men in the upper PRS quartile, 8,200 cases could be prevented.

Smoking behavior results from the complex interplay of social, cultural, behavioral, and genetic factors, and recent discoveries on the genetic basis of smoking behavior (29) have opened a debate on whether individualized strategies based on genetic information could improve the effectiveness of smoking prevention (30). Our findings provide proof-of-principle for the potential use of information on genetic risk for smoking-related diseases in targeted smoking cessation programs. However, additional data on gene–smoking interactions for all major smoking-related diseases, for example, lung and other smoking-related cancers, respiratory tract, and cardiovascular diseases, as well as considerations on the acceptability and ethical aspects of using genetic information in public health interventions, is needed before any recommendations can be made.

This report focuses on smoking as this is the strongest and most common risk factor for bladder cancer. However, accounting for other known risk factors such as occupational exposures and arsenic (1) and their potential interactions with genetic factors could result in larger discrimination in risk stratification. A bladder cancer risk assessment model based on case–control data and including smoking, occupational, environmental exposures, and a biomarker of susceptibility showed good risk discriminatory power (31). Further studies are needed to evaluate the risk stratification of bladder cancer by combined effects of all known environmental factors and the emerging susceptibility SNPs from GWASs.

Given the link between biologic interactions (i.e., when the 2 exposures need to be present to cause the disease through a particular pathway) and superadditive effects at the population level (32), it is noteworthy that among the 12 individual SNPs studied, the strongest evidence for additive interactions were seen for NAT2 and UGT1A6. These loci code for enzymes that play a key role in phase II detoxification and specifically enhance excretion of aromatic amines (4, 12), which are considered the most potent bladder carcinogens in tobacco smoke. In addition, our data provide support for an additive interaction between another detoxifying enzyme (GSTM1) and smoking that was previously reported in a meta-analysis of studies not included in this report (33). Thus, our analyses support the presence of biologic interactions between these loci and cigarette smoking on bladder cancer risk.

To our knowledge, this is one for the first studies to evaluate gene–environment interactions on the risk difference rather than relative risk of bladder cancer or any disease, and provides a model for future studies in the post-GWAS era. Although this is one of the largest studies of bladder cancer, missing data on genotypes resulted in reduced sample size to evaluate effects of the PRS. Missing genotype data should be nondifferential with respect to disease status and exposures, and thus will result in a loss of power but no bias in absolute risk, relative risk, or risk difference estimates. Although the sample size was adequate to obtain risk estimates for overall bladder cancer risk, larger studies are needed to obtain subtype-specific estimates, particularly low-grade/noninvasive papillary tumors and the muscle-invasive tumors. Different reference time for smoking assessment in case–control and cohort studies can lead to heterogeneity of smoking effects across studies. However, we observed no significant differences in estimates by study design within U.S. studies (P_{heterogeneity} = 0.691 for ever vs. never OR, 0.813 for current vs. never OR, and 0.734 for former vs. never OR). Of note, our estimate of the proportion of bladder cancer cases that could be attributable to smoking (PAR) based on white males participating in the 3 U.S. studies agreed with the estimate recently reported in a prospective cohort study in the United States (PAR of 50% for both men and women; ref. 34).

In conclusion, our analysis provides strong evidence for additive gene–smoking interactions on the risk of bladder cancer, suggesting the potential value of risk stratification in targeted prevention strategies.

G.L. Andriole has a commercial research grant from Johnson & Johnson, Medivation, and Wilex; has ownership interest (including patents) in Envisioneering Medical; and is a consultant/advisory board member of Amgen, Augmenix, Steba Biotech, Viking Medical, Bayer, Bristol Myers Squibb, Cambridge Endo, Caris, GlaxoSmithKline, Janssen Biotech Inc., Myriad Genetics, and Ortho-Clinical Diagnostics. No potential conflicts of interest were disclosed by the other authors.

Conception and design: M. Garcia-Closas, N. Rothman, J.D. Figueroa, J.F. Fraumeni, S.J. Chanock, D.T. Silverman, N. Chatterjee

Development of methodology: M. Garcia-Closas, N. Rothman, J.D. Figueroa, S.J. Chanock, D.T. Silverman, N. Chatterjee

Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): M. Garcia-Closas, N. Rothman, J.D. Figueroa, L. Prokunina-Olsson, D. Baris, E.J. Jacobs, N. Malats, I.D. Vivo, D. Albanes, M.P. Purdue, M. Kogevinas, A. Tardon, C. Serra, A. Carrato, R. Garcia-Closas, J. Lloreta, A.T. Johnson, M. Schwenn, M. Karagas, G.L. Andriole, A. Black, M. Thun, W.R. Diver, S.J. Weinstein, J. Virtamo, D.J. Hunter, N.E. Caporaso, M.T. Landi, A. Hutchinson, L. Burdett, J.F. Fraumeni, D.T. Silverman

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): M. Garcia-Closas, J.D. Figueroa, L. Prokunina-Olsson, S. Han, D. Baris, E.J. Jacobs, N. Malats, S. Sharma, Y.-P. Fu, Z. Wang, J. Lloreta, N.E. Caporaso, K. Jacobs, M. Yeager, J.F. Fraumeni, S.J. Chanock, D.T. Silverman, N. Chatterjee

Writing, review, and/or revision of the manuscript: M. Garcia-Closas, N. Rothman, J.D. Figueroa, L. Prokunina-Olsson, D. Baris, E.J. Jacobs, N. Malats, I.D. Vivo, M.P. Purdue, S. Sharma, M. Kogevinas, Z. Wang, A. Tardon, A. Carrato, R. Garcia-Closas, J. Lloreta, A.T. Johnson, M. Karagas, A. Schned, G.L. Andriole, R.L. Grubb, S.M. Gapstur, W.R. Diver, S.J. Weinstein, J. Virtamo, D.J. Hunter, N.E. Caporaso, M.T. Landi, M. Yeager, J.F. Fraumeni, S.J. Chanock, D.T. Silverman, N. Chatterjee

Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): J.D. Figueroa, D. Albanes, S. Sharma, Y.-P. Fu, W. Tang, M. Karagas, A. Schned

Study supervision: M. Garcia-Closas, N. Rothman, J.D. Figueroa, G.L. Andriole, D.J. Hunter, J.F. Fraumeni, D.T. Silverman

The authors thank the following individuals for their valuable contributions to the study: Leslie Carroll, Kirk Snyder, Anne Taylor, and Jane Wang (Information Management Services, Silver Spring, MD); Gemma Castaño-Vinyals, Fernando Fernández, Maria Sala, and Montserrat Torà (Institut Municipal d'Investigació Mèdica, Barcelona, Spain); Paul Hurwitz, Charles Lawrence, Robert Saal, and Anna McIntosh (Westat, Inc., Rockville); Francisco X. Real (Spanish National Cancer Research Centre, Madrid and Departament de Ciències Experimentals i de la Salut, Universitat Pompeu Fabra, Barcelona, Spain); and Fernando Rivera (Marqués de Valdecilla University Hospital, Santander, Cantabria, Spain)

The NCI bladder cancer GWAS was supported by the intramural research program of the NIH, NCI. This work was funded in part with federal funds from the NCI, NIH, under contract no. HHSN261200800001E. Support for individual studies that participated in the effort is as follows:

SBCS (D.T. Silverman): Intramural Research Program of the NIH, NCI, Division of Cancer Epidemiology and Genetics and intramural contract number NCI N02-CP-11015. FIS/Spain 98/1274, FIS/Spain 00/0745, PI061614, and G03/174, Fundació Marató TV3, Red Temática Investigación Cooperativa en Cáncer (RTICC), Consolíder ONCOBIO, EU-FP7-201663; and RO1- CA089715 and CA34627. NEBCS (D.T. Silverman): Intramural research program of the NIH, NCI, Division of Cancer Epidemiology and Genetics and intramural contract number NCI N02-CP-01037. PLCO (M.P. Purdue): The NIH Genes, Environment, and Health Initiative (GEI) partly funded DNA extraction and statistical analyses (HG-06-033-NCI-01 and RO1HL091172-01), genotyping at the Johns Hopkins University Center for Inherited Disease Research (U01HG004438 and NIH HHSN268200782096C), and study coordination at the GENEVA (N. Caporaso): The NIH GEI partly funded DNA extraction and statistical analyses (HG-06-033-NCI-01 and RO1HL091172-01), genotyping at the Johns Hopkins University Center for Inherited Disease Research (U01HG004438 and NIH HHSN268200782096C), and study coordination at the GENEVA Coordination Center (U01 HG004446) for EAGLE and part of PLCO studies. Genotyping for the remaining part of PLCO and all ATBC and CPS-II samples were supported by the Intramural Research Program of the NIH, NCI, Division of Cancer Epidemiology and Genetics. The PLCO is supported by the Intramural Research Program of the Division of Cancer Epidemiology and Genetics and supported by contracts from the Division of Cancer Prevention, NCI, NIH. ATBC (D. Albanes): This research was supported in part by the Intramural Research Program of the NIH and the NCI. In addition, this research was supported by U.S. Public Health Service contracts N01-CN-45165, N01-RC-45035, N01-RC-37004, and HHSN261201000006C from the NCI, Department of Health and Human Services. NHS and HPFS (I.D. Vivo): CA055075 and CA087969.

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