Multistage analysis of variants in the Inflammation pathway and lung cancer risk in smokers

doi:10.1158/1055-9965.EPI-12-0352-T

Cancer Epidemiol Biomarkers Prev. Author manuscript; available in PMC 2012 November 2.

Published in final edited form as:

Cancer Epidemiol Biomarkers Prev. 2012 July; 21(7): 1213–1221.

Published online 2012 May 9. doi: 10.1158/1055-9965.EPI-12-0352-T

PMCID: PMC3487592

NIHMSID: NIHMS404102

Multistage analysis of variants in the Inflammation pathway and lung cancer risk in smokers

Margaret R. Spitz,¹ Ivan P. Gorlov,² Qiong Dong,³ Xifeng Wu,³ Wei Chen,⁴ David W. Chang,³ Carol J. Etzel,³ Neil E. Caporaso,⁵ Yang Zhao,⁶ David C. Christiani,⁶ Paul Brennan,⁷ Demetrius Albanes,⁸ Jianxin Shi,⁹ Michael Thun,¹⁰ Maria Teresa Landi,⁵ and Christopher I. Amos⁴

¹Dan L. Duncan Cancer Center, Baylor College of Medicine

²Department of Genitourinary Medical Oncology, Houston, TX, 77030, USA

³Department of Epidemiology, Houston, TX, 77030, USA

⁴Department of Genetics, The University of Texas M.D. Anderson Cancer Center, Houston, TX, 77030, USA

⁵Genetic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, MD 20892

⁶Environmental and Occupational Medicine and Epidemiology Program, Department of Environmental Health, Harvard School of Public Health, Boston, MA 02115, USA

⁷International Agency for Research on Cancer (IARC), Lyon, France

⁸Nutritional Epidemiology Branch, National Institutes of Health, Department of Health and Human Services, Bethesda, MD 20892

⁹Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD 20892

¹⁰Epidemiology & Surveillance Research, American Cancer Society, Atlanta, GA 30303, USA

Correspondence to: Margaret R. Spitz, Dan L. Duncan Cancer Center, Baylor College of Medicine, One Baylor Plaza, MS:BCM305, Ste 450A, Cullen Bldg, Houston, Tx 77030. 713.798.2115 (telephone) 713.798.2716 (fax). Email: spitz/at/bcm.edu

The publisher's final edited version of this article is available at Cancer Epidemiol Biomarkers Prev

Abstract

BACKGROUND

Tobacco-induced lung cancer is characterized by a deregulated inflammatory microenvironment. Variants in multiple genes in inflammation pathways may contribute to risk of lung cancer.

METHODS

We therefore conducted a three-stage comprehensive pathway analysis (discovery, replication and meta-analysis) of inflammation gene variants in ever smoking lung cancer cases and controls. A discovery set (1096 cases; 727 controls) and an independent and non-overlapping internal replication set (1154 cases; 1137 controls) were derived from an ongoing case-control study. For discovery, we used an iSelect BeadChip to interrogate a comprehensive panel of 11737 inflammation pathway SNPs and selected nominally significant (p<0.05) SNPs for internal replication.

RESULTS

There were 6 SNPs that achieved statistical significance (p<0.05) in the internal replication dataset with concordant risk estimates for former smokers and 5 concordant and replicated SNPs in current smokers. Replicated hits were further tested in a subsequent meta-analysis using external data derived from two published GWAS and a case-control study. Two of these variants (a BCL2L14 SNP in former smokers and a SNP in IL2RB in current smokers) were further validated. In risk score analyses, there was a 26% increase in risk with each additional adverse allele when we combined the genotyped SNP and the most significant imputed SNP in IL2RB in current smokers and a 36% similar increase in risk for former smokers associated with genotyped and imputed BCL2L14 SNPs.

CONCLUSIONS/IMPACT

Before they can be applied for risk prediction efforts, these SNPs should be subject to further external replication and more extensive fine mapping studies.

Keywords: Inflammation SNPS, lung cancer, smokers

Introduction

Tobacco-induced lung cancer is characterized by generation of reactive oxidant species leading to tissue destruction and an abundant and deregulated inflammatory microenvironment. Chronic airway inflammation contributes to alterations in the bronchial epithelium and lung microenvironment, provoking a milieu conducive to pulmonary carcinogenesis (1). Selection has endowed humans with a balance between an appropriately limited inflammatory response that protects the host against infection, and an abnormally prolonged or intense inflammatory response that could result in a dysfunctional immune system and create a microenvironment that might promote carcinogenesis (2). Epidemiologic evidence also supports a role of inflammation in lung carcinogenesis (3). For example, besides the well documented association between lung cancer and obstructive pulmonary disease (with its inflammatory microenvironment), there is reported to be an increased risk of lung cancer among patients with lung infections (e.g. tuberculosis and bacterial pneumonia) as well as in immunosuppressed individuals (3). We and others have previously shown that tobacco-induced chronic obstructive airways disease, likewise characterized by a sustained inflammatory reaction in the airways and lung parenchyma, is a significant contributor to lung cancer risk in smokers (4, 5). However, there is considerable inter-individual variation in susceptibility of long-term smokers to develop chronic obstructive airways disease and/or lung cancer. There is also extensive evidence of familial aggregation of both diseases suggesting genetic components exist. Genetic variants in key inflammation-related genes could alter gene function and cause a shift in balance resulting in deregulation of the inflammatory response and corresponding modulation of susceptibility to cigarette-induced normal tissue damage (6).

Loza et al (6) have stressed the advantages of conducting pathway-focused analyses, using pre-defined functional sub pathways to evaluate biologically feasible interactions. We therefore conducted an in depth three-stage analysis (discovery, replication and meta-analysis) of gene variants in inflammatory pathways as susceptibility factors for lung cancer in ever smokers to evaluate their role in the context of relevant covariates. A parallel study in never smokers has been previously reported (7).

Methods

Study subjects

Discovery and Internal replication sample

The discovery and replication populations were non overlapping sets of cases and controls derived from an ongoing multi-racial/ethnic lung cancer case-control study at MD Anderson Cancer Center (8, 9). Cases were consecutive patients with newly diagnosed, histopathologically confirmed and previously untreated non-small cell lung cancer with no age, gender, ethnicity, tumor histology, or disease stage restrictions. Medical history, family history of cancer, smoking habits, and occupational history were obtained through an interviewer-administered risk-factor questionnaire. Institutional review board approval at M. D. Anderson Cancer Center was obtained for this study. Case exclusion criteria included a history of prior cancer, prior chemotherapy or radiotherapy for the lung cancer, or recent blood transfusion.

We recruited our control population from the Kelsey-Seybold Foundation, Houston’s largest multidisciplinary physician practice (9). Potential controls were first surveyed with a short questionnaire for their willingness to participate in research studies and provide preliminary data for matching demographic characteristics with those of cases (9). Controls were frequency matched to the cases on the basis of age (±5 years), sex, smoking status and ethnicity. Control exclusion criteria for the study included prior chemotherapy or radiotherapy or recent blood transfusion, and any previous cancer. To date, the response rate among both the cases and controls has been approximately 75%. Upon receiving informed consent, a 40-mL blood sample was drawn into coded heparinized tubes from all study participants for the assays. Genomic DNA was extracted from peripheral blood lymphocytes and stored at −80 °C until use.

This analysis focuses on Caucasian case and control subjects who reported being ever smokers, i.e. had smoked more than 100 cigarettes over a lifetime. Former smokers were defined as those who had quit smoking more than a year before their diagnosis (cases) or before interview (controls). The internal replication set was comprised of the 1154 ever smoking Caucasian lung cancer cases and 1137 controls that were the population used for the published lung cancer GWAS conducted by Amos et al. and who were enrolled into the case-control study from 8/95 through 10/05 (10). The discovery set was based on case and control subjects who were not included in the lung cancer GWAS and who were selected from the entire lung study database through 10/08 (excluding those enrolled in the GWAS) on the basis of histology (non small cell lung cancer), ethnicity (Caucasian) and ever smoking status.

Meta-analysis sample

For the third stage meta-analysis, three additional studies (two GWAS and a case-control study) contributed data using the same inclusion criteria of non-small cell lung cancer in Caucasian ever smokers. The International Agency for Research on Cancer (IARC) GWAS (11) (1,426 cases; 1564 controls) included a lung cancer case-control study conducted in 6 central European countries (Czech Republic, Hungary, Poland, Romania, Russia and Slovakia). The NCI GWAS (12) (3164 cases; 2983 controls) was drawn from a population-based case-control study, The Environment And Genetics in Lung cancer Etiology (EAGLE) study in Lombardy, Northern Italy, as well as three cohort studies; specifically: the Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study (ATBC), a randomized primary prevention trial including nearly 30,000 male smokers enrolled in Finland between 1985 and 1993; the Prostate, Lung, Colon, Ovary Screening Trial (PLCO), a randomized trial including 150,000 individuals enrolled in ten U.S. study centers between 1992 and 2001; and the Cancer Prevention Study II Nutrition Cohort (CPS-II), of over 183,000 subjects enrolled by the American Cancer Society between 1992 and 2001 across all U.S. states.. The third data set for meta-analysis was derived from a case-control study of lung cancer at Massachusetts General Hospital, Boston, MA. Patients were recruited between December 1992 and April 2007. Controls were either case related (healthy friends and spouses) or case unrelated (friends or spouses of other hospital patients from oncology or thoracic surgery units). This study included 892 cases and 809 controls for whom genotyping data were available (13). Each study was approved by institutional review boards, and participants signed an informed consent.

Gene and SNP selection

A comprehensive list of candidate genes for the discovery phase were selected as reported previously (7) using the Gene Oncology database (14) and the National Center for Biotechnology Information (NCBI) PubMed (15) to identify inflammation pathway-related genes. We also used the inflammation pathway gene list and functionally-defined subpathways as outlined in Loza et al. (6). For each gene, we selected tagging SNPs (tagSNPs) located within 10 kb upstream of the transcriptional start site or 10 kb downstream of the transcriptional stop site based on data from the International HapMap Project (16). Using the LD select program (17) and the UCSC Golden Path Gene Sorter program (18), we further divided identified SNPs into bins based on an r² threshold of 0.8 and minor allele frequency (MAF) greater than 0.05 in Caucasians to select tagging SNPs. We also included additional inflammation pathway SNPs located in coding (synonymous SNPs, nonsynonymous SNPs) and regulatory regions (promoter, splicing site, 5-UTR, and 3-UTR) of inflammation-related genes. Functional SNPs and SNPs in inflammation pathway genes previously reported to be associated with cancer were also included. The complete set of selected SNPs was submitted to Illumina technical support for Infinium chemistry designability, beadtype analyses, and iSelect Infinium Beadchip synthesis.

Genotyping

A total of 11,930 SNPs mapped to 904 genes that were in or near inflammation pathways (Supplementary Table) were genotyped in the discovery samples using Illumina’s Infinium iSelect HD Custom Genotyping BeadChip according to the standard 3 day protocol (San Diego, CA). Genotypes were autocalled using the BeadStudio software. We excluded any SNP with a call rate lower than 95% or with MAF=0. Theduplicate sample error rate of 0.137%) was derived from 64 duplicate samples, The final set included 11,737 SNPs in the inflammation pathway.

Statistical Analysis

For each SNP, Hardy-Weinberg equilibrium was assessed among the controls using a chi-squared test. All subsequent analyses were stratified by smoking status (former and current). Single-SNP association tests were carried out using PLINK 1.07 (19). Unconditional logistic regression analysis, implemented using SAS version 9.2, was used to calculate the odds ratios (ORs) and 95% confidence intervals (CIs) for the association between a single locus and lung cancer risk with and without adjustment for age, sex, or smoking intensity, and assuming an additive model on the logistic scale.

We applied the Bayesian false discovery probability test (BFDP) (20) to evaluate the chance of obtaining a false positive association during the replication and validation stages This approach calculates the probability of declaring no association given the data and a specified prior on the presence of an association, and has a noteworthy threshold that is defined in terms of the costs of false discovery and non-discovery. We set a level of noteworthiness for BFDP at 0.8, i.e. false non-discovery rate is four times as costly as false discovery. We tested priors ranging from 0.01 through 0.07, and ORs from 1.2 through 1.5. For this analysis, we used a prior of 0.05 to determine that the association was unlikely to represent a false-positive result, and selected an OR of 1.5 since this was a hypothesis driven pathway-based, rather than an agnostic approach.

For the Stage 2 analysis, we conducted an internal fast-track replication separately for current and former smokers, by testing those significant SNPs (P <0.05) from the discovery phase in an independent set of cases and controls drawn from the same study population source and that had been included in the published GWAS, for which the HumanHap300 BeadChip was used for genotyping. For imputation of un-genotyped SNPs, we applied the MACH1.0.16 program (21) using HapMap 2 CEU reference data (release 21) for GWAS data plus 1,000 Genomes CEU reference data (March 2010 release) for the candidate regions, that had imputation R² values ≥ 0.8. Linkage disequilibrium for the top SNPs was visualized using Haploview v. 4.1 (22) to summarize R² statistics.

For the meta-analysis, we combined data from the two published GWAS and the case-control study using R Software version 1.6-1 (23). Between-study heterogeneity was tested by the Cochran’s Q test, with p<0.05 as the significance level. If there was evidence of significant heterogeneity, we applied the random effects model, using the method of DerSimonian and Laird (24). For the fixed-effects model, we used the Mantel-Haenszel method. The significance of the pooled OR was determined by the Z-test, and P < 0.05 was considered as statistically significant. All the P-values reported here were two sided.

Results

Discovery Phase

The Discovery set (Table 1) included 1096 case patients and 727 control subjects (608 former smoking cases and 325 controls and 488 current smoker cases and 402 controls). The cases (64.7 years) were older than the controls (57.5 years). This exceeds the five year matching criterion and reflects ongoing incomplete control recruitment since frequency matching is employed. The cases were also heavier smokers, both in terms of cigarettes per day (27.4 vs. 23.7) and years smoked 36.8 vs. 29.5). Former smoker cases were more likely to have quit at an older age than their respective controls. Cases were also more likely to report a prior history of physician diagnosed emphysema, dust exposure, family history of cancer in first degree relatives, exposure to asbestos and less likely to report suffering from hay fever. The distribution of genes by functional sub-pathway and number of SNPs/gene included in the Discovery phase is summarized in the Supplementary Table. Before selection of SNPs for replication, there were 653 SNPs for former smokers and 608 SNPs for current smokers that achieved nominal P values of <0.05 in the discovery set.

Table 1

Characteristics of Ever-smoking Lung Cancer Cases and Controls in Discovery and Replication Populations, MD Anderson Cancer Center

Internal Replication

The independent replication set included 1154 cases and 1137 controls from our GWAS (603 and 657, respectively, former smoking cases and controls; 551 and 480 current smoking cases and controls). The distribution of risk factors between cases and controls in the second phase closely resembled those in the discovery phase.

From the SNPs that were statistically significant in the discovery phase, we matched 295 SNPs that were directly genotyped from the GWAS database and an additional 200 imputed SNPs. The remaining 741 SNPs could not be evaluated. Using these genotyped and imputed data for the replication phase, we performed logistic regression analysis assuming an additive model for each available SNP, and conducting separate analyses for current and former smokers. On univariate analysis, there were 6 SNPs that achieved statistical significance (p<0.05) in the replication dataset with risk estimates concordant for direction for former smokers and 5 SNPs that were concordant for risk estimates and also achieved significance in current smokers (Table 2). For the two SNPs in TGFB1, r² for LD was 0.412. For the two SNPs in IL2RB, r² for LD was 0.586. All other SNPs that were statistically significant on univariate analysis had r² values that were <0.002.

Table 2

SNPs significant in discovery set and verified in replication set

We also applied the Bayesian false discovery probability test (BFDP) (20) to evaluate the likelihood of any of these 11 SNP associations being false positive associations. Based on the criteria outlined above, ten of the eleven SNPs had BFDP<0.8 and rs1544669 in former smokers had BFDP=0.86. Similar results were obtained for OR = 1.2 and priors of 0.03 and 0.07.

In current smokers, the significant SNPs belonged to either the leukocyte (ICOS, rs1896286 and CD86, rs12106790) or cytokine (IL2RB, rs1003694 and rs2235330 and CSF2RB, rs20722707) signaling sub-pathways. In former smokers, besides the leukocyte (LILRA4, rs1205316) and cytokine (TGFB1, rs 2241715 and rs4803455) signaling pathways, rs4747064 in PRF1 (ROS/glutathione/cytotoxic granules) and rs1544669 in BCL2L14 (apoptosis signaling) were also replicated. Of the 741 SNPs significant in the discovery phase that we could not internally replicate, 12 SNPs belonged to genes with replicated SNPs, including 4 in ICOS, 3 each in IL2RB and PRF1, and I each in CD86 and BCL2L14.

Meta-analysis

We elected to move all eleven SNPs on to phase 3 replication in the meta-analysis. Three of these SNPs were not included on the 317k chip, and had to be imputed in our replication set and in the IARC data set. rs17146857 (r² =0.84) and rs1544669 (r² =0.49) were included, but we were unable to reliably impute rs1205316.

The meta-analysis results for the three external data sets are summarized in Figs. 1a and 1b for current and former smokers, respectively. Two SNPs were successfully replicated with ORs in similar direction. In current smokers, (Fig. 1a) we replicated rs2235330 in IL2RB (OR=0.92 (0.84, 1.00)) while the other IL2RB SNP, rs1003694 was borderline significant (OR= 0.95 (0.88, 1.02)). Both SNPs in IL2RB are intronic. For former smokers, (Fig 1b) rs1544669 in BCL2L14 in a 5' UTR flanking region was associated with a summary OR of 0.91 (0.83, 1.00).

Fig. 1

a and 1b. Forest plots for Current (Fig 1a) and Former Smokers (1b). The squares and horizontal lines correspond to the study-specific OR and 95% CI. The area of the squares reflects the weight (inverse of the variance). The diamond represents the summary (more ...)

There were no substantial differences when the discovery and replication data were stratified by gender, histology, or smoking intensity. To verify whether there were stronger signals in the two identified loci, we performed SNP imputation in the discovery dataset to increase coverage in the regions surrounding rs1003694 and rs2235330 for current smokers and around rs1544669 in former smokers, based on the 1000 genomes March 2010 and June 2010 release CEU reference panels using MACH version 1.016 (21). For IL2RB, there were 91 genotyped SNPs 1mb from each side of the gene used for imputation. A/T or C/G SNPs were checked for strand orientation and flipped to forward strand as needed before the imputation. There were 480 imputed SNPs with R² for imputation quality over 0.8. The most significant SNPs (Fig. 2a) were rs5995385 (genotyped) and rs5750383 and rs5756527 (both imputed), all with P= 0.001124, and in tight LD. Since these SNPs were not included in the GWAS, we were not able to attempt replication. They are all located in the flanking_3UTR region of IL2RB, that is not highly conserved, but could tag functional polymorphisms in IL2RB. We performed a similar imputation analysis for BCL2L14 and identified 351 imputed SNPs with R² for imputation quality over 0.8 (Fig 2b). The top three SNPs, all with P value <0.004 (rs11054701,rs2075241 and rs2160521) were in complete LD, and only one (rs2075241) was selected for inclusion in the risk score.

Fig. 2

a Association of imputed and genotyped SNPs in the chromosome 22 region around IL2RB with lung cancer risk in current smokers.

Risk Score

We computed genetic risk scores by summing the adverse alleles from the replicated and most significant imputed SNPs in IL2RB for current smokers and BCL2L14 SNPs for former smokers (Table 3). We restricted this analysis to our discovery dataset for which we had performed imputation. For current smokers, compared with those carrying 0 or 1 adverse allele, risks increased from 1.19 (0.73–1.94) for those carrying 2 adverse alleles; 1.45 (0.91–2.31) for those carrying 3 adverse alleles and 2.11 (1.25–3.55) for those with 4 adverse alleles. There was a 26% increase in risk with each additional allele (P for trend=0.002). For former smokers, the risk for those carrying 3 or 4 adverse alleles was 2.84 (1.47 – 5.48) and there was a 36% increase in risk for each adverse allele (P for trend was 0.0005). There was no association between the no. of adverse alleles (risk score) and smoking intensity (pack-years) in either current or former smokers.

Table 3

Genetic Risk Score

Discussion

In this multi stage analysis, using independent sets of cases and controls for discovery and replication, we were able to successfully replicate 6 SNPs in the inflammation pathways in former smokers and 5 different SNPs in current smokers that were statistically significant in both groups with almost identical risk estimates in the discovery and replication phases. In a subsequent meta-analysis from three additional external studies, two of these variants achieved statistical significance. These were rs1544669 in BCL2L14 in former smokers and rs2235330 in IL2RB in current smokers.

Inflammation is a physiological response to cellular and tissue damage. Appropriate response to this damage is tightly regulated through a balance between pro-inflammatory and anti-inflammatory cytokines and signaling molecules (25). It is suggested, in fact, that the tumor microenvironment, and especially its inflammatory component may be a critical element of carcinogenesis (26). Since common variations in a single gene contribute only modestly to risk, it seems logical that rather than focusing on a few SNPs and/or genes with the strongest evidence of disease association, one considers multiple variants in interacting or related genes in the same pathway to improve the power to detect causal pathways and disease mechanisms (27). Loza et al. (6) constructed a comprehensive inflammation pathway gene list and functionally defined subpathways that formed the basis for our own analysis.

The replicated SNP in current smokers was in the Interleukin-2 receptor subunit beta (IL2RB) gene, a cytokine signaling gene. IL-2 exerts both stimulatory and regulatory functions in the immune system and is a member of the cytokine family that is central to immune homeostasis (28). IL-2 binds to its receptor complex, IL-2R alpha, beta, and gamma chains, and exerts its effect via second messengers, mainly tyrosine kinases, which ultimately are involved in T cell-mediated immune responses. Local blockade of the beta-chain of the IL-2R restored an immunosuppressive cytokine milieu that ameliorated both inflammation and airway hyperresponsiveness in experimental allergic asthma (29). Interleukin-2 (IL-2) is the major growth factor for activated T-lymphocytes and stimulates clonal expansion and maturation of these lymphocytes.

In former smokers, we replicated a SNP in the BCL2L14 gene, located on chromosome 12, and that encodes Apoptosis facilitator Bcl-2-like protein 14. Loss of heterozygosity of the short arm of chromosome 12 is a frequent event in both hematological malignancies and solid tumors. (30). BCL2L14, also known as Bcl-G, is a proapoptotic member of the Bcl-2 family which regulates cell death (31). This gene has been shown to be a transcriptional target of TP53 (32) and is a candidate tumor suppressor. Association of this gene with lung cancer has not been previously reported. Since apoptosis plays an essential role in protecting against cellular carcinogenesis, such as one due to oxidative damage from cigarette smoke, it is biologically plausible that this cell death regulator might influence the pathogenesis of lung cancer through the TP53 pathway. It is not surprising that different findings were observed in current versus former smokers. For example, current and former smokers differ with respect to the role that COX-2 plays in maintaining bronchial epithelial proliferation (33), and differences between these two groups with respect to bronchial epithelial biology have been reported (34, 35). Different biology is also suggested by the observation that active smokers have faired poorly in large-scale chemoprevention trials, whereas former smokers have exhibited no effect or favorable trends (33).

There are a few published, generally small, candidate gene studies on polymorphisms in inflammation-related genes that have been linked with increased lung cancer risk, but with limited replication of the study results, as reviewed by Engels (36). For example, Hart et al (37) studied 11 SNPs in nine genes in 882 subjects and reported risk by combination of adverse genotypes, but there was no replication phase. Vogel et al (38) included 7 inflammation pathway SNPS in their case-cohort study that included 428 cases. Carriers of a variant allele of IL10 and IL1B were at increased risk, although the latter was statistically significant only in current smokers. We (39) have previously published case control data on over 1000 cases and a similar number of controls from the study base and reported a significant association with a SNP in ILIB. In the International Lung Cancer Consortium (ILCCO), we conducted a coordinated genotyping study of 10 common variants including this IL1B variant in 4588 cases and 6453 controls but found no association with this IL1B variant (40), nor was this specific IL1B variant included in our discovery analysis.

Our discovery and replication data are derived from a retrospective case-control analysis and therefore we are unable to effectively evaluate any meaningful association with inflammatory marker levels since pre-diagnostic sera are not available. However, an analysis of pre-diagnostic C reactive protein levels in the PLCO data showed a significant association with subsequent lung cancer risk (41). Another limitation of our study was the lack of genotyping for all of the SNPs included in our discovery study so that we could not validate risk score predictions. Further studies that independently evaluate the risk scores we developed are needed.

There are clear advantages to this pathway-based approach. Since we restricted our analysis to a specific pathway, we have reduced to some extent the issue of false positive reporting, and increased the power of our analyses. Our genes are classified by functional subpathways and thus we were able to evaluate associations in a more biologically driven manner. We cannot exclude the possibility that, despite our three stage analytic approach, the findings represent false positives. However, the relatively large sample sizes with dual discovery and replication populations, detailed epidemiologic information, and comprehensive query of genes and SNPs ensure robust power and greater genetic coverage to detect true-positive findings. Follow-up analysis in African American lung cancer cases and controls is ongoing to confirm and extend these findings.

Supplementary Material

Supplementary table

Click here to view.^{(54K, pdf)}

Acknowledgments

Grant support: RO1CA55769 (MR Spitz), RO1CA127219 (MR Spitz) RO1CA074386, P01CA090578 (DC Christiani), U19 CA148127, RP100443, CA121197 (CI Amos)

References

1. Lee G, Walser TC, Dubinett SM. Chronic inflammation, chronic obstructive pulmonary disease and lung cancer. Curr Opin Pulm Med. 2009;15(4):303–307. [PubMed]

2. Forrester JS, Bick-Forrester J. Persistence of inflammatory cytokines cause a spectrum of chronic progressive diseases: implications for therapy. Med Hypotheses. 2005;65:227–231. [PubMed]

3. Engels EA. Inflammation in the development of lung cancer: epidemiological evidence. Expert Review of Anticancer Therapy. 2008;8(4):605–615. [PubMed]

4. Spitz MR, Hong WK, Amos CI, Schabath MB, Dong Q, Shete S, et al. A risk model for prediction of lung cancer. J Natl Cancer Inst. 2007;99(9):715–726. et al. [PubMed]

5. Brenner DR, McLaughlin JR, Hung RJ. Previous lung diseases and lung cancer risk: a systematic review and meta-analysis. PLoS One. 2011;6(3):e17479. Review. [PMC free article] [PubMed]

6. Loza MJ, McCall CE, Li L, Isaacs WB, Xu J, Chang BL. Assembly of Inflammation- Related Genes for Pathway-Focused Genetic Analysis. PLoS ONE. 2007;2(10):e1035. [PMC free article] [PubMed]

7. Spitz MR, Gorlov IP, Amos CI, Spitz MR, Dong Q, Chen W, et al. Variants in inflammation genes are implicated in risk of lung cancer in never smokers exposed to secondhand smoke. Cancer Discovery. 2011;1:420–429. Published OnlineFirst August 25, 2011. [PubMed]

8. Spitz MR, Wei Q, Dong Q, Amos CI, Wu X. Genetic susceptibility to lung cancer: the role of DNA damage and repair. Cancer Epidemiol. Biomarkers Prev. 2003;12:689–698. [PubMed]

9. Hudmon KS, Honn SE, Jiang H, Chamberlain RM, Xiang W, Ferry G, et al. Identifying and recruiting healthy control subjects from a managed care organization: a methodology for molecular epidemiological case-control studies of cancer. Cancer Epidemiol. Biomarkers Prev. 1997;6:565–571. [PubMed]

10. Amos CI, Wu X, Broderick P, Gorlov IP, Gu J, Eisen T, et al. Genome-wide association scan of tag SNPs identifies a susceptibility locus for lung cancer at 15q25.1. Nat Genet. 2008;40:616–622. [PMC free article] [PubMed]

11. Hung RJ, McKay JD, Gaborieau Boffetta P, Hashibe M, Zaridze D, et al. A susceptibility locus for lung cancer maps to nicotinic acetylcholine receptor subunit genes on 15q25. Nature. 2008;452:633–637. [PubMed]

12. Landi MT, Chatterjee N, Yu K, Goldin LR, Goldstein AM, Rotunno M, et al. A genome-wide association study of lung cancer identifies a region of chromosome 5p15 associated with risk for adenocarcinoma. Am J Hum Genet. 2009;85:679–691. [PubMed]

13. Miller DP, Liu G, De Vivo I, Lynch TJ, Wain JC, Su L, et al. Combinations of the variant genotypes of GSTP1, GSTM1, and p53 are associated with an increased lung cancer risk. Cancer Res. 2002 May 15;62(10):2819–2823. [PubMed]

14. The Gene Ontology Consortium. [cited 2010 August 25];Gene ontology: tool for the unification of biology. Available from: http://www.geneontology.org.

15. The National Center for Biotechnology Information (NCBI) [cited 2010 August 25]; Available from: http://www.ncbi.nlm.nih.gov/pubmed.

16. International HapMap Consortium. [cited 2011 August 25];The international HapMap project. Available from: http://hapmap.ncbi.nlm.nih.gov/cgi-perl/gbrowse/hapmap24_B36/

17. Nickerson Deborah A, Mark Rieder, Chris Carlson, Qian Yi. Documentation for ldSelect Version 1.0. University of Washington; [cited 2010 August 25]. Available from: http://droog.gs.washington.edu/ldSelect.html.

18. UCSC Genome Bioinformatics Genome Browser. [cited 2010 August 25]; Available from: http://genome.ucsc.edu.

19. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet. 2007;81:559–575. Available from: http://pngu.mgh.harvard.edu/purcell/plink/ [PubMed]

20. Wakefield J, et al. A Bayesian measure of the probability of false discovery in genetic epidemiology studies. Am. J. Hum. Genet. 2007;81:208–227. [PubMed]

21. [cited 2008 April];Markov Chain Haplotyping (MACH) software tool for haplotype estimation and genotype imputation. Developed by Goncalo Abecasis and Yun Li. Available from: http://www.sph.umich.edu/csg/abecasis/MACH/index.html release MACH1.016MACH1.0.16.

22. Barrett JC, Fry B, Maller J, Daly MJ. Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics. 2005;21:263–265. [PubMed]

23. Guido Schwarzer. meta: Meta-Analysis with R. R package version 1.6-1. 2010 <sc@imbi.uni-freiburg.de> Available from: http://CRAN.R-project.org/package=meta.

24. DerSimonian R, Laird N. Meta-analysis in clinical trials. Control Clin Trials. 1986;7:177–188. [PubMed]

25. Han J, Ulevitch RJ. Limiting inflammatory responses during activation ofinnate immunity. Nat Immunol. 2005;6:1198–1205. [PubMed]

26. Prendergast GC. Inflammatory mediators in cancer etiology and targets for therapy and prevention. Can Rev. 2008;9 online.

27. Wang K, Li M, Bucan M. Pathway-Based Approaches for Analysis of Genomewide Association Studies. Am J Hum Genet. 2007;81(6) [PubMed]

28. Krieg C, Létourneau S, Pantaleo G, Boyman O. Improved IL-2 immunotherapy by selective stimulation of IL-2 receptors on lymphocytes and endothelial cells. Proc Natl Acad Sci U S A. 2010 Jun 29;107(26):11906–11911. [PubMed]

29. Doganci A, Karwot R, Maxeiner JH, Scholtes P, Schmitt E, Neurath MF, et al. IL-2 receptor beta-chain signaling controls immunosuppressive CD4+ T cells in the draining lymph nodes and lung during allergic airway inflammation in vivo. J Immunol. 2008;181:1917–1926. [PubMed]

30. Montpetit A, Boily G, Sinnett D. A detailed transcriptional map of the chromosome 12p12 tumour suppressor locus. Eur J Hum Genet. 2002;10:62–71. [PubMed]

31. Guo B, Godzik A, Reed JC. Bcl-G, a novel pro-apoptotic member of the Bcl-2 family. J Biol Chem. 2001;276:2780–2785. [PubMed]

32. Miled C, Pontoglio M, Garbay S, Yaniv M, Weitzman JB. A genomic map of p53 binding sites identifies novel p53 targets involved in an apoptotic network. Cancer Res. 2005;65:5096–5104. [PubMed]

33. Kim ES, Hong WK, Lee JJ, Mao L, Morice RC, Liu DD, et al. Biological activity of celecoxib in the bronchial epithelium of current and former smokers. Cancer Prev Res. 2010;3(2):148–159. [PubMed]

34. Lee JJ, Liu D, Lee JS, Kurie JM, Khuri FR, Ibarguen H, et al. Long-term impact of smoking on lung epithelial proliferation in current and former smokers. J Natl Cancer Inst. 2001;93:1081–1088. [PubMed]

35. Spira A, Beane J, Shah V, Liu G, Schembri F, Yang X, et al. Effects of cigarette smoke on the human airway epithelial cell transcriptome. Proc Natl Acad Sci U S A. 2004;101:10143–10148. [PubMed]

36. Engels A. Inflammation in the development of lung cancer: epidemiologic evidence. Expert. Rev. Anticancer Ther. 2008;8:605–615. [PubMed]

37. Hart K, Landvik NE, Lind H, Skaug V, Haugen A, Zienolddiny S. A combination of functional polymorphisms in the CASP8, MMP1, IL10 and SEPS1 genes affects risk of nonsmall cell lung cancer. Lung Cancer. 2011;71(2):123–129. [PubMed]

38. Vogel U, Christensen J, Wallin H, Friis S, Nexø BA, Raaschou-Nielsen O, et al. Polymorphisms in genes involved in the inflammatory response and interaction with NSAID use or smoking in relation to lung cancer risk in a prospective study. Mutat Res. 2008;639(1–2):89–100. 1. [PubMed]

39. Engels EA, Wu X, Gu J, Dong Q, Liu J, Spitz MR. Systematic evaluation of genetic variants in the inflammation pathway and risk of lung cancer. Cancer Res. 2007;67:6520–6527. [PubMed]

40. Truong T, Sauter W, McKay JD, Hosgood HD, 3rd, Gallagher C, Amos CI, et al. International Lung Cancer Consortium: coordinated association study of 10 potential lung cancer susceptibility variants. Carcinogenesis. 2010;31(4):625–633. [PMC free article] [PubMed]

41. Chaturvedi AK, Caporaso NE, Katki HA, Wong HL, Chatterjee N, Pine SR, et al. C-reactive protein and risk of lung cancer. J Clin Oncol. 2010;28:2719–2726. [PMC free article] [PubMed]

42. Diabetes Genetics Initiative of Broad Institute of Harvard and MIT, Lund University, and Novartis Institutes of BioMedical Research Genome-wide association analysis identifies loci for type 2 diabetes and triglyceride levels. Science. 2007;316:1331–1336. [PubMed]