Abstract
Background
Pulmonary inflammation may contribute to lung cancer etiology. We conducted a broad evaluation of the association of single nucleotide polymorphisms (SNPs) in innate immunity and inflammation pathways with lung cancer risk, and conducted comparisons with a lung cancer genome wide association study (GWAS).
Methods
We included 378 lung cancer cases and 450 controls from the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial. An Illumina GoldenGate oligonucleotide pool assay was used to genotype 1,429 SNPs. Odds ratios (ORs) and 95% confidence intervals (CIs) were estimated for each SNP, and p-values for trend were calculated. For statistically significant SNPs (p-trend<0.05), we replicated our results with genotyped or imputed SNPs in the GWAS, and adjusted p-values for multiple testing.
Results
In our PLCO analysis, we observed a significant association between 81 SNPs located in 44 genes and lung cancer (p-trend<0.05). Of these 81 SNPS, there was evidence for confirmation in the GWAS for 10 SNPs. However, after adjusting for multiple comparisons, the only SNP that remained significantly associated with lung cancer in the replication phase was rs4648127 (NFKB1; multiple testing adjusted p-trend=0.02). The CT/TT genotype of NFKB1 was associated with reduced odds of lung cancer in the PLCO study (OR=0.56; 95% CI 0.37–0.86) and the GWAS (OR=0.79; 95% CI 0.69–0.90).
Conclusions
We found a significant association between a variant in the NFKB1 gene and lung cancer risk. Our findings add to evidence implicating inflammation and immunity in lung cancer etiology.
Keywords: lung cancer, genetics, inflammation, immunity, epidemiology
Introduction
With 1.38 million deaths annually, lung cancer causes the largest number of cancer-related deaths worldwide (1). Cigarette smoking is the primary cause of lung cancer, increasing risk by 15 to 30-fold (2). In addition, pulmonary inflammation may contribute to lung cancer etiology through the production of reactive oxygen and nitrogen species, the proliferation of cells and increase in angiogenesis during tissue repair, and the up-regulation of anti-apoptotic genes through the nuclear factor kappa B (NF-κB) pathway (3). Understanding the role of inflammation in lung cancer etiology may inform chemoprevention efforts and help identify high-risk individuals for screening.
Different strategies have been used to study the association between inflammation and lung cancer. Previous studies have shown associations of lung cancer risk with inflammatory lung conditions, such as chronic obstructive pulmonary disease, pulmonary tuberculosis and Chlamydia pneumonia (4–6). Further, circulating levels of C-reactive protein (CRP), interleukin (IL)-6 and IL-8, and polymorphisms in immunity and inflammation-related genes (e.g., IL1B, IL1A, FCER2, IL-10, TNF-α, IL8RA, ICAM1 and IL12A) have also been associated with lung cancer risk (7–12). However, these genetic studies have been small, associations have not been replicated, and few studies have comprehensively evaluated polymorphisms in innate immunity and inflammation genes.
Information on genome-wide variation and lung cancer is available from genome-wide association studies (GWAS) (13–16). None of the SNPs found to be significantly associated with lung cancer were those previously identified in smaller genetic studies of inflammation-related genes. However, due to the stringent p-values required for statistical significance in GWAS to prevent false positive results, it is possible that moderate associations between polymorphisms in inflammation-related genes and lung cancer were missed.
We conducted a comprehensive evaluation of the association of 1,429 SNPs across 211 genes in innate immunity and inflammation pathways with lung cancer risk in the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial. We independently confirmed our observations by conducting comparisons with the National Cancer Institute’s (NCI’s) lung cancer GWAS (13).
Methods
Study Population
Study participants were enrolled in the PLCO Cancer Screening Trial, which included approximately 155,000 people, aged 50–74 years (17). During 1992–2001 participants were randomized to receive cancer screening or routine health care. Our case-control study was nested within the screening arm of PLCO (N=77,464). Lung cancer screening included a baseline chest x-ray and two (never smokers) or three (current/former smokers) annual chest x-rays. Additionally, at baseline and annually for six years, blood samples were obtained and questionnaires were administered to collect demographic, behavioral and dietary information.
Lung cancers were ascertained through self-report and confirmed by medical chart and death certificate review. Of the 898 lung cancers that occurred between baseline and December 31, 2004, 378 were included in our analysis. Cases were excluded if they were missing the baseline questionnaire (n=17) or information on smoking behaviors (n=11), had a history of cancer (n=56), had multiple cancers during follow-up (n=88), did not consent to the use of their biological specimens for genetic analyses (n=217), or did not have available DNA (n=131). Controls were matched to cases based on the following criteria: age at randomization (55–59, 60–64, 65–69, 70–74 years), sex, year of randomization (1993–1995, 1996–1997, 1998–1999, 2000–2001), length of study follow-up (1-year intervals), and smoking behavior (status [current, former, never], pack-years smoked at baseline [0–29, 30–39, 40–49, 50+], and time since quitting [<15 and 15+ years]). Controls were matched to cases 1:1 for current and former smokers and 3:1 for never smokers.
Laboratory Methods
DNA was extracted from buffy coats collected at baseline using a Qiagen kit (Qiagen Inc., Valencia, CA). Genotyping for 1,536 SNPs across 148 gene regions was performed using an Illumina GoldenGate oligonucleotide pool assay (OPA, Illumina, San Diego, CA). Genes were selected onto the OPA platform based on their function in innate immunity and inflammation pathways (oxidative response, pattern recognition molecules and antimicrobials, integrins and adhesion molecules, complement, chemokines with their receptors and signaling molecules, and response genes and tissue factors). SNPs in these gene regions (defined as 20 kb upsteam to 10 kb past the polyA tail signal) from the International HapMap project were included using the Tagzilla algorithm (18). The following criteria were used for SNP inclusion: minor allele frequencies of >5% in the HapMap Caucasian samples, r2 values of >0.8, and a greater weight for SNPs with a design score of 1.1. Additionally, <5% of SNPs were included based on prior evidence from association studies. High-sensitivity CRP was measured using a chemiluminescent immunoassay (Diagnostic Products Corporation, Los Angeles, CA).
Of the 1,536 SNPs genotyped, 99 were excluded due to assay failure and 8 were excluded due to deviations from Hardy-Weinberg equilibrium among controls (p-value<0.001). The assay completion rate for the included SNPs ranged from 91.2–94.9%, and the concordance among 23 quality control samples was 87.0–95.7% for 29 SNPs and 100% for the remaining 1,400 SNPs.
Statistical Analysis
Odds ratios (ORs) and 95% confidence intervals (CIs) were estimated for lung cancer, comparing heterozygous and homozygous variant genotypes to homozygous wild type genotypes for each of 1,429 SNPs. P-values for trend were calculated, treating the number of alleles as continuous variables. We carried out stratified analyses by lung cancer histology, smoking status and family history, and evaluated interactions between SNPs and smoking status, family history and CRP levels. We conducted analyses adjusted for age, race, sex and smoking behavior (status, pack-years and time since quit) utilizing unconditional logistic regression to retain the maximum number of cases and controls. As adjustment did not alter the associations between SNPs and lung cancer, all results presented are unadjusted. In a sensitivity analysis restricted to white participants, the results also did not change. Therefore, we have presented results for all participants.
We identified SNPs that were significantly (p-trend<0.05) associated with lung cancer in our case-control study and then replicated our results in an independent sample, the NCI’s lung cancer GWAS (13). The NCI GWAS included data on 5,739 lung cancer cases and 5,848 controls from the PLCO, the Environment and Genetics in Lung Cancer Etiology Study, the Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study, and the Cancer Prevention Study II Nutrition Cohort (13). We estimated ORs with logistic regression in the NCI GWAS for 1) the same SNP estimated in the case-control study, if genotyped in the GWAS; or 2) an imputed value for the SNP estimated in the case-control study, if not genotyped in the GWAS. Imputed values were estimated from genotyped SNPs in the same gene region (20 kb upsteam to 10 kb past the polyA tail signal) as the SNP of interest using the IMPUTE statistical program (19) and HapMap release #24 as the reference panel. We excluded PLCO participants from the GWAS analyses who were included in our case-control study (n=355).
We performed permutations to adjust for multiple testing of 81 SNPs in the replication stage. Briefly, we permuted the case-control status in the PLCO data 1000 times, as genotype data were not available from the GWAS. For each permutation, we computed the one-sided p-value for each SNP with direction specified by the PLCO results, and then computed the minimum p-value. Multiple testing adjusted p-values were calculated as the proportion of permutations (n=1000) where the minimum p-value was below the p-value in the GWAS data. When the direction of the association differed in the PLCO and GWAS datasets, the multiple testing adjusted p-value was 1.0. The multiple testing adjusted p-values calculated through permutation were very similar to those estimated with a Bonferroni correction.
Results
Baseline characteristics of cases (n=378) and controls (n=450) from the PLCO study are presented in Table 1. No significant differences were observed for the matching characteristics, with the exception of smoking status, as the study design oversampled never smoking controls. Cases were significantly more likely than controls to have a history of bronchitis/emphysema at baseline (p=0.007) and a family history of lung cancer (p=0.01). The most common lung cancer histologic types included adenocarcinoma (n=170), small cell carcinoma (n=88), squamous cell carcinoma (n=41) and large cell carcinoma (n=20).
Table 1.
Characteristics of 378 lung cancer cases and 450 controls in the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial
| Controls | Cases | p-value | |||
|---|---|---|---|---|---|
|
| |||||
| N | % | N | % | ||
| Total | 450 | 378 | |||
| Age at enrollment, years | |||||
| ≤59 | 89 | 19.8 | 80 | 21.2 | † |
| 60–64 | 131 | 29.1 | 106 | 28.0 | |
| 65–69 | 147 | 32.7 | 128 | 33.9 | |
| 70–74 | 83 | 18.4 | 64 | 16.9 | |
| Sex | |||||
| Female | 180 | 40.0 | 131 | 34.7 | † |
| Male | 270 | 60.0 | 247 | 65.3 | |
| Smoking status* | |||||
| Never smoker | 99 | 22.0 | 30 | 7.9 | † |
| Former smoker | 217 | 48.2 | 217 | 57.4 | |
| 0–29 pack-years and quit for <15 years | 21 | 4.7 | 23 | 6.1 | |
| 0–29 pack-years and quit for ≥15 years | 49 | 10.7 | 44 | 11.6 | |
| 30–39 pack-years and quit for <15 years | 32 | 7.1 | 30 | 7.9 | |
| 30–39 pack-years and quit for ≥15 years | 16 | 3.6 | 17 | 4.5 | |
| 40–49 pack-years and quit for <15 years | 15 | 3.3 | 14 | 3.7 | |
| 40–49 pack-years and quit for ≥15 years | 8 | 1.8 | 11 | 2.9 | |
| 50+ pack-years and quit for <15 years | 64 | 14.2 | 66 | 17.5 | |
| 50+ pack-years and quit for ≥15 years | 12 | 2.7 | 12 | 3.2 | |
| Current smoker | 134 | 29.8 | 131 | 34.7 | |
| 0–29 pack-years | 32 | 7.1 | 26 | 6.9 | |
| 30–39 pack-years | 41 | 9.1 | 39 | 10.3 | |
| 40–49 pack-years | 9 | 2.0 | 11 | 2.9 | |
| 50+ pack-years | 52 | 11.6 | 55 | 14.6 | |
| Race/ethnicity | |||||
| White | 410 | 91.1 | 338 | 89.4 | 0.35 |
| Black | 20 | 4.4 | 27 | 7.1 | |
| Hispanic | 7 | 1.6 | 4 | 1.1 | |
| Other | 13 | 2.9 | 9 | 2.4 | |
| Education | |||||
| High school graduate or less | 157 | 34.9 | 148 | 39.2 | 0.21 |
| More than high school graduate | 293 | 65.1 | 230 | 60.8 | |
| Body mass index at enrollment | |||||
| <18.5 | 8 | 1.8 | 7 | 1.9 | 0.59 |
| 18.5–24.9 | 142 | 31.6 | 121 | 32.0 | |
| 25.0–29.9 | 211 | 46.9 | 162 | 42.9 | |
| ≥30 | 89 | 19.8 | 88 | 23.3 | |
| Regular use of aspirin or ibuprofen | |||||
| Yes | 302 | 67.1 | 242 | 64.0 | 0.35 |
| No | 148 | 32.9 | 136 | 36.0 | |
| History of bronchitis/emphysema | |||||
| Yes | 47 | 10.4 | 62 | 16.4 | 0.007 |
| No | 394 | 87.6 | 299 | 79.1 | |
| Missing | 9 | 2.0 | 17 | 4.5 | |
| History of heart disease | |||||
| Yes | 55 | 12.2 | 62 | 16.4 | 0.06 |
| No | 386 | 85.8 | 300 | 79.4 | |
| Missing | 9 | 2.0 | 16 | 4.2 | |
| Family history of lung cancer†† | |||||
| Yes | 48 | 10.7 | 66 | 17.5 | 0.01 |
| No | 387 | 86.0 | 288 | 76.2 | |
| Missing | 15 | 3.3 | 24 | 6.4 | |
Proportions of cases and controls appear different due to differences in the number of controls per case for never smokers (3:1), former smokers (1:1) and current smokers (1:1).
Matching variable.
Family history of lung cancer in any first degree relative was self-reported.
Of the 1,429 SNPs included in our analysis, we observed significant associations between 81 SNPs located in 44 genes and lung cancer (p-trend<0.05). Of these 81 SNPS, there was evidence for confirmation in the GWAS data for 4 SNPs through direct replication, and 6 SNPs through imputation. Table 2 presents the ORs for the 10 confirmed SNPs in the PLCO and GWAS.
Table 2.
Association of selected innate immunity and inflammation SNPs with lung cancer in the PLCO case-control study and NCI GWAS
| PLCO Study | Typed or imputed in GWAS | P-trend corrected for multiple comparisons** | |||
|---|---|---|---|---|---|
| Controls | Cases | OR (95% CI) | OR (95% CI) | ||
| NFKB1 (rs4648127) | |||||
|
| |||||
| CC | 379 | 342 | 1.0 | 1.0† | 0.023‡ |
| CT | 69 | 35 | 0.56 (0.36–0.87) | 0.78 (0.68–0.90) | |
| TT | 2 | 1 | 0.55 (0.05–6.14) | 0.83 (0.55–1.25) | |
| CT+TT | 0.56 (0.37–0.86) | 0.79 (0.69–0.90) | |||
| p-trend** | 0.0092 | 0.0006 | |||
|
| |||||
| C2 (rs497309) | |||||
|
| |||||
| AA | 374 | 293 | 1.0 | 1.0† | 0.18 |
| AC | 71 | 76 | 1.37 (0.96–1.95) | 1.26 (1.09–1.46) | |
| CC | 5 | 8 | 2.04 (0.66–6.31) | 1.10 (0.86–1.41) | |
| AC+CC | 1.41 (1.00–1.99) | 1.21 (1.08–1.37) | |||
| p-trend** | 0.040 | 0.0058 | |||
|
| |||||
| TLR3 (rs3775291) | |||||
|
| |||||
| CC | 229 | 172 | 1.0 | 1.0* | 0.32 |
| CT | 192 | 165 | 1.14 (0.86–1.52) | 1.05 (0.97–1.13) | |
| TT | 28 | 41 | 1.95 (1.16–3.28) | 1.21 (1.06–1.38) | |
| CT+TT | 1.25 (0.95–1.64) | 1.07 (1.00–1.16) | |||
| p-trend** | 0.024 | 0.010 | |||
|
| |||||
| ALOX5 (rs1487562) | |||||
|
| |||||
| CC | 321 | 237 | 1.0 | 1.0* | 0.44 |
| CT | 119 | 122 | 1.39 (1.03–1.88) | 1.11 (1.03–1.21) | |
| TT | 10 | 19 | 2.57 (1.18–5.64) | 1.09 (0.91–1.30) | |
| CT+TT | 1.48 (1.11–1.98) | 1.11 (1.03–1.20) | |||
| p-trend** | 0.0030 | 0.02 | |||
|
| |||||
| C2 (rs2734335) | |||||
|
| |||||
| AA | 149 | 96 | 1.0 | 1.0* | 0.46 |
| AG | 192 | 180 | 1.46 (1.05–2.02) | 1.02 (0.93–1.11) | |
| GG | 108 | 102 | 1.47 (1.01–2.13) | 1.14 (1.03–1.27) | |
| AG+GG | 1.46 (1.08–1.98) | 1.06 (0.97–1.14) | |||
| p-trend** | 0.038 | 0.017 | |||
|
| |||||
| FCER1G (rs4489574) | |||||
|
| |||||
| CC | 229 | 169 | 1.0 | 1.0* | 0.70 |
| CT | 169 | 152 | 1.22 (0.91–1.64) | 1.14 (1.05–1.23) | |
| TT | 51 | 57 | 1.51 (0.99–2.32) | 1.05 (0.92–1.19) | |
| CT+TT | 1.29 (0.98–1.69) | 1.12 (1.04–1.21) | |||
| p-trend** | 0.041 | 0.032 | |||
|
| |||||
| TLR4 (rs10759932) | |||||
|
| |||||
| TT | 341 | 265 | 1.0 | 1.0† | 0.75 |
| CT | 98 | 100 | 1.31 (0.95–1.81) | 1.10 (1.01–1.21) | |
| CC | 9 | 12 | 1.72 (0.71–4.13) | 1.08 (0.88–1.34) | |
| CT+CC | 1.35 (0.99–1.84) | 1.10 (1.01–1.20) | |||
| p-trend** | 0.049 | 0.039 | |||
|
| |||||
| TLR10 (rs7660429) | |||||
|
| |||||
| CC | 329 | 299 | 1.0 | 1.0† | 1.0 |
| CG | 110 | 73 | 0.73 (0.52–1.02) | 1.09 (0.99–1.19) | |
| GG | 11 | 6 | 0.60 (0.22–1.64) | 1.14 (0.92–1.42) | |
| CG+GG | 0.72 (0.52–0.99) | 1.10 (1.00–1.20) | |||
| p-trend** | 0.044 | 0.036 | |||
|
| |||||
| ACAD11 (rs11927882) | |||||
|
| |||||
| GG | 273 | 201 | 1.0 | 1.0† | 1.0 |
| AG | 142 | 136 | 1.30 (0.97–1.75) | 0.85 (0.72–0.99) | |
| AA | 35 | 40 | 1.55 (0.95–2.53) | 0.71 (0.42–1.16) | |
| AG+AA | 1.35 (1.02–1.78) | 0.83 (0.72–0.97) | |||
| p-trend** | 0.027 | 0.013 | |||
|
| |||||
| CCR6 (rs9459883) | |||||
|
| |||||
| GG | 378 | 293 | 1.0 | 1.0† | 1.0 |
| CG | 70 | 73 | 1.35 (0.94–1.93) | 0.90 (0.80–1.02) | |
| CC | 2 | 12 | 7.74 (1.72–34.8) | 0.72 (0.42–1.22) | |
| CG+CC | 1.52 (1.07–2.16) | 0.89 (0.79–1.00) | |||
| p-trend** | 0.003 | 0.045 | |||
Typed in GWAS;
Imputed in GWAS;
Statistically significant after adjustment for multiple comparisons;
P-trends in the PLCO study and the GWAS were two-sided and the p-trend corrected for multiple comparisons was one-sided.
After adjusting for multiple comparisons, only NFKB1 (rs4648127) remained significantly associated with lung cancer in the replication phase (multiple testing adjusted p-value=0.02). The CT/TT genotype of NFKB1 (rs4648127) was associated with a 44% reduction in the odds (OR=0.56; 95% CI 0.37–0.86) of lung cancer in the PLCO case-control study and a 21% reduction in the odds (OR=0.79; 95% CI 0.69–0.90) of lung cancer when imputed in the GWAS study. None of the 13 additional NFKB1 SNPs genotyped in the PLCO case-control study were significantly associated with lung cancer risk (all p-trend>0.05). Of note, rs4648127 was not correlated with the twelve other NFKB1 SNPs genotyped in PLCO and available in HapMap (r2 range 0–0.49).
We carried out additional analyses in the PLCO case-control study. Among controls, NFKB1 (rs4648127) was not associated with family history of lung cancer, bronchitis/emphysema or CRP (all p>0.3). Additionally, we found no significant interaction between NFKB1 (rs4648127) and smoking status (p-interaction=0.19), CRP level (p-interaction=0.97), or family history (p-interaction=0.78). Across lung cancer histologies, we found the association between NFKB1 (rs4648127) and lung cancer to be limited to adenocarcinoma, though we had limited power to detect associations with other histology groups.
Discussion
A SNP in the NFKB1 gene (rs4648127) was associated with lung cancer in the screening arm of the PLCO Cancer Screening Trial, with a 44% reduction in lung cancer risk with at least one T allele, compared to the CC genotype. Additionally, lung cancer risk was reduced by 21% when the same SNP was imputed with data from the NCI GWAS. Our observation of a significant association between the NFKB1 gene and lung cancer risk underscores the etiologic role of inflammation and immunity in lung carcinogenesis
NF-κB, a transcription factor that is primarily activated by pro-inflammatory cytokines, plays an important role in development, immunity, tissue homeostasis, and inflammation, and regulates gene expression, cell apoptosis, and proliferation (20). Though variation in the NFKB1 gene has not been previously associated with lung cancer risk, a polymorphism in the promoter region of NFKB1 is associated with the severity of acute respiratory distress syndrome (21), suggesting biologic plausibility for NFKB1’s role in pulmonary diseases. SNPs in NFKB1 have been associated with the risk of non-Hodgkin lymphoma, Hodgkin lymphoma, colon cancer, rectal cancer, meningioma and cervical cancer (18;22–25). Further, haplotypes including the same NFKB1 SNP as our study (rs4648127) were significantly associated with rectal cancer risk (25). It is important to note that NF-κB can also be activated by inducers other than cytokines, including viral and bacterial products, DNA damage, and hypoxia (26). Thus, the association between NFKB1and lung cancer may also reflect associations between other factors and lung cancer risk.
It is important to place results from candidate gene studies in the context of GWAS results. The evaluation of 515,922 SNPs in the NCI GWAS necessitated the use of very stringent p-values to prevent false positive associations. Therefore, false negatives may have arisen for SNPs with more moderate associations and weaker p-values. Indeed, although 8 of 20 NFKB1 SNPs on the GWAS were significant at nominal levels (p<0.05, range=0.0004–0.7) (13), none of these met the GWAS p-value threshold. Additionally, there may have been poor coverage of gene regions associated with inflammation on the GWAS chip. Nonetheless, using a targeted approach focused on the immune and inflammation pathways, we found support for an association between NFKB1 and lung cancer.
The main limitation of our study was the relatively small number of cases and controls, which limited our ability to detect weaker associations in a large number of SNPs or conduct pathway-based analyses in PLCO. However, we focused our analyses on genes with functions related to inflammation and innate immunity, which support emerging biologically plausible mechanisms in lung cancer development. Further, we confirmed our results with the NCI GWAS study. We believe that this method has a higher sensitivity than GWAS alone and can be used when there is a biologically-motivated hypothesis.
In conclusion, our study supports the role of genetic variation in innate immunity in the development of lung cancer. Future studies should further examine the NFKB1 region to identify functional SNPs and corresponding protein levels that are associated with lung cancer risk. These findings add to the growing body of literature implicating inflammation and immunity in lung cancer etiology.
Acknowledgments
This study was supported by the Intramural Research Program of the National Cancer Institute. The Environment and Genetics in Lung Cancer Etiology (EAGLE), Prostate, Lung, Colon, Ovary Screening Trial (PLCO), and Alpha-Tocopherol, Beta-Carotene Cancer Prevention (ATBC) studies were supported by the Intramural Research Program of the National Institutes of Health, National Cancer Institute (NCI), Division of Cancer Epidemiology and Genetics. PLCO was also supported by individual contracts from the NCI to the University of Colorado Denver (NO1-CN-25514), Georgetown University (NO1-CN-25522), the Pacific Health Research Institute (NO1-CN-25515), the Henry Ford Health System (NO1-CN-25512), the University of Minnesota, (NO1-CN-25513), Washington University (NO1-CN-25516), the University of Pittsburgh (NO1-CN-25511), the University of Utah (NO1-CN-25524), the Marshfield Clinic Research Foundation (NO1-CN-25518), the University of Alabama at Birmingham (NO1-CN-75022), Westat, Inc. (NO1-CN-25476), and the University of California, Los Angeles (NO1-CN-25404). ATBC was also supported by U.S. Public Health Service contracts (N01-CN-45165, N01-RC-45035, and N01-RC-37004) from the NCI. The Cancer Prevention Study-II (CPS-II) Nutrition Cohort was supported by the American Cancer Society.
For the PLCO: The authors thank Drs. Christine Berg and Philip Prorok, Division of Cancer Prevention, National Cancer Institute; the Screening Center investigators and staff of the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial; Mr. Tom Riley and staff, Information Management Services, Inc.; Ms. Barbara O’Brien and staff, Westat, Inc.; Mr. Tim Sheehy and staff, DNA Extraction and Staging Laboratory, SAIC-Frederick, Inc; and Ms. Jackie King and staff, BioReliance, Inc. Most importantly, we acknowledge the study participants for their contributions to making this study possible.
For the NCI GWAS: The authors thank the participants and collaborators of the EAGLE, ATBC, PLCO and CPS-II Nutrition Cohort studies; the the staff of the Core Genotyping Facility—specifically Amy Hutchinson, Aurelie Vogt, Kevin Jacobs and Zhaoming Wang; the National Center for Biotechnology for assistance with data cleaning; Justin Paschall and Mike Feolo for data manipulation; and Adam Risch, Bill Wheeler and Sihui Zhao of Information Management Services, Inc. for database support.
Reference List
- 1.Ferlay J, Shin HR, Forman D, Mathers C, Parkin DM. GLOBOCAN 2008, Cancer Incidence and Mortality Worldwide: IARC CancerBase No 10 (Internet) Lyon, France: International Agency for Research on Cancer; 2010. [Google Scholar]
- 2.Sasco AJ, Secretan MB, Straif K. Tobacco smoking and cancer: a brief review of recent epidemiological evidence. Lung cancer (Amsterdam, Netherlands) 2004;45 (Suppl 2):S3–S9. doi: 10.1016/j.lungcan.2004.07.998. [DOI] [PubMed] [Google Scholar]
- 3.Ballaz S, Mulshine JL. The potential contributions of chronic inflammation to lung carcinogenesis. Clin Lung Cancer. 2003;5:46–62. doi: 10.3816/CLC.2003.n.021. [DOI] [PubMed] [Google Scholar]
- 4.Chaturvedi AK, Gaydos CA, Agreda P, et al. Chlamydia pneumoniae infection and risk for lung cancer. Cancer Epidemiol Biomarkers Prev. 2010;19:1498–505. doi: 10.1158/1055-9965.EPI-09-1261. [DOI] [PubMed] [Google Scholar]
- 5.Koshiol J, Rotunno M, Consonni D, et al. Chronic obstructive pulmonary disease and altered risk of lung cancer in a population-based case-control study. PLoS One. 2009;4:e7380. doi: 10.1371/journal.pone.0007380. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Shiels MS, Albanes D, Virtamo J, Engels EA. Increased risk of lung cancer in men with tuberculosis in the alpha-tocopherol, Beta-carotene cancer prevention study. Cancer Epidemiol Biomarkers Prev. 2011;20:672–8. doi: 10.1158/1055-9965.EPI-10-1166. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.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;67:6520–7. doi: 10.1158/0008-5472.CAN-07-0370. [DOI] [PubMed] [Google Scholar]
- 8.Landvik NE, Hart K, Skaug V, Stangeland LB, Haugen A, Zienolddiny S. A specific interleukin-1B haplotype correlates with high levels of IL1B mRNA in the lung and increased risk of non-small cell lung cancer. Carcinogenesis. 2009;30:1186–92. doi: 10.1093/carcin/bgp122. [DOI] [PubMed] [Google Scholar]
- 9.Vogel U, Christensen J, Wallin H, 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;639:89–100. doi: 10.1016/j.mrfmmm.2007.11.004. [DOI] [PubMed] [Google Scholar]
- 10.Lee KM, Shen M, Chapman RS, et al. Polymorphisms in immunoregulatory genes, smoky coal exposure and lung cancer risk in Xuan Wei, China. Carcinogenesis. 2007;28:1437–41. doi: 10.1093/carcin/bgm030. [DOI] [PubMed] [Google Scholar]
- 11.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 non-small cell lung cancer. Lung cancer (Amsterdam, Netherlands) 2010 doi: 10.1016/j.lungcan.2010.04.016. [DOI] [PubMed]
- 12.Pine SR, Mechanic LE, Enewold L, et al. Increased levels of circulating interleukin 6, interleukin 8, C-reactive protein, and risk of lung cancer. J Natl Cancer Inst. 2011;103:1112–22. doi: 10.1093/jnci/djr216. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Landi MT, Chatterjee N, Yu K, 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;85:679–91. doi: 10.1016/j.ajhg.2009.09.012. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Amos CI, Wu X, Broderick P, et al. Genome-wide association scan of tag SNPs identifies a susceptibility locus for lung cancer at 15q25.1. Nat Genet. 2008;40:616–22. doi: 10.1038/ng.109. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.McKay JD, Hung RJ, Gaborieau V, et al. Lung cancer susceptibility locus at 5p15.33. Nat Genet. 2008;40:1404–6. doi: 10.1038/ng.254. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Hung RJ, McKay JD, Gaborieau V, et al. A susceptibility locus for lung cancer maps to nicotinic acetylcholine receptor subunit genes on 15q25. Nature. 2008;452:633–7. doi: 10.1038/nature06885. [DOI] [PubMed] [Google Scholar]
- 17.Prorok PC, Andriole GL, Bresalier RS, et al. Design of the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial. Control Clin Trials. 2000;21:273S–309S. doi: 10.1016/s0197-2456(00)00098-2. [DOI] [PubMed] [Google Scholar]
- 18.Rajaraman P, Brenner AV, Neta G, et al. Risk of meningioma and common variation in genes related to innate immunity. Cancer Epidemiol Biomarkers Prev. 2010;19:1356–61. doi: 10.1158/1055-9965.EPI-09-1151. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Howie BN, Donnelly P, Marchini J. A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genet. 2009;5:e1000529. doi: 10.1371/journal.pgen.1000529. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Lin Y, Bai L, Chen W, Xu S. The NF-kappaB activation pathways, emerging molecular targets for cancer prevention and therapy. Expert Opin Ther Targets. 2010;14:45–55. doi: 10.1517/14728220903431069. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Adamzik M, Frey UH, Rieman K, et al. Insertion/deletion polymorphism in the promoter of NFKB1 influences severity but not mortality of acute respiratory distress syndrome. Intensive Care Med. 2007;33:1199–203. doi: 10.1007/s00134-007-0649-4. [DOI] [PubMed] [Google Scholar]
- 22.Cerhan JR, Liu-Mares W, Fredericksen ZS, et al. Genetic variation in tumor necrosis factor and the nuclear factor-kappaB canonical pathway and risk of non-Hodgkin’s lymphoma. Cancer Epidemiol Biomarkers Prev. 2008;17:3161–9. doi: 10.1158/1055-9965.EPI-08-0536. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Chang ET, Birmann BM, Kasperzyk JL, et al. Polymorphic variation in NFKB1 and other aspirin-related genes and risk of Hodgkin lymphoma. Cancer Epidemiol Biomarkers Prev. 2009;18:976–86. doi: 10.1158/1055-9965.EPI-08-1130. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Zhou B, Qie M, Wang Y, et al. Relationship between NFKB1 -94 insertion/deletion ATTG polymorphism and susceptibility of cervical squamous cell carcinoma risk. Ann Oncol. 2010;21:506–11. doi: 10.1093/annonc/mdp507. [DOI] [PubMed] [Google Scholar]
- 25.Curtin K, Wolff RK, Herrick JS, Abo R, Slattery ML. Exploring multilocus associations of inflammation genes and colorectal cancer risk using hapConstructor. BMC Med Genet. 2010;11:170. doi: 10.1186/1471-2350-11-170. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Perkins ND. The diverse and complex roles of NF-kappaB subunits in cancer. Nat Rev Cancer. 2012;12:121–32. doi: 10.1038/nrc3204. [DOI] [PubMed] [Google Scholar]