Risk of Meningioma and Common Variation in Genes Related to Innate Immunity

Meningioma is the second most common type of brain/central nervous system tumor in the United States, comprising ∼30% of all brain/central nervous system tumors (1). Despite their largely benign histology, these tumors can cause serious morbidity by virtue of their intracranial location. Other than ionizing radiation and certain rare predisposing genetic syndromes, very little is known about the etiology of these tumors. Evidence from prior epidemiologic studies, although inconsistent, suggests a possible inverse association between the risk of meningioma and personal history of allergic disease (2,3), raising the possibility that alterations in the immune system may contribute to the etiology of these tumors.

The innate immune system is phylogenetically ancient and works closely with the adaptive immune system in an integrated process to ensure effective responses to a wide range of antigenic challenges, including tumors (4). Genetic polymorphisms as well as functional alterations in the innate immune system have been implicated in the pathogenesis of several intracranial conditions including glioma, meningioma, and neurodegenerative diseases (5-10).

Given the epidemiologic and biological evidence suggesting a link between innate immune system alterations and central nervous system disorders, we evaluated common genetic germline variants in innate immune genes to estimate the effect of low penetrance alleles on risk of meningioma. Using data from non-Hispanic whites in a hospital-based case-control study conducted by the National Cancer Institute (NCI) between 1994 and 1998, we evaluated the risk of meningioma (n = 101) compared with noncancer controls (n = 330) with respect to 1,407 tag single nucleotide polymorphisms (SNP) in 148 innate immune genes and their surrounding regions.

A detailed description of study methods can be found elsewhere (11). Briefly, the parent study was a U.S. hospital-based case-control study of the three most common brain tumor types. Eligible patients were 18 y or older with a first intracranial glioma or neuroepitheliomatous tumor (ICD-O-2 codes 9380-9473 and 9490-9506), meningioma (ICD-O-2 codes 9530-9538), or acoustic neuroma (ICD-O-2 code 9560) diagnosed during 1994 to 1998 at one of three hospitals specializing in brain tumor treatment (in Boston, Phoenix, and Pittsburgh) within the 8 wk preceding hospitalization. Ninety-two percent of eligible brain tumor patients agreed to participate and were enrolled (489 patients with glioma, 197 with meningioma, and 96 patients with acoustic neuroma). For this study, only histologically confirmed cases of meningioma were included.

Controls were chosen from individuals admitted to the same hospitals for injuries (25%), circulatory system disorders (22%), musculoskeletal disorders (22%), digestive disorders (12%), or a variety of other nonneoplastic conditions, and were frequency matched in a one-to-one ratio to a total case series (glioma, meningioma, and acoustic neuroma) based on age (18-29, 30-39, 40-49, 50-59,60-69, 70-79, 80-99 y), race/ethnicity (non-Hispanic white, Hispanic, African-American, other), sex, hospital, and residential proximity to the hospital. Seven hundred ninety-nine control patients (86% of all contacted) were enrolled.

Shortly following hospitalization, a trained research nurse administered a structured in-person interview with each subject and subjects later completed and mailed back a self-administered questionnaire. The information collected included data on use of cellular telephones, prior medical conditions, diet, and a detailed occupational history. In addition, participants were asked to provide whole blood samples. This analysis was restricted to individuals of European ancestry (89% of all study participants) who provided blood samples and for whom an adequate amount of DNA was extracted from blood samples. The study protocol was approved by the Institutional Review Board of each participating institution and written informed consent was obtained from each patient or proxy.

DNA was extracted from blood using a phenol-chloroform protocol. Samples were genotyped for 101 patients with meningioma and 330 controls of European ancestry. Genotyping was done at the NCI Core Genotyping Facility (Advanced Technology Corp.) using an Illumina GoldenGate OPA panel designed to tag 148 candidate innate immunity genes and their surrounding regions (20 kb 5′ of the start of the first exon and 10 kb 3′ of the end of the last exon of each candidate gene). The innate immunity panel was composed of genes selected from known innate immune 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). Tag SNPs were chosen from the SNPs that were genotyped as part of the International HapMap (12) using the TagZilla (13) algorithm with the following parameters: minor allele frequency of >5% in HapMap Caucasian (CEU) samples, r² > 0.8, and greater weighting for SNPs with a design score of 1.1 (SNPs with a design score of <0.4 were designated as “obligate excludes”).

Quality control specimens included replicate samples from 3 nonstudy participants and blinded duplicate samples from 21 participants interspersed among cases and controls. Seventy-seven of 1,536 SNPs originally chosen failed in assay manufacture or provided only monoallelic calls. For the study analyses, any SNPs that did not satisfy the Hardy-Weinberg Equilibrium at P < 0.001 (n = 10) were excluded. Additional genotype assays were excluded for low completion rates of <90% (n = 26) or poor concordance rates of <95% (n = 16). Percent agreement between the three nonstudy replicates for the remaining 1,407 SNPs was 100% for all SNPs. Concordance for study duplicates ranged from 95% to 100% (mean, 98.8%) and the genotyping success rate ranged from 92% to 100% (mean, 99.8%). Principal component analysis of these data revealed that population stratification was negligible in this data set. A list of genes, chromosomal location, and position for the SNPs included in the analysis is available online (5).

Unconditional multivariate logistic regression models were used to estimate odds ratios and calculate 95% confidence intervals for the main effects of each individual SNP, using the homozygous wild-type genotype as the reference category. Odds ratios were estimated separately for heterozygotes and rare homozygous allele groups, adjusting for study-matching factors (age, sex, hospital, and residential distance from hospital). A likelihood ratio test of linear trend was conducted for each SNP using a three-level ordinal variable corresponding to the number of minor alleles for that SNP.

We used the rank-truncated product to adjust SNP and gene region–based findings for multiple testing while accounting for correlations among SNPs induced by linkage disequilibrium (14). The rank-truncated product, which is based on the product of the most significant P values within a gene or pathway, is an appropriate test in scenarios with a small set of true effects among a large number of null effects. Permutation based P values for the rank-truncated product statistics were computed based on 20,000 permutations of case-control status under the null hypotheses of no association with genotype.

One hundred and one cases of meningioma and 330 controls were successfully genotyped for 1,407 SNPs in 148 genetic regions. Demographic characteristics did not differ appreciably between all participants and those that were genotyped, with genotyped cases being slightly more likely to be female and younger (Table 1). The observed distribution of P values of trend for all 1,407 SNPs did not differ significantly from the expected (uniform) null distribution, making the possibility of systematic bias in the study unlikely.

Sixty-eight SNPs in 36 genetic regions were significantly associated with the risk of meningioma at P < 0.05 and 17 SNPs in 12 genetic regions were associated at P < 0.01 (Table 2). After correcting for multiple testing, none of the SNPs from the single SNP analysis remained statistically significant at P < 0.05. However, gene region–based tests identified seven regions as statistically significant: TNFRSF18 (P = 0.003), NFKB1 (P = 0.008), FCER1G (P = 0.009), CD14 (P = 0.01), C1D (P = 0.03), CCR6 (P = 0.03), and VCAM1 (P = 0.03).

In our exploratory, hypothesis-generating study of risk of adult meningioma with common tagging SNPs in 148 innate immune genes and their surrounding regions, we identified seven genetic regions of particular interest within four innate immune pathways: TNFSRF18, FCER1G and VCAM1 (integrins/cell surface receptors), NFKB1 and CD14 (pattern recognition and antimicrobials), C1D (complement), and CCR6 (chemokines).

The gene regions for NFKB1 and FCER1G were particularly intriguing given the relationship between T-cell regulation and chronic inflammation (NFKB1), and IgE and allergic reactions (FCER1G). Three statistically significant SNPs were observed in each of these regions at the P < 0.01 level. NFKB1 encodes the subunit p50/p105 for a pleiotropic transcription regulator activated by a variety of intracellular and extracellular stimuli. The transcription inhibitor is part of the DNA-binding subunit of the NFKB protein complex involved with T-cell regulation and chronic inflammation (15). Epidemiologic studies have observed associations between NFKB1 polymorphisms and risk of some cancers, including glioma, non–Hodgkin lymphoma and Hodgkin's lymphoma (5, 16, 17), and inflammatory diseases (18) but not for other cancers such as breast, colorectal, or renal cell cancers (19, 20). The NFKB pathway has also been associated with inflammatory conditions in the brain, such as Alzheimer's disease (7, 8). Less is known about FCER1G and its relevance to the etiology of meningiomas. The FCER1G is a subunit of the high-affinity IgE receptor mediating allergic reactions. Functional polymorphisms in FCER1A, which also codes for a subunit of the IgE receptor, were strongly associated with serum IgE levels in a genome-wide association study (21).

Several additional SNPs of interest lay within gene regions coding for integrin/cell surface receptors, including TNFSRF18, FCER1G, and VCAM1. This pathway is of particular interest given that changes in integrin pattern expression have been observed in a variety of meningiomas (9, 10) and may influence their invasive biological behavior.

The few existing studies of common genetic variation and risk of meningioma have reported statistically significant associations in genes involved in metabolism (22), DNA repair (23-25), apoptosis/cell cycle (26), folate metabolism(27, 28), p53 (25, 29, 30), and oxidative response pathways (31). To our knowledge, this is the first study of meningioma to explore a large number of genetic variants in the innate immunity pathway. In a previous candidate SNP/gene study on oxidative response and risk of meningioma, we found that the minor allele variant of SOD3 rs699473 was associated with increased risk of meningioma. This variant was not included in the tag SNPs selected for the current study (31). A small subset of the SNPs included in this study were examined in terms of interaction with lead exposure, and XDH rs7574920 as well as GPX1 rs1050450 and rs18006688 were found to modify the effect of lead exposure on risk of meningioma (32).

Comparing our results for innate immunity genes and risk of meningioma with a previous examination of the same SNPs for glioma risk (5), we found that among the top 10 hits, NFKB1 was significant in both studies (P < 0.01). However, gene region–based hits (P < 0.05) differed between glioma (ALOX5, SELP, and SOD) and meningioma (TNFSRF18, FCER1G, VCAM1, NFKB1, CD14, C1D, and CCR6).

While our results provide interesting clues, they are subject to some caveats. Although the hospital-based design of this study allowed the accrual of incident meningiomas and we imposed strict quality control criteria for blood collection and genotyping of DNA samples, the sample size for this study remains small. The SNPs in this study were chosen as tagging markers for the genetic region and not based on known function; thus, the observed associations could be due to linkage disequilibrium with the true unobserved causal SNPs. Replication of these findings with increased coverage of the identified genes of interest and larger sample size (for example, in multicenter studies of meningioma) is required to rule out the possibility of chance findings.

No potential conflicts of interest were disclosed.

We thank Henry Chen, Michael Stagner, Bob Wheeler, Jane Wang, and Leslie Carroll of Information Management Systems for the help with statistical programming and biospecimen coordination.

Grant Support: This research was supported by intramural funds from the NCI, NIH, Department of Health and Human Services, and has been funded in whole or in part with federal funds from the NCI, NIH, under contract N01-CO-12400. The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations imply endorsement by the U.S. Government.

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