Genetic risk variants in the CDKN2A/B, RTEL1 and EGFR genes are associated with somatic biomarkers in glioma.

During the last years, genome wide association studies have discovered common germline genetic variants associated with specific glioma subtypes. We aimed to study the association between these germline risk variants and tumor phenotypes, including copy number aberrations and protein expression. A total of 91 glioma patients were included. Thirteen well known genetic risk variants in TERT, EGFR, CCDC26, CDKN2A, CDKN2B, PHLDB1, TP53, and RTEL1 were selected for investigation of possible correlations with the glioma somatic markers: EGFR amplification, 1p/19q codeletion and protein expression of p53, Ki-67, and mutated IDH1. The CDKN2A/B risk variant, rs4977756, and the CDKN2B risk variant, rs1412829 were inversely associated (p = 0.049 and p = 0.002, respectively) with absence of a mutated IDH1, i.e., the majority of patients homozygous for the risk allele showed no or low expression of mutated IDH1. The RTEL1 risk variant, rs6010620 was associated (p = 0.013) with not having 1p/19q codeletion, i.e., the majority of patients homozygous for the risk allele did not show 1p/19q codeletion. In addition, the EGFR risk variant rs17172430 and the CDKN2B risk variant rs1412829, both showed a trend for association (p = 0.055 and p = 0.051, respectively) with increased EGFR copy number, i.e., the majority of patients homozygote for the risk alleles showed chromosomal gain or amplification of EGFR. Our findings indicate that CDKN2A/B risk genotypes are associated with primary glioblastoma without IDH mutation, and that there is an inverse association between RTEL1 risk genotypes and 1p/19q codeletion, suggesting that these genetic variants have a molecular impact on the genesis of high graded brain tumors. Further experimental studies are needed to delineate the functional mechanism of the association between genotype and somatic genetic aberrations.

Immunohistochemical analysis of LRIG proteins in meningiomas: correlation between estrogen receptor status and LRIG expression.

The leucine-rich repeats and immunoglobulin-like domains (LRIG) protein family is comprised of three integral membrane proteins: LRIG1, LRIG2, and LRIG3. LRIG1 is a negative regulator of growth factor signaling. The expression and subcellular localization of LRIG proteins have prognostic implications in primary brain tumors, such as oligodendrogliomas and astrocytomas. The expression of LRIG proteins has not previously been studied in meningiomas. In this study, the expression of LRIG1, LRIG2, and LRIG3 was analyzed in 409 meningiomas by immunohistochemistry, and potential associations between LRIG protein expression and tumor grade, gender, progesterone receptor status, and estrogen receptor (ER) status were investigated. The LRIG proteins were most often expressed in the cytoplasm, though LRIG1 also showed prominent nuclear expression. Cytoplasmic expression of LRIG1 and LRIG2 correlated with histological subtypes of meningiomas (p = 0.038 and 0.013, respectively). Nuclear and cytoplasmic expression of LRIG1 was correlated with ER status (p = 0.003 and 0.004, respectively), as was cytoplasmic expression of LRIG2 (p = 0.006). This study is the first to examine the expression of LRIG proteins in meningiomas, and it shows a correlation between ER status and the expression of LRIG1 and LRIG2, which suggests a possible role for LRIG proteins in meningioma pathogenesis.

Relation between Established Glioma Risk Variants and DNA Methylation in the Tumor.

Genome-wide association studies and candidate gene studies have identified several genetic variants that increase glioma risk. The majority of these variants are non-coding and the mechanisms behind the increased risk in carriers are not known. In this study, we hypothesize that some of the established glioma risk variants induce aberrant DNA methylation in the developing tumor, either locally (gene-specific) or globally (genome-wide). In a pilot data set including 77 glioma patients, we used Illumina beadchip technology to analyze genetic variants in blood and DNA methylation in matched tumor samples. To validate our findings, we used data from the Cancer Genome Atlas, including 401 glioblastoma patients. Consensus clustering identified the glioma CpG island methylator phenotype (gCIMP) and two additional subgroups with distinct patterns of global DNA methylation. In the pilot dataset, gCIMP was associated with two genetic variants in CDKN2B-AS1, rs1412829 and rs4977756 (9p21.3, p = 8.1 x 10-7 and 4.8 x 10-5, respectively). The association was in the same direction in the TCGA dataset, although statistically significant only when combining individuals with AG and GG genotypes. We also investigated the relation between glioma risk variants and DNA methylation in the promoter region of genes located within 30 kb of each variant. One association in the pilot dataset, between the TERT risk variant rs2736100 and lower methylation of cg23827991 (in TERT; p = 0.001), was confirmed in the TCGA dataset (p = 0.001). In conclusion, we found an association between rs1412829 and rs4977756 (9p21.3, CDKN2B-AS1) and global DNA methylation pattern in glioma, for which a trend was seen also in the TCGA glioblastoma dataset. We also found an association between rs2736100 (in TERT) and levels of methylation at cg23827991 (localized in the same gene, 3.3 kbp downstream of the risk variant), which was validated in the TCGA dataset. Except for this one association, we did not find strong evidence for gene-specific DNA methylation mediated by glioma risk variants.

Metabolomic Screening of Tumor Tissue and Serum in Glioma Patients Reveals Diagnostic and Prognostic Information.

Glioma grading and classification, today based on histological features, is not always easy to interpret and diagnosis partly relies on the personal experience of the neuropathologists. The most important feature of the classification is the aimed correlation between tumor grade and prognosis. However, in the clinical reality, large variations exist in the survival of patients concerning both glioblastomas and low-grade gliomas. Thus, there is a need for biomarkers for a more reliable classification of glioma tumors as well as for prognosis. We analyzed relative metabolite concentrations in serum samples from 96 fasting glioma patients and 81 corresponding tumor samples with different diagnosis (glioblastoma, oligodendroglioma) and grade (World Health Organization (WHO) grade II, III and IV) using gas chromatography-time of flight mass spectrometry (GC-TOFMS). The acquired data was analyzed and evaluated by pattern recognition based on chemometric bioinformatics tools. We detected feature patterns in the metabolomics data in both tumor and serum that distinguished glioblastomas from oligodendrogliomas (p(tumor) = 2.46 × 10(-8), p(serum) = 1.3 × 10(-5)) and oligodendroglioma grade II from oligodendroglioma grade III (p(tumor) = 0.01, p(serum) = 0.0008). Interestingly, we also found patterns in both tumor and serum with individual metabolite features that were both elevated and decreased in patients that lived long after being diagnosed with glioblastoma compared to those who died shortly after diagnosis (p(tum)(o)(r) = 0.006, p(serum) = 0.004; AUROCC(tumor) = 0.846 (0.647-1.000), AUROCC(serum) = 0.958 (0.870-1.000)). Metabolic patterns could also distinguish long and short survival in patients diagnosed with oligodendroglioma (p(tumor) = 0.01, p(serum) = 0.001; AUROCC(tumor) = 1 (1.000-1.000), AUROCC(serum) = 1 (1.000-1.000)). In summary, we found different metabolic feature patterns in tumor tissue and serum for glioma diagnosis, grade and survival, which indicates that, following further verification, metabolomic profiling of glioma tissue as well as serum may be a valuable tool in the search for latent biomarkers for future characterization of malignant glioma.

EGFR gene variants are associated with specific somatic aberrations in glioma.

A number of gene variants have been associated with an increased risk of developing glioma. We hypothesized that the reported risk variants may be associated with tumor genomic instability. To explore potential correlations between germline risk variants and somatic genetic events, we analyzed matched tumor and blood samples from 95 glioma patients by means of SNP genotyping. The generated genotype data was used to calculate genome-wide allele-specific copy number profiles of the tumor samples. We compared the copy number profiles across samples and found two EGFR gene variants (rs17172430 and rs11979158) that were associated with homozygous deletion at the CDKN2A/B locus. One of the EGFR variants (rs17172430) was also associated with loss of heterozygosity at the EGFR locus. Our findings were confirmed in a separate dataset consisting of matched blood and tumor samples from 300 glioblastoma patients, compiled from publically available TCGA data. These results imply there is a functional effect of germline EGFR variants on tumor progression.