Identification of Novel SNPs in Glioblastoma Using Targeted Resequencing

Andreas Keller,Benjamin Meder,Nicole Ludwig,Mark Matzas,Eckart Meese,Christian Harz,Ulrike Fischer,Hugo A Katus,Amanda Ewart Toland

doi:10.1371/journal.pone.0018158

Abstract

High-throughput sequencing opens avenues to find genetic variations that may be indicative of an increased risk for certain diseases. Linking these genomic data to other “omics” approaches bears the potential to deepen our understanding of pathogenic processes at the molecular level. To detect novel single nucleotide polymorphisms (SNPs) for glioblastoma multiforme (GBM), we used a combination of specific target selection and next generation sequencing (NGS). We generated a microarray covering the exonic regions of 132 GBM associated genes to enrich target sequences in two GBM tissues and corresponding leukocytes of the patients. Enriched target genes were sequenced with Illumina and the resulting reads were mapped to the human genome. With this approach we identified over 6000 SNPs, including over 1300 SNPs located in the targeted genes. Integrating the genome-wide association study (GWAS) catalog and known disease associated SNPs, we found that several of the detected SNPs were previously associated with smoking behavior, body mass index, breast cancer and high-grade glioma. Particularly, the breast cancer associated allele of rs660118 SNP in the gene SART1 showed a near doubled frequency in glioblastoma patients, as verified in an independent control cohort by Sanger sequencing. In addition, we identified SNPs in 20 of 21 GBM associated antigens providing further evidence that genetic variations are significantly associated with the immunogenicity of antigens.

Highlights

Glioblastoma (GBM) is the most frequent and most malignant human primary brain tumor that is characterized by a rapid and invasive growth pattern, high level of cellular heterogeneity and poor prognosis [1,2]
A manifold of additional aberrations have been identified for GBM with high-throughput whole genome and whole transcriptome analysis tools, including amplicons detected by aCGH data [6,7,8], aberrant gene expression profiles detected by microarray screenings [9,10,11], deregulated microRNAs [12,13,14] or single nucleotide polymorphisms (SNPs) detected by genome wide association studies (GWAS) [15,16]
While chromosomal imbalances and aberrant mRNA and miRNA expression profiles can be identified in tumor cells after tumor formation, putative SNPs that might be associated with an increased risk for developing glioblastoma can be identified prior to tumor formation in DNA from normal samples of a patient

Summary

Introduction

Glioblastoma (GBM) is the most frequent and most malignant human primary brain tumor that is characterized by a rapid and invasive growth pattern, high level of cellular heterogeneity and poor prognosis [1,2]. While chromosomal imbalances and aberrant mRNA and miRNA expression profiles can be identified in tumor cells after tumor formation, putative SNPs that might be associated with an increased risk for developing glioblastoma can be identified prior to tumor formation in DNA from normal samples of a patient. These genetic predispositions offer essential screening tools to identify high-risk populations for specific cancers. The knowledge of such predispositions and of the resulting molecular changes in cellular processes will further our understanding of these tumors and will contribute to a personalized medicine

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