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Identification of Variants in Primary and Recurrent Glioblastoma Using a Cancer-Specific Gene Panel and Whole Exome Sequencing
Authors:Selene M. Virk  Richard M. Gibson  Miguel E. Quinones-Mateu  Jill S. Barnholtz-Sloan
Affiliation:1Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, Ohio, USA;2University Hospital Translational Laboratory, University Hospitals Case Medical Center, Cleveland, Ohio, USA;3Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland, Ohio, USA;4Department of Pathology, Case Western Reserve University, Cleveland, Ohio, USA;University of Navarra, SPAIN
Abstract:Glioblastoma (GBM) is an aggressive, malignant brain tumor typically resulting in death of the patient within one year following diagnosis; and those who survive beyond this point usually present with tumor recurrence within two years (5-year survival is 5%). The genetic heterogeneity of GBM has made the molecular characterization of these tumors an area of great interest and has led to identification of molecular subtypes in GBM. The availability of sequencing platforms that are both fast and economical can further the adoption of tumor sequencing in the clinical environment, potentially leading to identification of clinically actionable genetic targets. In this pilot study, comprised of triplet samples of normal blood, primary tumor, and recurrent tumor samples from three patients; we compared the ability of Illumina whole exome sequencing (ExomeSeq) and the Ion AmpliSeq Comprehensive Cancer Panel (CCP) to identify somatic variants in patient-paired primary and recurrent tumor samples. Thirteen genes were found to harbor variants, the majority of which were exclusive to the ExomeSeq data. Surprisingly, only two variants were identified by both platforms and they were located within the PTCH1 and NF1 genes. Although preliminary in nature, this work highlights major differences in variant identification in data generated from the two platforms. Additional studies with larger samples sizes are needed to further explore the differences between these technologies and to enhance our understanding of the clinical utility of panel based platforms in genomic profiling of brain tumors.
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