PNNL

Abstract

Gliomas, the most common type of cancer that starts in the glial cells of the brain, are clinically derived from various neural cells including astrocytes, oligodendrocytes, and ependymal cells. Glioblastoma (GBM), as the most aggressive and commonly occurring type of glioma, has an average length of survival following diagnosis of only 12 to 15 months and less than 3–7% of patients survive longer than five years.1 The causes of most cases of glioblastoma remains unclear, and majority of glioblastoma diagnoses are de novo whereas others start as the low-grade type of gliomas (LGG) and progress into glioblastoma. To date, The Cancer Genome Atlas (TCGA) and other studies have performed large scale next-generation sequencing of the genome of gliomas patients and revealed the mutation landscape and intratumor heterogeneity of gliomas patients involved in tumorigenesis.2,3,4,5,6 Several key genomic features such as the mutation of gene IDH and the deletion of chromosome arms 1p and 19q were identified as new biomarkers to stratify subgroups with distinct clinical outcomes and clinical treatment plans, this further reshaped and update the World Health Organization (WHO) classification of glioma.5,7,8 However, these studies primarily focused on the somatic events among subtypes. Numerous studies demonstrated mutation signature and copy number alterations signature documented the characteristics occurring throughout the whole life of cancer cells including DNA repair or exogenous processes such as chemotherapy treatment.9,10 It was expected that the genomic signatures would have a great influence on the clinical outcome and treatment response of glioma patients. One recent study analyzing the mutational spectral following radiotherapy in glioma patients revealed that a radiotherapy-derived deletion signature was associated with worse clinical outcomes and may be used to predict sensitivity to radiation therapy.11 Therefore, there is a need to identify and incorporate prognostic genomic signatures as additional molecular features that may enhance the treatments performance of gliomas. Here, we sequence the whole exomes of a cohort of Chinese glioma patients and additionally also obtain the published large-scale genomic data from Chinese Glioma Genome Atlas (CGGA) and The Cancer Genome Atlas (TCGA).2,3,4,12,13 We identify genomic variations, extract mutational and copy number alteration signatures, and evaluate their clinical relevance in a total of 1, 477 patients. Our results generate a full picture of genomic variation signature and discover several additional genomic features including focal amplification or deletion and genomic signatures as potential prognosis markers. We further develop a prognostic risk model of glioma based on genomic features to stratify glioma patients into high-risk and low-risk two subgroups with significant distinct outcomes in Chinese and TCGA cohort (p