GENE-41. UNDERSTANDING THE DIFFERENCES IN ADOLESCENT AND YOUNG ADULT (AYA) GLIOMAS AND OLDER ADULT (OA) GLIOMAS BASED ON FUNCTIONAL MOLECULAR SUBGROUPS. (11th November 2019)

• Record Type: Journal Article
• Authors: Bi, Catherine; Subramaniam, Ashwin; Xiu, Joanne; Heimberger, Amy; Michelhaugh, Sharon; Mittal, Sandeep; Sumrall, Ashley; Subramaniam, Deepa
• Is Part Of: Neuro-oncology. Volume 21(2019)Supplement 6
• Page Start: vi106
• Page End: vi106
• Publication Date: 2019-11-11
• Subjects: Brain Neoplasms -- Periodicals; Brain -- Tumors -- Periodicals; Brain -- Cancer -- Periodicals; Nervous system -- Cancer -- Periodicals; 616.99481
• DOI: 10.1093/neuonc/noz175.443
• Languages: English
• ISSNs: 1522-8517

Abstract

Abstract: BACKGROUND: Gliomas in the AYA population (15–39 years of age) have unique biological characteristics and need to be better characterized. METHODS: Glioma tumors in AYA subjects and subjects >65 years of age (OA) were analyzed by next generation sequencing using a 592 gene panel. Pathogenic mutations were classified into five functional groups, viz. metabolic pathways genes (IDH1/2, FH), tumor suppressor genes (TP53, RB1, APC, NF1/2, PTEN, TSC1/2), genes involved in DNA repair (MMR genes, BRCA1/2, POLE, ARID1A, CHEK2, ATM, BLM, BRIP1, WRN, BARD1, POT1, MUTYH), oncogenes (BRAF, NRAS, HRAS, EGFR, PDGFRA, FGFR1, NOTCH1, MYCN), and genes involved in transcriptional regulation (SETD2, H3F3A, KMTD2A/2C/2D, KDM6A, PIK3CA). Mutation frequency in AYA tumors and OA tumors were compared using Chi-squared analysis (Pearson's score χ2; likelihood ratio LR). RESULTS: 720 unique gliomas tumors were analyzed: 118 AYA, 602 OA; 420 males, 300 females. When both groups are considered together, glioblastoma was the most common histology (75%), followed by grade 3 astrocytoma (13%), glioma NOS (3.8%), oligodendrogliomas (3%), low grade gliomas (2.9%) and other (2.3%). AYA tumors harbored more metabolic pathway gene mutations (χ2 137.7, p< 0.0001) driven primarily by IDH1 mutations, while OA tumors had a higher mutation frequency in oncogenes (χ2 9.22, p=0.0024) driven by EGFR mutations (LR 27.567) and tumor suppressor genes (χ2 40.35, p< 0.0001) driven by NF1 (LR 18.147) and PTEN (LR 66.216). No significant differences were noted in mutation frequency in DNA repair or transcriptional regulation genes. However, AYA glioblastoma tumors had a significant increase in mutations in genes involved in chromatin remodeling, (χ2 11.43, p=0.0007) even after excluding H3F3A. CONCLUSIONS: Functional genomic classification of AYA tumors may help develop better targeted therapies, especially focused on genes involved in metabolic pathways and transcriptional regulation.