Comprehensive Genomic Profiling of 282 Pediatric Low- and High-Grade Gliomas Reveals Genomic Drivers, Tumor Mutational Burden, and Hypermutation Signatures

Author:

Johnson Adrienne1,Severson Eric1,Gay Laurie1,Vergilio Jo-Anne1,Elvin Julia1,Suh James1,Daniel Sugganth1,Covert Mandy1,Frampton Garrett M.1,Hsu Sigmund2,Lesser Glenn J.3,Stogner-Underwood Kimberly3,Mott Ryan T.3,Rush Sarah Z.4,Stanke Jennifer J.4,Dahiya Sonika5,Sun James1,Reddy Prasanth1,Chalmers Zachary R.1,Erlich Rachel1,Chudnovsky Yakov1,Fabrizio David1,Schrock Alexa B.1,Ali Siraj1,Miller Vincent1,Stephens Philip J.1,Ross Jeffrey1,Crawford John R.6,Ramkissoon Shakti H.1

Affiliation:

1. Foundation Medicine, Inc., Morrisville, North Carolina and Cambridge, Massachusetts, USA

2. Department of Neurosurgery, University of Texas Health Science Center, Houston, Texas, USA

3. Wake Forest Baptist Comprehensive Cancer Center, Winston-Salem, North Carolina, USA

4. Department of Pediatrics, Division of Hematology and Oncology, Children's Hospital Medical Center of Akron, Akron, Ohio, USA

5. Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, Missouri, USA

6. Department of Neurosciences and Pediatrics, University of California San Diego, San Diego, California, USA

Abstract

Abstract Background Pediatric brain tumors are the leading cause of death for children with cancer in the U.S. Incorporating next-generation sequencing data for both pediatric low-grade (pLGGs) and high-grade gliomas (pHGGs) can inform diagnostic, prognostic, and therapeutic decision-making. Materials and Methods We performed comprehensive genomic profiling on 282 pediatric gliomas (157 pHGGs, 125 pLGGs), sequencing 315 cancer-related genes and calculating the tumor mutational burden (TMB; mutations per megabase [Mb]). Results In pLGGs, we detected genomic alterations (GA) in 95.2% (119/125) of tumors. BRAF was most frequently altered (48%; 60/125), and FGFR1 missense (17.6%; 22/125), NF1 loss of function (8.8%; 11/125), and TP53 (5.6%; 7/125) mutations were also detected. Rearrangements were identified in 35% of pLGGs, including KIAA1549-BRAF, QKI-RAF1, FGFR3-TACC3, CEP85L-ROS1, and GOPC-ROS1 fusions. Among pHGGs, GA were identified in 96.8% (152/157). The genes most frequently mutated were TP53 (49%; 77/157), H3F3A (37.6%; 59/157), ATRX (24.2%; 38/157), NF1 (22.2%; 35/157), and PDGFRA (21.7%; 34/157). Interestingly, most H3F3A mutations (81.4%; 35/43) were the variant K28M. Midline tumor analysis revealed H3F3A mutations (40%; 40/100) consisted solely of the K28M variant. Pediatric high-grade gliomas harbored oncogenic EML4-ALK, DGKB-ETV1, ATG7-RAF1, and EWSR1-PATZ1 fusions. Six percent (9/157) of pHGGs were hypermutated (TMB >20 mutations per Mb; range 43–581 mutations per Mb), harboring mutations deleterious for DNA repair in MSH6, MSH2, MLH1, PMS2, POLE, and POLD1 genes (78% of cases). Conclusion Comprehensive genomic profiling of pediatric gliomas provides objective data that promote diagnostic accuracy and enhance clinical decision-making. Additionally, TMB could be a biomarker to identify pediatric glioblastoma (GBM) patients who may benefit from immunotherapy.

Publisher

Oxford University Press (OUP)

Subject

Cancer Research,Oncology

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