Comprehensive Genomic Profiling of 282 Pediatric Low- and High-Grade Gliomas Reveals Genomic Drivers, Tumor Mutational Burden, and Hypermutation Signatures
ONCOLOGIST
Authors: Johnson, Adrienne; Severson, Eric; Gay, Laurie; Vergilio, Jo-Anne; Elvin, Julia; Suh, James; Daniel, Sugganth; Covert, Mandy; Frampton, Garrett M.; Hsu, Sigmund; Lesser, Glenn J.; Stogner-Underwood, Kimberly; Mott, Ryan T.; Rush, Sarah Z.; Stanke, Jennifer J.; Dahiya, Sonika; Sun, James; Reddy, Prasanth; Chalmers, Zachary R.; Erlich, Rachel; Chudnovsky, Yakov; Fabrizio, David; Schrock, Alexa B.; Ali, Siraj; Miller, Vincent; Stephens, Philip J.; Ross, Jeffrey; Crawford, John R.; Ramkissoon, Shakti H.
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 perMb; range 43-581 mutations perMb), 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.
Genetic Predisposition to Long-Term Nondiabetic Deteriorations in Glucose Homeostasis Ten-Year Follow-Up of the GLACIER Study
DIABETES
Authors: Renstrom, Frida; Shungin, Dmitry; Johansson, Ingegerd; Florez, Jose C.; Hallmans, Goran; Hu, Frank B.; Franks, Paul W.
Abstract
OBJECTIVE-To assess whether recently discovered genetic loci associated with hyperglycemia also predict long-term changes in glycemic traits. RESEARCH DESIGN AND METHODS-Sixteen fasting glucose-raising loci were genotyped in middle-aged adults from the Gene x Lifestyle interactions And Complex traits Involved in Elevated disease Risk (GLACIER) Study, a population-based prospective cohort study from northern Sweden. Genotypes were tested for association with baseline fasting and 2-h postchallenge glycemia (N = 16,330), and for changes in these glycemic traits during a 10-year follow-up period (N = 4,059). RESULTS-Cross-sectional directionally consistent replication with fasting glucose concentrations was achieved for 12 of 16 variants; 10 variants were also associated with impaired fasting glucose (IFG) and 7 were independently associated with 2-h postchallenge glucose concentrations. In prospective analyses, the effect alleles at four loci (GCK rs4607517, ADRA2A rs10885122, DGKB-TMEM195 rs2191349, and G6PC2 rs560887) were nominally associated with worsening fasting glucose concentrations during 10-years of follow-up. MTNR1B rs10830963, which was predictive of elevated fasting glucose concentrations in cross-sectional analyses, was associated with a protective effect on postchallenge glucose concentrations during follow-up; however, this was only when baseline fasting and 2-h glucoses were adjusted for. An additive effect of multiple risk alleles on glycemic traits was observed: a weighted genetic risk score (80th vs. 20th centiles) was associated with a 0.16 mmol/l (P = 2.4 x 10(-6)) greater elevation in fasting glucose and a 64% (95% CI: 33-201%) higher risk of developing IFG during 10 years of follow-up. CONCLUSIONS-Our findings imply that genetic profiling might facilitate the early detection of persons who are genetically susceptible to deteriorating glucose control; studies of incident type 2 diabetes and discrete cardiovascular end points will help establish whether the magnitude of these changes is clinically relevant. Diabetes 60:345-354, 2011