Molecular profiling reveals biologically discrete subsets and pathways of progression in diffuse glioma

By Michele Ceccarelli, F. Barthel, T. Malta, Thais S. Sabedot, S. Salama, Bradley A. Murray, Olena Morozova, Y. Newton, Amie Radenbaugh, S. Pagnotta and 196 more. Cell, 2016

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SUMMARY Therapy development for adult diffuse glioma is hindered by incomplete knowledge of somatic glioma driving alterations and suboptimal disease classification. We defined the complete set of genes associated with 1,122 diffuse grade II-III-IV gliomas from The Cancer Genome Atlas and used molecular profiles to improve disease classification, identify molecular correlations, and provide insights into the progression from low- to high-grade disease. Whole genome sequencing data analysis determined that ATRX but not TERT promoter mutations are associated with increased telomere length. Recent advances in glioma classification based on IDH mutation and 1p/19q co-deletion status were recapitulated through analysis of DNA methylation profiles, which identified clinically relevant molecular subsets. A subtype of IDH-mutant glioma was associated with DNA demethylation and poor outcome; a group of IDH-wildtype diffuse glioma showed molecular similarity to pilocytic astrocytoma and relatively favorable survival. Understanding of cohesive disease groups may aid improved clinical outcomes.

Subfields: Cancer Research, Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/eb669a8e04d648c2b434294293c672f56f90b924

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Citations in the corpus

Deep-Learning Convolutional Neural Networks Accurately Classify Genetic Mutations in Gliomas (2018, background): “tained, including IDH1 status, 1p/19q codeletion, and MGMT promoter methylation.(13)”
Glioma Subclassifications and Their Clinical Significance (2017, background): “In gliomas, they were initially identified in a high percentage of LGGs and secondary GBMs and a lower percentage of primary GBMs [12] .”
Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation (2018, background): “Compared to GCIMP-high tumors, G-CIMP-low tumors are known to be more proliferative, express cellcycle-related genes, and have various stem cell-like genomic features (Ceccarelli et al., 2016).”
TCGAbiolinks: an R/Bioconductor package for integrative analysis of TCGA data (2015, methodology): “These types of analyses have been performed by our group in our recent TCGA papers (3,6,16,35) but were never implemented in a reproducible and standard package until now.”
DNA methylation-based classification of central nervous system tumours (2018, neutral): “Due to the subjective nature of histological assessment of tumour purity, we additionally used the Ceccarelli et al. 2016 dataset 18 to train a Random Forest regression (continuous response variable) model to predict tumour purity 50 .”
The Immune Landscape of Cancer (2018, background): “Glioma subtypes (Ceccarelli et al., 2016) CpG island methylator phenotype-high (CIMP-H), the 1p/19q codeletion subtype and pilocytic astrocytoma-like (PA-like) were prevalent in C5, with remaining subtypes enriched in C4.”