A machine learning analysis of a "normal-like" IDH-WT diffuse glioma transcriptomic subgroup associated with prolonged survival reveals novel immune and neurotransmitter-related actionable targets

• Published in: BMC medicine 2020-10, Vol.18 (1), p.280-18, Article 280
• Main Authors: Nguyen, H D, Allaire, A, Diamandis, P, Bisaillon, M, Scott, M S, Richer, M
• Format: Article
• Language: English
• Subjects: Adult; Aged; Aged, 80 and over; Algorithms; Amino acid neurotransmission; Amino acids; Amplification; Biomarkers; Brain Neoplasms - genetics; Brain Neoplasms - mortality; Brain Neoplasms - pathology; Brain tumors; Cancer; Central nervous system; Chromosome 10; Chromosome 7; Chromosomes; Classification; Clustering; Datasets; Diagnostic systems; Epidermal growth factor receptors; Female; Gene expression; Genes; Genomes; Glioma; Glioma - genetics; Glioma - mortality; Glioma - pathology; Humans; IDH-WT; Immune checkpoint; Isocitrate dehydrogenase; Isocitrate Dehydrogenase - genetics; Learning algorithms; Machine learning; Machine Learning - standards; Male; Medical prognosis; Middle Aged; Mutation; Nervous system; Neurotransmitters; Pipelines; Prognosis; Receptors; Scavenger receptors; Signatures; Subgroups; Survival; Survival Analysis; Synaptotagmin; Transcriptome - genetics; Transcriptomic; Tumor immune checkpoints; Tumors; Young Adult; γ-Aminobutyric acid
• ISSN: 1741-7015; 1741-7015
• DOI: 10.1186/s12916-020-01748-x

Classification of primary central nervous system tumors according to the World Health Organization guidelines follows the integration of histologic interpretation with molecular information and aims at providing the most precise prognosis and optimal patient management. According to the cIMPACT-NOW update 3, diffuse isocitrate dehydrogenase-wild type (IDH-WT) gliomas should be graded as grade IV glioblastomas (GBM) if they possess one or more of the following molecular markers that predict aggressive clinical course: EGFR amplification, TERT promoter mutation, and whole-chromosome 7 gain combined with chromosome 10 loss. The Cancer Genome Atlas (TCGA) glioma expression datasets were reanalyzed in order to identify novel tumor subcategories which would be considered as GBM-equivalents with the current diagnostic algorithm. Unsupervised clustering allowed the identification of previously unrecognized transcriptomic subcategories. A supervised machine learning algorithm (k-nearest neighbor model) was also used to identify gene signatures specific to some of these subcategories. We identified 14 IDH-WT infiltrating gliomas displaying a "normal-like" (NL) transcriptomic profile associated with a longer survival. Genes such as C5AR1 (complement receptor), SLC32A1 (vesicular gamma-aminobutyric acid transporter), MSR1 (or CD204, scavenger receptor A), and SYT5 (synaptotagmin 5) were differentially expressed and comprised in gene signatures specific to NL IDH-WT gliomas which were validated further using the Chinese Glioma Genome Atlas datasets. These gene signatures showed high discriminative power and correlation with survival. NL IDH-WT gliomas represent an infiltrating glioma subcategory with a superior prognosis which can only be detected using genome-wide analysis. Differential expression of genes potentially involved in immune checkpoint and amino acid signaling pathways is providing insight into mechanisms of gliomagenesis and could pave the way to novel treatment targets for infiltrating gliomas.