DNA-methylome-assisted classification of patients with poor prognostic subventricular zone associated IDH-wildtype glioblastoma.
Sebastian AdebergMaximilian KnollChristian KoelscheDenise BernhardtDaniel SchrimpfFelix SahmLaila KönigSemi Ben HarrabiJuliane Hörner-RieberVivek VermaMelanie Bewerunge-HudlerAndreas UnterbergDominik SturmChristine JungkChristel Herold-MendeWolfgang WickAndreas von DeimlingJuergen DebusStefan RiekenAmir AbdollahiPublished in: Acta neuropathologica (2022)
Glioblastoma (GBM) derived from the "stem cell" rich subventricular zone (SVZ) may constitute a therapy-refractory subgroup of tumors associated with poor prognosis. Risk stratification for these cases is necessary but is curtailed by error prone imaging-based evaluation. Therefore, we aimed to establish a robust DNA methylome-based classification of SVZ GBM and subsequently decipher underlying molecular characteristics. MRI assessment of SVZ association was performed in a retrospective training set of IDH-wildtype GBM patients (n = 54) uniformly treated with postoperative chemoradiotherapy. DNA isolated from FFPE samples was subject to methylome and copy number variation (CNV) analysis using Illumina Platform and cnAnalysis450k package. Deep next-generation sequencing (NGS) of a panel of 130 GBM-related genes was conducted (Agilent SureSelect/Illumina). Methylome, transcriptome, CNV, MRI, and mutational profiles of SVZ GBM were further evaluated in a confirmatory cohort of 132 patients (TCGA/TCIA). A 15 CpG SVZ methylation signature (SVZM) was discovered based on clustering and random forest analysis. One third of CpG in the SVZM were associated with MAB21L2/LRBA. There was a 14.8% (n = 8) discordance between SVZM vs. MRI classification. Re-analysis of these patients favored SVZM classification with a hazard ratio (HR) for OS of 2.48 [95% CI 1.35-4.58], p = 0.004 vs. 1.83 [1.0-3.35], p = 0.049 for MRI classification. In the validation cohort, consensus MRI based assignment was achieved in 62% of patients with an intraclass correlation (ICC) of 0.51 and non-significant HR for OS (2.03 [0.81-5.09], p = 0.133). In contrast, SVZM identified two prognostically distinct subgroups (HR 3.08 [1.24-7.66], p = 0.016). CNV alterations revealed loss of chromosome 10 in SVZM- and gains on chromosome 19 in SVZM- tumors. SVZM- tumors were also enriched for differentially mutated genes (p < 0.001). In summary, SVZM classification provides a novel means for stratifying GBM patients with poor prognosis and deciphering molecular mechanisms governing aggressive tumor phenotypes.
Keyphrases
- poor prognosis
- copy number
- end stage renal disease
- machine learning
- deep learning
- contrast enhanced
- stem cells
- magnetic resonance imaging
- ejection fraction
- newly diagnosed
- chronic kidney disease
- genome wide
- long non coding rna
- dna methylation
- peritoneal dialysis
- circulating tumor
- high resolution
- diffusion weighted imaging
- single cell
- computed tomography
- clinical trial
- cell free
- low grade
- mass spectrometry
- radiation therapy
- mesenchymal stem cells
- patient reported outcomes
- circulating tumor cells
- wild type
- neural network
- bioinformatics analysis