Epigenetic landscape reorganization and reactivation of embryonic development genes are associated with malignancy in IDH-mutant astrocytoma.

Accurate grading of IDH-mutant gliomas defines patient prognosis and guides the treatment path. Histological grading is however difficult and, apart from CDKN2A/B homozygous deletions in IDH-mutant astrocytomas, there are no other objective molecular markers used for grading. Experimental Design: RNA-sequencing was conducted on primary IDH-mutant astrocytomas (n=138) included in the prospective CATNON trial, which was performed to assess the prognostic effect of adjuvant and concurrent temozolomide. We integrated the RNA sequencing data with matched DNA-methylation and NGS data. We also used multi-omics data from IDH-mutant astrocytomas included in the TCGA dataset and validated results on matched primary and recurrent samples from the GLASS-NL study. We used the DNA-methylation profiles to generate a Continuous Grading Coefficient (CGC) that is based on classification scores derived from a CNS-tumor classifier. We found that the CGC was an independent predictor of survival outperforming current WHO-CNS5 and methylation-based classification. Our RNA-sequencing analysis revealed four distinct transcription clusters that were associated with i) an upregulation of cell cycling genes; ii) a downregulation of glial differentiation genes; iii) an upregulation of embryonic development genes (e.g. HOX, PAX and TBX) and iv) an upregulation of extracellular matrix genes. The upregulation of embryonic development genes was associated with a specific increase of CpG island methylation near these genes.

Transcriptome analysis reveals tumor microenvironment changes in glioblastoma.

A better understanding of transcriptional evolution of IDH-wild-type glioblastoma may be crucial for treatment optimization. Here, we perform RNA sequencing (RNA-seq) (n = 322 test, n = 245 validation) on paired primary-recurrent glioblastoma resections of patients treated with the current standard of care. Transcriptional subtypes form an interconnected continuum in a two-dimensional space. Recurrent tumors show preferential mesenchymal progression. Over time, hallmark glioblastoma genes are not significantly altered. Instead, tumor purity decreases over time and is accompanied by co-increases in neuron and oligodendrocyte marker genes and, independently, tumor-associated macrophages. A decrease is observed in endothelial marker genes. These composition changes are confirmed by single-cell RNA-seq and immunohistochemistry. An extracellular matrix-associated gene set increases at recurrence and bulk, single-cell RNA, and immunohistochemistry indicate it is expressed mainly by pericytes. This signature is associated with significantly worse survival at recurrence. Our data demonstrate that glioblastomas evolve mainly by microenvironment (re-)organization rather than molecular evolution of tumor cells.

Alternative normalization and analysis pipeline to address systematic bias in NanoString GeoMx Digital Spatial Profiling data.

Spatial transcriptomics is a novel technique that provides RNA-expression data with tissue-contextual annotations. Quality assessments of such techniques using end-user generated data are often lacking. Here, we evaluated data from the NanoString GeoMx Digital Spatial Profiling (DSP) platform and standard processing pipelines. We queried 72 ROIs from 12 glioma samples, performed replicate experiments of eight samples for validation, and evaluated five external datasets. The data consistently showed vastly different signal intensities between samples and experimental conditions that resulted in biased analysis. We evaluated the performance of alternative normalization strategies and show that quantile normalization can adequately address the technical issues related to the differences in data distributions. Compared to bulk RNA sequencing, NanoString DSP data show a limited dynamic range which underestimates differences between conditions. Weighted gene co-expression network analysis allowed extraction of gene signatures associated with tissue phenotypes from ROI annotations. Nanostring GeoMx DSP data therefore require alternative normalization methods and analysis pipelines.

The Why of YY1: Mechanisms of Transcriptional Regulation by Yin Yang 1.

First described in 1991, Yin Yang 1 (YY1) is a transcription factor that is ubiquitously expressed throughout mammalian cells. It regulates both transcriptional activation and repression, in a seemingly context-dependent manner. YY1 has a well-established role in the development of the central nervous system, where it is involved in neurogenesis and maintenance of homeostasis in the developing brain. In neurodevelopmental and neurodegenerative disease, the crucial role of YY1 in cellular processes in the central nervous system is further underscored. In this mini-review, we discuss the various mechanisms leading to the transcriptional activating and repressing roles of YY1, including its role as a traditional transcription factor, its interactions with cofactors and chromatin modifiers, the role of YY1 in the non-coding genome and 3D chromatin organization and the possible implications of the phase-separation mechanism on YY1 function. We provide examples on how these processes can be involved in normal development and how alterations can lead to various diseases.