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.

Abstract 6140: Transcriptional evolution of glioblastoma reveals changes in bulk composition, mesenchymal sub-type as end-state, and a prognostic association with increased extracellular matrix gene expression

Background: Glioblastoma is the most prevalent and severe type of malignant brain tumor in adults. Although the genetic make-up initiating glioblastoma is increasingly better understood, a better understanding in the mechanisms that drive its evolution, heterogeneity and therapy resistance may reveal new directions for therapy development. To get better insights into glioblastoma evolution, we analyzed and de-convoluted transcriptomes of primary and recurrent glioblastoma resections.

Material and Methods: Matching primary and secondary resections from n=185 glioblastoma patients were collected as part of EORTC Study 1542. The study was extended with tumor pairs from n=51 patients from the international GLASS study. The datasets were subjected to differential and deconvolution analysis using in-house algorithms.

Results: When mapping the tumor samples into a reduced Glioblastoma Intrinsic Transcriptional Subtype space, we visualized subtype traversal, indicating that the CL subtype most often switches. As we found no more transitions from MES to other subtypes than to be expected by chance, we concluded that MES is an end-state. On average, tumor cell percentages decreased from ~67% to ~50% mostly due to an increase in TAM/microglia. Differential expression analysis was performed with correction for tumor cell percentages. While expression of most known oncogenes did not change considerably over time, marker genes of TAM/microglia, neurons and oligodendrocytes were up-regulated whereas endothelial cell markers were down-regulated over time. Furthermore, a cluster of ~30 extracellular matrix-associated genes increase significantly over time. A signature representing the gene-set was significantly associated with poor survival; high signatures were in particular associated to survival in secondary resections (P = 6.613e-06, Kaplan-Meier estimator). This suggests that the increase of extracellular matrix expression fulfils an important role in glioblastoma evolution.

Conclusion: Using a large cohort, we interrogated changes in the glioblastoma transcriptome over time and found that in particular the composition of the tumor and its environment changes. The tumor cell percentage drops, suggesting more invasion or recruitment of non-malignant cells or a combination of both. This change is independent of an increase in the prognostic increase in extracellular matrix expression.

Citation Format: Youri Hoogstrate, Kaspar Draaisma, Santoesha A. Ghisai, Iris de Heer, Levi van Hijfte, Wouter Coppieters, Melissa Kerkhof, Astrid Weyerbrock, Marc Sanson, Ann Hoeben, Slávka Lukacova, Giuseppe Lombardi, Sieger Leenstra, Monique Hanse, Ruth Fleischeuer, Colin Watts, Joseph McAbee, Nicos Angelopoulos, Thierry Gorlia, Vassilis Golfinopoulos, Johan M. Kros, Vincent Bours, Martin J. van den Bent, Pierre A. Robe, Pim J. French. Transcriptional evolution of glioblastoma reveals changes in bulk composition, mesenchymal sub-type as end-state, and a prognostic association with increased extracellular matrix gene expression [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 6140.

Abstract 1228: Spatial transcriptomics: Data processing revisited to address noise interference

Background: Spatially resolved transcriptomics is a novel and already highly recognized method that allows RNA sequencing results to be annotated with local tissue phenotypes. The NanoString GeoMx Digital Spatial Profiling (DSP) Platform allows users to collect RNA expression data from manually selected Regions of Interest (ROIs) on FFPE tissue sections. Here, we extensively evaluated data from the DSP platform with its associated pipeline and identify significant background noise interference issues which compromise data interpretation. Alternative and more suitable workflows are presented for correct data analysis.

Methods: In this study, 12 paired tumor samples were collected from six glioma patients who underwent two separate resections. For all patients, the first resection was a low grade astrocytoma (WHO grade II or III) and the second resection was a high grade astrocytoma (WHO grade IV). The DSP platform was used to collect expression data of 1,800 genes from 72 ROIs (i.e. 6 per sample). Biological replicates were made of eight tumors from four patients. Gene expression data was normalized with both standard NanoString methods and several alternative methods (e.g. DeSeq2, gamma fit correction and quantile normalization). Weighted Gene Co-expression Network analysis (WGCNA) was used for biological validation. In addition to our own study, six publicly available NanoString DSP datasets were evaluated.

Results: Data distributions of all glioma samples, when exposed to standard data processing, were burdened with significant background noise interference. Notably, differences in noise interference were largest between biologically distinct tumor subgroups (i.e. between first and second glioma resections), which was confirmed in replicate experiments. The noise interference patterns were also present in all six publicly available NanoString DSP datasets which will invariably lead to incorrect interpretation of the underlying biology. To correct for noise interference, we tested several normalization methods. The relatively crude quantile normalization method provided the least biased result and showed the highest concordance with bulk RNA sequencing data. To evaluate the biological validity of our alternative approach, we used T cell counts from our tissue regions as an independent parameter, that were quantified using immune fluorescence. Unsupervised WGCNA identified gene clusters enriched for lymphocyte genes that highly correlated with T cell quantities in ROIs, confirming that alternative normalization can extract a biological signal from the DSP platform.

Conclusion: The DSP Platform platform suffers from significant noise interference when using standard analysis tools that obscure its results. Here, we revised the workflow and provide an alternative normalization that adequately addresses noise interference and enables correct interpretation of gene expression data.

Citation Format: Levi van Hijfte, Marjolein Geurts, Wies R. Vallentgoed, Paul H. Eilers, Peter A. Sillevis Smitt, Reno Debets, Pim J. French. Spatial transcriptomics: Data processing revisited to address noise interference [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1228.

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.