2019
DOI: 10.1084/jem.20190196
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Chromatin landscapes reveal developmentally encoded transcriptional states that define human glioblastoma

Abstract: Mack et al. defined active chromatin landscapes of glioblastoma stem cells (GSCs) and primary tumor specimens, revealing novel transcriptional regulatory circuits and therapeutic targets. Super-enhancers identified essential transcription factors that underlie GSC identity and intertumoral diversity, potentially informing precision medicine.

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Cited by 107 publications
(144 citation statements)
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“…making cancer networks distinct from their normal counterparts [27,29,41]. Although conventional tumor genetics categorize mutations into oncogenes and tumor-suppressor genes, recent genetic analyses of gliomas and other malignancies have detected dysregulation of chromatin regulators, which may lead to plasticity in the epigenetic cell state [42][43][44][45]. When analyzed in this context, cancer networks reside at a net higher entropy than do corresponding normal tissues, and display distinct energy relationships between stem and non-stem populations [29].…”
Section: Glossarymentioning
confidence: 99%
“…making cancer networks distinct from their normal counterparts [27,29,41]. Although conventional tumor genetics categorize mutations into oncogenes and tumor-suppressor genes, recent genetic analyses of gliomas and other malignancies have detected dysregulation of chromatin regulators, which may lead to plasticity in the epigenetic cell state [42][43][44][45]. When analyzed in this context, cancer networks reside at a net higher entropy than do corresponding normal tissues, and display distinct energy relationships between stem and non-stem populations [29].…”
Section: Glossarymentioning
confidence: 99%
“…Commonly referred to as DNA motif analysis, enhancer maps from ChIP‐seq and ATAC‐seq experiments combined with gene expression profiling can provide valuable insight into the identity of upstream TFs and their activity within a given patient brain tumour or cell line(s). This is relevant as active TFs across several brain tumour types have been shown to be highly essential for tumour growth [16,30,40]. When ranked based upon predicted TF target binding, SEs can be used to model and predict the most active and interconnected TFs within a given cell or tissue type [41].…”
Section: Reverse‐engineering Enhancers To Characterize Transcription mentioning
confidence: 99%
“…CRC TFs can be shared and unique across brain tumour types, and when disrupted, are capable of inhibiting tumour cell growth [30,43]. In glioblastoma, OLIG2 and RUNX2 TFs (among many other TFs) have been shown to robustly segregate two distinct cell states that have differential response to drug therapy; and suggest that these cells may contribute uniquely to therapeutic resistance [16]. Furthermore, OLIG2 has been shown to mark tumour propagating cells in both glioma and medulloblastoma, and associated with tumorigenesis and relapse [36,44].…”
Section: Reverse‐engineering Enhancers To Characterize Transcription mentioning
confidence: 99%
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“…This includes high-resolution Hi-C maps of chromatin contacts in human embryonic brain , ATAC-seq on postmortem brain (Fullard et al, 2017) or glioma stem like cells from glioblastoma (Tome-Garcia et al, 2018), ChIP-seq analysis for H3K27me3 modification on 11 glioblastoma and 2 diffuse astrocytoma samples (Sharma et al, 2017). Only recently researchers began integrating H3K27ac profiles with gene expression, DNA methylomes, copy number variations, and whole exomes demonstrating distinct chromatin and epigenetic profiles in glioblastomas, distinguishing this entity from other brain tumors (Mack et al, 2019). Combination of those features in a single patient and intersections of acquired profiles in gliomas of different grades have not yet been achieved.…”
Section: Introductionmentioning
confidence: 99%