ASCL1 Reorganizes Chromatin to Direct Neuronal Fate and Suppress Tumorigenicity of Glioblastoma Stem Cells

Park, Nicole I.; Guilhamon, Paul; Desai, Kinjal; McAdam, Rochelle F.; Langille, Ellen; O’Connor, Madlen; Lan, Xiaoyang; Whetstone, Heather; Coutinho, Fiona J.; Vanner, Robert; Ling, Erick; Prinos, Panagiotis; Lee, Lilian; Selvadurai, Hayden; Atwal, Gurnit; Kushida, Michelle; Clarke, Ian D.; Voisin, Véronique; Cusimano, Michael D.; Bernstein, Mark; Das, Sunit; Bader, Gary D.; Arrowsmith, C.H.; Angers, Stéphane; Huang, Xi; Lupien, Mathieu; Dirks, Peter B.

doi:10.1016/j.stem.2017.06.004

Cited by 156 publications

(143 citation statements)

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“…The prognostic and pathway associations of HOXA10-AS transcript abundance prompted us to investigate this lncRNA functionally. We performed a siRNA-mediated knockdown experiment of HOXA10-AS followed by a six-day cell proliferation experiment using the primary patient-derived GBM cell line G797 [53, 54]. To minimize off-target effects on the protein-coding gene HOXA10 antisense to the lncRNA, we used two siRNAs against the unique exon three of the lncRNA.…”

Section: Resultsmentioning

confidence: 99%

“…The human glioma G797 cells were prepared as described previously as a bulk patient-derived cell cultures [53, 54]. We selected the G797 patient-derived cell line as a suitable candidate for our experiments based on previously generated RNA-seq data [53] that indicated a relatively high native transcript abundance of lncRNA HOXA10-AS in these cells.…”

Section: Methodsmentioning

confidence: 99%

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Comprehensive analysis of lncRNAs reveals candidate prognostic biomarkers in multiple cancer types

Isaev

Jiang

Lee

et al. 2019

Preprint

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12Long non-coding RNAs (lncRNAs) are increasingly recognized as functional units in can-13 cer pathways and powerful molecular biomarkers, however most lncRNAs remain un-14 characterized. Here we performed a systematic discovery of prognostic lncRNAs in 9,326 15 patient tumors of 29 types using a proportional-hazards elastic net machine-learning 16 framework. lncRNAs showed highly tissue-specific transcript abundance patterns. We 17 identified 179 prognostic lncRNAs whose abundance correlated with patient risk and im-18 proved the performance of common clinical variables and molecular tumor subtypes. 19 Pathway analysis revealed a large diversity of the high-risk tumors stratified by lncRNAs 20and suggested their functional associations. In lower-grade gliomas, discrete activation 21 of HOXA10-AS indicated poor patient prognosis, neurodevelopmental pathway activation 22 and a transcriptomic similarity to glioblastomas. HOXA10-AS knockdown in patient-de-23 rived glioblastoma cells caused decreased cell proliferation and deregulation of glioma 24 driver genes and proliferation pathways. Our study underlines the pan-cancer potential 25 of the non-coding transcriptome for developing molecular biomarkers and innovative 26 therapeutic strategies. 27 28 cancer patient prognosis with transcript abundance of protein-coding genes and their genetic 61 and epigenetic alterations [22][23][24][25]. However, associations of lncRNAs with cancer patient sur-62 vival and biological function remain largely unexplored. A recent study characterized recurrent 63 hypomethylation patterns affecting a thousand lncRNAs in the TCGA PanCanAtlas cohort and 64 identified the EPIC1 lncRNA as a marker of poor prognosis in a subset of breast cancers [26]. 65Another TCGA study associated mutations and transcript abundance profiles of lncRNAs with 66 regulatory networks and molecular pathways and nominated candidate oncogenic and tumor 67 suppressive lncRNAs, some of which were functionally validated in cancer cell lines [27]. Analy-68 sis of cell-cycle correlated lncRNAs revealed a subset of S-phase enriched lncRNAs whose 69 transcript abundance profiles correlated with patient survival in multiple TCGA cohorts [28]. 70However, those studies did not analyze robust prognostic performance of lncRNAs using ma-71 chine-learning and cross-validation approaches, indicating further potential to systematically dis-72 cover lncRNAs as candidate prognostic biomarkers of multiple cancer types. 73Here we evaluated the transcript abundance profiles of nearly 6,000 lncRNAs as prognostic bi-74 omarkers in human cancers. Using a comprehensive machine-learning analysis, we compiled a 75 robust catalogue of prognostic lncRNAs across nearly 10,000 tumors of 29 types from the 76 TCGA PanCanAtlas project [22, 29]. The majority of our candidate lncRNAs showed improved 77 prognostic potential compared to standard clinical features and molecular tumor subtypes. We 78 associated prognostic lncRNAs with large-scale deregulation of hallmark cancer pathways, re-79 vealing e...

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Comprehensive analysis of lncRNAs reveals candidate prognostic biomarkers in multiple cancer types

Isaev

Jiang

Lee

et al. 2019

Preprint

Self Cite

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“…1C) [26,31,61,62]. Next, the cell cycle arrest-promoting activity of Ascl1 and Ngn2 can be enhanced by knocking down TP53, serum withdrawal, CDK2 and mTOR inhibition, and chemical JAK/ STAT inhibition, which further helps iN conversion by inhibiting both epithelial-to-mesenchymal transition and apoptosis [41, 58,63]. 1D) [33].…”

Section: Nongenetic In Boostersmentioning

confidence: 99%

“…Subsequently, it has been shown that additional pathway modulations, including SIRT1 activation and HDAC inhibition, can be applied to increase the efficacy and maturity of the neuronal population obtained [62]. Next, the cell cycle arrest-promoting activity of Ascl1 and Ngn2 can be enhanced by knocking down TP53, serum withdrawal, CDK2 and mTOR inhibition, and chemical JAK/ STAT inhibition, which further helps iN conversion by inhibiting both epithelial-to-mesenchymal transition and apoptosis [41,58,63]. Enabling cytoskeletal dynamics through inhibition of integrin signaling facilitates the cell-type switch [41].…”

Section: Nongenetic In Boostersmentioning

confidence: 99%

Next‐generation disease modeling with direct conversion: a new path to old neurons

2019

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Within just over a decade, human reprogramming-based disease modeling has developed from a rather outlandish idea into an essential part of disease research. While iPSCs are a valuable tool for modeling developmental and monogenetic disorders, their rejuvenated identity poses limitations for modeling age-associated diseases. Direct cell-type conversion of fibroblasts into induced neurons (iNs) circumvents rejuvenation and preserves hallmarks of cellular aging. iNs are thus advantageous for modeling diseases that possess strong age-related and epigenetic contributions and can complement iPSCbased strategies for disease modeling. In this review, we provide an overview of the state of the art of direct iN conversion and describe the key epigenetic, transcriptomic, and metabolic changes that occur in converting fibroblasts. Furthermore, we summarize new insights into this fascinating process, particularly focusing on the rapidly changing criteria used to define and characterize in vitro-born human neurons. Finally, we discuss the unique features that distinguish iNs from other reprogramming-based neuronal cell models and how iNs are relevant to disease modeling.

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“…Although SP1 is essential for growth in only one of the three C1 samples tested, other members of the SP1 regulatory network are found to be exclusively essential in the other two C1 samples, suggesting the SP1 network as a whole is the key regulator of this GSC subtype. Of the C2-enriched essential transcription factors, OLIG2 is a known GSC marker 19 , while ASCL1 is a critical regulator of GSC differentiation 11 . FOXD1, enriched in C3, is a known pluripotency regulator and determinant of tumourigenicity in GSCs where it regulates the transcriptional activity of the aldehyde dehydrogenase ALDH1A3, an established functional marker for mesenchymal GSCs 20,21 .…”

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confidence: 99%

Single-cell chromatin accessibility in glioblastoma delineates cancer stem cell heterogeneity predictive of survival

Guilhamon

Kushida

Nikolić

et al. 2018

Preprint

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41Chromatin accessibility discriminates stem from mature cell populations, enabling the 42 identification of primitive stem-like cells in primary tumors, such as Glioblastoma (GBM) where 43 self-renewing cells driving cancer progression and recurrence are prime targets for therapeutic 44 intervention. We show, using single-cell chromatin accessibility, that primary GBMs harbor a 45 heterogeneous self-renewing population whose diversity is captured in patient-derived 46 glioblastoma stem cells (GSCs). In depth characterization of chromatin accessibility in GSCs 47 identifies three GSC states: Reactive, Constructive, and Invasive, each governed by uniquely 48 essential transcription factors and present within GBMs in varying proportions. Orthotopic 49 xenografts reveal that GSC states associate with survival, and identify an invasive GSC signature 50 predictive of low patient survival. Our chromatin-driven characterization of GSC states improves 51 prognostic precision and identifies dependencies to guide combination therapies. 52 53 56only an additional 2.5 months in the small subset of responsive patients [2]. Despite extensive 57 characterization and stratification of the bulk primary tumors, no targeted therapies have been 58 successfully developed [1,3]. GBM tumors are rooted in self-renewing tumor-initiating cells 59 commonly referred to as glioblastoma stem cells (GSCs)[4] that drive disease progression in 60 vivo [5,6] and display resistance to chemo-and radiotherapy leading to disease recurrence [7]. 61 The promise of therapeutically targeting self-renewing tumor-initiating cancer cells depends on 62 3 our capacity to capture the full range of heterogeneity within this population from individual 63 tumors. Intratumoral heterogeneity within primary GBM has recently been documented 64 through single cell RNA-seq experiments and revealed a continuum between four cellular 65 states[8]: neural-progenitor-like (NPC), oligodendrocyte-progenitor-like (OPC), astrocyte-like 66 (AC), and mesenchymal-like (MES)[8]. A subsequent study[9] using single-cell gene-centric 67 enrichment analysis placed GBM cells along a single axis of variation from proneural to 68 mesenchymal transcriptional profiles, with cells expressing stem-associated genes lying at the 69 extremes of this axis. Hence, primary GBM consists of distinct states, across which stem-like 70 cells appear to be found. Whether these stem-like cells found across GBM states represent 71 functionally distinct GSC populations with tumor-initiating properties and unique dependencies 72 remains to be established to guide therapeutic progress. To address this issue, we combined 73 single-cell technologies to define GSC composition in primary GBM with functional assays to 74 reveal the unique dependencies across GSCs, reflective of invasive, constructive and reactive 75 states that relate to patient outcome. 76 77 RESULTS 78Chromatin accessibility readily discriminates stem from mature cell populations [10], 79 which can be resolved at the single cell level thro...

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ASCL1 Reorganizes Chromatin to Direct Neuronal Fate and Suppress Tumorigenicity of Glioblastoma Stem Cells

Cited by 156 publications

References 72 publications

Comprehensive analysis of lncRNAs reveals candidate prognostic biomarkers in multiple cancer types

Comprehensive analysis of lncRNAs reveals candidate prognostic biomarkers in multiple cancer types

Next‐generation disease modeling with direct conversion: a new path to old neurons

Single-cell chromatin accessibility in glioblastoma delineates cancer stem cell heterogeneity predictive of survival

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