Single-cell chromatin accessibility profiling of glioblastoma identifies an invasive cancer stem cell population associated with lower survival

Guilhamon, Paul; Chesnelong, Charles; Kushida, Michelle; Nikolić, Ana; Singhal, Divya; MacLeod, Graham; Tonekaboni, Seyed Ali Madani; Cavalli, Florence M.G.; Arlidge, Christopher; Rajakulendran, Nishani; Rastegar, Naghmeh; Hao, Xiaoguang; Hassam, Rozina; Smith, Laura J.; Whetstone, Heather; Coutinho, Fiona J.; Nadorp, Bettina; Ellestad, Katrina; Luchman, H. Artee; Chan, Jennifer A.; Shoichet, Molly S.; Taylor, Michael D.; Haibe‐Kains, Benjamin; Weiss, Samuel; Angers, Stéphane; Gallo, Marco; Dirks, Peter B.; Lupien, Mathieu

doi:10.7554/elife.64090

Cited by 63 publications

(52 citation statements)

References 61 publications

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“…Programs 1 and 2 had some notable differences from all previously described subtypes. Program 1 was similar to both AC and MES, which agrees with the recently reported stratification of the MES subtype and heterogeneity of the AC subtype in GBM 72 . It was strongly enriched for genes associated with cell respiration, and was negatively associated with endothelial cells.…”

Section: Resultssupporting

confidence: 90%

Bayesian cell-type deconvolution and gene expression inference reveals tumor-microenvironment interactions

Chu

Danko

2020

Preprint

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Understanding the dynamic interactions between malignant cells and the tumor stroma is a major goal of cancer research. Here we developed a Bayesian model that jointly infers both cellular composition and gene expression in each cell type, including heterogeneous malignant cells, from bulk RNA-seq using scRNA-seq as prior information. We conducted an integrative analysis of 85 single-cell and 1,412 bulk RNA-seq datasets in primary human glioblastoma, head and neck squamous cell carcinoma, and melanoma. We identified cell types correlated with clinical outcomes and explored regional heterogeneity in tumor state and stromal composition. We redefined common molecular subtypes using gene expression in malignant cells, after excluding confounding non-malignant cell types. Finally, we identified genes whose expression in malignant cells correlated with infiltration of macrophages, T-cells, fibroblasts, and endothelial cells across multiple tumor types. Our work provides a new lens that we used to measure cellular composition and expression in a statistically powered cohort of three primary human malignancies. Results Bayesian inference of cell type composition and tumor expressionTED uses a scRNA-seq reference dataset to infer two parameters of interest from bulk RNA-seq data: (i) the proportion of cell types in the bulk population and (ii) the average expression profiles of each cell type. TED describes the proportion and expression profiles of each cell type as latent variables that it infers from the data ( Fig. 1a, Supplementary Fig. 1, Supplementary Note 1). TED makes the key simplifying assumption that each non-malignant cell type shares a common gene expression profile across patients, as observed in the cases analyzed to date 28,30,31 .Critically, each bulk RNA-seq sample is then assumed to have a unique tumor expression profile that we infer from the data.Expression in the reference and bulk RNA-seq data often differ substantially due to batch effects or tumor heterogeneity. To account for uncertainty in the reference cell type expression matrix, TED implements a fully Bayesian inference of tumor composition. First, TED uses Gibbs sampling to estimate the posterior joint distribution of cell type composition, θ0, and gene expression profiles, Z, i.e. P(θ0, Z | φ, X; α) ( Fig. 1a, red, top). Second, to account for tumor cells, or other cell types which cannot be observed in the reference dataset, TED infers a maximum likelihood estimate (MLE) for tumor expression profiles, ψtum (Fig. 1a, red, mid). During this step TED also infers the maximum a posterior estimate (MAP) of the expression profile of non-malignant stromal cells, ψstr, to correct for batch effects between bulk and single cell RNAseq platforms. Last, TED uses the updated expression profile for each patient and cell type to resample the posterior distribution of cell type composition, θ, i.e. P(θ | ψtum , ψstr ,X; α) ( Fig. 1a, red, bottom). Optionally, TED has an additional mode which can be used to learn common patterns of expression heterogeneit...

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Section: Resultssupporting

confidence: 90%

Bayesian cell-type deconvolution and gene expression inference reveals tumor-microenvironment interactions

Chu

Danko

2020

Preprint

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“…These sites include motifs for factors that affect invasion (FOXD1 and ALDH1A3), response to immune signalling (SP1) and neural commitment (OLIG2, AHR). Combined with scRNA-seq, these findings confirm the heterogeneity even within the tumour-initiating CSCs and highlight the potential challenges in targeted therapies [ 66 , 67 ].…”

Section: The Epigenetic Foundation Of Tumour Plasticitymentioning

confidence: 64%

Deregulation of Transcriptional Enhancers in Cancer

Azad

Atlasi

2021

Cancers

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Epigenetic regulations can shape a cell’s identity by reversible modifications of the chromatin that ultimately control gene expression in response to internal and external cues. In this review, we first discuss the concept of cell plasticity in cancer, a process that is directly controlled by epigenetic mechanisms, with a particular focus on transcriptional enhancers as the cornerstone of epigenetic regulation. In the second part, we discuss mechanisms of enhancer deregulation in adult stem cells and epithelial-to-mesenchymal transition (EMT), as two paradigms of cell plasticity that are dependent on epigenetic regulation and serve as major sources of tumour heterogeneity. Finally, we review how genetic variations at enhancers and their epigenetic modifiers contribute to tumourigenesis, and we highlight examples of cancer drugs that target epigenetic modifications at enhancers.

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“…The profound shift in co-accessibility of DNA elements observed with KMT5B/C loss demonstrates the complexity of cis-regulation in these tumours. Heterogenous chromatin landscapes have recently been shown to play an important role in glioma stem-like cell diversity in adult glioblastoma (Guilhamon et al, 2021), whilst in H3G34R/V mutant glioma, it was recently shown that the specific lineage context of these tumours may facilitate PDGFRA co-option through a chromatin loop connecting PDGFRA to GSX2 regulatory elements, promoting PDGFRA overexpression (Chen et al, 2020). A fundamental consequence of the altered gene expression landscape observed with KMT5B loss is a significant increase in the transcriptional heterogeneity, in patient samples as well as model systems, as has also been observed with the histone demethylase KDM5B in breast cancer (Hinohara et al, 2018).…”

Section: Discussionmentioning

confidence: 99%

Loss of the H4 lysine methyltransferase KMT5B drives tumorigenic phenotypes by depleting H3K27me3 at loci otherwise retained in H3K27M mutant DIPG cells

Kessler

Mackay

Grabovska

et al. 2021

Preprint

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DIPG are characterised by histone H3K27M mutations, resulting in global loss of the repressive mark H3K27me3, although certain key loci are retained. We recently identified subclonal loss-of-function mutations in the H4 lysine methyltransferase KMT5B to be associated with enhanced invasion/migration, but the mechanism by which this occurred was unclear. Here we use integrated ChIP-seq, ATAC-seq and RNA-seq on patient-derived, subclonal and CRISPR-Cas9-KD DIPG cells to show that loss of KMT5B/C causes depletion of these retained H3K27me3 loci via changes in chromatin accessibility, causing a raft of transcriptional changes which promote tumorigenesis. De-repression occurred at bivalent loci marked by H3K4me3, driving increased transcriptional heterogeneity and elevated gene expression associated with increased invasion, abrogated DNA repair and mesenchymal transition, along with a markedly altered secretome. These data suggest a previously unrecognised trans-histone (H4/H3) interaction in DIPG cells with a potentially profound effect on their diffusely infiltrating phenotype.

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Single-cell chromatin accessibility profiling of glioblastoma identifies an invasive cancer stem cell population associated with lower survival

Cited by 63 publications

References 61 publications

Bayesian cell-type deconvolution and gene expression inference reveals tumor-microenvironment interactions

Bayesian cell-type deconvolution and gene expression inference reveals tumor-microenvironment interactions

Deregulation of Transcriptional Enhancers in Cancer

Loss of the H4 lysine methyltransferase KMT5B drives tumorigenic phenotypes by depleting H3K27me3 at loci otherwise retained in H3K27M mutant DIPG cells

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