Resolving medulloblastoma cellular architecture by single-cell genomics

Hovestadt, Volker; Smith, Kyle; Bihannic, Laure; Filbin, Mariella G.; Shaw, McKenzie; Baumgartner, Alicia; DeWitt, John; Groves, Andrew; Mayr, Lisa; Weisman, Hannah; Richman, Alyssa; Shore, Marni E.; Goumnerova, Liliana; Rosencrance, Celeste; Carter, Robert A.; Phoenix, Timothy N.; Hadley, Jennifer; Tong, Yiai; Houston, Jim; Ashmun, Richard A.; DeCuypere, Michael; Sharma, Tanvi; Flasch, Diane A.; Silkov, Antonina; Ligon, Keith L.; Pomeroy, Scott L.; Rivera, Miguel N.; Rozenblatt-Rosen, Orit; Rusert, Jessica M.; Wechsler‐Reya, Robert J.; Li, Xiao Nan; Peyrl, Andreas; Gojo, Johannes; Kirchhofer, Dominik; Lötsch, Daniela; Czech, Thomas; Dorfer, Christian; Haberler, Christine; Geyeregger, René; Halfmann, Angela; Gawad, Charles; Easton, John; Pfister, Stefan M.; Regev, Aviv; Gajjar, Amar; Orr, Brent A.; Slavc, Irene; Robinson, Giles; Bernstein, B; Suvà, Mario L.; Northcott, Paul A.

doi:10.1038/s41586-019-1434-6

Cited by 306 publications

(466 citation statements)

References 43 publications

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“…4a). To accommodate recent observations that malignant cells in different tumors are heterogeneous mixtures of functionally distinct cell types 27,32,46 , we modelled each patient as a linear combination of different pathways comprised of genes whose expression covaries in a similar manner. TED selected genes in each pathway and the weights for each tumor using the expectation maximization (EM) algorithm, such that the linear combination of all pathways most accurately approximates malignant cell expression in all patients.…”

Section: Tumor Pathway Embeddings Identify New Distinctions Between Mmentioning

confidence: 99%

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: Tumor Pathway Embeddings Identify New Distinctions Between Mmentioning

confidence: 99%

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

Chu

Danko

2020

Preprint

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“…A recent comprehensive scRNA-seq study showed that infant SHH-MB and adult SHH-MB have different molecular characteristics. While adult SHH-MBs express Atoh1, infant SHH-MBs express genes such as Neurod1, Map1b and Tubb2b (Hovestadt et al, 2019), which are strongly expressed in ND+GCPs (Fig. 1e).…”

Section: Canonical Wnt Signaling Controls the Dynamic Expressions Of mentioning

confidence: 99%

CyclinD1 controls development of cerebellar granule cell progenitors through phosphorylation and stabilization of ATOH1

Miyashita

Owa

Seto

et al. 2020

Preprint

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Here we report that CyclinD1 (CCND1) directly regulates both the proliferative and immature states of cerebellar granule cell progenitors (GCPs). CCND1 not only accelerates cell cycle but also upregulates ATOH1 protein, an essential transcription factor that maintains GCPs in an immature state. In cooperation with CDK4, CCND1 directly phosphorylates Ser309 of ATOH1, which inhibits additional phosphorylation at S328, consequently preventing Ser328 phosphorylation-dependent ATOH1 degradation. PROX1 downregulates Ccnd1 expression by histone-deacetylation of Ccnd1 promoter inGCPs, leading to cell cycle exit and differentiation. WNT signaling upregulates PROX1 expression in GCPs. These findings suggest that WNT-PROX1-CCND1-ATOH1 signaling cascade cooperatively controls proliferation and immaturity of GCPs. We revealed that the expression and phosphorylation levels of these molecules dynamically change during cerebellar development, which was suggested to determine appropriate differentiation rates from GCPs to GCs at distinct developmental stages. This study contributes to understanding the regulatory mechanism of GCPs as well as neural progenitors.3 do not express ATOH1 but are NEUROD1-positive. The reduction of ATOH1 promotes GC development by accelerating transition from AT+GCPs to ND+GCPs (AT-ND transition), which eventually leads to differentiation into GCs. We found that CCND1 not only promotes cell cycle progression but maintains immaturity of GCPs by protecting against ATOH1 degradation. CCND1 and CDK4 cooperatively phosphorylate S309 of ATOH1, which suppresses further phosphorylation at S328, preventing ATOH1 degradation. Furthermore, the WNT-PROX1 pathway down-regulates CCND1 expression via chromatin histone deacetylation, which accelerates AT-ND transition of GCPs, eventually leading to GC development. We found that levels of the ATOH1 phosphorylation, CCND1, PROX1 proteins and WNT signaling vary during cerebellar development, which may control properly timed proliferation and differentiation of GCPs at distinct stages. These results give insights into the molecular machinery to coordinately regulate proliferation and immaturity in neural progenitors. Results ATOH1 expression controls the transition between subpopulations of GCPs.

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“…We first determined whether Bcor is normally expressed in developing cerebellar GNPs in mice, the cellof-origin for SHH-medulloblastoma (Yang et al 2008;Schüller et al 2008;Hovestadt et al 2019;Vladoiu et al 2019). Using RNA in situ hybridization (ISH), we found that Bcor is expressed in the outer external granule layer the C-terminal PUFD domain is inferred to be absent because of the introduction of premature STOP codons in 6/7 cases (Table 1).…”

Section: Bcor Is Expressed In Proliferating Granule Neuron Progenitormentioning

confidence: 99%

Functional loss of a non-canonical BCOR-PRC1.1 complex accelerates SHH-driven medulloblastoma formation

Kutscher

Okonechnikov

Batora

et al. 2020

Preprint

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Medulloblastoma is a childhood brain tumor arising from the developing cerebellum. In Sonic Hedgehog (SHH)-subgroup medulloblastoma, aberrant activation of SHH signaling causes increased proliferation of granule neuron progenitors (GNPs) and predisposes these cells to tumorigenesis. A second, cooperating genetic hit is often required to push these hyperplastic cells to malignancy and confer mutation-specific characteristics associated with oncogenic signaling. Somatic loss-of-function mutations of the transcriptional co-repressor BCOR are recurrent and highly enriched in SHH-medulloblastoma. To investigate BCOR as a putative tumor suppressor, we used a germline genetically engineered mouse model to delete exons 9/10 of Bcor (BcorΔE9-10) in GNPs during development. This leads to reduced expression of C-terminally truncated BCOR (BCORΔE9-10). While BcorΔE9-10 alone did not promote tumorigenesis or affect GNP differentiation, BcorΔE9-10 combined with loss of the SHH-receptor gene Ptch1 resulted in highly penetrant medulloblastomas. In Ptch1+/-;BcorΔE9-10 tumors, the growth factor gene Igf2 was aberrantly upregulated, and ectopic Igf2 overexpression was sufficient to drive tumorigenesis in Ptch1+/- GNPs. BCOR directly regulates Igf2, likely through the PRC1.1 complex; the repressive histone mark H2AK119Ub is decreased at the Igf2 promoter in Ptch1+/-;BcorΔE9-10 tumors. Overall, our data suggests that BCOR-PRC1.1 disruption leads to Igf2 overexpression, which transforms preneoplastic cells to malignant tumors.

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Resolving medulloblastoma cellular architecture by single-cell genomics

Cited by 306 publications

References 43 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

CyclinD1 controls development of cerebellar granule cell progenitors through phosphorylation and stabilization of ATOH1

Functional loss of a non-canonical BCOR-PRC1.1 complex accelerates SHH-driven medulloblastoma formation

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