. 2019 Aug 8;178(4):835-849.e21.

doi: 10.1016/j.cell.2019.06.024. Epub 2019 Jul 18.

An Integrative Model of Cellular States, Plasticity, and Genetics for Glioblastoma

Cyril Neftel¹, Julie Laffy², Mariella G Filbin³, Toshiro Hara⁴, Marni E Shore⁵, Gilbert J Rahme⁵, Alyssa R Richman⁵, Dana Silverbush⁵, McKenzie L Shaw³, Christine M Hebert⁵, John Dewitt⁵, Simon Gritsch⁵, Elizabeth M Perez⁵, L Nicolas Gonzalez Castro⁶, Xiaoyang Lan⁵, Nicholas Druck⁷, Christopher Rodman⁷, Danielle Dionne⁸, Alexander Kaplan⁹, Mia S Bertalan⁹, Julia Small¹⁰, Kristine Pelton¹¹, Sarah Becker¹¹, Dennis Bonal¹², Quang-De Nguyen¹², Rachel L Servis⁷, Jeremy M Fung⁷, Ravindra Mylvaganam⁷, Lisa Mayr¹², Johannes Gojo¹³, Christine Haberler¹⁴, Rene Geyeregger¹⁵, Thomas Czech¹⁶, Irene Slavc¹³, Brian V Nahed¹⁰, William T Curry¹⁰, Bob S Carter¹⁰, Hiroaki Wakimoto¹⁰, Priscilla K Brastianos⁹, Tracy T Batchelor⁹, Anat Stemmer-Rachamimov⁷, Maria Martinez-Lage⁷, Matthew P Frosch⁷, Ivan Stamenkovic¹⁷, Nicolo Riggi¹⁷, Esther Rheinbay¹⁸, Michelle Monje¹⁹, Orit Rozenblatt-Rosen⁸, Daniel P Cahill¹⁰, Anoop P Patel²⁰, Tony Hunter²¹, Inder M Verma²¹, Keith L Ligon²², David N Louis⁷, Aviv Regev²³, Bradley E Bernstein⁵, Itay Tirosh²⁴, Mario L Suvà²⁵

Affiliations

¹ Department of Pathology and Center for Cancer Research, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA; Klarman Cell Observatory, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Institute of Pathology, Faculty of Biology and Medicine, Centre Hospitalier Universitaire Vaudois, Lausanne, 1011, Switzerland.
² Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot 761001, Israel.
³ Department of Pathology and Center for Cancer Research, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA; Klarman Cell Observatory, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Department of Pediatric Oncology, Dana-Farber Boston Children's Cancer and Blood Disorders Center, Boston, MA 02215, USA.
⁴ Department of Pathology and Center for Cancer Research, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA; Klarman Cell Observatory, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Laboratory of Genetics, Salk Institute for Biological Studies, La Jolla, CA 92037, USA.
⁵ Department of Pathology and Center for Cancer Research, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA; Klarman Cell Observatory, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA.
⁶ Department of Pathology and Center for Cancer Research, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA; Klarman Cell Observatory, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Departments of Neurology and Radiation Oncology, Division of Hematology/Oncology, Massachusetts General Hospital Cancer Center, Harvard Medical School, Boston, MA, 02114, USA.
⁷ Department of Pathology and Center for Cancer Research, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA.
⁸ Klarman Cell Observatory, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA.
⁹ Departments of Neurology and Radiation Oncology, Division of Hematology/Oncology, Massachusetts General Hospital Cancer Center, Harvard Medical School, Boston, MA, 02114, USA.
¹⁰ Department of Neurosurgery, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114 USA.
¹¹ Department of Oncologic Pathology, Brigham and Women's Hospital, Boston Children's Hospital, Dana-Farber Cancer Institute, Boston, MA 02215, USA.
¹² Center for Biomedical Imaging in Oncology, Lurie Family Imaging Center, Dana-Farber Cancer Institute, Boston, MA 02215, USA.
¹³ Department of Pediatrics and Adolescent Medicine, Medical University of Vienna, Vienna, 1090, Austria.
¹⁴ Institute of Neurology, Medical University of Vienna, Vienna, 1090, Austria.
¹⁵ Children's Cancer Research Institute (CCRI), St. Anna Kinderspital, Medical University of Vienna, Vienna, 1090, Austria.
¹⁶ Department of Neurosurgery, Medical University of Vienna, Vienna, 1090, Austria.
¹⁷ Institute of Pathology, Faculty of Biology and Medicine, Centre Hospitalier Universitaire Vaudois, Lausanne, 1011, Switzerland.
¹⁸ Department of Pathology and Center for Cancer Research, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA; Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA.
¹⁹ Departments of Neurology, Neurosurgery, Pediatrics and Pathology, Stanford University School of Medicine, Stanford, CA, 94305, USA.
²⁰ Department of Neurological Surgery, University of Washington, Seattle, USA.
²¹ Laboratory of Genetics, Salk Institute for Biological Studies, La Jolla, CA 92037, USA.
²² Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Department of Oncologic Pathology, Brigham and Women's Hospital, Boston Children's Hospital, Dana-Farber Cancer Institute, Boston, MA 02215, USA.
²³ Klarman Cell Observatory, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Howard Hughes Medical Institute, Koch Institute for Integrative Cancer Research, Department of Biology, MIT, Cambridge, MA 02139, USA.
²⁴ Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot 761001, Israel. Electronic address: Itayt@weizmann.ac.il.
²⁵ Department of Pathology and Center for Cancer Research, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA; Klarman Cell Observatory, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA. Electronic address: Suva.Mario@mgh.harvard.edu.

PMID: 31327527
PMCID: PMC6703186
DOI: 10.1016/j.cell.2019.06.024

An Integrative Model of Cellular States, Plasticity, and Genetics for Glioblastoma

Cyril Neftel et al. Cell. 2019.

. 2019 Aug 8;178(4):835-849.e21.

doi: 10.1016/j.cell.2019.06.024. Epub 2019 Jul 18.

Authors

Affiliations

¹ Department of Pathology and Center for Cancer Research, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA; Klarman Cell Observatory, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Institute of Pathology, Faculty of Biology and Medicine, Centre Hospitalier Universitaire Vaudois, Lausanne, 1011, Switzerland.
² Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot 761001, Israel.
³ Department of Pathology and Center for Cancer Research, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA; Klarman Cell Observatory, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Department of Pediatric Oncology, Dana-Farber Boston Children's Cancer and Blood Disorders Center, Boston, MA 02215, USA.
⁴ Department of Pathology and Center for Cancer Research, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA; Klarman Cell Observatory, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Laboratory of Genetics, Salk Institute for Biological Studies, La Jolla, CA 92037, USA.
⁵ Department of Pathology and Center for Cancer Research, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA; Klarman Cell Observatory, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA.
⁶ Department of Pathology and Center for Cancer Research, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA; Klarman Cell Observatory, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Departments of Neurology and Radiation Oncology, Division of Hematology/Oncology, Massachusetts General Hospital Cancer Center, Harvard Medical School, Boston, MA, 02114, USA.
⁷ Department of Pathology and Center for Cancer Research, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA.
⁸ Klarman Cell Observatory, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA.
⁹ Departments of Neurology and Radiation Oncology, Division of Hematology/Oncology, Massachusetts General Hospital Cancer Center, Harvard Medical School, Boston, MA, 02114, USA.
¹⁰ Department of Neurosurgery, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114 USA.
¹¹ Department of Oncologic Pathology, Brigham and Women's Hospital, Boston Children's Hospital, Dana-Farber Cancer Institute, Boston, MA 02215, USA.
¹² Center for Biomedical Imaging in Oncology, Lurie Family Imaging Center, Dana-Farber Cancer Institute, Boston, MA 02215, USA.
¹³ Department of Pediatrics and Adolescent Medicine, Medical University of Vienna, Vienna, 1090, Austria.
¹⁴ Institute of Neurology, Medical University of Vienna, Vienna, 1090, Austria.
¹⁵ Children's Cancer Research Institute (CCRI), St. Anna Kinderspital, Medical University of Vienna, Vienna, 1090, Austria.
¹⁶ Department of Neurosurgery, Medical University of Vienna, Vienna, 1090, Austria.
¹⁷ Institute of Pathology, Faculty of Biology and Medicine, Centre Hospitalier Universitaire Vaudois, Lausanne, 1011, Switzerland.
¹⁸ Department of Pathology and Center for Cancer Research, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA; Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA.
¹⁹ Departments of Neurology, Neurosurgery, Pediatrics and Pathology, Stanford University School of Medicine, Stanford, CA, 94305, USA.
²⁰ Department of Neurological Surgery, University of Washington, Seattle, USA.
²¹ Laboratory of Genetics, Salk Institute for Biological Studies, La Jolla, CA 92037, USA.
²² Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Department of Oncologic Pathology, Brigham and Women's Hospital, Boston Children's Hospital, Dana-Farber Cancer Institute, Boston, MA 02215, USA.
²³ Klarman Cell Observatory, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Howard Hughes Medical Institute, Koch Institute for Integrative Cancer Research, Department of Biology, MIT, Cambridge, MA 02139, USA.
²⁴ Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot 761001, Israel. Electronic address: Itayt@weizmann.ac.il.
²⁵ Department of Pathology and Center for Cancer Research, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA; Klarman Cell Observatory, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA. Electronic address: Suva.Mario@mgh.harvard.edu.

PMID: 31327527
PMCID: PMC6703186
DOI: 10.1016/j.cell.2019.06.024

Abstract

Diverse genetic, epigenetic, and developmental programs drive glioblastoma, an incurable and poorly understood tumor, but their precise characterization remains challenging. Here, we use an integrative approach spanning single-cell RNA-sequencing of 28 tumors, bulk genetic and expression analysis of 401 specimens from the The Cancer Genome Atlas (TCGA), functional approaches, and single-cell lineage tracing to derive a unified model of cellular states and genetic diversity in glioblastoma. We find that malignant cells in glioblastoma exist in four main cellular states that recapitulate distinct neural cell types, are influenced by the tumor microenvironment, and exhibit plasticity. The relative frequency of cells in each state varies between glioblastoma samples and is influenced by copy number amplifications of the CDK4, EGFR, and PDGFRA loci and by mutations in the NF1 locus, which each favor a defined state. Our work provides a blueprint for glioblastoma, integrating the malignant cell programs, their plasticity, and their modulation by genetic drivers.

Keywords: CDK4; EGFR; NF1; PDGFRA; glioblastoma IDH-wildtype; glioblastoma stem cells; glioblastoma subtypes; lineage tracing; single-cell RNA-sequencing.

PubMed Disclaimer

Conflict of interest statement

DECLARATION OF INTERESTS

The authors declare no competing interests.

Figures

**Figure 1.. Classification of single cells from 28 glioblastomas.**
(A) Inference of chromosomal CNAs based on average relative expression in windows of 100 analyzed genes. Row correspond to cells, with non-malignant (NM) cells that lack CNAs at the top followed by malignant cells (with CNAs, as defined in Figure S1) ordered by tumor and within a tumor clustered by overall CNA patterns. (B) tSNE plot of all single cells. Cells are colored based on presence of CNAs (blue) or high expression of sets of marker genes for macrophages (cyan), oligodendrocytes (magenta) or T-cells (green). (C) tSNE plot of all malignant cells, colored by tumor.

**Figure 2.. Expression signatures of intra-tumoral heterogeneity among malignant cells.**
(A) Top: cell-to-cell correlation matrix of malignant cells from MGH105, with cells ordered by hierarchical clustering. Bottom: assignment of cells to potential overlapping clusters. (B) Hierarchical clustering of signatures for 269 potential clusters defined from 27 tumors. Groups of potential clusters are highlighted at the top and were used to define meta-modules. (C) Meta-modules, composed of genes consistently upregulated in potential clusters of the same group. Selected genes are indicated (see Table S2 for a full list). (D) Relative expression of meta-modules across neurodevelopment-related cell types as measured by scRNA-seq (Darmanis et al., 2017; Darmanis et al., 2015; Tirosh et al., 2016b). Error bars correspond to standard error.

**Figure 3.. Assignment of malignant cells to cellular states and their hybrids.**
(A) Heatmap showing the meta-module scores of all non-cycling cells (left) and cycling cells (right). Within each group, the cells are ordered by their maximal score, for cells mapping to one meta-module, followed by cells mapping to two meta-modules (hybrid states, denoted as “H”). (B) Bar plot showing the percentage of cells with highest score for each meta-module. Adult and pediatric tumors are separated in order to demonstrate their distinct distributions. Error bars correspond to standard error, calculated by bootstrapping. (C) Bar plot showing the percentage of cycling cells among cells with highest score for each of the meta-modules. Error bars correspond to standard error, calculated by bootstrapping. (D) Bar plot showing the observed and expected percentages of hybrid cells (co-expressing two distinct meta-modules) out of all malignant cells. Expected percentages and their standard errors were calculated by shuffling the cell scores (STAR Methods). (E) In situ RNA hybridization of glioblastoma for NPC-like (CD24), MES-like (CD44) and proliferation (Ki67) markers. Arrows highlight representative cells positive for CD24 (blue) or CD44 (red). Arrowhead highlights a cell coexpressing CD24 and Ki67. (F) Two-dimensional representation of cellular states. Each quadrant corresponds to one cellular state, the exact position of malignant cells (dots) reflect their relative scores for the meta-modules, and their colors reflect the density of cycling cells (STAR Methods).

**Figure 4.. Intra-tumoral heterogeneity at the genetic and expression levels.**
(**A-B**) Identification of genetic subclones by CNAs. Shown are the inferred CNAs of malignant cells in MGH125 (A) and MGH102 (B), separated into genetic subclones based on amplifications/deletions of specific chromosomes (STAR Methods). (C) Cell-state plots (as in Figure 3F) for six tumors with CNA-based subclones. Cells are colored by their subclone.

**Figure 5.. The distribution of glioblastoma cellular states is associated with chromosomal amplifications across the TCGA glioblastoma cohort.**
(A) Pie charts displaying the fraction of cells in four cellular states in each glioblastoma from our cohort. Tumor indices are above each pie chart, with pediatric tumors indicated in red and recurrent tumors with “R”. Tumors are grouped by bulk TCGA subtype as labelled. (B) Analysis of the TCGA glioblastoma cohort shows that high-level amplifications of *EGFR*, *PDGFRA* and *CDK4* are associated with high bulk scores for the AC-like, OPC-like, and NPC-like cellular states, respectively. Shown are significance values (-log₁₀(P-value)) for the association of chromosomal amplifications with high bulk scores (shown above the zero line, indicating enrichment of the cellular state) or with low bulk scores (shown below the zero line, indicating depletion of the cellular state). Single chromosome gains are distinguished from high-level amplifications (Brennan et al., 2013) which are found to have significant associations with three cellular states (AC-like, OPC-like, NPC-like) while no associations of chromosomal amplifications were found with the MES-like state.

**Figure 6.. Glioblastoma oncogenes drive defined cellular states.**
**(A)** Micrographs of immunofluorescence of mouse NPC over-expressing *EGFR*, *CDK4* or eGFP immunostained for the astrocytic marker GFAP (red). (B) Quantification of GFAP+ cells shown in panel (A) (STAR Methods). (C) scRNA-seq scores for the AC-like signature (Y axis) of ranked cells (X axis) overexpressing EGFR (red) or GFP (black) (STAR Methods). (D) scRNA-seq scores for the NPC-like signature (Y axis) of ranked cells (X axis) overexpressing CDK4 (blue) or GFP (black). (E) Growth curve using NPCs over-expressing eGFP, *EGFR* or *CDK4* shows increased proliferation (p<0.0001) in *CDK4* expressing cells. RLU: Relative Light Units (arbitrary value). (F) Growth curve of astrocytes derived from the engineered NPCs (STAR Methods) shows significant (p<0.002, ANOVA) increase in growth of astrocytes overexpressing EGFR.

**Figure 7.. Cellular transitions in glioblastoma.**
(A) Experimental workflow. Different fractions of cells were sorted from patient sample MGH143 and injected orthotopically into immunocompromised mice to generate PDXs. The patient sample and the PDXs subpopulations were subjected to scRNA-seq. (B) Samples described in (A) are each represented by a pie chart depicting the fraction of cells in four states. Pie charts are positioned on the X axis based on their sorted fraction and whether they represent injected or PDX sample, and on the Y axis based on their compositional similarity to the original patient sample (one minus the Manhattan distance over the fractions of four states). (C) Experimental workflow. Lentiviruses harboring oncogenes and unique barcodes were injected into the mouse hippocampus (STAR Methods) and the resulting tumors were analyzed by scRNA-seq. (D) Barcodes which were identified in multiple cells are each represented by a pie chart depicting the fraction of cells in each state. Pie charts are positioned based on the number of cells with the respective barcode (X axis), and the number of cellular states observed among these cells (Y axis). Pie chart sizes are proportional to log₂ of the number of cells. (E) Experimental workflow. Primary cultures are established from glioblastoma samples (MGH143 and MGG23) and infected with lentiviruses harboring unique barcodes, xenografted into mouse brains and tumor formed are analyzed by scRNA-seq. (F) Unique barcodes from (E) are displayed as shown in (D). (G) Model for the cellular states of glioblastoma and their genetic and micro-environmental determinants. Mitotic spindles indicate cycling cells. Lighter/darker tones indicate strength of each program. Intermediate states are shown in between the four states and indicate transitions.

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Comment in

Plasticity and Clonality of Cancer Cell States.
Barkley D, Yanai I. Barkley D, et al. Trends Cancer. 2019 Nov;5(11):655-656. doi: 10.1016/j.trecan.2019.09.002. Epub 2019 Oct 14. Trends Cancer. 2019. PMID: 31735281

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An Integrative Model of Cellular States, Plasticity, and Genetics for Glioblastoma

Affiliations

An Integrative Model of Cellular States, Plasticity, and Genetics for Glioblastoma

Authors

Affiliations

Abstract

Conflict of interest statement

Figures

Comment in

References

Publication types

MeSH terms

Grants and funding

LinkOut - more resources

Full Text Sources

Other Literature Sources

Medical

Molecular Biology Databases

Research Materials

Miscellaneous