Glioblastoma (GBM) is a lethal form of brain cancer with standard surgery and radiation giving a median survival of only 12.6 months (Carlsson et al., 2014). The addition of temozolomide chemotherapy provides only an additional 2.5 months in the small subset of responsive patients (Stupp et al., 2005). Despite extensive characterization and stratification of the bulk primary tumors, no targeted therapies have been successfully developed (Carlsson et al., 2014; von Neubeck et al., 2015). GBM tumors are rooted in self-renewing tumor-initiating cells commonly referred to as glioblastoma stem cells (GSCs) (Venere et al., 2011) that drive disease progression in vivo (Chen et al., 2012; Gallo et al., 2015) and display resistance to chemo- and radiotherapy leading to disease recurrence (Bao et al., 2006). The promise of therapeutically targeting self-renewing tumor-initiating cancer cells depends on our capacity to capture the full range of heterogeneity within this population from individual tumors. Intratumoral heterogeneity within primary GBM has recently been documented through single-cell RNA-seq experiments and revealed a continuum between four cellular states (Neftel et al., 2019): neural-progenitor-like (NPC), oligodendrocyte-progenitor-like (OPC), astrocyte-like (AC), and mesenchymal-like (MES) (Neftel et al., 2019). A subsequent study (Wang et al., 2019) using single-cell gene-centric enrichment analysis placed GBM cells along a single axis of variation from proneural to mesenchymal transcriptional profiles, with cells expressing stem-associated genes lying at the extremes of this axis. Hence, primary GBM consists of distinct states, across which stem-like cells appear to be found. Whether these stem-like cells found across GBM states represent functionally distinct GSC populations with tumor-initiating properties and unique dependencies remains to be established to guide therapeutic progress. To address this issue, we combined single-cell technologies to define GSC composition in primary GBM with functional assays to reveal the unique dependencies across GSCs, reflective of invasive, constructive, and reactive states that relate to patient outcome.

Chromatin accessibility readily discriminates stem from mature cell populations (Stergachis et al., 2013), which can be resolved at the single-cell level through single-cell ATAC-seq (Buenrostro et al., 2018; Corces et al., 2016), taking into account non-gene centric features, such as accessibility of noncoding elements and total amount of accessible DNA sequences. Applying single-cell chromatin accessibility profiling (scATAC-seq) across four primary adult GBM tumors (3797 cells), wild type for both IDH1 and IDH2, revealed seven to nine accessibility modules in each tumor based on unsupervised clustering (Figure 1A). We assigned cells to each of the four scRNA-seq-derived cellular states (Neftel et al., 2019) based on individual cells’ chromatin accessibility enrichment scores for the promoter regions of each state’s signature genes. Across the four tumors, 35–55.2% of the cells were significantly enriched for at least one state’s signature genes (Figure 1—figure supplement 1A). The MES state reported from scRNA-seq (Neftel et al., 2019) dominates the identity of two or more modules reported from chromatin accessibility in every tumor (Figure 1B,C, Figure 1—figure supplement 1B). In contrast, the NPC and OPC states are mixed within the same module defined based on chromatin accessibility, dominating over the other states typically in at least two modules. Cells assigned to the AC state did not preferentially cluster within a single module reported from chromatin accessibility (Figure 1B,C, Figure 1—figure supplement 1B). Collectively, our results suggest that chromatin accessibility reflects a greater stratification of the MES state and detects similarities between the OPC and NPC states and heterogeneity within the AC state.

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To identify putative cancer stem cells within each primary tumor, we next focused on the level of chromatin accessibility at promoters of 19 transcription factors previously associated with self-renewal and tumor-propagating capacity in GBM (Suvà et al., 2014; Figure 1D). Individual cells scoring as putative cancer stem cells were not restricted to a unique module defined by chromatin accessibility but were distributed across a subset of modules, suggesting heterogeneity across cancer stem cells in primary GBM, in agreement with reports relying on single GBM cell labeling (Corces et al., 2016; Gallo et al., 2015; Lan et al., 2017; Liau et al., 2017; Meyer et al., 2015; Miller et al., 2017; Orzan et al., 2017; Park et al., 2017; Rheinbay et al., 2013; Stergachis et al., 2013; Suvà et al., 2014), assessing the heterogeneity of self-renewing tumor-initiating cells. Putative cancer stem cells identified in primary GBM through scATAC-seq were found in modules ascribed to every one of the four cellular states defined by gene expression (Neftel et al., 2019), predominantly within NPC and OPC containing modules and a smaller fraction (<10%) in MES-specific modules across all four tumors (Figure 1E). This suggests that the core transcriptional unit of cancer stem cells in primary GBM (Suvà et al., 2014) is not restricted to a unique population defined by its global transcriptional or chromatin accessibility profile with the resolution achieved with current single-cell technologies.

To further probe the heterogeneity in chromatin accessibility within the GBM cancer stem cell pool, we derived GSC populations from 27 adult IDH wild-type GBM tumors (Pollard et al., 2009) and profiled their chromatin accessibility by bulk ATAC-seq (Figure 2A). Each patient-derived GSC showed a similar enrichment for accessible chromatin regions in promoters and 5′UTRs, and depletion in introns and distal intergenic regions (Figure 2B). Collectively, we uncovered 92% of the total predicted regions of accessible chromatin (255,891 regions) within GSCs based on a saturation analysis using a self-starting non-linear regression model across the 27 samples (Figure 2C). We next assessed the similarity between these GSCs and the putative cancer stem cells found by scATAC-seq in the four primary GBMs. GSCs were identified within each tumor by calculating the enrichment of accessible chromatin regions shared by a majority of GSCs (>14/27) in each tumor cell (Figure 2D). On average, 11.3% of cells in each primary GBM were labeled as GSCs. Comparing the distribution of GSCs across the seven to nine modules defined by scATAC-seq to that of the 19 transcription factor-derived cancer stem cell signature demonstrates concordance between the two signatures (Figure 2E). Moreover, the enrichment z-scores for both cancer stem signatures (i.e. stem transcription factors signature and GSC chromatin accessibility signature) are significantly correlated across cells in all four tumors (p≤1.6×10⁻⁵) (Figure 2F). The overlap in cells significantly enriched for either stem signatures across the four tumors is also significant (hypergeometric test p-value=8.8×10⁻⁸) Additionally, an average of 91.2% (85.1–100%) of the cells identified by either signature display the hallmark GBM copy number changes at chromosomes 7 and 10, confirming their neoplastic status (Figure 2—figure supplement 1). Collectively, these results demonstrate that the patient-derived GSC populations reflect the chromatin identity of putative cancer stem cells found in primary brain tumors, highlighting the value of these GSCs as models to deepen our understanding of individual cells within primary GBM with features found in self-renewing tumor-initiating cells. Accordingly, spectral clustering of the 27 patient-derived GSC ATAC profiles identifies three distinct states of self-renewing tumor-initiating cells (Figure 3A). Expression profiling of these 27 GSCs by RNA-seq reveals GSCs significantly enriched for the signatures of each of the three TCGA GBM subtypes (proneural, classical, and mesenchymal) (Wang et al., 2017; Figure 3B). However, the assignment of the proneural, classical, and mesenchymal subtypes across GSCs did not match the three clusters identified from ATAC-seq (Figure 3B). Conversely, clustering the GSCs by gene expression, independently of their chromatin accessibility, did largely recapitulate the GSC states defined by chromatin accessibility (Figure 3B). This suggests that the mismatch between the GSC cluster from chromatin accessibility profiles and TCGA expression subtypes is not mainly due to differences between chromatin accessibility and gene expression. Potential alternative causes for this observed mismatch include the absence of a tumor microenvironment in the GSC populations or that given the TCGA subtypes were determined from bulk GBM, they may not fully capture the nature of rarer populations found within a tumor, such as the cancer stem cell populations.

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Gene set enrichment analysis with GSEA (Gene Set Enrichment Analysis) (Subramanian et al., 2005) and g:profiler (Reimand et al., 2016) using genes exclusively enriched for both expression and promoter chromatin accessibility in each subtype reported significantly enriched terms defining the largest GSC state as a Reactive state, with terms related to immune cells and response (Figure 3C, top panel). A second GSC state was enriched for Constructive gene sets involved in brain, neuron, and glial cell development (Figure 3C, middle panel). The third and smallest GSC state presented an Invasive state characterized by terms relating to the extracellular matrix and angiogenesis (Figure 3C, bottom panel). We next mapped copy number alterations (CNAs) across the 27 GSCs by applying the molecular neuropathology classifier tool (Capper et al., 2018) to DNA methylation data from the GSCs (Figure 3D). While common GBM CNAs, including EGFR gains and CDKN2A/B loss, were observed, the CNA-based classification of GSCs failed to match the three chromatin accessibility-derived states, suggesting the three GSC states are not defined by somatic copy number events (Figure 3D). Further comparison of the accessible chromatin in each GSC state reveals that only a small subset of accessible chromatin regions drives the three GSC states (Figure 3E, Figure 3—figure supplement 1). Our ability to discriminate GSC state-specific regions of accessible chromatin is reflective of the comprehensiveness of our cohort to saturate the detection of accessible regions to 93%, 88%, and 71% across the Reactive (n = 13), Constructive (n = 9), and Invasive (n = 5) state GSCs, respectively (Figure 3F).

Considering that regions of accessible chromatin serve as binding sites for transcription factors engaging in gene expression regulation, we next tested for DNA recognition motif family enrichment across regions exclusively accessible in Reactive, Constructive, or Invasive GSC states (Figure 4A, Figure 4—figure supplement 1A). The most enriched DNA recognition motif families in each state were either depleted or showed low-level enrichment in the other states. Specifically, the DNA recognition motifs for the interferon-regulatory factor (IRF) and Cys2-His2 zinc finger (C2H2 ZF) transcription factor families were enriched in the Reactive state (Figure 4A, top panel). Regulatory factor X (RFX) and basic helix-loop-helix (bHLH) DNA recognition motifs were enriched in the Constructive state (Figure 4A, middle panel), while the Forkhead motif family was enriched in the Invasive state (Figure 4A, bottom panel). Genome-wide CRISPR/Cas9 essentiality screens (Figure 4—figure supplement 1B) in three Reactive, two Constructive and one Invasive GSC (MacLeod et al., 2019) revealed the preferential requirement for expressed transcription factors (Figure 4—figure supplement 1C–E) recognizing the enriched DNA recognition motif in a state-specific manner (Figure 4B). Specifically, the SP1 regulatory network is preferentially essential in the Reactive state GSCs (Figure 4B, top panel), ASCL1, OLIG2, AHR, and NPAS3 are uniquely essential in the Constructive state GSCs (Figure 4B, middle panel) and FOXD1 is essential only in the Invasive state GSC (Figure 4B, bottom panel). Notably, SP1 itself is exclusively essential in only one Reactive GSC (G564). However, of the 36 transcription factors from the Reactive-enriched families (IRF and C2H2 ZF) that were essential in at least one Reactive GSC and not in any of the Constructive or Invasive GSCs, 13 are directly regulated by SP1 (Figure 4—figure supplement 1C), thus suggesting that the SP1 regulatory network as a whole, rather than SP1 on its own, is key in the Reactive GSC state. Notably, all six transcription factors display significantly higher expression in GBM compared to normal brain (Tang et al., 2017; Figure 4—figure supplement 1F), further supporting their function as key regulators of tumor initiation and development. An additional gene set enrichment analysis combining genes exclusively essential to each state with the putative targets of the key transcription factors outlined above identifies additional enriched terms supporting the identities of the three GSC states as Reactive, Constructive, and Invasive (Figure 4—figure supplement 1G).

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Previous work suggests that GBM tumors harbor a heterogeneous population of GSCs (Lan et al., 2017; Meyer et al., 2015; Patel et al., 2014). We therefore quantified the presence of Reactive, Constructive, and Invasive cancer stem cells in our four primary GBM based on their scATAC-seq profiles. The Constructive state was dominant in every primary tumor, ranging from 9 to 21% of all cells captured by scATAC-seq (Figure 4C). The Reactive and Invasive states accounted for only 0–9% of all cells, in varying proportions from one tumor to another (Figure 4C). Collectively, our results further support the heterogeneous nature of cancer stem cells that populate primary tumors.

While various classifications of GBMs and/or their constitutive bulk and stem tumor cells have been reported, with some associating with patient survival (Neftel et al., 2019; Patel et al., 2014; Cancer Genome Atlas Research Network et al., 2010; Wang et al., 2017; Yin et al., 2019; Zuo et al., 2019), molecular signatures in adult GBM stratifying the poorer prognosis IDH wild-type patients by survival are lacking. We performed intracranial xenografts of 37 IDH wild-type GSC populations and classified the transplanted cells by their GSC state to perform a differential survival analysis (Figure 5A). The overall survival times of the transplanted mice grouped by GSC state were significantly different (LogRank test p=0.041), with the Invasive state GSCs leading to the worst prognosis. Relative to the mice injected with Reactive state GSCs, mice with Constructive state GSCs and Invasive state GSCs had hazard ratios of 1.3 (95% confidence interval [CI]: 0.57–2.97) and 3.5 (95% CI: 1.2–10.49), respectively.

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In light of this finding, we sought to evaluate the invasive properties of the Invasive GSC state population. To that end, we conducted an invasion assay (Restall et al., 2018) wherein neurospheres from representative GSCs of each state (four Reactive, three Constructive, and three Invasive; three independent biological replicates for each GSC) are embedded in a collagen matrix and invasion is monitored in real time (Figure 5B,C, Figure 5—figure supplement 1A). GSCs of the Invasive state invaded strikingly faster and further into the collagen matrix and overall displayed a three times greater invasive ability than that of the Reactive and Constructive states. This invasiveness was further confirmed in vivo through human-specific staining of mouse brains injected with Invasive-state GSCs (Figure 5—figure supplement 1B–C).

Next, we investigated the prognostic value of the GSC states using the TCGA GBM cohort (IDH1 and IDH2 wild type, n = 144). When classified by dominant GSC state, TCGA tumors display the same trend as the xenografts with Invasive state-dominated tumors showing the lowest survival (Figure 5—figure supplement 2A). However, with only two tumors classified as Invasive-dominant, the difference in survival between the three patient groups was not statistically significant (p=0.3) (Figure 5—figure supplement 2A). We proceeded to rank the TCGA tumors solely by their concordance to Invasive GSCs and classified the patient tumors into Invasive-low (z-score < 1), Invasive-mid (z-score = 1–1.65), and Invasive-high (z-score ≥ 1.65) groups (Figure 5D). With this stratification method, median patient survival per group not only decreased with increasing Invasive GSC score, but we also identified an Invasive-high subset of tumors with significantly lower survival (p=0.019, HR (Hazard Ratio) = 2.8, 95% CI: [1.3–5.81]) (Figure 5E). This was further validated using an additional cohort of TCGA samples with microarray gene expression (Figure 5—figure supplement 2D–E). These results show that cancer stem cell states defined based on the chromatin accessibility in GSCs can identify transcriptional programs associated with poor prognosis and can serve as a signature to identify high-risk patients in IDH wild-type GBM.

Defining the nature of self-renewing tumor-initiating cells in primary GBM is required to identify vulnerabilities for therapeutic intervention. Quantifying their heterogeneity within tumors can guide treatment strategies and assist in predicting the course of disease progression. Here we show that chromatin accessibility assays capture a heterogeneity across self-renewing tumor-initiating cells in primary GBM that extends beyond their genetic diversity and underlies the heterogeneity in bulk progeny (Meyer et al., 2015). This heterogeneity aligns with diversity in the three-dimensional genome organization of GSCs (Johnston et al., 2019) and agrees with how the three-dimensional genome organization instructs cis-regulatory plexuses underlying gene regulation (Bailey et al., 2016; Kim et al., 2016; Sallari et al., 2017; Schmitt et al., 2016; Zheng and Xie, 2019). While the chromatin accessibility signature derived from GSCs robustly identified cancer stem cells across the four primary GBM tumors used in this study, a larger cohort will be required to establish whether this signature is always sufficient to capture all cancer stem cells in primary GBMs.

We further reveal a specific cancer stem state that is significantly predictive of patient survival and can be used as a signature to identify high-risk patients. Specifically, the Invasive GSC signature draws from a population with enhanced ability to invade and spread compared to other GSC states. Collectively, our results argue for distinct GSC populations whose composition in tumors impacts survival.

Our results also highlight dependencies unique to each cancer stem state. Specifically, the Reactive GSC state-associated transcription factor SP1 and its regulatory partners are involved in cellular differentiation and growth, apoptosis, response to DNA damage, chromatin remodeling (O'Connor et al., 2016), stimulation of TERT expression in cancer stem cells (Liu et al., 2016), and increased stemness and invasion in GBM (Lee et al., 2014). In contrast, the Constructive GSC state relies on transcription factors including OLIG2, a known GSC marker (Trépant et al., 2015); AHR involved in tumor microenvironment responses and metabolic adaptation (Gabriely et al., 2017); NPAS3, a regulator of Notch signaling and neurogenesis (Michaelson et al., 2017); and ASCL1, a critical regulator of GSC differentiation and marker of sensitivity to Notch inhibition in GSCs (Park et al., 2017; Rajakulendran et al., 2019). Finally, the Invasive GSC state relies on FOXD1, a pluripotency regulator and determinant of tumorigenicity in GSCs regulating expression of the aldehyde dehydrogenase ALDH1A3, a functional marker for invasive GSCs (Cheng et al., 2016; Koga et al., 2014). These results support developing combination therapy using targeting agents against each GSC state, such as Notch inhibitors (Park et al., 2017) and small molecule inhibitors of ALDH (Cheng et al., 2016), to eradicate self-renewing tumor-initiating cells with the hope to cure GBM patients.

The GSCs are available upon reasonable request from PBD and SW. The GSC ATAC-seq and DNA methylation data have been deposited at GEO (GSE109399). The scATAC-seq data has been deposited at GEO (GSE139136). RNA-seq data are available at EGA (EGAS00001003070).

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Author details

1. Paul Guilhamon

   1. Princess Margaret Cancer Centre, University Health Network, Toronto, Canada
   2. Developmental and Stem Cell Biology Program and Arthur and Sonia Labatt Brain tumor Research Centre, The Hospital for Sick Children, Toronto, Canada

   Contribution

   Conceptualization, Formal analysis, Funding acquisition, Validation, Investigation, Visualization, Writing - original draft, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-8276-5987

2. Charles Chesnelong

   Developmental and Stem Cell Biology Program and Arthur and Sonia Labatt Brain tumor Research Centre, The Hospital for Sick Children, Toronto, Canada

   Contribution

   Investigation

   Competing interests

   No competing interests declared

3. Michelle M Kushida

   Developmental and Stem Cell Biology Program and Arthur and Sonia Labatt Brain tumor Research Centre, The Hospital for Sick Children, Toronto, Canada

   Contribution

   Investigation

   Competing interests

   No competing interests declared

4. Ana Nikolic

   1. Clark Smith Brain Tumour Centre, Arnie Charbonneau Cancer Institute, University of Calgary, Calgary, Canada
   2. Alberta Children’s Hospital Research Institute, University of Calgary, Calgary, Canada
   3. Department of Biochemistry and Molecular Biology, Cumming School of Medicine, University of Calgary, Calgary, Canada

   Contribution

   Investigation

   Competing interests

   No competing interests declared

5. Divya Singhal

   1. Clark Smith Brain Tumour Centre, Arnie Charbonneau Cancer Institute, University of Calgary, Calgary, Canada
   2. Alberta Children’s Hospital Research Institute, University of Calgary, Calgary, Canada
   3. Department of Biochemistry and Molecular Biology, Cumming School of Medicine, University of Calgary, Calgary, Canada

   Contribution

   Investigation

   Competing interests

   No competing interests declared

6. Graham MacLeod

   Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, Canada

   Contribution

   Resources

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-6401-9307

7. Seyed Ali Madani Tonekaboni

   1. Princess Margaret Cancer Centre, University Health Network, Toronto, Canada
   2. Department of Medical Biophysics, University of Toronto, Toronto, Canada

   Contribution

   Formal analysis

   Competing interests

   No competing interests declared

8. Florence MG Cavalli

   Developmental and Stem Cell Biology Program and Arthur and Sonia Labatt Brain tumor Research Centre, The Hospital for Sick Children, Toronto, Canada

   Contribution

   Data curation

   Competing interests

   No competing interests declared

9. Christopher Arlidge

   Princess Margaret Cancer Centre, University Health Network, Toronto, Canada

   Contribution

   Investigation

   Competing interests

   No competing interests declared

10. Nishani Rajakulendran

    Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, Canada

    Contribution

    Resources

    Competing interests

    No competing interests declared

11. Naghmeh Rastegar

    Developmental and Stem Cell Biology Program and Arthur and Sonia Labatt Brain tumor Research Centre, The Hospital for Sick Children, Toronto, Canada

    Contribution

    Investigation

    Competing interests

    No competing interests declared

12. Xiaoguang Hao

    1. Clark Smith Brain Tumour Centre, Arnie Charbonneau Cancer Institute, University of Calgary, Calgary, Canada
    2. Hotchkiss Brain Institute, University of Calgary, Calgary, Canada
    3. Department of Cell Biology & Anatomy, University of Calgary, Calgary, Canada

    Contribution

    Investigation

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-2695-0111

13. Rozina Hassam

    1. Clark Smith Brain Tumour Centre, Arnie Charbonneau Cancer Institute, University of Calgary, Calgary, Canada
    2. Hotchkiss Brain Institute, University of Calgary, Calgary, Canada
    3. Department of Cell Biology & Anatomy, University of Calgary, Calgary, Canada

    Contribution

    Investigation

    Competing interests

    No competing interests declared

14. Laura J Smith

    Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Canada

    Contribution

    Investigation, Visualization

    Competing interests

    No competing interests declared

15. Heather Whetstone

    Developmental and Stem Cell Biology Program and Arthur and Sonia Labatt Brain tumor Research Centre, The Hospital for Sick Children, Toronto, Canada

    Contribution

    Investigation

    Competing interests

    No competing interests declared

16. Fiona J Coutinho

    Developmental and Stem Cell Biology Program and Arthur and Sonia Labatt Brain tumor Research Centre, The Hospital for Sick Children, Toronto, Canada

    Contribution

    Project administration

    Competing interests

    No competing interests declared

17. Bettina Nadorp

    Princess Margaret Cancer Centre, University Health Network, Toronto, Canada

    Contribution

    Formal analysis

    Competing interests

    No competing interests declared

18. Katrina I Ellestad

    1. Clark Smith Brain Tumour Centre, Arnie Charbonneau Cancer Institute, University of Calgary, Calgary, Canada
    2. Alberta Children’s Hospital Research Institute, University of Calgary, Calgary, Canada

    Contribution

    Investigation

    Competing interests

    No competing interests declared

19. H Artee Luchman

    1. Clark Smith Brain Tumour Centre, Arnie Charbonneau Cancer Institute, University of Calgary, Calgary, Canada
    2. Hotchkiss Brain Institute, University of Calgary, Calgary, Canada
    3. Department of Cell Biology & Anatomy, University of Calgary, Calgary, Canada

    Contribution

    Supervision

    Competing interests

    No competing interests declared

20. Jennifer Ai-wen Chan

    1. Clark Smith Brain Tumour Centre, Arnie Charbonneau Cancer Institute, University of Calgary, Calgary, Canada
    2. Alberta Children’s Hospital Research Institute, University of Calgary, Calgary, Canada
    3. Department of Pathology and Laboratory Medicine, University of Calgary, Calgary, Canada

    Contribution

    Resources

    Competing interests

    No competing interests declared

21. Molly S Shoichet

    Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Canada

    Contribution

    Supervision

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-1830-3475

22. Michael D Taylor

    1. Developmental and Stem Cell Biology Program and Arthur and Sonia Labatt Brain tumor Research Centre, The Hospital for Sick Children, Toronto, Canada
    2. Division of Neurosurgery, University of Toronto, Toronto, Canada
    3. Departments of Molecular Genetics and Surgery, University of Toronto, Toronto, Canada

    Contribution

    Supervision

    Competing interests

    No competing interests declared

23. Benjamin Haibe-Kains

    1. Princess Margaret Cancer Centre, University Health Network, Toronto, Canada
    2. Department of Medical Biophysics, University of Toronto, Toronto, Canada
    3. Department of Computer Science, University of Toronto, Toronto, Canada
    4. Ontario Institute for Cancer Research, Toronto, Canada
    5. Vector Institute, Toronto, Canada

    Contribution

    Supervision

    Competing interests

    No competing interests declared

24. Samuel Weiss

    1. Clark Smith Brain Tumour Centre, Arnie Charbonneau Cancer Institute, University of Calgary, Calgary, Canada
    2. Hotchkiss Brain Institute, University of Calgary, Calgary, Canada
    3. Department of Cell Biology & Anatomy, University of Calgary, Calgary, Canada
    4. Department of Physiology & Pharmacology, University of Calgary, Calgary, Canada

    Contribution

    Supervision

    Competing interests

    No competing interests declared

25. Stephane Angers

    1. Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, Canada
    2. Department of Biochemistry, Faculty of Medicine, University of Toronto, Toronto, Canada

    Contribution

    Supervision

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-7241-9044

26. Marco Gallo

    1. Clark Smith Brain Tumour Centre, Arnie Charbonneau Cancer Institute, University of Calgary, Calgary, Canada
    2. Alberta Children’s Hospital Research Institute, University of Calgary, Calgary, Canada
    3. Department of Biochemistry and Molecular Biology, Cumming School of Medicine, University of Calgary, Calgary, Canada
    4. Department of Physiology & Pharmacology, University of Calgary, Calgary, Canada

    Contribution

    Supervision, Writing - review and editing

    Competing interests

    No competing interests declared

27. Peter B Dirks

    1. Developmental and Stem Cell Biology Program and Arthur and Sonia Labatt Brain tumor Research Centre, The Hospital for Sick Children, Toronto, Canada
    2. Division of Neurosurgery, University of Toronto, Toronto, Canada
    3. Ontario Institute for Cancer Research, Toronto, Canada

    Contribution

    Conceptualization, Supervision, Writing - original draft, Writing - review and editing

    For correspondence

    peter.dirks@sickkids.ca

    Competing interests

    No competing interests declared

28. Mathieu Lupien

    1. Princess Margaret Cancer Centre, University Health Network, Toronto, Canada
    2. Department of Medical Biophysics, University of Toronto, Toronto, Canada

    Contribution

    Conceptualization, Supervision, Funding acquisition, Writing - original draft, Project administration, Writing - review and editing

    For correspondence

    mlupien@uhnres.utoronto.ca

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-0929-9478

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