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Fate mapping of human glioblastoma reveals an invariant stem cell hierarchy


Human glioblastomas harbour a subpopulation of glioblastoma stem cells that drive tumorigenesis. However, the origin of intratumoural functional heterogeneity between glioblastoma cells remains poorly understood. Here we study the clonal evolution of barcoded glioblastoma cells in an unbiased way following serial xenotransplantation to define their individual fate behaviours. Independent of an evolving mutational signature, we show that the growth of glioblastoma clones in vivo is consistent with a remarkably neutral process involving a conserved proliferative hierarchy rooted in glioblastoma stem cells. In this model, slow-cycling stem-like cells give rise to a more rapidly cycling progenitor population with extensive self-maintenance capacity, which in turn generates non-proliferative cells. We also identify rare ‘outlier’ clones that deviate from these dynamics, and further show that chemotherapy facilitates the expansion of pre-existing drug-resistant glioblastoma stem cells. Finally, we show that functionally distinct glioblastoma stem cells can be separately targeted using epigenetic compounds, suggesting new avenues for glioblastoma-targeted therapy.

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Figure 1: Serial transplantation scheme and characterization of barcoded glioblastoma xenografts.
Figure 2: Clonal dynamics of GBM is consistent with a conserved proliferative hierarchy.
Figure 3: Chemotherapy reveals clonal transformations in GBM.

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  1. Stupp, R. et al. Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N. Engl. J. Med. 352, 987–996 (2005)

    CAS  PubMed  Google Scholar 

  2. Singh, S. K. et al. Identification of human brain tumour initiating cells. Nature 432, 396–401 (2004)

    ADS  CAS  PubMed  Google Scholar 

  3. Chen, J. et al. A restricted cell population propagates glioblastoma growth after chemotherapy. Nature 488, 522–526 (2012)

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  4. Patel, A. P. et al. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science 344, 1396–1401 (2014)

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  5. Tirosh, I. et al. Single-cell RNA-seq supports a developmental hierarchy in human oligodendroglioma. Nature 539, 309–313 (2016)

    ADS  PubMed  PubMed Central  Google Scholar 

  6. Sottoriva, A. et al. Intratumor heterogeneity in human glioblastoma reflects cancer evolutionary dynamics. Proc. Natl Acad. Sci. USA 110, 4009–4014 (2013)

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  7. Johnson, B. E. et al. Mutational analysis reveals the origin and therapy-driven evolution of recurrent glioma. Science 343, 189–193 (2014)

    ADS  CAS  PubMed  Google Scholar 

  8. Greaves, M. Cancer stem cells: back to Darwin? Semin. Cancer Biol. 20, 65–70 (2010)

    PubMed  Google Scholar 

  9. Piccirillo, S. G. et al. Genetic and functional diversity of propagating cells in glioblastoma. Stem Cell Reports 4, 7–15 (2015)

    CAS  PubMed  Google Scholar 

  10. Snuderl, M. et al. Mosaic amplification of multiple receptor tyrosine kinase genes in glioblastoma. Cancer Cell 20, 810–817 (2011)

    CAS  PubMed  Google Scholar 

  11. Driessens, G., Beck, B., Caauwe, A., Simons, B. D. & Blanpain, C. Defining the mode of tumour growth by clonal analysis. Nature 488, 527–530 (2012)

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  12. Sánchez-Danés, A. et al. Defining the clonal dynamics leading to mouse skin tumour initiation. Nature 536, 298–303 (2016)

    ADS  PubMed  PubMed Central  Google Scholar 

  13. Rulands, S. & Simons, B. D. Tracing cellular dynamics in tissue development, maintenance and disease. Curr. Opin. Cell Biol. 43, 38–45 (2016)

    CAS  PubMed  Google Scholar 

  14. Simons, B. D. Deep sequencing as a probe of normal stem cell fate and preneoplasia in human epidermis. Proc. Natl Acad. Sci. USA 113, 128–133 (2016)

    ADS  CAS  PubMed  Google Scholar 

  15. Nguyen, L. V. et al. Clonal analysis via barcoding reveals diverse growth and differentiation of transplanted mouse and human mammary stem cells. Cell Stem Cell 14, 253–263 (2014)

    CAS  PubMed  Google Scholar 

  16. Nguyen, L. V. et al. Barcoding reveals complex clonal dynamics of de novo transformed human mammary cells. Nature 528, 267–271 (2015)

    ADS  CAS  PubMed  Google Scholar 

  17. Bhang, H. E. et al. Studying clonal dynamics in response to cancer therapy using high-complexity barcoding. Nat. Med. 21, 440–448 (2015)

    CAS  PubMed  Google Scholar 

  18. Wagenblast, E. et al. A model of breast cancer heterogeneity reveals vascular mimicry as a driver of metastasis. Nature 520, 358–362 (2015)

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  19. Vanner, R. J. et al. Quiescent Sox2+ cells drive hierarchical growth and relapse in sonic hedgehog subgroup medulloblastoma. Cancer Cell 26, 33–47 (2014)

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Verhaak, R. G. et al. Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR, and NF1. Cancer Cell 17, 98–110 (2010)

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Brennan, C. W. et al. The somatic genomic landscape of glioblastoma. Cell 155, 462–477 (2013)

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Nguyen, L. V. et al. DNA barcoding reveals diverse growth kinetics of human breast tumour subclones in serially passaged xenografts. Nat. Commun. 5, 5871 (2014)

    ADS  CAS  PubMed  Google Scholar 

  23. Sottoriva, A. et al. A Big Bang model of human colorectal tumor growth. Nat. Genet. 47, 209–216 (2015)

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Waclaw, B. et al. A spatial model predicts that dispersal and cell turnover limit intratumour heterogeneity. Nature 525, 261–264 (2015)

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  25. Bailey, N. T. J. The Elements of Stochastic Processes with Applications to the Natural Sciences (John Wiley & Sons, 1990)

  26. Pollard, S. M. et al. Glioma stem cell lines expanded in adherent culture have tumor-specific phenotypes and are suitable for chemical and genetic screens. Cell Stem Cell 4, 568–580 (2009)

    CAS  PubMed  Google Scholar 

  27. Funato, K., Major, T., Lewis, P. W., Allis, C. D. & Tabar, V. Use of human embryonic stem cells to model pediatric gliomas with H3.3K27M histone mutation. Science 346, 1529–1533 (2014)

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  28. Borkin, D. et al. Pharmacologic inhibition of the Menin-MLL interaction blocks progression of MLL leukemia in vivo. Cancer Cell 27, 589–602 (2015)

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Gallo, M. et al. MLL5 orchestrates a cancer self-renewal state by repressing the histone variant H3.3 and globally reorganizing chromatin. Cancer Cell 28, 715–729 (2015)

    CAS  PubMed  Google Scholar 

  30. Suvà, M. L. et al. EZH2 is essential for glioblastoma cancer stem cell maintenance. Cancer Res. 69, 9211–9218 (2009)

    PubMed  Google Scholar 

  31. Meyer, M. et al. Single cell-derived clonal analysis of human glioblastoma links functional and genomic heterogeneity. Proc. Natl Acad. Sci. USA 112, 851–856 (2015)

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  32. Williams, M. J., Werner, B., Barnes, C. P., Graham, T. A. & Sottoriva, A. Identification of neutral tumor evolution across cancer types. Nat. Genet. 48, 238–244 (2016)

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Pollen, A. A. et al. Molecular identity of human outer radial glia during cortical development. Cell 163, 55–67 (2015)

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Hu, Y & Smyth, G. K. ELDA: extreme limiting dilution analysis for comparing depleted and enriched populations in stem cell and other assays. J. Immunol. Methods 347, 70–78 (2009)

    CAS  PubMed  Google Scholar 

  35. Li, H. & Durbin, R. Fast and accurate long-read alignment with Burrows–Wheeler transform. Bioinformatics 26, 589–595 (2010)

    PubMed  PubMed Central  Google Scholar 

  36. McKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010)

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  37. Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009)

    Article  PubMed  PubMed Central  Google Scholar 

  38. Koboldt, D. C. et al. VarScan 2: somatic mutation and copy number alteration discovery in cancer by exome sequencing. Genome Res. 22, 568–576 (2012)

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Wang, K., Li, M. & Hakonarson, H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 38, e164 (2010)

    PubMed  PubMed Central  Google Scholar 

  40. Sherry, S. T. et al. dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 29, 308–311 (2001)

    CAS  PubMed  PubMed Central  Google Scholar 

  41. Lek, M. et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature 536, 285–291 (2016)

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Auton, A. et al. A global reference for human genetic variation. Nature 526, 68–74 (2015)

    ADS  PubMed  Google Scholar 

  43. García-Alcalde, F. et al. Qualimap: evaluating next-generation sequencing alignment data. Bioinformatics 28, 2678–2679 (2012)

    PubMed  Google Scholar 

  44. Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010)

    CAS  PubMed  PubMed Central  Google Scholar 

  45. Cibulskis, K. et al. Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nat. Biotechnol. 31, 213–219 (2013)

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Ramos, A. H. et al. Oncotator: cancer variant annotation tool. Hum. Mutat. 36, E2423–E2429 (2015)

    PubMed  PubMed Central  Google Scholar 

  47. Landrum, M. J. et al. ClinVar: public archive of relationships among sequence variation and human phenotype. Nucleic Acids Res. 42, D980–D985 (2014)

    CAS  PubMed  Google Scholar 

  48. Abecasis, G. R. et al. An integrated map of genetic variation from 1,092 human genomes. Nature 491, 56–65 (2012)

    ADS  PubMed  Google Scholar 

  49. Forbes, S. A. et al. COSMIC: mining complete cancer genomes in the Catalogue of Somatic Mutations in Cancer. Nucleic Acids Res. 39, D945–D950 (2011)

    CAS  PubMed  Google Scholar 

  50. Favero, F. et al. Sequenza: allele-specific copy number and mutation profiles from tumor sequencing data. Ann. Oncol. 26, 64–70 (2015)

    CAS  PubMed  Google Scholar 

  51. Dobin, A . et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013)

    CAS  PubMed  Google Scholar 

  52. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014)

    PubMed  PubMed Central  Google Scholar 

  53. Crisman, T. J. et al. Identification of an efficient gene expression panel for glioblastoma classification. PLoS ONE 11, e0164649 (2016)

    PubMed  PubMed Central  Google Scholar 

  54. Buenrostro, J. D., Giresi, P. G., Zaba, L. C., Chang, H. Y. & Greenleaf, W. J. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat. Methods 10, 1213–1218 (2013)

    CAS  PubMed  PubMed Central  Google Scholar 

  55. Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012)

    CAS  PubMed  PubMed Central  Google Scholar 

  56. Zhang, Y. et al. Model-based analysis of ChIP-seq (MACS). Genome Biol. 9, R137 (2008)

    PubMed  PubMed Central  Google Scholar 

  57. Rendeiro, A. F. et al. Chromatin accessibility maps of chronic lymphocytic leukaemia identify subtype-specific epigenome signatures and transcription regulatory networks. Nat. Commun. 7, 11938 (2016)

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  58. Palmisano, W. A. et al. Predicting lung cancer by detecting aberrant promoter methylation in sputum. Cancer Res. 60, 5954–5958 (2000)

    CAS  PubMed  Google Scholar 

  59. Uren, A. G. et al. A high-throughput splinkerette-PCR method for the isolation and sequencing of retroviral insertion sites. Nat. Protocols 4, 789–798 (2009)

    CAS  PubMed  Google Scholar 

  60. Schüffler, P. J. et al. TMARKER: A free software toolkit for histopathological cell counting and staining estimation. J. Pathol. Inform. 4 (Suppl), S2 (2013)

    PubMed  PubMed Central  Google Scholar 

  61. Thielecke, L. et al. Limitations and challenges of genetic barcode quantification. Sci. Rep. 7, 43249 (2017)

    ADS  PubMed  PubMed Central  Google Scholar 

  62. Micallef, L. & Rodgers, P. eulerAPE: drawing area-proportional 3-Venn diagrams using ellipses. PLoS ONE 9, e101717 (2014)

    ADS  PubMed  PubMed Central  Google Scholar 

  63. Gillespie, D. T. Exact stochastic simulation of coupled chemical reactions. J. Phys. Chem. 81, 2340–2361 (1977)

    CAS  Google Scholar 

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We thank R. D. Corbett, P. Plettner, N. Khuu and G. Edin for technical advice, the SickKids-UHN Flow Cytometry Facility for assistance with fluorescence-activated cell sorting, SickKids Laboratory Animal Services for animal housing and veterinary support, and The Centre for Applied Genomics, Princess Margaret Genomics Centre, and Canada’s Michael Smith Genome Sciences Centre for sequencing and bioinformatics support. This study was supported by the Canadian Institutes of Health Research (funding reference number 142434), the Ontario Institute for Cancer Research through funding provided by the Government of Ontario, and Stand Up To Cancer (SU2C) Canada. P.B.D. is also supported by the Terry Fox Research Institute, the Canadian Cancer Society, the Hospital for Sick Children Foundation, Jessica’s Footprint Foundation, the Hopeful Minds Foundation, the Bresler family, and B.R.A.I.N. Child. P.B.D. holds a Garron Family Chair in Childhood Cancer Research at The Hospital for Sick Children. B.D.S. acknowledges the support of the Wellcome Trust (grant number 098357/Z/12/Z). C.J.E. acknowledges grant support from the Canadian Cancer Society and the Terry Fox Run. Research was supported by SU2C Canada Cancer Stem Cell Dream Team Research Funding (SU2C-AACR-DT-19-15) provided by the Government of Canada through Genome Canada and the Canadian Institutes of Health Research, with supplementary support from the Ontario Institute for Cancer Research through funding provided by the Government of Ontario. Stand Up To Cancer Canada is a program of the Entertainment Industry Foundation Canada. Research funding is administered by the American Association for Cancer Research International – Canada, the scientific partner of SU2C Canada. The Structural Genomics Consortium is funded by AbbVie, Bayer, Boehringer Ingelheim, GSK, Genome Canada, Ontario Genomics Institute, Janssen, Lilly, Merck, Novartis, the government of Ontario, Pfizer, Takeda, and the Wellcome Trust.

Author information

Authors and Affiliations



X.L. and P.B.D. conceptualized the study and were assisted by L.V.N., R.J.V., N.I.P., F.J.C., H.J.S., M.G. and C.J.E. in experimental design. P.B.D. and B.D.S. supervised the study. X.L. performed in vivo and in vitro barcoding experiments and drug validation studies. D.J.J., B.D.S., X.L., D.P., A.C. and P.B.D. analysed and interpreted barcoding results. D.J.J. and B.D.S. developed the theoretical model of tumour growth, performed simulations and wrote the supplemental theory section. F.M.G.C., L.M.R., M.D.T. and T.J.P. analysed whole-exome sequencing and RNA sequencing results. P.G. and M.L. performed ATAC–seq and analysed results. R.J.V., L.L., M.M.K., N.I.P., F.J.C., H.W., C.C., B.L., N.R., R.H. and S. Do assisted in performing the experiments. M.M., A.J.M., R.A.M., Y.M. and M.H. oversaw the generation of sequencing data. L.V.N. and C.J.E. designed, generated, and validated the barcode library. P.P. and C.H.A. assisted with in vitro drug assays. M.D.C., S. Da and M.B. contributed all GBM tumour samples used in the study. X.L., D.J.J., C.J.E., B.D.S. and P.B.D. wrote the manuscript, and all authors contributed to data interpretation and approved the manuscript.

Corresponding authors

Correspondence to Benjamin D. Simons or Peter B. Dirks.

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Competing interests

The authors declare no competing financial interests.

Additional information

Reviewer Information Nature thanks T. Graham, J. Seoane, M. Suva and the other anonymous reviewer(s) for their contribution to the peer review of this work.

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Extended data figures and tables

Extended Data Figure 1 Barcode data processing.

a, Summary of GBM models used for barcoding experiments indicating TCGA subgroups as determined by RNA–seq20, self-renewing frequency as assessed by primary LDA, the number of primary xenografts successfully established and the cell dose used for primary xenografts (n.d.: not determined, n.s.: no spheres). b, Proliferation kinetics of GSC cultures in vitro. Data are shown as mean ± s.d. of 3 technical replicates. c, Cell doubling times of GSCs grown in culture calculated using the data in b. Data are shown as mean ± s.d. of 3 technical replicates, horizontal line marks 24 h. df, Relationship between fractional read value (FRV) and input cell numbers in spiked-in controls for the three sequencing runs. The highly influential data points (Cook’s distance >4/n) are greyed out and not used for regression analysis to estimate relative clone sizes. The black line is the line of best fit, and the grey box indicates sequencing noise threshold. g, Analysis of barcode sequence saturation across six in vivo experiments. h, Position weight matrices depicting the representation of variable nucleotides in the barcode library, the (1)719 ipsilateral sample, as well as the largest and smallest 100 clones in that sample. The height of nucleotides at each position represents their relative frequency, with the most frequently occurring nucleotide shown in the top position. i, Summary of unique barcode integration sites identified by splinkerette PCR.

Source data

Extended Data Figure 2 Molecular characterization of GBMs and GBM xenografts.

a, Oncoprint plot of mutations identified in primary GBM tissue samples that are of the top 200 recurrently mutated genes in the provisional TCGA dataset21. b, Multidimensional scaling plot for the 32-gene simple GBM classification method using RNA-seq53. Shown are the TCGA core samples and five patient samples used in the current study. TCGA samples are labelled and coloured according to their original subgroup as determined from microarray expression analysis20. c, Methylation-specific PCR assay for the MGMT promoter in six primary GBMs. L, ladder; -ve, water only control; M, methylated PCR product; U, unmethylated PCR product. Specific ladder marker sizes are shown in base pairs. d, Pairwise correlation of ATAC–seq peak intensities across GSC culture models and compared with a chronic lymphocytic leukaemia (CLL) control57. Black outline highlights correlations for GSC cultures derived from the GBMs used for the in vivo barcoding study (G719, G729, G754). e, Summary of somatic mutations identified using exome sequencing from representative GBM-719 barcoded xenografts, grouped according to type. p2 Veh: passage 2, treated with vehicle; p2 TMZ: passage 2, treated with TMZ; p3 Veh Veh: passage 3, treated with vehicle at passages 2 and 3; p3 TMZ TMZ: passage 3, treated with TMZ at passages 2 and 3 and briefly expanded in vitro before sequencing. f, Heat map representing relative copy number profiles from whole exome sequencing of GBM-719 xenograft samples. Segments of gains (red) or deletions (blue) are colour-coded on the basis of log2 copy number ratios. Frequent loss of chromosome 10 is a common observation in GBM. g, Summary of patient characteristics for all tumour samples used throughout the study, and the experiment(s) that each sample is used for.

Extended Data Figure 3 Functional characterization of GBMs and GBM xenografts.

a, Haematoxylin and eosin (H&E) and human-specific nestin staining in primary glioblastoma specimens. Scale bar, 100 μm. b, H&E and human-specific nestin staining for representative GBM xenografts. Scale bar, 100 μm. c, Survival analysis of xenografts derived from the indicated GBM model and treatment conditions. All survival analyses were performed using a log-rank test (n = 4 mice per group with the exception of the GBM-754 experiment; vehicle, vehicle group which contains 3 mice). d, Quantification of percentage proliferative activity in serial xenografts by Ki-67 staining and percentage apoptosis by cleaved caspase-3 staining, mean ± s.d. of 6 representative sections from the same xenograft sample.

Source data

Extended Data Figure 4 GSCs are able to invade contralaterally and have heterogeneous clonal outputs.

a, Human-specific nestin staining in representative xenografts between ipsilateral and contralateral hemispheres (scale bar, 1 mm; contra, contralateral hemisphere; ipsi, ipsilateral hemisphere). b, Comparison of cell numbers recovered from xenografts between the ipsilateral and contralateral fractions, two-sided paired t-tests. Single data points are overlaid over the box plot, the horizontal line represents the median, and the lower and upper hinges represent the 25th and 75th quartiles, respectively. The lower and upper whiskers extend from the hinge to the lowest and highest values within 1.5 times the inter-quartile range (IQR). c, Plot of Pearson correlation coefficients comparing relative clone sizes between two hemispheres, for the indicated sample groups. The box-plots are displayed as for panel b. d, Clonal composition of tumours generated serially from contralateral fractions, grouped according to the geographical distribution of each detected clone in the previous (primary) passage. e, Clone size distributions for representative xenograft samples. All data shown are from ipsilateral hemispheres, not treated with TMZ, and generated from ipsilateral-derived cells from the previous passage (in the case of secondary and tertiary xenografts). Fits to a negative binomial distribution (curve) are included for patients with rich datasets (GBM-719, GBM-742, and GBM-754), used for quantitative analyses. Plot titles identify the respective sequence of serial passages by the nomenclature introduced in the Supplementary Theory. f, Representative correlation of clone size between successive serial passages of GBM-719 untreated xenografts with Pearson’s r indicated. P1, primary passage; P2, secondary passage; P3, tertiary passage. g, Representative correlations of clone size between different secondary passage replicate experiments derived from the same primary xenograft as panel f, with Pearson’s r indicated. The red arrowhead shows deviations from a linear correlation due to large outliers. R1, replicate 1; R2, replicate 2; R3, replicate 3.

Source data

Extended Data Figure 5 First incomplete moment of clone size distributions for GBM-719, -729, and -735 xenografts.

ac, First incomplete moments of the clone size distributions for all xenograft samples derived from patient tumours GBM-719 (a), GBM-729 (b), and GBM-735 (c). Samples are named according to the sequence of samples injected: C, generated from the contralateral fraction of the previous passage; T, TMZ treated; V, vehicle treated. For illustrative purposes, GBM-719 xenografts (a) that are TMZ-treated are marked with a red arrowhead where the distribution appears to deviate from the negative binomial. The indicated fit parameter n0 describes a characteristic clone size of the population (Supplementary Theory 2, 3). Where group B clones (large outliers) were removed to generate a more accurate fit, the number of clones removed is indicated and the re-calculated first incomplete moment distributions with outliers removed are plotted in grey. d, Schematic description of how a sequence of treatments resulting in a particular xenograft sample is incorporated into the sample nomenclatures.

Extended Data Figure 6 First incomplete moment of clone size distributions for GBM-742, -743, and -754 xenografts and variant allele frequencies for GBM-719 xenografts.

ac, First incomplete moments of the clone size distributions for all xenografts derived from the tumours GBM-742 (a), GBM-743 (b), and GBM-754 (c). Sample and plot annotations are as described for Extended Data Fig. 5. d, Distribution of variant allele frequencies (VAFs) across GBM-719 xenograft samples. Mutations with a VAF of 0.5 are likely to correspond to variants in the clonal population (found in all cells within the tumour), whereas less prevalent mutations correspond to subclonal populations defined by recent mutational events found only in a subset of cells. e, Comparison of VAF values for mutations in paired secondary and tertiary passages. f, First incomplete moments show a negative binomial distribution for VAF values below 0.5 across xenograft samples. The dashed line shows a fit to the exponential and the vertical line marks a VAF of 0.5. g, First incomplete moments for mutations that are newly detected in the tertiary vehicle- and TMZ-treated passage. h, Same as panel f after filtering out mutations that do not occur in diploid regions of the genome. i, Same as panel g after filtering out mutations that do not occur in diploid regions of the genome.

Extended Data Figure 7 Barcode analysis of xenograft-derived cultures.

a, Proportional Venn diagrams depicting the number of unique and shared barcoded clones as defined by the in vivo passages (primary, secondary, or tertiary), that are also detectable within the specified xenograft-derived cultures. b, Comparison of clone sizes between paired primary xenografts and primary xenograft-derived GSC cultures. c, Correlation of clone sizes between TMZ-treated GBM-719 xenografts, and cultures derived from these xenografts. A select cluster of clones that become outcompeted after secondary xenografts are outlined in blue, and Spearman’s rho coefficients are as indicated. d, First incomplete moments of the full clone size distributions for GBM-754 primary xenograft cultures at different times throughout culture expansion. e, First incomplete moments of the clone size distributions used in panel d, with the 14 largest outlier clones removed from each sample. f, Pairwise clone size comparisons between replicate cultures in d, with Spearman’s rho indicated.

Extended Data Figure 8 First incomplete moment of clone size distributions for remaining GBM xenograft derived cultures.

a, Plots of first incomplete moment for cultures derived from the indicated GBM xenografts. b, Same as a, with the indicated number of large outlier clones removed from the analysis.

Extended Data Figure 9 Epigenetic drug screening of GBM-754 primary xenograft culture.

a, Primary drug screen of GBM-754 primary xenograft-derived culture, with growth assessed as culture density relative to DMSO control. Compounds highlighted in blue were used in subsequent experiments. b, Strategy to identify clonal differences in drug response. Cells are treated in duplicate with each compound, and allowed to repopulate to the same density as DMSO controls before barcode sequencing. c, Summary of results from drug repopulation experiments. The top plot shows the ratio between sum relative clone sizes of group B and group A, technical replicates are denoted as 1, 2, or 3. The horizontal line marks the mean group B/group A ratio for DMSO treated cultures. The bottom plot shows the number of reads obtained from each sample after repopulation, relative to DMSO. The horizontal line marks the mean number of reads for DMSO samples. d, Additional technical replicate experiments related to Fig. 3g, demonstrating selectivity of UNC1999 and MI-2-2 on group A and B clones, respectively. e, Dose response assays for the indicated GSC culture models upon UNC1999 and MI-2-2 treatment, mean ± s.d. of six technical replicates. f, Two additional independent experiments related to Fig. 3h. P values for the left and right replicates, respectively, are 6.95 × 10−4, 0.148 for DMSO vs CI-994; 0.338, 0.55 for DMSO vs GSK591; 3.31 × 10−3, 0.0177 for DMSO vs UNC1999; 2.15 × 10−11, 1.59 × 10−7 for DMSO vs MI-2-2; 1.75 × 10−4, 4.02 × 10−3 for UNC1999 vs MI-2-2; 1.49 × 10−10, 3.7 × 10−12 for MI-nc vs M; 0.963, 0.408 for M vs M + C; 0.355, 0.408 for M vs M + G; 2.68 × 10−9, 6.06 × 10−8 for M vs M + U. g, Combined effect of GSK343 and MI-2-2 on self-renewal. P = 4.42 × 10−6 for DMSO vs GSK343; 2.96 × 10−12 for DMSO vs MI-2-2; 3.62 × 10−6 for GSK343 vs M + G; 0.0125 for MI-2-2 vs M + G. h, Combined effect of UNC1999 and MI-2-2 on self-renewal when used at 1 μM, representative of 3 independent experiments. P = 0.147 for DMSO vs UNC1999; 0.129 for DMSO vs MI-2-2; 9.84 × 10−4 for DMSO vs M + U. i, Two additional independent experiments related to Fig. 3i. P values for the left and right replicates, respectively, are 4.59 × 10−5, 4.81 × 10−15 for DMSO vs UNC1999; 3.28 × 10−25, 1.13 × 10−31 for DMSO vs MI-2-2; 1.86 × 10−11, 3.61 × 10−6 for UNC1999 vs MI-2-2. jm, Combined effect of UNC1999 and MI-2-2 on self-renewal in the indicated GSC culture models. P values for the G523, G549, G564, G566 experiments, respectively, are 1.9 × 10−5, 1, 0.758, 0.799 for DMSO vs UNC1999; 8.14 × 10−18, 2.14 × 10−4, 0.503, 6.12 × 10−4 for DMSO vs MI-2-2; 2.72 × 10−12, 3.28 × 10−30, 1.15 × 10−21, 2.54 × 10−8 for UNC1999 vs M + U; 7.69 × 10−3, 1.26 × 10−15, 2.61 × 10−18, 8.82 × 10−3 for MI-2-2 vs M + U. n, Combined effect of UNC1999 and MI-2-2 on self-renewal of uncultured GBM-851 cells. P = 3.01 × 10−3 for DMSO vs UNC1999; 1.36 × 10−4 for DMSO vs MI-2-2; 3.11 × 10−3 for UNC1999 vs M + U; 0.0276 for MI-2-2 vs M + U. Analysis of LDA results was performed using ELDA software34, error bars represent 95% confidence interval (NS, P > 0.05; *P ≤ 0.05; **P ≤ 0.01; ***P ≤ 0.001).

Source data

Extended Data Figure 10 First incomplete moment of the clone size distributions for drug-treated GBM-754 primary xenograft cultures.

a, First incomplete moments of the full clone size distributions of GBM-754 primary xenograft cultures treated with different drugs. b, First incomplete moments of the clone size distributions used in panel a, with five group B clones removed.

Supplementary information

Supplementary Theory

This file contains the Supplementary Theory section, which details the quantitative analysis of barcoding data that led to our minimal model of GBM growth, and the stochastic simulation strategy used to test the model predictions. It further describes how the clone survival and cross-correlation values obtained from simulation results are compared with experimental findings. Building upon the minimal model, a potential explanation for the existence of Group B clones (resistance to apoptosis) is also presented. Lastly, the use of exome sequencing analysis of xenografts as an additional consistency check for the model is described. (PDF 1136 kb)

Reporting Summary (PDF 69 kb)

Supplementary Table 1

This file contains the normalized clone size quantification of barcoded xenografts for the GBM-719, -729, -735, -742, -743, and -754 series. The data is related to Figure 2 and Extended Data Figures 4-6. (XLSX 523 kb)

Supplementary Table 2

This file contains clone growth trajectories derived from an instance of the stochastic simulation algorithm, as well as the change in mean clone size over time. The data is represented graphically in Figure 2d and Figure S1 in the Supplementary Theory. (XLSX 177 kb)

Supplementary Table 3

This file contains the experimental and simulated clone survival probability values for the GBM-719, -742, and -754 series. The data is related to Figure 2g and Figure S2 in the Supplementary Theory. (XLSX 77 kb)

Supplementary Table 4

This file contains the experimental and simulated clone size cross correlation values for the GBM-719, -742, and -754 series. The data is related to Figure 2f and Figure S3 in the Supplementary Theory. (XLSX 70 kb)

Supplementary Table 5

This file contains the first incomplete moment distribution obtained from the stochastic simulation algorithm with random induction times. The data is related to Figure S4 in the Supplementary Theory. (XLSX 147 kb)

Supplementary Table 6

This file contains the normalized clone size quantification for the proliferation assay from (1)754 culture. The data is related to Extended Data Figure 7d-f. (XLSX 109 kb)

Supplementary Table 7

This file contains the normalized clone size quantification for the additional xenograft-derived cultures. The data is related to Extended Data Figure 8. (XLSX 100 kb)

Supplementary Table 8

This file contains the normalized clone size quantification for the drug screen using (1)754 culture. The data is related to Figure 3g, Extended Data Figure 9c-d and Extended Data Figure 10. (XLSX 311 kb)

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Lan, X., Jörg, D., Cavalli, F. et al. Fate mapping of human glioblastoma reveals an invariant stem cell hierarchy. Nature 549, 227–232 (2017).

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