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A biobank of patient-derived pediatric brain tumor models

Nature Medicine volume 24, pages 1752–1761 (2018)Cite this article

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Abstract

Brain tumors are the leading cause of cancer-related death in children. Genomic studies have provided insights into molecular subgroups and oncogenic drivers of pediatric brain tumors that may lead to novel therapeutic strategies. To evaluate new treatments, better preclinical models adequately reflecting the biological heterogeneity are needed. Through the Children’s Oncology Group ACNS02B3 study, we have generated and comprehensively characterized 30 patient-derived orthotopic xenograft models and seven cell lines representing 14 molecular subgroups of pediatric brain tumors. Patient-derived orthotopic xenograft models were found to be representative of the human tumors they were derived from in terms of histology, immunohistochemistry, gene expression, DNA methylation, copy number, and mutational profiles. In vivo drug sensitivity of targeted therapeutics was associated with distinct molecular tumor subgroups and specific genetic alterations. These models and their molecular characterization provide an unprecedented resource for the cancer community to study key oncogenic drivers and to evaluate novel treatment strategies.

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Fig. 1: PDOX of pediatric brain tumors.
Fig. 2: Histology and immunohistochemistry are conserved in PDOX.
Fig. 3: Molecular subgrouping of PDOX models and cell lines.
Fig. 4: Molecular characterization of PDOX models and cell lines.
Fig. 5: Molecular fidelity of PDOX models.
Fig. 6: Preclinical evaluation of targeted therapeutics shows differential response to therapy based on molecular drivers.

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Data availability

PDOX lines described here, as well as additional lines being developed and characterized, are catalogued online and shared with other researchers at the Brain Tumor Resource Laboratory (http://www.btrl.org). In addition, an online summary of each model and interactive access to the molecular and histopathological data can be found in the R2 PDX Explorer (http://www.r2platform.com/pdxexplorer). Short-read sequencing data are available at the European Genome-phenome Archive (http://www.ebi.ac.uk/ega/), hosted by the European Bioinformatics Institute, under accession EGAS00001002536. Methylation and gene expression data have been deposited in the Gene Expression Omnibus (GEO; http://www.ncbi.nlm.nih.gov/geo) under accessions GSE99994 and GSE99961.

References

  1. Taylor, M. D. et al. Molecular subgroups of medulloblastoma: the current consensus. Acta Neuropathol. 123, 465–472 (2012).

    Article  CAS  PubMed  Google Scholar 

  2. Johann, P. D. et al. Atypical teratoid/rhabdoid tumors are comprised of three epigenetic subgroups with distinct enhancer landscapes. Cancer Cell 29, 379–393 (2016).

    Article  CAS  PubMed  Google Scholar 

  3. Torchia, J. et al. Integrated (epi)-genomic analyses identify subgroup-specific therapeutic targets in CNS rhabdoid tumors. Cancer Cell 30, 891–908 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Sturm, D. et al. Hotspot mutations in H3F3A and IDH1 define distinct epigenetic and biological subgroups of glioblastoma. Cancer Cell 22, 425–437 (2012).

    Article  CAS  PubMed  Google Scholar 

  5. Johnson, R. A. et al. Cross-species genomics matches driver mutations and cell compartments to model ependymoma. Nature 466, 632–636 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Witt, H. et al. Delineation of two clinically and molecularly distinct subgroups of posterior fossa ependymoma. Cancer Cell 20, 143–157 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Gajjar, A., Pfister, S. M., Taylor, M. D. & Gilbertson, R. J. Molecular insights into pediatric brain tumors have the potential to transform therapy. Clin. Cancer Res. 20, 5630–5640 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Capper, D. et al. DNA methylation-based classification of central nervous system tumours. Nature 555, 469–474 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Louis, D. N. et al. The2016 World Health Organization classification of tumors of the central nervous system: a summary. Acta Neuropathol. 131, 803–820 (2016).

    Article  PubMed  Google Scholar 

  10. Pajtler, K. W. et al. The current consensus on the clinical management of intracranial ependymoma and its distinct molecular variants. Acta Neuropathol. 133, 5–12 (2017).

    Article  CAS  PubMed  Google Scholar 

  11. Pajtler, K. W. et al. Molecular classification of ependymal tumors across all CNS compartments, histopathological grades, and age groups. Cancer Cell 27, 728–743 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Shou, Y. et al. A five-gene hedgehog signature developed as a patient preselection tool for hedgehog inhibitor therapy in medulloblastoma. Clin. Cancer Res. 21, 585–593 (2015).

    Article  CAS  PubMed  Google Scholar 

  13. Gilbertson, R. J. Brain tumors provide new clues to the source of cancer stem cells: does oncology recapitulate ontogeny? Cell Cycle 5, 135–137 (2006).

    Article  CAS  PubMed  Google Scholar 

  14. Packer, R. J. et al. Phase III study of craniospinal radiation therapy followed by adjuvant chemotherapy for newly diagnosed average-risk medulloblastoma. J Clin. Oncol. 24, 4202–4208 (2006).

    Article  CAS  PubMed  Google Scholar 

  15. Gao, H. et al. High-throughput screening using patient-derived tumor xenografts to predict clinical trial drug response. Nat. Med. 21, 1318–1325 (2015).

    Article  CAS  PubMed  Google Scholar 

  16. Townsend, E. C. et al. The public repository of xenografts enables discovery and randomized phase II-like trials in mice. Cancer Cell 30, 183 (2016).

    Article  CAS  PubMed  Google Scholar 

  17. Bruna, A. et al. A biobank of breast cancer explants with preserved intra-tumor heterogeneity to screen anticancer compounds. Cell 167, 260–274.e222 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  18. Houghton, P. J. et al. The pediatric preclinical testing program: description of models and early testing results. Pediatr. Blood Cancer 49, 928–940 (2007).

    Article  PubMed  Google Scholar 

  19. Zhao, X. et al. Global gene expression profiling confirms the molecular fidelity of primary tumor-based orthotopic xenograft mouse models of medulloblastoma. Neuro. Oncol. 14, 574–583 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Grasso, C. S. et al. Functionally defined therapeutic targets in diffuse intrinsic pontine glioma. Nat. Med. 21, 827 (2015).

    Article  CAS  PubMed  Google Scholar 

  21. Goldenberg, D. M. & Pavia, R. A. In vivo horizontal oncogenesis by a human tumor in nude mice. Proc. Natl Acad. Sci. USA 79, 2389–2392 (1982).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Triviai, I. et al. Endogenous retrovirus induces leukemia in a xenograft mouse model for primary myelofibrosis. Proc.Natl Acad. Sci. USA 111, 8595–8600 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Hovestadt, V. et al. Decoding the regulatory landscape of medulloblastoma using DNA methylation sequencing. Nature 510, 537–541 (2014).

    Article  CAS  PubMed  Google Scholar 

  24. Sturm, D. et al. New brain tumor entities emerge from molecular classification of CNS-PNETs. Cell 164, 1060–1072 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Korshunov, A. et al. H3-/IDH-wild type pediatric glioblastoma is comprised of molecularly and prognostically distinct subtypes with associated oncogenic drivers. Acta Neuropathol. 134, 507–516 (2017).

    Article  CAS  PubMed  Google Scholar 

  26. Mackay, A. et al. Integrated molecular meta-analysis of 1,000 pediatric high-grade and diffuse intrinsic pontine glioma. Cancer Cell 32, 520–537 (2017). e525.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Northcott, P. A. et al. The whole-genome landscape of medulloblastoma subtypes. Nature 547, 311–317 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Callari, M. et al. Computational approach to discriminate human and mouse sequences in patient-derived tumour xenografts. BMC Genomics 19, 19 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  29. Northcott, P. A. et al. Enhancer hijacking activates GFI1 family oncogenes in medulloblastoma. Nature 511, 428–434 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Pajtler, K. W. et al. Molecular heterogeneity and CXorf67 alterations in posterior fossa group A (PFA) ependymomas. Acta Neuropathol. 136, 211–226 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Kilday, J. P. et al. Copy number gain of 1q25 predicts poor progression-free survival for pediatric intracranial ependymomas and enables patient risk stratification: a prospective European clinical trial cohort analysis on behalf of the Children’s Cancer Leukaemia Group (CCLG), Societe Francaise d’Oncologie Pediatrique (SFOP), and International Society for Pediatric Oncology (SIOP). Clin. Cancer Res. 18, 2001–2011 (2012).

    Article  CAS  PubMed  Google Scholar 

  32. Parker, M. et al. C11orf95-RELA fusions drive oncogenic NF-kappaB signalling in ependymoma. Nature 506, 451–455 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Biegel, J. A. Molecular genetics of atypical teratoid/rhabdoid tumor. Neurosurg. Focus 20, E11 (2006).

    Article  PubMed  Google Scholar 

  34. Shih, D. J. et al. Cytogenetic prognostication within medulloblastoma subgroups. J. Clin. Oncol. 32, 886–896 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  35. Pei, Y. et al. HDAC and PI3K antagonists cooperate to inhibit growth of MYC-driven medulloblastoma. Cancer Cell 29, 311–323 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Lee, S. J. et al. Sonic hedgehog-induced histone deacetylase activation is required for cerebellar granule precursor hyperplasia in medulloblastoma. PLoS ONE. 8, e71455 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Spiller, S. E., Ditzler, S. H., Pullar, B. J. & Olson, J. M. Response of preclinical medulloblastoma models to combination therapy with 13-cis retinoic acid and suberoylanilide hydroxamic acid (SAHA). J. Neurooncol. 87, 133–141 (2008).

    Article  CAS  PubMed  Google Scholar 

  38. Cook Sangar, M. L. et al. Inhibition of CDK4/6 by palbociclib significantly extends survival in medulloblastoma patient-derived xenograft mouse models. Clin. Cancer Res. 23, 5802–5813 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Varley, K. E. et al. Dynamic DNA methylation across diverse human cell lines and tissues. Genome Res. 23, 555–567 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Goodspeed, A., Heiser, L. M., Gray, J. W. & Costello, J. C. Tumor-derived cell lines as molecular models of cancer pharmacogenomics. Mol. Cancer Res. 14, 3–13 (2016).

    Article  CAS  PubMed  Google Scholar 

  41. Ramaswamy, V. et al. Medulloblastoma subgroup-specific outcomes in irradiated children: who are the true high-risk patients? Neuro Oncol. 18, 291–297 (2016).

    Article  PubMed  Google Scholar 

  42. Hidalgo, M. et al. Patient-derived xenograft models: an emerging platform for translational cancer research. Cancer Discov. 4, 998–1013 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Peereboom, D. M. et al. Phase II trial of erlotinib with temozolomide and radiation in patients with newly diagnosed glioblastoma multiforme. J. Neurooncol. 98, 93–99 (2010).

    Article  CAS  PubMed  Google Scholar 

  44. Wen, P. Y. et al. Phase I/II study of erlotinib and temsirolimus for patients with recurrent malignant gliomas: North American Brain Tumor Consortium trial 04-02. Neuro Oncol. 16, 567–578 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Raizer, J. J. et al. A phase II study of bevacizumab and erlotinib after radiation and temozolomide in MGMT unmethylated GBM patients. J. Neurooncol. 126, 185–192 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Sflomos, G. et al. A preclinical model for ERα-positive breast cancer points to the epithelial microenvironment as determinant of luminal phenotype and hormone response. Cancer Cell 29, 407–422 (2016).

    Article  CAS  PubMed  Google Scholar 

  47. Stewart, E. et al. Orthotopic patient-derived xenografts of paediatric solid tumours. Nature 549, 96–100 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Eirew, P. et al. Dynamics of genomic clones in breast cancer patient xenografts at single-cell resolution. Nature 518, 422–426 (2015).

    Article  CAS  PubMed  Google Scholar 

  49. Hill, R. M. et al. Combined MYC and P53 defects emerge at medulloblastoma relapse and define rapidly progressive, therapeutically targetable disease. Cancer Cell 27, 72–84 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Lee, J. et al. Tumor stem cells derived from glioblastomas cultured in bFGF and EGF more closely mirror the phenotype and genotype of primary tumors than do serum-cultured cell lines. Cancer Cell 9, 391–403 (2006).

    Article  CAS  PubMed  Google Scholar 

  51. Saeed, A. I. et al. TM4: a free, open-source system for microarray data management and analysis. Biotechniques 34, 374–378 (2003).

    Article  CAS  PubMed  Google Scholar 

  52. Smyth, G. K. Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat. Appl. Genet. Mol. Biol. 3, Article3 (2004).

    Article  PubMed  Google Scholar 

  53. Wilson, C. L. & Miller, C. J. Simpleaffy: a bioconductor package for affymetrix quality control and data analysis. Bioinformatics 21, 3683–3685 (2005).

    Article  CAS  PubMed  Google Scholar 

  54. Huang da, W., Sherman, B. T. & Lempicki, R. A. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc. 4, 44–57 (2009).

    Article  PubMed  CAS  Google Scholar 

  55. Huang da, W., Sherman, B. T. & Lempicki, R. A. Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res. 37, 1–13 (2009).

    Article  PubMed  CAS  Google Scholar 

  56. Hovestadt, V. et al. Robust molecular subgrouping and copy-number profiling of medulloblastoma from small amounts of archival tumour material using high-density DNA methylation arrays. Acta Neuropathol. 125, 913–916 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  57. van der Maaten, L. & Hinton, G. Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008).

    Google Scholar 

  58. Mazor, T. et al. DNA methylation and somatic mutations converge on the cell cycle and define similar evolutionary histories in brain tumors. Cancer Cell 28, 307–317 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  60. Futreal, P. A. et al. A census of human cancer genes. Nat. Rev. Cancer 4, 177–183 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Zhao, M., Sun, J. & Zhao, Z. TSGene: a web resource for tumor suppressor genes. Nucleic Acids Res. 41, D970–D976 (2013).

    Article  CAS  PubMed  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  63. Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. McPherson, A. et al. deFuse: an algorithm for gene fusion discovery in tumor RNA-Seq data. PLoS Comput. Biol. 7, e1001138 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Campbell, I. Chi-squared and Fisher-Irwin tests of two-by-two tables with small sample recommendations. Stat. Med. 26, 3661–3675 (2007).

    Article  PubMed  Google Scholar 

  66. Richardson, J. T. The analysis of 2×2 contingency tables—yet again. Stat. Med. 30, 890; author reply 891–892. (2011).

    Article  PubMed  Google Scholar 

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Acknowledgements

We are grateful to the patients and families who consented to provide tissue to generate these resources. We thank A. Richards, M. Merrill, D. Acorn, M. Biery, A. Wittmann, and L. Sieber for experimental contributions. We thank the DKFZ Genomics and Proteomics Core Facility, DKFZ Heidelberg, Germany, and the AMC Department of Oncogenomics, Amsterdam, The Netherlands, for performing high-throughput sequencing and microarray analyses to a very high standard. We also thank the DKFZ data management group, especially I. Scholz, for their excellent support in processing the sequencing data, and Z. Gu for the defuse pipeline. We thank X.-N. Li, R. J. Wechsler-Reya, and T. Milde for allowing us to list their PDOX lines on our websites. This work was supported by NIH 1U10CA180886-01 (J.M.O.), NIH 1R01CA155360 (J.M.O.), NIH R01 CA114567 (J.M.O.), the Seattle Run of Hope, the Seattle Children’s Brain Tumor Research Endowment, the Dutch Cancer Foundations KWF (2010-4713) (M.K.) and KIKA (90) (M.K.), Deutsche Krebshilfe (111537) (S.M.P., M.K.), BMBF (01KT1605) (S.M.P., M.K.), IMI-JU ITCC-P4 (116064) (D.T.W.J., J.K., S.M.P., M.K.), and the Helmholtz International Graduate School for Cancer Research (S.B.).

Author information

Author notes

  1. Joyoti Dey

    Present address: Presage Biosciences, Seattle, WA, USA

  2. Lukas Chavez

    Present address: Department of Medicine, Division of Medical Genetics, University of California San Diego School of Medicine, La Jolla, CA, USA

  3. These authors contributed equally: Sebastian Brabetz, Sarah E. S. Leary, Susanne N. Gröbner.

  4. These authors jointly supervised this work: Stefan M. Pfister, Marcel Kool, James M. Olson.

Authors and Affiliations

  1. Hopp Children’s Cancer Center, NCT Heidelberg (KiTZ), Heidelberg, Germany

    Sebastian Brabetz, Susanne N. Gröbner, Norman L. Mack, Benjamin Schwalm, Gnana Prakash Balasubramanian, Lukas Chavez, David T. W. Jones, Stefan M. Pfister & Marcel Kool

  2. Division of Pediatric Neurooncology, German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany

    Sebastian Brabetz, Susanne N. Gröbner, Huriye Şeker-Cin, Norman L. Mack, Benjamin Schwalm, Gnana Prakash Balasubramanian, Paul A. Northcott, Lukas Chavez, David T. W. Jones, Stefan M. Pfister & Marcel Kool

  3. Faculty of Biosciences, Heidelberg University, Heidelberg, Germany

    Sebastian Brabetz

  4. Seattle Children’s and University of Washington, Seattle, WA, USA

    Sarah E. S. Leary, Bonnie Cole & James M. Olson

  5. Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA

    Sarah E. S. Leary, Madison W. Nakamoto, Emily J. Girard, Andrew D. Strand, Karina L. Bloom, Fiona Pakiam, Kyle D. Pedro, Joyoti Dey, Stacey Hansen, Sally Ditzler & James M. Olson

  6. Division of Molecular Genetics, German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany

    Volker Hovestadt, Norman L. Mack & Peter Lichter

  7. Department of Pathology and Center for Cancer Research, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA

    Volker Hovestadt

  8. Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Volker Hovestadt

  9. CCU Neuropathology, German Cancer Research Center (DKFZ), Heidelberg, Germany

    Andrey Korshunov

  10. Department of Neuropathology, Heidelberg University, Heidelberg, Germany

    Andrey Korshunov

  11. Department of Developmental Neurobiology, St Jude Children’s Research Hospital, Memphis, TN, USA

    Paul A. Northcott

  12. Department of Oncogenomics, Academic Medical Center, Amsterdam, The Netherlands

    Jan Koster

  13. Department of Pediatric Oncology, Hematology and Immunology, Heidelberg University Hospital, Heidelberg, Germany

    Stefan M. Pfister

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Contributions

S.B., S.E.S.L., S.N.G., M.W.N., H.S.-C, E.J.G., B.C., A.D.S., K.L.B., N.L.M., F.P., B.S., A.K., K.D.P., J.D., S.H., and S.D. performed and/or coordinated the experimental work. S.B., S.E.S.L., S.N.G., M.W.N., H.S.-C., E.J.G, B.C., A.D.S., K.L.B., V.H., A.K., G.P.B., P.A.N., K.D.P., S.H., S.D., P.L., L.C., D.T.W.J., J.K., and M.K. performed data analysis and interpretation. S.B., S.E.S.L., S.N.G., S.M.P., M.K., and J.M.O. provided project leadership. S.B., S.E.S.L., S.N.G., S.M.P., M.K., and J.M.O. prepared the initial manuscript and figures. All authors contributed to the critical review of the manuscript.

Corresponding authors

Correspondence to Stefan M. Pfister, Marcel Kool or James M. Olson.

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Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–10

Reporting Summary

Supplementary Table 1

PDOX model information

Supplementary Table 2

Pathological characterization of PDOX models and matching human tumors

Supplementary Table 3

Overview about molecular data

Supplementary Table 4

Molecular alterations in PDOX models and matching human tumors

Supplementary Table 5

Genes with increased and decreased expression in PDOX compared to human tumors

Supplementary Table 6

Primers used for Sanger sequencing validations and for qRT–PCR

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Brabetz, S., Leary, S.E.S., Gröbner, S.N. et al. A biobank of patient-derived pediatric brain tumor models. Nat Med 24, 1752–1761 (2018). https://doi.org/10.1038/s41591-018-0207-3

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