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Spatial heterogeneity in medulloblastoma

Abstract

Spatial heterogeneity of transcriptional and genetic markers between physically isolated biopsies of a single tumor poses major barriers to the identification of biomarkers and the development of targeted therapies that will be effective against the entire tumor. We analyzed the spatial heterogeneity of multiregional biopsies from 35 patients, using a combination of transcriptomic and genomic profiles. Medulloblastomas (MBs), but not high-grade gliomas (HGGs), demonstrated spatially homogeneous transcriptomes, which allowed for accurate subgrouping of tumors from a single biopsy. Conversely, somatic mutations that affect genes suitable for targeted therapeutics demonstrated high levels of spatial heterogeneity in MB, malignant glioma, and renal cell carcinoma (RCC). Actionable targets found in a single MB biopsy were seldom clonal across the entire tumor, which brings the efficacy of monotherapies against a single target into question. Clinical trials of targeted therapies for MB should first ensure the spatially ubiquitous nature of the target mutation.

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Figure 1: Medulloblastomas, but not glioblastomas, show reliable transcriptome-based subgroup prediction.
Figure 2: The variable intratumoral heterogeneity of somatic alterations in all tumor entities.
Figure 3: Spatial intermixing of clonal lineages.
Figure 4: Genetically distinct clonal lineages yield ON/OFF mutation patterns between spatially separated biopsies.
Figure 5: Quantification of variable genetic heterogeneity across tumor entities.
Figure 6: Genetic heterogeneity at recurrence greatly exceeds spatial heterogeneity in MB.

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References

  1. Northcott, P.A. et al. Medulloblastoma comprises four distinct molecular variants. J. Clin. Oncol. 29, 1408–1414 (2011).

    PubMed  Google Scholar 

  2. Kleinman, C.L. et al. Fusion of TTYH1 with the C19MC microRNA cluster drives expression of a brain-specific DNMT3B isoform in the embryonal brain tumor ETMR. Nat. Genet. 46, 39–44 (2014).

    CAS  PubMed  Google Scholar 

  3. Versteege, I. et al. Truncating mutations of hSNF5/INI1 in aggressive paediatric cancer. Nature 394, 203–206 (1998).

    CAS  PubMed  Google Scholar 

  4. Pietsch, T. et al. Prognostic significance of clinical, histopathological, and molecular characteristics of medulloblastomas in the prospective HIT2000 multicenter clinical trial cohort. Acta Neuropathol. 128, 137–149 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. Remke, M., Ramaswamy, V. & Taylor, M.D. Medulloblastoma molecular dissection: the way toward targeted therapy. Curr. Opin. Oncol. 25, 674–681 (2013).

    CAS  PubMed  Google Scholar 

  6. Kool, M. et al. Genome sequencing of SHH medulloblastoma predicts genotype-related response to smoothened inhibition. Cancer Cell 25, 393–405 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. Kieran, M.W. Targeted treatment for sonic hedgehog-dependent medulloblastoma. Neuro-oncol. 16, 1037–1047 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Louis, D.N. et al. The 2007 WHO classification of tumours of the central nervous system. Acta Neuropathol. 114, 97–109 (2007).

    PubMed  PubMed Central  Google Scholar 

  9. Gerlinger, M. et al. Genomic architecture and evolution of clear cell renal cell carcinomas defined by multiregion sequencing. Nat. Genet. 46, 225–233 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. Gerlinger, M. et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N. Engl. J. Med. 366, 883–892 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Gulati, S. et al. Systematic evaluation of the prognostic impact and intratumour heterogeneity of clear cell renal cell carcinoma biomarkers. Eur. Urol. 66, 936–948 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  13. 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 

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

    CAS  PubMed  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Northcott, P.A. et al. Subgroup-specific structural variation across 1,000 medulloblastoma genomes. Nature 488, 49–56 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. 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 

  18. Beuselinck, B. et al. Molecular subtypes of clear cell renal cell carcinoma are associated with sunitinib response in the metastatic setting. Clin. Cancer Res. 21, 1329–1339 (2015).

    CAS  PubMed  Google Scholar 

  19. Thibodeau, B.J. et al. Characterization of clear cell renal cell carcinoma by gene expression profiling. Urol. Oncol. 34, 168.e1–168.e9 (2016).

    CAS  Google Scholar 

  20. Gravendeel, L.A. et al. Intrinsic gene expression profiles of gliomas are a better predictor of survival than histology. Cancer Res. 69, 9065–9072 (2009).

    CAS  PubMed  Google Scholar 

  21. Ha, G. et al. TITAN: inference of copy number architectures in clonal cell populations from tumor whole-genome sequence data. Genome Res. 24, 1881–1893 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Andor, N., Harness, J.V., Müller, S., Mewes, H.W. & Petritsch, C. EXPANDS: expanding ploidy and allele frequency on nested subpopulations. Bioinformatics 30, 50–60 (2014).

    CAS  PubMed  Google Scholar 

  23. Andor, N. et al. Pan-cancer analysis of the extent and consequences of intratumor heterogeneity. Nat. Med. 22, 105–113 (2016).

    CAS  PubMed  Google Scholar 

  24. Hiley, C., de Bruin, E.C., McGranahan, N. & Swanton, C. Deciphering intratumor heterogeneity and temporal acquisition of driver events to refine precision medicine. Genome Biol. 15, 453 (2014).

    PubMed  PubMed Central  Google Scholar 

  25. Northcott, P.A. et al. Medulloblastomics: the end of the beginning. Nat. Rev. Cancer 12, 818–834 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Sturm, D. et al. Paediatric and adult glioblastoma: multiform (epi)genomic culprits emerge. Nat. Rev. Cancer 14, 92–107 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

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

    PubMed  PubMed Central  Google Scholar 

  28. Linehan, W.M. et al. Comprehensive molecular characterization of papillary renal-cell carcinoma. N. Engl. J. Med. 374, 135–145 (2016).

    PubMed  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Griffith, M. et al. DGIdb: mining the druggable genome. Nat. Methods 10, 1209–1210 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. Morrissy, A.S. et al. Divergent clonal selection dominates medulloblastoma at recurrence. Nature 529, 351–357 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  33. Geldres, C. et al. T lymphocytes redirected against the chondroitin sulfate proteoglycan-4 control the growth of multiple solid tumors both in vitro and in vivo. Clin. Cancer Res. 20, 962–971 (2014).

    CAS  PubMed  Google Scholar 

  34. Stein, R. et al. CD74: a new candidate target for the immunotherapy of B-cell neoplasms. Clin. Cancer Res. 13, 5556s–5563s (2007).

    CAS  PubMed  Google Scholar 

  35. Wu, M.R., Zhang, T., DeMars, L.R. & Sentman, C.L. B7H6-specific chimeric antigen receptors lead to tumor elimination and host antitumor immunity. Gene Ther. 22, 675–684 (2015).

    PubMed  PubMed Central  Google Scholar 

  36. Chinnasamy, D. et al. Gene therapy using genetically modified lymphocytes targeting VEGFR-2 inhibits the growth of vascularized syngenic tumors in mice. J. Clin. Invest. 120, 3953–3968 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. Craddock, J.A. et al. Enhanced tumor trafficking of GD2 chimeric antigen receptor T cells by expression of the chemokine receptor CCR2b. J. Immunother. 33, 780–788 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Hong, H. et al. Diverse solid tumors expressing a restricted epitope of L1-CAM can be targeted by chimeric antigen receptor redirected T lymphocytes. J. Immunother. 37, 93–104 (2014).

    CAS  PubMed  Google Scholar 

  39. Kakarla, S. et al. Antitumor effects of chimeric receptor engineered human T cells directed to tumor stroma. Mol. Ther. 21, 1611–1620 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Lanitis, E. et al. Primary human ovarian epithelial cancer cells broadly express HER2 at immunologically-detectable levels. PLoS One 7, e49829 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. Pule, M.A. et al. Virus-specific T cells engineered to coexpress tumor-specific receptors: persistence and antitumor activity in individuals with neuroblastoma. Nat. Med. 14, 1264–1270 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Tang, X. et al. T cells expressing a LMP1-specific chimeric antigen receptor mediate antitumor effects against LMP1-positive nasopharyngeal carcinoma cells in vitro and in vivo. J. Biomed. Res. 28, 468–475 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Wang, W. et al. Specificity redirection by CAR with human VEGFR-1 affinity endows T lymphocytes with tumor-killing ability and anti-angiogenic potency. Gene Ther. 20, 970–978 (2013).

    CAS  PubMed  Google Scholar 

  44. Irizarry, R.A. et al. Summaries of Affymetrix GeneChip probe level data. Nucleic Acids Res. 31, e15 (2003).

    PubMed  PubMed Central  Google Scholar 

  45. Tibshirani, R., Hastie, T., Narasimhan, B. & Chu, G. Diagnosis of multiple cancer types by shrunken centroids of gene expression. Proc. Natl. Acad. Sci. USA 99, 6567–6572 (2002).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Northcott, P.A. et al. Rapid, reliable, and reproducible molecular sub-grouping of clinical medulloblastoma samples. Acta Neuropathol. 123, 615–626 (2012).

    CAS  PubMed  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  48. 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  PubMed  PubMed Central  Google Scholar 

  49. 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 

  50. Fraley, C., Raftery, A., Murphy, T.B. & Scrucca,, L. mclust Version 4 for R: Normal Mixture Modeling for Model-Based Clustering, Classification, and Density Estimation (University of Washington, 2012).

  51. Stephens, P.J. et al. The landscape of cancer genes and mutational processes in breast cancer. Nature 486, 400–404 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

The MAGIC project (M.D.T. and M.A.M.) is financially supported by Genome Canada, Genome BC, Terry Fox Research Institute, Ontario Institute for Cancer Research, Pediatric Oncology Group Ontario, funds from The Family of Kathleen Lorette and the Clark H. Smith Brain Tumour Centre, Montreal Children's Hospital Foundation, Hospital for Sick Children: Sonia and Arthur Labatt Brain Tumour Research Centre, Chief of Research Fund, Cancer Genetics Program, Garron Family Cancer Centre, B.R.A.I.N. Child, M.D.T.'s Garron Family Endowment, and the BC Childhood Cancer Parents Association. M.D.T. is supported by a Stand Up To Cancer St. Baldrick's Pediatric Dream Team Translational Research Grant (SU2C-AACR-DT1113); Stand Up To Cancer is a program of the Entertainment Industry Foundation administered by the American Association for Cancer Research. M.D.T. is also supported by The Garron Family Chair in Childhood Cancer Research, and grants from the Cure Search Foundation, the US National Institutes of Health (R01CA148699 and R01CA159859), The Pediatric Brain Tumor Foundation, The Terry Fox Research Institute, and Brainchild. This study was conducted with the support of the Ontario Institute for Cancer Research through funding provided by the Government of Ontario, as well as The Brain Tumour Foundation of Canada Impact Grant of the Canadian Cancer Society and Brain Canada with the financial assistance of Health Canada (grant 703202 to M.D.T.). This work was also supported by a Program Project Grant from the Terry Fox Research Institute (to M.D.T.), a Grand Challenge Award from CureSearch for Children's Cancer (to M.D.T.), and the PedBrain Tumor Project contributing to the International Cancer Genome Consortium, funded by German Cancer Aid (109252) and by the German Federal Ministry of Education and Research (BMBF; grants 01KU1201A and MedSys 0315416C to S.M.P. and P.L.). We acknowledge the Labatt Brain Tumour Research Centre Tumour and Tissue Repository, which is supported by B.R.A.I.N. Child and Megan's Walk (M.D.T.). M.A.M. acknowledges support from the Canadian Institutes of Health Research (CIHR; FDN-143288). M.R. is supported by a fellowship from the Dr. Mildred Scheel Foundation for Cancer Research/German Cancer Aid. F.M.G.C. is supported by the Stephen Buttrum Brain Tumour Research Fellowship, granted by the Brain Tumour Foundation of Canada. V.R. is supported by a CIHR fellowship and an Alberta Innovates–Health Solutions Clinical Fellowship. For technical support and expertise in next-generation sequencing efforts, we thank The Centre for Applied Genomics (Toronto, Ontario, Canada). We thank S. Archer for technical writing, and C. Smith for artwork.

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A.S.M., F.M.G.C., M.R., M.D.T., and M.A.M. led the study and wrote the manuscript. A.S.M. and F.M.G.C. designed, supervised, and performed bioinformatic analyses. M.R. led the collection of samples and data generation, and performed bioinformatic analyses. B.L. extracted nucleic acids, managed biobanking, and maintained the patient database. S.H., A.M.F., B.L.H., C.D., D.J.H.S., D.M.M., D.P., D.T.W.J., E.N.K., H.F., J.M., J.P., J.R., J.T., L.G., L.K.D., M.V., P.A.N., S. Agnihotri, S. Albrecht, S.C.M., S.P.-C., V.H., V.R., X. Wu, X. Wang, and Y.Y.T. provided technical and bioinformatic support. A.A., A.T., C.M., D.L., E.C., E.M., H.I.L., J.E.S., K.T., M.M., N.D., P.P., R.C., R.D.C., T.W., W.L., Y.C., and Y.L. led and performed RNA-seq and whole-genome sequencing library preparation and sequencing experiments, and performed data analyses. N.T. and Y.M. supervised bioinformatic analyses at the Genome Sciences Center. H.N. and T.G. performed whole-exome sequencing library preparation and sequencing experiments, and performed data analyses. B.R.R., C.S., C.E.H., J.L., J.S.M., N.J., P.B., R.J.P., S.D., and U.S. provided the patient samples and clinical details that made the study possible. A.H., A.J.M., A.K., D.M., E.B., G.D.B., J.T.R., M.K., P.D., P.L., R.A.M., S.J.M.J., S.M.P., and U.T. provided valuable input regarding study design, data analysis, and interpretation of results. M.D.T. and M.A.M. provided financial and technical infrastructure and oversaw the study, and served as joint senior authors and project co-leaders.

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Correspondence to Marco A Marra or Michael D Taylor.

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Morrissy, A., Cavalli, F., Remke, M. et al. Spatial heterogeneity in medulloblastoma. Nat Genet 49, 780–788 (2017). https://doi.org/10.1038/ng.3838

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