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Recurrent MET fusion genes represent a drug target in pediatric glioblastoma

Abstract

Pediatric glioblastoma is one of the most common and most deadly brain tumors in childhood. Using an integrative genetic analysis of 53 pediatric glioblastomas and five in vitro model systems, we identified previously unidentified gene fusions involving the MET oncogene in 10% of cases. These MET fusions activated mitogen-activated protein kinase (MAPK) signaling and, in cooperation with lesions compromising cell cycle regulation, induced aggressive glial tumors in vivo. MET inhibitors suppressed MET tumor growth in xenograft models. Finally, we treated a pediatric patient bearing a MET-fusion-expressing glioblastoma with the targeted inhibitor crizotinib. This therapy led to substantial tumor shrinkage and associated relief of symptoms, but new treatment-resistant lesions appeared, indicating that combination therapies are likely necessary to achieve a durable clinical response.

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Figure 1: The genomic landscape of pediatric glioblastomas.
Figure 2: Oncogenic MET fusions.
Figure 3: MET fusion animal model and preclinical testing of a MET inhibitor.
Figure 4: Translation of MET inhibitor treatment into a clinical setting.

References

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

    CAS  PubMed  PubMed Central  Google Scholar 

  2. Jones, C. & Baker, S.J. Unique genetic and epigenetic mechanisms driving pediatric diffuse high-grade glioma. Nat. Rev. Cancer 14, 651–661 (2014).

    CAS  Google Scholar 

  3. Louis, D.N. et al. WHO Classification of Tumors of the Central Nervous System, Revised 4th edn. (IARC, 2016).

  4. Wu, G. et al. Somatic histone H3 alterations in pediatric diffuse intrinsic pontine gliomas and non-brainstem glioblastomas. Nat. Genet. 44, 251–253 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. Schwartzentruber, J. et al. Driver mutations in histone H3.3 and chromatin remodeling genes in pediatric glioblastoma. Nature 482, 226–231 (2012).

    CAS  PubMed  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  7. Korshunov, A. et al. Integrated analysis of pediatric glioblastoma reveals a subset of biologically favorable tumors with associated molecular prognostic markers. Acta Neuropathol. 129, 669–678 (2015).

    CAS  PubMed  Google Scholar 

  8. Dias-Santagata, D. et al. BRAF V600E mutations are common in pleomorphic xanthoastrocytoma: diagnostic and therapeutic implications. PLoS One 6, e17948 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Weber, R.G. et al. Frequent loss of chromosome 9, homozygous CDKN2A/p14(ARF)/CDKN2B deletion and low TSC1 mRNA expression in pleomorphic xanthoastrocytomas. Oncogene 26, 1088–1097 (2007).

    CAS  PubMed  Google Scholar 

  10. Wu, G. et al. The genomic landscape of diffuse intrinsic pontine glioma and pediatric non-brainstem high-grade glioma. Nat. Genet. 46, 444–450 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Jones, D.T. et al. Recurrent somatic alterations of FGFR1 and NTRK2 in pilocytic astrocytoma. Nat. Genet. 45, 927–932 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Zhang, J. et al. Whole-genome sequencing identifies genetic alterations in pediatric low-grade gliomas. Nat. Genet. 45, 602–612 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Singh, D. et al. Transforming fusions of FGFR and TACC genes in human glioblastoma. Science 337, 1231–1235 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. Stephens, P.J. et al. Massive genomic rearrangement acquired in a single catastrophic event during cancer development. Cell 144, 27–40 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Shlien, A. et al. Combined hereditary and somatic mutations of replication error repair genes result in rapid onset of ultra-hypermutated cancers. Nat. Genet. 47, 257–262 (2015).

    CAS  PubMed  Google Scholar 

  16. Greco, A. et al. The DNA rearrangement that generates the TRK-T3 oncogene involves a novel gene on chromosome 3 whose product has a potential coiled-coil domain. Mol. Cell. Biol. 15, 6118–6127 (1995).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Hernández, L. et al. TRK-fused gene (TFG) is a new partner of ALK in anaplastic large cell lymphoma producing two structurally different TFG-ALK translocations. Blood 94, 3265–3268 (1999).

    PubMed  Google Scholar 

  18. Bao, Z.S. et al. RNA-seq of 272 gliomas revealed a novel, recurrent PTPRZ1-MET fusion transcript in secondary glioblastomas. Genome Res. 24, 1765–1773 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Navis, A.C. et al. Identification of a novel MET mutation in high-grade glioma resulting in an auto-active intracellular protein. Acta Neuropathol. 130, 131–144 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Laser-Azogui, A., Diamant-Levi, T., Israeli, S., Roytman, Y. & Tsarfaty, I. Met-induced membrane blebbing leads to amoeboid cell motility and invasion. Oncogene 33, 1788–1798 (2014).

    CAS  PubMed  Google Scholar 

  21. Shin, C.H., Grossmann, A.H., Holmen, S.L. & Robinson, J.P. The BRAF kinase domain promotes the development of gliomas in vivo. Genes Cancer 6, 9–18 (2015).

    PubMed  PubMed Central  Google Scholar 

  22. Worst, B.C. et al. Next-generation personalised medicine for high-risk pediatric cancer patients—The INFORM pilot study. Eur. J. Cancer 65, 91–101 (2016).

    PubMed  Google Scholar 

  23. Chi, A.S. et al. Rapid radiographic and clinical improvement after treatment of a MET-amplified recurrent glioblastoma with a mesenchymal-epithelial transition inhibitor. J. Clin. Oncol. 30, e30–e33 (2012).

    PubMed  Google Scholar 

  24. Birchmeier, C., Birchmeier, W., Gherardi, E. & Vande Woude, G.F. Met, metastasis, motility and more. Nat. Rev. Mol. Cell Biol. 4, 915–925 (2003).

    CAS  PubMed  Google Scholar 

  25. Gherardi, E., Birchmeier, W., Birchmeier, C. & Vande Woude, G. Targeting MET in cancer: rationale and progress. Nat. Rev. Cancer 12, 89–103 (2012).

    CAS  PubMed  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Mak, H.H. et al. Oncogenic activation of the Met receptor tyrosine kinase fusion protein, Tpr-Met, involves exclusion from the endocytic degradative pathway. Oncogene 26, 7213–7221 (2007).

    CAS  PubMed  Google Scholar 

  28. Cooper, C.S. et al. Molecular cloning of a new transforming gene from a chemically transformed human cell line. Nature 311, 29–33 (1984).

    CAS  PubMed  Google Scholar 

  29. Yoshihara, K. et al. The landscape and therapeutic relevance of cancer-associated transcript fusions. Oncogene 34, 4845–4854 (2015).

    CAS  PubMed  Google Scholar 

  30. Diamond, J.R. et al. Initial clinical sensitivity and acquired resistance to MET inhibition in MET-mutated papillary renal cell carcinoma. J. Clin. Oncol. 31, e254–e258 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. Lai, A.Z. et al. Dynamic reprogramming of signaling upon met inhibition reveals a mechanism of drug resistance in gastric cancer. Sci. Signal. 7, ra38 (2014).

    PubMed  Google Scholar 

  32. Truffaux, N. et al. Preclinical evaluation of dasatinib alone and in combination with cabozantinib for the treatment of diffuse intrinsic pontine glioma. Neuro-oncol. 17, 953–964 (2015).

    CAS  PubMed  Google Scholar 

  33. Mossé, Y.P. et al. Safety and activity of crizotinib for pediatric patients with refractory solid tumors or anaplastic large-cell lymphoma: a Children's Oncology Group phase 1 consortium study. Lancet Oncol. 14, 472–480 (2013).

    PubMed  PubMed Central  Google Scholar 

  34. Jia, W. et al. SOAPfuse: an algorithm for identifying fusion transcripts from paired-end RNA-Seq data. Genome Biol. 14, R12 (2013).

    PubMed  PubMed Central  Google Scholar 

  35. Kim, D. et al. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 14, R36 (2013).

    PubMed  PubMed Central  Google Scholar 

  36. 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 

  37. Tischler, G. & Leonard, S. biobambam: tools for read pair collation based algorithms on BAM files. Source Code Biol. Med. 9, 13 (2014).

    PubMed Central  Google Scholar 

  38. Jones, D.T. et al. Dissecting the genomic complexity underlying medulloblastoma. Nature 488, 100–105 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Rimmer, A. et al. Integrating mapping-, assembly- and haplotype-based approaches for calling variants in clinical sequencing applications. Nat. Genet. 46, 912–918 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. 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 

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

    CAS  PubMed  Google Scholar 

  42. 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 

  43. Korbel, J.O. & Campbell, P.J. Criteria for inference of chromothripsis in cancer genomes. Cell 152, 1226–1236 (2013).

    CAS  PubMed  Google Scholar 

  44. 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 

  45. Röring, M. et al. Distinct requirement for an intact dimer interface in wild-type, V600E and kinase-dead B-Raf signalling. EMBO J. 31, 2629–2647 (2012).

    PubMed  PubMed Central  Google Scholar 

  46. Eisenhardt, A.E. et al. Functional characterization of a BRAF insertion mutant associated with pilocytic astrocytoma. Int. J. Cancer 129, 2297–2303 (2011).

    CAS  PubMed  Google Scholar 

Download references

Acknowledgements

For technical support and expertise, we thank A. Wittmann, L. Sieber, C. Xanthopoulos, D. Sohn and N. Mack, the DKFZ Genomics and Proteomics Core Facility, the DKFZ Center for Preclinical Research, R. Kabbe (Division of Theoretical Bioinformatics, DKFZ), M. Bieg and M. Schlesner (Division of Applied Bioinformatics, DKFZ), C. Jäger-Schmidt (Data Management Group, DKFZ), S. Rüffer and T. Giese from the Heidelberg University Hospital, M. Rabenstein from the NCT Heidelberg, S. Thamm, D. Balzereit, S. Dökel, M. Linser, A. Kovacsovics and V. Amstislavskiy from the Max Planck Institute for Molecular Genetics (MPIMG) in Berlin, the tissue bank of the National Center for Tumor Diseases (NCT, Heidelberg), and the Department of Oncogenomics (University of Amsterdam). Ntv-a; Cdkn2a−/−; Ptenfl/fl mice were kindly provided by E. Holland (Fred Hutchinson Cancer Research Center). This work was principally supported by 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, MedSys #0315416C, NGFNplus #01GS0883 and e:Med Joint Research Projects SYS-GLIO #031A425A and CancerTelSys #01ZX1302). Additional support came from the German Cancer Research Center–Heidelberg Center for Personalized Oncology (DKFZ-HIPO), the German Cancer Consortium (DKTK, INFORM project), the Max Planck Society (Munich, Germany), the European Union (FP7/2007-2013, grant ESGI #262055), the Helmholtz Alliance Preclinical Comprehensive Cancer Center (PCCC, grant number HA-305), the German Research Foundation (DFG, grant LA2983/2-1), the EDM and the Lemos Foundations, the New York University Langone Human Specimen Resource Center, Laura and Isaac Perlmutter Cancer Center, supported in part by the Cancer Center Support Grant, P30 CA16087 from the National Cancer Institute, US National Institutes of Health, UL 1 TR000038 from the National Center for the Advancement of Translational Science (NCATS), US National Institutes of Health, and grants from the Making Headway Foundation. J. Gronych was supported by a Dr. Mildred Scheel Foundation Scholarship. The authors acknowledge NHS funding to the NIHR Biomedical Research Centre at The Royal Marsden and the ICR as well as the project (Ministry of Health, Czech Republic) for conceptual development of research organization 00064203 (University Hospital Motol, Prague, Czech Republic).

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S.B., J. Gronych, H.-J.W., E.P., F.W., S. Halbach, D. Sturm, L.B., A.M. Stütz, K.S., B.R., D.M., S. Heiland, C.v.K., S.S., S.W., J.F., T.B. performed and/or coordinated the experimental work. S.B., J. Gronych, H.-J.W., B. Hutter, S.G., V.H., M.K., P.A.N., T.Z., B. Huang, M.R., I.B., M.H., T.R., M.Z., C.P., C.L., B.C.W. performed data analysis. M.R., A.E.K., A.U., O.W., A.v.D., D.C., N.J., A.M. Sehested, D. Sumerauer, M.A.K., C.J., C.H.-M., A.K., J. Grill, N.T., C.M.v.T., B.C.W., D.H.-B.B., S.T., H.-K.N., D.Z., J.C.A., N.G.G. collected data and provided patient materials. S.B., J. Gronych, H.-J.W., B. Hutter, S.M.P., P.L. and D.T.W.J. prepared the initial manuscript and figures. S.B., J. Gronych, U.D.W., J.O.K., G.R., B.B., H.L., T.B., R.E., M.-L.Y., S.M.P., P.L. and D.T.W.J. provided project leadership.

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International Cancer Genome Consortium PedBrain Tumor Project. Recurrent MET fusion genes represent a drug target in pediatric glioblastoma. Nat Med 22, 1314–1320 (2016). https://doi.org/10.1038/nm.4204

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