Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Clinical genome sequencing uncovers potentially targetable truncations and fusions of MAP3K8 in spitzoid and other melanomas

Nature Medicine volume 25, pages 597–602 (2019)Cite this article

Subjects

Abstract

Spitzoid melanoma is a specific morphologic variant of melanoma that most commonly affects children and adolescents, and ranges on the spectrum of malignancy from low grade to overtly malignant. These tumors are generally driven by fusions of ALK, RET, NTRK1/3, MET, ROS1 and BRAF1,2. However, in approximately 50% of cases no genetic driver has been established2. Clinical whole-genome and transcriptome sequencing (RNA-Seq) of a spitzoid tumor from an adolescent revealed a novel gene fusion of MAP3K8, encoding a serine-threonine kinase that activates MEK3,4. The patient, who had exhausted all other therapeutic options, was treated with a MEK inhibitor and underwent a transient clinical response. We subsequently analyzed spitzoid tumors from 49 patients by RNA-Seq and found in-frame fusions or C-terminal truncations of MAP3K8 in 33% of cases. The fusion transcripts and truncated genes all contained MAP3K8 exons 1–8 but lacked the autoinhibitory final exon. Data mining of RNA-Seq from the Cancer Genome Atlas (TCGA) uncovered analogous MAP3K8 rearrangements in 1.5% of adult melanomas. Thus, MAP3K8 rearrangements—uncovered by comprehensive clinical sequencing of a single case—are the most common genetic event in spitzoid melanoma, are present in adult melanomas and could be amenable to MEK inhibition.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Get just this article for as long as you need it

$39.95

Learn more

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Diagnosis and treatment history.
Fig. 2: Clinical genomics sequencing.
Fig. 3: Recurrent MAP3K8 fusions/truncations in a spitzoid melanoma cohort.
Fig. 4: MAP3K8 fusions and truncations in TCGA melanoma samples.

Data availability

The raw whole-genome sequencing, exome and RNA-Seq dataset generated for the index patient are available in St. Jude Cloud, https://platform.stjude.cloud/requests/publications?publication_accession=SJC-PB-1018, and have also been deposited in European Genome-Phenome Archive under accession number EGAS00001003430. RNA-Seq data from archival samples are available from the authors upon reasonable request and with permission from the St. Jude Children’s Research Hospital IRB.

References

  1. Lu, C. et al. The genomic landscape of childhood and adolescent melanoma. J. Invest. Dermatol. 135, 816–823 (2015).

    Article  CAS  Google Scholar 

  2. Wiesner, T. et al. Kinase fusions are frequent in Spitz tumours and spitzoid melanomas. Nat. Commun. 5, 3116 (2014).

    Article  Google Scholar 

  3. Salmeron, A. et al. Activation of MEK-1 and SEK-1 by Tpl-2 proto-oncoprotein, a novel MAP kinase kinase kinase. EMBO J. 15, 817–826 (1996).

    Article  CAS  Google Scholar 

  4. Hagemann, D., Troppmair, J. & Rapp, U. R. Cot protooncoprotein activates the dual specificity kinases MEK-1 and SEK-1 and induces differentiation of PC12 cells. Oncogene 18, 1391–1400 (1999).

    Article  CAS  Google Scholar 

  5. Alexandrov, L. B. et al. Signatures of mutational processes in human cancer. Nature 500, 415–421 (2013).

    Article  CAS  Google Scholar 

  6. Johannessen, C. M. et al. COT drives resistance to RAF inhibition through MAP kinase pathway reactivation. Nature 468, 968–972 (2010).

    Article  CAS  Google Scholar 

  7. Little, A. S. et al. Amplification of the driving oncogene, KRAS or BRAF, underpins acquired resistance to MEK1/2 inhibitors in colorectal cancer cells. Sci. Signal. 4, ra17 (2011).

    Article  Google Scholar 

  8. Gándara, M. L., López, P., Hernando, R., Castaño, J. G. & Alemany, S. The COOH-terminal domain of wild-type Cot regulates its stability and kinase specific activity. Mol. Cell. Biol. 23, 7377–7390 (2003).

    Article  Google Scholar 

  9. Lazova, R. et al. Spitz nevi and spitzoid melanomas - exome sequencing and comparison to conventional melanocytic nevi and melanomas. Mod. Pathol. 30, 640–649 (2017).

    Article  CAS  Google Scholar 

  10. Shain, A. H. et al. The genetic evolution of melanoma from precursor lesions. N. Engl. J. Med. 373, 1926–1936 (2015).

    Article  Google Scholar 

  11. Pollock, P. M. et al. High frequency of BRAF mutations in nevi. Nat. Genet. 33, 19–20 (2003).

    Article  CAS  Google Scholar 

  12. Cancer Genome Atlas Network. Genomic classification of cutaneous melanoma. Cell 161, 1681–1696 (2015).

    Article  Google Scholar 

  13. Hu, X. et al. TumorFusions: an integrative resource for cancer-associated transcript fusions. Nucleic Acids Res. 46, D1144–D1149 (2018).

    Article  CAS  Google Scholar 

  14. Hayward, N. K. et al. Whole-genome landscapes of major melanoma subtypes. Nature 545, 175–180 (2017).

    Article  CAS  Google Scholar 

  15. Forbes, S. A. et al. COSMIC (the Catalogue of Somatic Mutations in Cancer): a resource to investigate acquired mutations in human cancer. Nucleic Acids Res. 38, D652–D657 (2010).

    Article  CAS  Google Scholar 

  16. Cerami, E. et al. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2, 401–404 (2012).

    Article  Google Scholar 

  17. Gutman, D. A. et al. The digital slide archive: a software platform for management, integration, and analysis of histology for cancer research. Cancer Res. 77, e75–e78 (2017).

    Article  CAS  Google Scholar 

  18. Liang, W. S. et al. Integrated genomic analyses reveal frequent TERT aberrations in acral melanoma. Genome Res. 27, 524–532 (2017).

    Article  CAS  Google Scholar 

  19. Gruosso, T. et al. MAP3K8/TPL-2/COT is a potential predictive marker for MEK inhibitor treatment in high-grade serous ovarian carcinomas. Nat. Commun. 6, 8583 (2015).

    Article  CAS  Google Scholar 

  20. Sourvinos, G., Tsatsanis, C. & Spandidos, D. A. Overexpression of the Tpl-2/Cot oncogene in human breast cancer. Oncogene 18, 4968–4973 (1999).

    Article  CAS  Google Scholar 

  21. Clark, A. M., Reynolds, S. H., Anderson, M. & Wiest, J. S. Mutational activation of the MAP3K8 protooncogene in lung cancer. Genes Chromosomes Cancer 41, 99–108 (2004).

    Article  CAS  Google Scholar 

  22. Lee, J.-H. et al. TPL2 is an oncogenic driver in keratocanthoma and squamous cell carcinoma. Cancer Res. 76, 6712–6722 (2016).

    Article  CAS  Google Scholar 

  23. Ceci, J. D. et al. Tpl-2 is an oncogenic kinase that is activated by carboxy-terminal truncation. Genes Dev. 11, 688–700 (1997).

    Article  CAS  Google Scholar 

  24. Patriotis, C., Makris, A., Bear, S. E. & Tsichlis, P. N. Tumor progression locus 2 (Tpl-2) encodes a protein kinase involved in the progression of rodent T-cell lymphomas and in T-cell activation. Proc. Natl Acad. Sci. USA 90, 2251–2255 (1993).

    Article  CAS  Google Scholar 

  25. Erny, K. M., Peli, J., Lambert, J. F., Muller, V. & Diggelmann, H. Involvement of the Tpl-2/cot oncogene in MMTV tumorigenesis. Oncogene 13, 2015–2020 (1996).

    CAS  PubMed  Google Scholar 

  26. Poulikakos, P. I. & Solit, D. B. Resistance to MEK inhibitors: should we co-target upstream? Sci. Signal. 4, pe16 (2011).

    Article  Google Scholar 

  27. Samatar, A. A. & Poulikakos, P. I. Targeting RAS-ERK signalling in cancer: promises and challenges. Nat. Rev. Drug Discov. 13, 928–942 (2014).

    Article  CAS  Google Scholar 

  28. Kim, K. B. et al. Phase II study of the MEK1/MEK2 inhibitor trametinib in patients with metastatic BRAF-mutant cutaneous melanoma previously treated with or without a BRAF inhibitor. J. Clin. Oncol. 31, 482–489 (2013).

    Article  CAS  Google Scholar 

  29. Flaherty, K. T. et al. Improved survival with MEK inhibition in BRAF-mutated melanoma. N. Engl. J. Med. 367, 107–114 (2012).

    Article  CAS  Google Scholar 

  30. Ascierto, P. A. et al. MEK162 for patients with advanced melanoma harbouring NRAS or Val600 BRAF mutations: a non-randomised, open-label phase 2 study. Lancet Oncol. 14, 249–256 (2013).

    Article  CAS  Google Scholar 

  31. Ranzani, M. et al. BRAF/NRAS wild-type melanoma, NF1 status and sensitivity to trametinib. Pigment Cell Melanoma Res. 28, 117–119 (2015).

    Article  CAS  Google Scholar 

  32. Zhou, X. et al. Exploring genomic alteration in pediatric cancer using ProteinPaint. Nat. Genet. 48, 4–6 (2016).

    Article  CAS  Google Scholar 

  33. Robinson, J. T. et al. Integrative genomics viewer. Nat. Biotech. 29, 24–26 (2011).

    Article  CAS  Google Scholar 

  34. Lee, S. et al. TERT promoter mutations are predictive of aggressive clinical behavior in patients with spitzoid melanocytic neoplasms. Sci. Rep. 5, 11200 (2015).

    Article  CAS  Google Scholar 

  35. Rusch, M. et al. Clinical cancer genomic profiling by three-platform sequencing of whole genome, whole exome and transcriptome. Nat. Commun. 9, 3962 (2018).

    Article  Google Scholar 

  36. 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).

    Article  CAS  Google Scholar 

  37. Roberts, K. G. et al. Targetable kinase-activating lesions in Ph-like acute lymphoblastic leukemia. N. Engl. J. Med. 371, 1005–1015 (2014).

    Article  Google Scholar 

  38. Anders, S., Pyl, P. T. & Huber, W. HTSeq–a Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169 (2015).

    Article  CAS  Google Scholar 

  39. Blokzijl, F., Janssen, R., van Boxtel, R. & Cuppen, E. MutationalPatterns: comprehensive genome-wide analysis of mutational processes. Genome Med. 10, 33 (2018).

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

  41. Seynnaeve, B. et al. Genetic and epigenetic alterations of TERT are associated with inferior outcome in adolescent and young adult patients with melanoma. Sci. Rep. 7, 45704 (2017).

    Article  CAS  Google Scholar 

  42. Borowicz, S. et al. The soft agar colony formation assay. J. Vis. Exp., e51998. https://doi.org/10.3791/51998 (2014).

  43. Chen, X. et al. CONSERTING: integrating copy-number analysis with structural-variation detection. Nat. Methods 12, 527–530 (2015).

    Article  CAS  Google Scholar 

  44. Dinkel, H. et al. ELM 2016–data update and new functionality of the eukaryotic linear motif resource. Nucleic Acids Res. 44, D294–D300 (2016).

    Article  CAS  Google Scholar 

  45. Sharrocks, A. D., Yang, S. H. & Galanis, A. Docking domains and substrate-specificity determination for MAP kinases. Trends Biochem. Sci. 25, 448–453 (2000).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We thank S. Brady for critical review of the manuscript. This work was funded by the American Lebanese Syrian Associated Charities of St. Jude Children’s Research Hospital.

Author information

Authors and Affiliations

  1. Department of Computational Biology, St. Jude Children’s Research Hospital, Memphis, TN, USA

    Scott Newman, Antonina Silkov, Stephen V. Rice, Ying Shao, Bridget Shaner, Heather Mulder, Gang Wu, Xin Zhou, John Easton & Jinghui Zhang

  2. Department of Chemical Biology & Therapeutics, St. Jude Children’s Research Hospital, Memphis, TN, USA

    Liying Fan & Philip M. Potter

  3. Department of Oncology, St. Jude Children’s Research Hospital, Memphis, TN, USA

    Allison Pribnow, Kim E. Nichols, Alberto Pappo & Armita Bahrami

  4. Department of Pediatrics, Division of Hematology/Oncology, Stanford University School of Medicine, Palo Alto, CA, USA

    Allison Pribnow

  5. Department of Pathology, St. Jude Children’s Research Hospital, Memphis, TN, USA

    Seungjae Lee, Joy Nakitandwe, Sheila Shurtleff, Elizabeth M. Azzato, David W. Ellison, James R. Downing & Armita Bahrami

  6. Departments of Pathology and Translational Research, Institute Curie, Paris, France

    Raymond Barnhill

Authors
  1. Scott Newman
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Liying Fan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Allison Pribnow
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Antonina Silkov
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Stephen V. Rice
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Seungjae Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Ying Shao
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Bridget Shaner
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Heather Mulder
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Joy Nakitandwe
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Sheila Shurtleff
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Elizabeth M. Azzato
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Gang Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Xin Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Raymond Barnhill
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. John Easton
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Kim E. Nichols
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. David W. Ellison
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. James R. Downing
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Alberto Pappo
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Philip M. Potter
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. Jinghui Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  23. Armita Bahrami
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

A.S. performed clinical genomics case reviews and analyzed RNA-Seq data. S.V.R., G.W. and X.Z. provided bioinformatics analysis and support. S.L. performed FISH assays. Y.S., B.S. and H.M. performed research sample quality control and sequencing under the supervision of J.E. J.N. performed clinical sequencing. S.S., K.E.N. and E.A. provided clinical genomics case review and reporting. R.B. provided archival samples for analysis and critical review of the data. L.F. performed functional validations and imunoblots under the supervision of P.M.P. and A.P. A.P. managed the original clinical case and performed data analysis and interpretation. K.E.N., D.W.E. and J.R.D. provided project oversight. A.B. provided pathology classifications and supervised IHC and FISH experiments. S.N., J.Z. and A.B. conceived of the study, performed the clinical review and data analysis and drafted the manuscript.

Corresponding authors

Correspondence to Scott Newman, Jinghui Zhang or Armita Bahrami.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Clinical Course.

Timeline of the clinical course imaging, and treatment regimens, are shown as colored rectangles along the horizontal timeline. For treatments, HLP is hyperthermic limb perfusion, TVEC is talimogene laherparaepvec and LTT462 is an ERK inhibitor. Subsequent Figures within this manuscript showing specimens or images from this timeline are indicated.

Extended Data Fig. 2 TERT rearrangement in the initial tumor.

a, Copy number segments from CONSERTING43; red shows a gain in copy number, blue a loss. b, Scatterplot of normalized sequencing coverage from whole-genome sequencing confirms a 75 kb region of neutral copy number encompassing the TERT locus flanked by deletions. c, Structural variant junctions are shown in blue for junctions within the same chromosome and red for junctions linking different chromosomes; the genome position of the partner locus is shown inline. d, RefSeq gene model showing the position of TERT. e, RNA-Seq coverage shows that TERT is expressed. We inspected RNA-Seq coverage at this locus for our 49 additional patient samples and found TERT expression to be absent. f, Splice junctions detected in RNA-Seq and quantified according to the y axis. Canonical splices are shown in blue and non-canonical splices are in red. g, An image of break-apart FISH for TERT performed on the primary tumor from the index case showing split red and green signals, consistent with TERT rearrangement. We performed FISH on 5 additional tumors and found this locus not to be rearranged. All FISH images are at ×100 magnification.

Extended Data Fig. 3 MAP3K8 and GNG2 copy number and expression in initial and relapsed tumors.

a, Chromosome 10 and b, chromosome 14 copy number profile from CONSERTING. Gray scatterplot represents normalized whole-genome sequencing read depth arranged by genome position on the x axis. Changes in copy number are evident as segmental shifts on the y axis. Although a deletion is evident at the start of 14q, no copy number changes are present at the MAP3K8 (chr10:30,720,950-30,752,762) or GNG2 (chr14:52,325,022-52,438,518) loci in the initial tumor sample (black arrows represent the locations of MAP3K8 and GNG2) on chromosomes 10 and 14, respectively. In the relapsed sample, the copy number of both loci is increased. c, RNA-Seq coverage with the initial tumor sample’s MAP3K8 and GNG2 loci in the upper panels and the relapsed tumor below. MAP3K8 and GNG2 expression are increased approximately threefold in the relapsed sample.

Extended Data Fig. 4 MAP3K8 mutations across cBioPortal studies.

ProteinPaint32 representation of MAP3K8 mutations found over 226 studies housed within cBioPortal16. Specific amino acid changes are shown as lollipops with a size proportional to their frequency in the cohort. The most frequently mutated tumor types were uterine endometrioid carcinoma (n = 25), colorectal adenocarcinoma (n = 21) and cutaneous melanoma (n = 20). A hotspot at R442 is indicated by the black arrow. According to ELM44 (http://elm.eu.org/), Arg442 is part of the sequence KRQRSLYIDL described previously as a MAPK docking site in MAP kinase substrates45. Interestingly, because the MAP3K8 truncation in SJMEL054992_D1 is at codon R440, only R442 is excluded from the truncated MAP3K8.

Extended Data Fig. 5 MAP3K8 fused and truncated samples from TCGA.

RNA-Seq coverage overlapping the MAP3K8 locus is shown in the blue histograms. Each histogram is scaled according to the corresponding y axis. Black arrows indicate the positions of fusions or truncations as outlined in Supplementary Table 6. The MAP3K8 locus of all seven rearranged samples is shown.

Supplementary information

Supplementary Information

Supplementary Data

Reporting Summary

Supplementary Tables

Supplementary Tables 1–8

Source data

Source Data Fig. 2

Unprocessed Western blot image

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Newman, S., Fan, L., Pribnow, A. et al. Clinical genome sequencing uncovers potentially targetable truncations and fusions of MAP3K8 in spitzoid and other melanomas. Nat Med 25, 597–602 (2019). https://doi.org/10.1038/s41591-019-0373-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41591-019-0373-y

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Cancer

Sign up for the Nature Briefing: Cancer newsletter — what matters in cancer research, free to your inbox weekly.

Get what matters in cancer research, free to your inbox weekly. Sign up for Nature Briefing: Cancer