Subjects

Abstract

Deleterious germline variants in CDKN2A account for around 40% of familial melanoma cases1, and rare variants in CDK4, BRCA2, BAP1 and the promoter of TERT have also been linked to the disease2,3,4,5. Here we set out to identify new high-penetrance susceptibility genes by sequencing 184 melanoma cases from 105 pedigrees recruited in the UK, The Netherlands and Australia that were negative for variants in known predisposition genes. We identified families where melanoma cosegregates with loss-of-function variants in the protection of telomeres 1 gene (POT1), with a proportion of family members presenting with an early age of onset and multiple primary tumors. We show that these variants either affect POT1 mRNA splicing or alter key residues in the highly conserved oligonucleotide/oligosaccharide-binding (OB) domains of POT1, disrupting protein-telomere binding and leading to increased telomere length. These findings suggest that POT1 variants predispose to melanoma formation via a direct effect on telomeres.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Accessions

References

  1. 1.

    et al. Features associated with germline CDKN2A mutations: a GenoMEL study of melanoma-prone families from three continents. J. Med. Genet. 44, 99–106 (2007).

  2. 2.

    et al. TERT promoter mutations in familial and sporadic melanoma. Science 339, 959–961 (2013).

  3. 3.

    Breast Cancer Linkage Consortium. Cancer risks in BRCA2 mutation carriers. J. Natl. Cancer Inst. 91, 1310–1316 (1999).

  4. 4.

    et al. Germline mutations in BAP1 predispose to melanocytic tumors. Nat. Genet. 43, 1018–1021 (2011).

  5. 5.

    et al. Germline mutations in the p16INK4a binding domain of CDK4 in familial melanoma. Nat. Genet. 12, 97–99 (1996).

  6. 6.

    , & Melanoma genetics: recent findings take us beyond well-traveled pathways. J. Invest. Dermatol. 132, 1763–1774 (2012).

  7. 7.

    & POT1 as a terminal transducer of TRF1 telomere length control. Nature 423, 1013–1018 (2003).

  8. 8.

    & Pot1, the putative telomere end–binding protein in fission yeast and humans. Science 292, 1171–1175 (2001).

  9. 9.

    & Maximum entropy modeling of short sequence motifs with applications to RNA splicing signals. J. Comput. Biol. 11, 377–394 (2004).

  10. 10.

    et al. POT1 mutations cause telomere dysfunction in chronic lymphocytic leukemia. Nat. Genet. 45, 526–530 (2013).

  11. 11.

    et al. A genome-wide association study identifies multiple susceptibility loci for chronic lymphocytic leukemia. Nat. Genet. 46, 56–60 (2014).

  12. 12.

    , & How telomeric protein POT1 avoids RNA to achieve specificity for single-stranded DNA. Proc. Natl. Acad. Sci. USA 107, 651–656 (2010).

  13. 13.

    , & Structure of human POT1 bound to telomeric single-stranded DNA provides a model for chromosome end-protection. Nat. Struct. Mol. Biol. 11, 1223–1229 (2004).

  14. 14.

    et al. Analysis of 6,515 exomes reveals the recent origin of most human protein-coding variants. Nature 493, 216–220 (2013).

  15. 15.

    et al. Estimating telomere length from whole genome sequence data. Nucleic Acids Res. 10.1093/nar/gku181 (7 March 2014).

  16. 16.

    et al. Telomere length and the risk of cutaneous malignant melanoma in melanoma-prone families with and without CDKN2A mutations. PLoS ONE 8, e71121 (2013).

  17. 17.

    , & POT1 association with TRF2 regulates telomere length. Mol. Cell. Biol. 29, 5611–5619 (2009).

  18. 18.

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

  19. 19.

    et al. IntOGen-mutations identifies cancer drivers across tumor types. Nat. Methods 10, 1081–1082 (2013).

  20. 20.

    et al. Rare missense variants in POT1 predispose to familial cutaneous malignant melanoma. Nat. Genet. 10.1038/ng.2941 (30 March 2014).

  21. 21.

    Loss of telomere protection: consequences and opportunities. Front. Oncol. 3, 88 (2013).

  22. 22.

    Density Estimation (Chapman and Hall, London, 1986).

  23. 23.

    & SIFT: predicting amino acid changes that affect protein function. Nucleic Acids Res. 31, 3812–3814 (2003).

  24. 24.

    et al. A method and server for predicting damaging missense mutations. Nat. Methods 7, 248–249 (2010).

  25. 25.

    et al. A combined functional annotation score for non-synonymous variants. Hum. Hered. 73, 47–51 (2012).

  26. 26.

    , , , & The Queensland Familial Melanoma Project: study design and characteristics of participants. Melanoma Res. 6, 155–165 (1996).

  27. 27.

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

  28. 28.

    et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat. Genet. 43, 491–498 (2011).

  29. 29.

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

  30. 30.

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

  31. 31.

    1000 Genomes Project Consortium. An integrated map of genetic variation from 1,092 human genomes. Nature 491, 56–65 (2012).

  32. 32.

    et al. Deriving the consequences of genomic variants with the Ensembl API and SNP Effect Predictor. Bioinformatics 26, 2069–2070 (2010).

  33. 33.

    et al. The variant call format and VCFtools. Bioinformatics 27, 2156–2158 (2011).

  34. 34.

    et al. Principal components analysis corrects for stratification in genome-wide association studies. Nat. Genet. 38, 904–909 (2006).

  35. 35.

    et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).

  36. 36.

    et al. Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega. Mol. Syst. Biol. 7, 539 (2011).

  37. 37.

    , , , & Jalview Version 2—a multiple sequence alignment editor and analysis workbench. Bioinformatics 25, 1189–1191 (2009).

  38. 38.

    PHYLIP—Phylogeny Inference Package (Version 3.2). Cladistics 5, 164–166 (1989).

  39. 39.

    , & Human Pot1 (protection of telomeres) protein: cytolocalization, gene structure, and alternative splicing. Mol. Cell. Biol. 22, 8079–8087 (2002).

  40. 40.

    , , , & Telomere length, cigarette smoking, and bladder cancer risk in men and women. Cancer Epidemiol. Biomarkers Prev. 16, 815–819 (2007).

  41. 41.

    Telomere measurement by quantitative PCR. Nucleic Acids Res. 30, e47 (2002).

  42. 42.

    et al. Telomere length in prospective and retrospective cancer case-control studies. Cancer Res. 70, 3170–3176 (2010).

  43. 43.

    et al. Multiple independent variants at the TERT locus are associated with telomere length and risks of breast and ovarian cancer. Nat. Genet. 45, 371–384 (2013).

  44. 44.

    & Functional impact bias reveals cancer drivers. Nucleic Acids Res. 40, e169 (2012).

Download references

Acknowledgements

We thank the UK10K Consortium (funded by the Wellcome Trust; WT091310) for access to control data. D.J.A., C.D.R.-E., Z.D., J.Z.L., J.C.T., M.P. and T.M.K. were supported by Cancer Research UK and the Wellcome Trust (WT098051). C.D.R.-E. was also supported by the Consejo Nacional de Ciencia y Tecnología of Mexico. K.A.P. and A.M.D. were supported by Cancer Research UK (grants C1287/A9540 and C8197/A10123) and by the Isaac Newton Trust. N.K.H. was supported by a fellowship from the National Health and Medical Research Council of Australia (NHMRC). L.G.A. was supported by an Australia and New Zealand Banking Group Limited Trustees PhD scholarship. A.L.P. is supported by Cure Cancer Australia. The work was funded in part by the NHMRC and Cancer Council Queensland. The work of N.A.G. was in part supported by the Dutch Cancer Society (UL 2012-5489). M.H., J.A.N.-B. and D.T.B. were supported by Cancer Research UK (programme awards C588/A4994 and C588/A10589 and the Genomics Initiative). C.L.-O., A.J.R. and V.Q. are funded by the Spanish Ministry of Economy and Competitiveness through the Instituto de Salud Carlos III (ISCIII), the Red Temática de Investigación del Cáncer (RTICC) del ISCIII and the Consolider-Ingenio RNAREG Consortium. C.L.-O. is an investigator with the Botín Foundation.

Author information

Author notes

    • Carla Daniela Robles-Espinoza
    • , Mark Harland
    • , Andrew J Ramsay
    •  & Lauren G Aoude

    These authors contributed equally to this work.

    • Nicholas K Hayward
    • , D Timothy Bishop
    • , Julia A Newton-Bishop
    •  & David J Adams

    These authors jointly directed this work.

Affiliations

  1. Experimental Cancer Genetics, Wellcome Trust Sanger Institute, Hinxton, UK.

    • Carla Daniela Robles-Espinoza
    • , Zhihao Ding
    • , Jessamy C Tiffen
    • , Mia Petljak
    • , Thomas M Keane
    •  & David J Adams
  2. Section of Epidemiology and Biostatistics, Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, UK.

    • Mark Harland
    • , Helen Snowden
    • , D Timothy Bishop
    •  & Julia A Newton-Bishop
  3. Departamento de Bioquímica y Biología Molecular, Instituto Universitario de Oncología del Principado de Asturias (IUOPA), Universidad de Oviedo, Oviedo, Spain.

    • Andrew J Ramsay
    • , Víctor Quesada
    •  & Carlos López-Otín
  4. Oncogenomics Laboratory, QIMR Berghofer Medical Research Institute, Herston, Brisbane, Queensland, Australia.

    • Lauren G Aoude
    • , Antonia L Pritchard
    • , Jane M Palmer
    • , Judith Symmons
    • , Peter Johansson
    • , Mitchell S Stark
    • , Michael G Gartside
    •  & Nicholas K Hayward
  5. Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.

    • Karen A Pooley
  6. Molecular Epidemiology Laboratory, QIMR Berghofer Medical Research Institute, Herston, Brisbane, Queensland, Australia.

    • Grant W Montgomery
  7. Genetic Epidemiology Laboratory, QIMR Berghofer Medical Research Institute, Herston, Brisbane, Queensland, Australia.

    • Nicholas G Martin
  8. Statistical Genetics, Wellcome Trust Sanger Institute, Hinxton, UK.

    • Jimmy Z Liu
  9. Laboratory of Translational Genomics, National Cancer Institute, Bethesda, Maryland, USA.

    • Jiyeon Choi
    • , Matthew Makowski
    •  & Kevin M Brown
  10. Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK.

    • Alison M Dunning
  11. Department of Dermatology, Leiden University Medical Centre, Leiden, The Netherlands.

    • Nelleke A Gruis

Authors

  1. Search for Carla Daniela Robles-Espinoza in:

  2. Search for Mark Harland in:

  3. Search for Andrew J Ramsay in:

  4. Search for Lauren G Aoude in:

  5. Search for Víctor Quesada in:

  6. Search for Zhihao Ding in:

  7. Search for Karen A Pooley in:

  8. Search for Antonia L Pritchard in:

  9. Search for Jessamy C Tiffen in:

  10. Search for Mia Petljak in:

  11. Search for Jane M Palmer in:

  12. Search for Judith Symmons in:

  13. Search for Peter Johansson in:

  14. Search for Mitchell S Stark in:

  15. Search for Michael G Gartside in:

  16. Search for Helen Snowden in:

  17. Search for Grant W Montgomery in:

  18. Search for Nicholas G Martin in:

  19. Search for Jimmy Z Liu in:

  20. Search for Jiyeon Choi in:

  21. Search for Matthew Makowski in:

  22. Search for Kevin M Brown in:

  23. Search for Alison M Dunning in:

  24. Search for Thomas M Keane in:

  25. Search for Carlos López-Otín in:

  26. Search for Nelleke A Gruis in:

  27. Search for Nicholas K Hayward in:

  28. Search for D Timothy Bishop in:

  29. Search for Julia A Newton-Bishop in:

  30. Search for David J Adams in:

Contributions

C.D.R.-E., M.H., J.A.N.-B., D.T.B., N.K.H. and D.J.A. designed the study and wrote the manuscript. C.D.R.-E., M.H., L.G.A., J.C.T., M.M., J.C., M.P., A.J.R., Z.D., V.Q., A.L.P., J.M.P., J.S., M.S.S., N.G.M., M.G.G., A.M.D., K.A.P., P.J., J.Z.L., K.M.B., C.L.-O. and T.M.K. performed experiments or analysis. N.A.G., G.W.M., H.S. and N.G.M. provided vital biological resources.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to David J Adams.

Integrated supplementary information

Supplementary information

PDF files

  1. 1.

    Supplementary Text and Figures

    Supplementary Figures 1–8, Supplementary Tables 1, 2 and 4–8 and Supplementary Note

Excel files

  1. 1.

    Supplementary Table 3

    Genes with cosegregating variants from the 28 pedigrees for which we had sequence data for 3 or more members and their GO terms.

About this article

Publication history

Received

Accepted

Published

DOI

https://doi.org/10.1038/ng.2947

Further reading

Newsletter Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing