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Genomic analysis identifies new drivers and progression pathways in skin basal cell carcinoma

Nature Genetics volume 48, pages 398–406 (2016)Cite this article

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Abstract

Basal cell carcinoma (BCC) of the skin is the most common malignant neoplasm in humans. BCC is primarily driven by the Sonic Hedgehog (Hh) pathway. However, its phenotypic variation remains unexplained. Our genetic profiling of 293 BCCs found the highest mutation rate in cancer (65 mutations/Mb). Eighty-five percent of the BCCs harbored mutations in Hh pathway genes (PTCH1, 73% or SMO, 20% (P = 6.6 × 10−8) and SUFU, 8%) and in TP53 (61%). However, 85% of the BCCs also harbored additional driver mutations in other cancer-related genes. We observed recurrent mutations in MYCN (30%), PPP6C (15%), STK19 (10%), LATS1 (8%), ERBB2 (4%), PIK3CA (2%), and NRAS, KRAS or HRAS (2%), and loss-of-function and deleterious missense mutations were present in PTPN14 (23%), RB1 (8%) and FBXW7 (5%). Consistent with the mutational profiles, N-Myc and Hippo-YAP pathway target genes were upregulated. Functional analysis of the mutations in MYCN, PTPN14 and LATS1 suggested their potential relevance in BCC tumorigenesis.

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Figure 1: Mutational landscape of BCC.
Figure 2: Mutations in copy-neutral LOH regions in BCC.
Figure 3: Functional characterization of N-Myc alterations in BCC.
Figure 4: PTPN14 mutations in BCC.
Figure 5: LATS1 mutations in BCC.
Figure 6: Clonality in sporadic BCCs.
Figure 7: The expression of N-Myc and YAP1 target genes is upregulated in BCC.
Figure 8: Signaling pathways involved in BCC.

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References

  1. Iwasaki, J.K., Srivastava, D., Moy, R.L., Lin, H.J. & Kouba, D.J. The molecular genetics underlying basal cell carcinoma pathogenesis and links to targeted therapeutics. J. Am. Acad. Dermatol. 66, e167–e178 (2012).

    Article  CAS  PubMed  Google Scholar 

  2. Youssef, K.K. et al. Identification of the cell lineage at the origin of basal cell carcinoma. Nat. Cell Biol. 12, 299–305 (2010).

    Article  CAS  PubMed  Google Scholar 

  3. Hahn, H. et al. Mutations of the human homolog of Drosophila patched in the nevoid basal cell carcinoma syndrome. Cell 85, 841–851 (1996).

    Article  CAS  PubMed  Google Scholar 

  4. Reifenberger, J. et al. Somatic mutations in the PTCH, SMOH, SUFUH and TP53 genes in sporadic basal cell carcinomas. Br. J. Dermatol. 152, 43–51 (2005).

    Article  CAS  PubMed  Google Scholar 

  5. Sharpe, H.J. et al. Genomic analysis of Smoothened inhibitor resistance in basal cell carcinoma. Cancer Cell 27, 327–341 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Atwood, S.X. et al. Smoothened variants explain the majority of drug resistance in basal cell carcinoma. Cancer Cell 27, 342–353 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Smith, M.J. et al. Germline mutations in SUFU cause Gorlin syndrome–associated childhood medulloblastoma and redefine the risk associated with PTCH1 mutations. J. Clin. Oncol. 32, 4155–4161 (2014).

    Article  CAS  PubMed  Google Scholar 

  8. Sweeney, R.T. et al. Identification of recurrent SMO and BRAF mutations in ameloblastomas. Nat. Genet. 46, 722–725 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Laurendeau, I. et al. Gene expression profiling of the hedgehog signaling pathway in human meningiomas. Mol. Med. 16, 262–270 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Brastianos, P.K. et al. Genomic sequencing of meningiomas identifies oncogenic SMO and AKT1 mutations. Nat. Genet. 45, 285–289 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Pomeroy, S.L. et al. Prediction of central nervous system embryonal tumour outcome based on gene expression. Nature 415, 436–442 (2002).

    Article  CAS  PubMed  Google Scholar 

  12. Clark, V.E. et al. Genomic analysis of non-NF2 meningiomas reveals mutations in TRAF7, KLF4, AKT1, and SMO. Science 339, 1077–1080 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Jayaraman, S.S., Rayhan, D.J., Hazany, S. & Kolodney, M.S. Mutational landscape of basal cell carcinomas by whole-exome sequencing. J. Invest. Dermatol. 134, 213–220 (2014).

    Article  CAS  PubMed  Google Scholar 

  14. Basset-Seguin, N. et al. Vismodegib in patients with advanced basal cell carcinoma (STEVIE): a pre-planned interim analysis of an international, open-label trial. Lancet Oncol. 16, 729–736 (2015).

    Article  CAS  PubMed  Google Scholar 

  15. Von Hoff, D.D. et al. Inhibition of the hedgehog pathway in advanced basal-cell carcinoma. N. Engl. J. Med. 361, 1164–1172 (2009).

    Article  CAS  PubMed  Google Scholar 

  16. Sekulic, A. et al. Efficacy and safety of vismodegib in advanced basal-cell carcinoma. N. Engl. J. Med. 366, 2171–2179 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Dreno, B., Basset-Seguin, N., Caro, I., Yue, H. & Schadendorf, D. Clinical benefit assessment of vismodegib therapy in patients with advanced basal cell carcinoma. Oncologist 19, 790–796 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Chang, A.L. & Oro, A.E. Initial assessment of tumor regrowth after vismodegib in advanced basal cell carcinoma. Arch. Dermatol. 148, 1324–1325 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  19. Lawrence, M.S. et al. Discovery and saturation analysis of cancer genes across 21 tumour types. Nature 505, 495–501 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Hodis, E. et al. A landscape of driver mutations in melanoma. Cell 150, 251–263 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

  22. Nikolaev, S.I. et al. Exome sequencing identifies recurrent somatic MAP2K1 and MAP2K2 mutations in melanoma. Nat. Genet. 44, 133–139 (2012).

    Article  CAS  Google Scholar 

  23. Drobetsky, E.A. & Sage, E. UV-induced G:C-->A:T transitions at the APRT locus of Chinese hamster ovary cells cluster at frequently damaged 5′-TCC-3′ sequences. Mutat. Res. 289, 131–138 (1993).

    Article  CAS  PubMed  Google Scholar 

  24. Marionnet, C., Benoit, A., Benhamou, S., Sarasin, A. & Stary, A. Characteristics of UV-induced mutation spectra in human XP-D/ERCC2 gene–mutated xeroderma pigmentosum and trichothiodystrophy cells. J. Mol. Biol. 252, 550–562 (1995).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Hanawalt, P.C., Donahue, B.A. & Sweder, K.S. Repair and transcription. Collision or collusion? Curr. Biol. 4, 518–521 (1994).

    Article  CAS  PubMed  Google Scholar 

  27. Besaratinia, A., Kim, S.I., Bates, S.E. & Pfeifer, G.P. Riboflavin activated by ultraviolet A1 irradiation induces oxidative DNA damage–mediated mutations inhibited by vitamin C. Proc. Natl. Acad. Sci. USA 104, 5953–5958 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Brash, D.E. UV signature mutations. Photochem. Photobiol. 91, 15–26 (2015).

    Article  CAS  PubMed  Google Scholar 

  29. Hu, J., Adar, S., Selby, C.P., Lieb, J.D. & Sancar, A. Genome-wide analysis of human global and transcription-coupled excision repair of UV damage at single-nucleotide resolution. Genes Dev. 29, 948–960 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. McGregor, J.M. et al. Spectrum of p53 gene mutations suggests a possible role for ultraviolet radiation in the pathogenesis of advanced cutaneous lymphomas. J. Invest. Dermatol. 112, 317–321 (1999).

    Article  CAS  PubMed  Google Scholar 

  31. da Silva Almeida, A.C. et al. The mutational landscape of cutaneous T cell lymphoma and Sézary syndrome. Nat. Genet. 47, 1465–1470 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Strano, S. et al. Mutant p53: an oncogenic transcription factor. Oncogene 26, 2212–2219 (2007).

    Article  CAS  PubMed  Google Scholar 

  33. Kato, G.J. & Dang, C.V. Function of the c-Myc oncoprotein. FASEB J. 6, 3065–3072 (1992).

    Article  CAS  PubMed  Google Scholar 

  34. Bahram, F., von der Lehr, N., Cetinkaya, C. & Larsson, L.G. c-Myc hot spot mutations in lymphomas result in inefficient ubiquitination and decreased proteasome-mediated turnover. Blood 95, 2104–2110 (2000).

    CAS  PubMed  Google Scholar 

  35. Welcker, M. et al. The Fbw7 tumor suppressor regulates glycogen synthase kinase 3 phosphorylation-dependent c-Myc protein degradation. Proc. Natl. Acad. Sci. USA 101, 9085–9090 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Freier, K. et al. Recurrent NMYC copy number gain and high protein expression in basal cell carcinoma. Oncol. Rep. 15, 1141–1145 (2006).

    CAS  PubMed  Google Scholar 

  37. Stacey, S.N. et al. New basal cell carcinoma susceptibility loci. Nat. Commun. 6, 6825 (2015).

    Article  CAS  PubMed  Google Scholar 

  38. Knoepfler, P.S. & Kenney, A.M. Neural precursor cycling at sonic speed: N-Myc pedals, GSK-3 brakes. Cell Cycle 5, 47–52 (2006).

    Article  CAS  PubMed  Google Scholar 

  39. Wong, Y.H. et al. KinasePhos 2.0: a web server for identifying protein kinase–specific phosphorylation sites based on sequences and coupling patterns. Nucleic Acids Res. 35, W588–W594 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  40. Wang, W. et al. PTPN14 is required for the density-dependent control of YAP1. Genes Dev. 26, 1959–1971 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Welcker, M. & Clurman, B.E. FBW7 ubiquitin ligase: a tumour suppressor at the crossroads of cell division, growth and differentiation. Nat. Rev. Cancer 8, 83–93 (2008).

    Article  CAS  PubMed  Google Scholar 

  42. King, B. et al. The ubiquitin ligase FBXW7 modulates leukemia-initiating cell activity by regulating MYC stability. Cell 153, 1552–1566 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Zhang, H., Pasolli, H.A. & Fuchs, E. Yes-associated protein (YAP) transcriptional coactivator functions in balancing growth and differentiation in skin. Proc. Natl. Acad. Sci. USA 108, 2270–2275 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Schlegelmilch, K. et al. Yap1 acts downstream of α-catenin to control epidermal proliferation. Cell 144, 782–795 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Wilson, K.E. et al. PTPN14 forms a complex with Kibra and LATS1 proteins and negatively regulates the YAP oncogenic function. J. Biol. Chem. 289, 23693–23700 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Hao, Y., Chun, A., Cheung, K., Rashidi, B. & Yang, X. Tumor suppressor LATS1 is a negative regulator of oncogene YAP. J. Biol. Chem. 283, 5496–5509 (2008).

    Article  CAS  PubMed  Google Scholar 

  47. Tate, G., Kishimoto, K. & Mitsuya, T. Biallelic alterations of the large tumor suppressor 1 (LATS1) gene in infiltrative, but not superficial, basal cell carcinomas in a Japanese patient with nevoid basal cell carcinoma syndrome. Med. Mol. Morphol. 48, 177–182 (2015).

    Article  CAS  PubMed  Google Scholar 

  48. Krauthammer, M. et al. Exome sequencing identifies recurrent somatic RAC1 mutations in melanoma. Nat. Genet. 44, 1006–1014 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Hammond, D. et al. Melanoma-associated mutations in protein phosphatase 6 cause chromosome instability and DNA damage owing to dysregulated Aurora-A. J. Cell Sci. 126, 3429–3440 (2013).

    Article  CAS  PubMed  Google Scholar 

  50. Duman-Scheel, M., Weng, L., Xin, S. & Du, W. Hedgehog regulates cell growth and proliferation by inducing cyclin D and cyclin E. Nature 417, 299–304 (2002).

    Article  CAS  PubMed  Google Scholar 

  51. Stefansson, B. & Brautigan, D.L. Protein phosphatase PP6 N terminal domain restricts G1 to S phase progression in human cancer cells. Cell Cycle 6, 1386–1392 (2007).

    Article  CAS  PubMed  Google Scholar 

  52. Couzens, A.L. et al. Protein interaction network of the mammalian Hippo pathway reveals mechanisms of kinase-phosphatase interactions. Sci. Signal. 6, rs15 (2013).

    Article  CAS  PubMed  Google Scholar 

  53. Jaju, P.D. et al. Mutations in the kinetochore gene KNSTRN in basal cell carcinoma. J. Invest. Dermatol. 135, 3197–3200 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Lee, C.S. et al. Recurrent point mutations in the kinetochore gene KNSTRN in cutaneous squamous cell carcinoma. Nat. Genet. 46, 1060–1062 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Santos, G.C., Zielenska, M., Prasad, M. & Squire, J.A. Chromosome 6p amplification and cancer progression. J. Clin. Pathol. 60, 1–7 (2007).

    Article  CAS  PubMed  Google Scholar 

  56. Zhang, H. et al. TEAD transcription factors mediate the function of TAZ in cell growth and epithelial-mesenchymal transition. J. Biol. Chem. 284, 13355–13362 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Valentijn, L.J. et al. Functional MYCN signature predicts outcome of neuroblastoma irrespective of MYCN amplification. Proc. Natl. Acad. Sci. USA 109, 19190–19195 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Pfister, S. et al. Outcome prediction in pediatric medulloblastoma based on DNA copy-number aberrations of chromosomes 6q and 17q and the MYC and MYCN loci. J. Clin. Oncol. 27, 1627–1636 (2009).

    Article  PubMed  Google Scholar 

  59. Xu, L. et al. Sonic Hedgehog pathway is essential for neuroblastoma cell proliferation and tumor growth. Mol. Cell. Biochem. 364, 235–241 (2012).

    Article  CAS  PubMed  Google Scholar 

  60. Oue, T., Yoneda, A., Uehara, S., Yamanaka, H. & Fukuzawa, M. Increased expression of the hedgehog signaling pathway in pediatric solid malignancies. J. Pediatr. Surg. 45, 387–392 (2010).

    Article  PubMed  Google Scholar 

  61. Murphy, A.J. et al. Aberrant activation, nuclear localization, and phosphorylation of Yes-associated protein-1 in the embryonic kidney and Wilms tumor. Pediatr. Blood Cancer 61, 198–205 (2014).

    Article  PubMed  Google Scholar 

  62. Pugh, T.J. et al. The genetic landscape of high-risk neuroblastoma. Nat. Genet. 45, 279–284 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Ellison, D.W. et al. Medulloblastoma: clinicopathological correlates of SHH, WNT, and non-SHH/WNT molecular subgroups. Acta Neuropathol. 121, 381–396 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Williams, R.D. et al. Multiple mechanisms of MYCN dysregulation in Wilms tumour. Oncotarget 6, 7232–7243 (2015).

    PubMed  PubMed Central  Google Scholar 

  65. Nikolaev, S.I. et al. Frequent cases of RAS-mutated Down syndrome acute lymphoblastic leukaemia lack JAK2 mutations. Nat. Commun. 5, 4654 (2014).

    Article  CAS  PubMed  Google Scholar 

  66. Vogelstein, B. et al. Cancer genome landscapes. Science 339, 1546–1558 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. 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 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Cibulskis, K. et al. Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nat. Biotechnol. 31, 213–219 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Nikolaev, S. et al. Extrachromosomal driver mutations in glioblastoma and low-grade glioma. Nat. Commun. 5, 5690 (2014).

    Article  CAS  PubMed  Google Scholar 

  71. Mermel, C.H. et al. GISTIC2.0 facilitates sensitive and confident localization of the targets of focal somatic copy-number alteration in human cancers. Genome Biol. 12, R41 (2011).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  72. Lawrence, M.S. et al. Mutational heterogeneity in cancer and the search for new cancer-associated genes. Nature 499, 214–218 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Roadmap Epigenomics Consortium. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015).

  74. Chen, C.L. et al. Impact of replication timing on non-CpG and CpG substitution rates in mammalian genomes. Genome Res. 20, 447–457 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Cancer Genome Atlas Network. Comprehensive genomic characterization of head and neck squamous cell carcinomas. Nature 517, 576–582 (2015).

  76. Nikolayeva, O. & Robinson, M.D. edgeR for differential RNA-seq and ChIP-seq analysis: an application to stem cell biology. Methods Mol. Biol. 1150, 45–79 (2014).

    Article  CAS  PubMed  Google Scholar 

  77. Huang, 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  CAS  Google Scholar 

  78. Huang, 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  CAS  Google Scholar 

  79. Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. USA 102, 15545–15550 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  80. Buchan, D.W., Minneci, F., Nugent, T.C., Bryson, K. & Jones, D.T. Scalable web services for the PSIPRED Protein Analysis Workbench. Nucleic Acids Res. 41, W349–W357 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  81. Dinkel, H. et al. The eukaryotic linear motif resource ELM: 10 years and counting. Nucleic Acids Res. 42, D259–D266 (2014).

    Article  CAS  PubMed  Google Scholar 

  82. Eswar, N. et al. Comparative protein structure modeling using MODELLER. Curr. Protoc. Protein Sci. Chapter 2, Unit 2.9 (2007).

  83. Guerois, R., Nielsen, J.E. & Serrano, L. Predicting changes in the stability of proteins and protein complexes: a study of more than 1000 mutations. J. Mol. Biol. 320, 369–387 (2002).

    Article  CAS  PubMed  Google Scholar 

  84. Lodrini, M. et al. MYCN and HDAC2 cooperate to repress miR-183 signaling in neuroblastoma. Nucleic Acids Res. 41, 6018–6033 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Xiao, W. et al. Mutual interaction between YAP and c-Myc is critical for carcinogenesis in liver cancer. Biochem. Biophys. Res. Commun. 439, 167–172 (2013).

    Article  CAS  PubMed  Google Scholar 

  86. Li, L. et al. MEK1 promotes YAP and their interaction is critical for tumorigenesis in liver cancer. FEBS Lett. 587, 3921–3927 (2013).

    Article  CAS  PubMed  Google Scholar 

  87. Gerdes, J., Becker, M.H., Key, G. & Cattoretti, G. Immunohistological detection of tumour growth fraction (Ki-67 antigen) in formalin-fixed and routinely processed tissues. J. Pathol. 168, 85–86 (1992).

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

We thank Z. Modrusan (next-generation sequencing), R. Piskol (computational biology), G. Pau (computational biology), F. Peale (pathology) and S. Jillo (collaboration management) from Genentech, Inc. This work was supported by Swiss Cancer League (LSCC 2939-02-2012), Dinu Lipatti 2014 and Novartis (14B065) research grants to S.I.N.

Author information

Author notes

  1. Ximena Bonilla and Laurent Parmentier: These authors contributed equally to this work.

Authors and Affiliations

  1. Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland

    Ximena Bonilla, Audrey Letourneau, Pascale G Ribaux, Konstantin Popadin, Rouaa Ben Chaabene, Federico A Santoni, Marco Garieri, Stylianos E Antonarakis & Sergey I Nikolaev

  2. Department of Dermatology, Hospital of Valais, Sierre, Switzerland

    Laurent Parmentier

  3. Department of Pathology, New York University School of Medicine, New York, New York, USA

    Bryan King & Iannis Aifantis

  4. Department of Physics, University of Connecticut, Storrs, Connecticut, USA

    Fedor Bezrukov

  5. RIKEN BNL Research Center, Brookhaven National Laboratory, Upton, New York, USA

    Fedor Bezrukov

  6. Department of Dermatology, University Hospitals of Geneva, Geneva, Switzerland

    Gürkan Kaya & Kerstin Grosdemange

  7. Swiss Institute of Bioinformatics, Lausanne, Switzerland

    Vincent Zoete & Olivier Michielin

  8. Institute of Information Transmission Problems, Russian Academy of Sciences, Moscow, Russia

    Vladimir B Seplyarskiy & Maria A Andrianova

  9. Pirogov Russian National Research Medical University, Moscow, Russia

    Vladimir B Seplyarskiy & Maria A Andrianova

  10. Lomonosov Moscow State University, Moscow, Russia

    Vladimir B Seplyarskiy & Maria A Andrianova

  11. Department of Molecular Oncology, Genentech, Inc., South San Francisco, California, USA

    Hayley J Sharpe & Frederic J de Sauvage

  12. Service of Clinical Pathology, University Hospitals of Geneva, Geneva, Switzerland

    Thomas McKee & Carole Verdan

  13. Department of Dermatology, Saint Louis Hospital, Paris 7 University, Paris, France

    Nicole Basset-Seguin

  14. Service of Genetic Medicine, University Hospitals of Geneva, Geneva, Switzerland

    Federico A Santoni, Michel Guipponi, Stylianos E Antonarakis & Sergey I Nikolaev

  15. Department of Biochemistry and Molecular Biology, University of Würzburg, Würzburg, Germany

    Olga Sumara & Martin Eilers

  16. Comprehensive Cancer Center Mainfranken, University of Würzburg, Würzburg, Germany

    Martin Eilers

  17. Department of Oncology, University of Lausanne and Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland

    Olivier Michielin

  18. Institute of Genetics and Genomics of Geneva (iGE3), Geneva, Switzerland

    Stylianos E Antonarakis

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  1. Ximena Bonilla
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  2. Laurent Parmentier
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  26. Stylianos E Antonarakis
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  27. Sergey I Nikolaev
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Contributions

S.I.N. and L.P. designed the project. S.I.N. and X.B. performed high-throughput sequencing data analysis. S.I.N., F.B., K.P., F.A.S. and R.B.C. performed statistical analysis. S.I.N., X.B., H.J.S., F.J.d.S. and S.E.A. wrote the manuscript. V.Z. and O.M. performed in silico modeling. X.B. and P.G.R. performed sample and high-throughput sequencing library preparation. L.P., N.B.-S., G.K. and K.G. collected samples. B.K. and I.A. performed MYCN in vivo experiments. T.M. and C.V. performed PTPN14 immunohistochemistry experiments. S.I.N., A.L. and M. Garieri performed RNA-seq analysis. V.B.S., M.A.A. and S.I.N. performed mutational signature analysis. M. Guipponi, P.G.R. and X.B. performed exome capture and high-throughput sequencing. M.E. and O.S. performed experiments.

Corresponding authors

Correspondence to Stylianos E Antonarakis or Sergey I Nikolaev.

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Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Mutational signatures in BCC compared to melanoma.

(a) The effect of transcription-coupled repair as a function of gene expression in BCC. Tumor type–specific gene expression levels were used for BCC (this study) and cutaneous melanoma21. Genes were grouped into five equally sized bins by their level of expression. 95% confidence intervals for the ratio between the two binomial distributions were calculated using the R function riskscoreci(). (b) Fraction of each nucleotide 5′ to C>A mutations.

Supplementary Figure 2 Distribution of mutations in BCC driver genes.

(a–n) Distribution of mutations in the full sample set (293 BCC samples) along the FBXW7 (a), PPP6C (b), STK19 (c), CASP8 (d), RB1 (e), KNSTRN (f), ERBB2 (g), NOTCH2 (h), NOTCH1 (i), ARID1A (j), SMO (k), TP53 (l), PTCH1 (m) and SUFU (n) protein diagrams. The orange lollipops represent truncating mutations, the purple lollipops represent missense mutations and the blue lollipops represent both truncating and missense events affecting the same amino acid. Protein functional domains are represented by colored boxes. The most recurrent events for each protein are labeled with the amino acid change. Protein diagrams were generated with cBioPortal tools.

Supplementary Figure 3 SCNAs in BCC exomes.

Top, overall profile of SCNAs in BCC. LOHs and cnLOHs are on the top (dark blue); amplified regions are on the bottom (red). The positions of relevant genes are marked. Bottom, per-tumor SCNA profile. Each line represents a sample, and each column represents a chromosome. Loss of an allele (LOH) is depicted in blue, cnLOH is indicated in black and copy number gain is indicated in yellow. Chromosomes X and Y have been excluded.

Supplementary Figure 4 LATS2 mutations in BCC.

(a) Distribution of mutations (in the 136 sequenced exomes only) along the LATS2 protein schema. The purple lollipops represent missense mutations, and the orange lollipops represent truncating mutations. Protein functional domains are represented by green boxes. Events occurring two or more times are labeled with the amino acid change. (b) Kinase domain structure of LATS2 highlighting the position of residue Pro1004. The protein diagram was generated with cBioPortal tools.

Supplementary Figure 5 Fraction of tumors with driver mutations per category.

The bars represent the fraction of samples with driver mutations per BCC category indicated in the header (only non-clonal samples were used in this analysis). Vismo, vismodegib. Genes harboring driver mutations are labeled on the x axis. MYCN-p.44 is a subcategory containing only tumors with MYCN p.44 mutations; PTPN14-tr corresponds to PTPN14 truncating mutations. The fraction of tumors represented by the bars can be found at the left.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–5 and Supplementary Note. (PDF 1459 kb)

Supplementary Table 1

Sample characteristics and sequencing statistics. (XLSX 121 kb)

Supplementary Table 2

Cancer panel genes. (XLSX 13 kb)

Supplementary Table 3

Somatic mutations identified in exome and cancer panel sequenced samples. (XLSX 40169 kb)

Supplementary Table 4

Fraction of mutations on the transcribed strand. (XLSX 9 kb)

Supplementary Table 5

MutSigCV analysis of cancer panel genes on exome-sequenced tumors (121 samples). (XLSX 2197 kb)

Supplementary Table 6

TumOnc output for cancer panel genes on the full data set (non-clonal tumors). (XLSX 21 kb)

Supplementary Table 7

Fraction of tumor cells and fraction of UV light–induced mutations per sample. (XLSX 23 kb)

Supplementary Table 8

Somatic copy number aberrations (SCNAs). (XLSX 33 kb)

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Bonilla, X., Parmentier, L., King, B. et al. Genomic analysis identifies new drivers and progression pathways in skin basal cell carcinoma. Nat Genet 48, 398–406 (2016). https://doi.org/10.1038/ng.3525

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