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.

  • Article
  • Published:

Comprehensive genomic analysis of malignant pleural mesothelioma identifies recurrent mutations, gene fusions and splicing alterations

Nature Genetics volume 48, pages 407–416 (2016)Cite this article

Subjects

Abstract

We analyzed transcriptomes (n = 211), whole exomes (n = 99) and targeted exomes (n = 103) from 216 malignant pleural mesothelioma (MPM) tumors. Using RNA-seq data, we identified four distinct molecular subtypes: sarcomatoid, epithelioid, biphasic-epithelioid (biphasic-E) and biphasic-sarcomatoid (biphasic-S). Through exome analysis, we found BAP1, NF2, TP53, SETD2, DDX3X, ULK2, RYR2, CFAP45, SETDB1 and DDX51 to be significantly mutated (q-score ≥ 0.8) in MPMs. We identified recurrent mutations in several genes, including SF3B1 (∼2%; 4/216) and TRAF7 (∼2%; 5/216). SF3B1-mutant samples showed a splicing profile distinct from that of wild-type tumors. TRAF7 alterations occurred primarily in the WD40 domain and were, except in one case, mutually exclusive with NF2 alterations. We found recurrent gene fusions and splice alterations to be frequent mechanisms for inactivation of NF2, BAP1 and SETD2. Through integrated analyses, we identified alterations in Hippo, mTOR, histone methylation, RNA helicase and p53 signaling pathways in MPMs.

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

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

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

Figure 1: Expression-based mesothelioma subtypes.
Figure 2: Mesothelioma somatic mutations.
Figure 3: Significantly mutated mesothelioma genes.
Figure 4: Multiple molecular mechanisms lead to activation or inactivation of genes.
Figure 5: Mutations in the splicing factor SF3B1 are associated with specific alterations in mRNA splicing.
Figure 6: Integrated analysis of pathway alterations observed in MPM.

Similar content being viewed by others

References

  1. Bueno, R. Multimodality treatments in the management of malignant pleural mesothelioma: an update. Hematol. Oncol. Clin. North Am. 19, 1089–1097, vii (2005).

    Article  PubMed  Google Scholar 

  2. Rudd, R. Asbestos and the lung. Medicine 36, 261–264 (2008).

    Article  Google Scholar 

  3. Carbone, M. et al. Malignant mesothelioma: facts, myths, and hypotheses. J. Cell. Physiol. 227, 44–58 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Frank, A.L. & Joshi, T.K. The global spread of asbestos. Ann. Glob. Health 80, 257–262 (2014).

    Article  PubMed  Google Scholar 

  5. Christoph, D.C. & Eberhardt, W.E. Systemic treatment of malignant pleural mesothelioma: new agents in clinical trials raise hope of relevant improvements. Curr. Opin. Oncol. 26, 171–181 (2014).

    Article  CAS  PubMed  Google Scholar 

  6. Bueno, R. et al. Pleural biopsy: a reliable method for determining the diagnosis but not subtype in mesothelioma. Ann. Thorac. Surg. 78, 1774–1776 (2004).

    Article  PubMed  Google Scholar 

  7. Sugarbaker, D.J., Richards, W.G. & Bueno, R. Extrapleural pneumonectomy in the treatment of epithelioid malignant pleural mesothelioma: novel prognostic implications of combined N1 and N2 nodal involvement based on experience in 529 patients. Ann. Surg. 260, 577–580, discussion 580–582 (2014).

    Article  PubMed  Google Scholar 

  8. Baldini, E.H. et al. Updated patterns of failure after multimodality therapy for malignant pleural mesothelioma. J. Thorac. Cardiovasc. Surg. 149, 1374–1381 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  9. Remon, J., Reguart, N., Corral, J. & Lianes, P. Malignant pleural mesothelioma: new hope in the horizon with novel therapeutic strategies. Cancer Treat. Rev. 41, 27–34 (2015).

    Article  CAS  PubMed  Google Scholar 

  10. Dong, L. et al. Differentially expressed alternatively spliced genes in malignant pleural mesothelioma identified using massively parallel transcriptome sequencing. BMC Med. Genet. 10, 149 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  11. Bueno, R. et al. Second generation sequencing of the mesothelioma tumor genome. PLoS One 5, e10612 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  12. Bott, M. et al. The nuclear deubiquitinase BAP1 is commonly inactivated by somatic mutations and 3p21.1 losses in malignant pleural mesothelioma. Nat. Genet. 43, 668–672 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Guo, G. et al. Whole-exome sequencing reveals frequent genetic alterations in BAP1, NF2, CDKN2A, and CUL1 in malignant pleural mesothelioma. Cancer Res. 75, 264–269 (2015).

    Article  CAS  PubMed  Google Scholar 

  14. Bianchi, A.B. et al. High frequency of inactivating mutations in the neurofibromatosis type 2 gene (NF2) in primary malignant mesotheliomas. Proc. Natl. Acad. Sci. USA 92, 10854–10858 (1995).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Cheng, J.Q. et al. p16 alterations and deletion mapping of 9p21–p22 in malignant mesothelioma. Cancer Res. 54, 5547–5551 (1994).

    CAS  PubMed  Google Scholar 

  16. Alexandrov, L.B., Nik-Zainal, S., Wedge, D.C., Campbell, P.J. & Stratton, M.R. Deciphering signatures of mutational processes operative in human cancer. Cell Reports 3, 246–259 (2013).

    Article  CAS  PubMed  Google Scholar 

  17. Testa, J.R. et al. Germline BAP1 mutations predispose to malignant mesothelioma. Nat. Genet. 43, 1022–1025 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Travis, W.D., Brambilla, E., Müller-Hermelink, H.K. & Harris, C.C. WHO Classification of Tumors of the Lung, Pleura, Thymus and Heart (IARC Press, 2004).

  19. Henderson, D.W., Reid, G., Kao, S.C., van Zandwijk, N. & Klebe, S. Challenges and controversies in the diagnosis of mesothelioma: Part 1. Cytology-only diagnosis, biopsies, immunohistochemistry, discrimination between mesothelioma and reactive mesothelial hyperplasia, and biomarkers. J. Clin. Pathol. 66, 847–853 (2013).

    Article  PubMed  Google Scholar 

  20. Gordon, G.J. et al. Using gene expression ratios to predict outcome among patients with mesothelioma. J. Natl. Cancer Inst. 95, 598–605 (2003).

    Article  CAS  PubMed  Google Scholar 

  21. Gordon, G.J. et al. Identification of novel candidate oncogenes and tumor suppressors in malignant pleural mesothelioma using large-scale transcriptional profiling. Am. J. Pathol. 166, 1827–1840 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Pass, H.I. et al. Gene expression profiles predict survival and progression of pleural mesothelioma. Clin. Cancer Res. 10, 849–859 (2004).

    Article  CAS  PubMed  Google Scholar 

  23. López-Ríos, F. et al. Global gene expression profiling of pleural mesotheliomas: overexpression of aurora kinases and P16/CDKN2A deletion as prognostic factors and critical evaluation of microarray-based prognostic prediction. Cancer Res. 66, 2970–2979 (2006).

    Article  PubMed  CAS  Google Scholar 

  24. de Reyniès, A. et al. Molecular classification of malignant pleural mesothelioma: identification of a poor prognosis subgroup linked to the epithelial-to-mesenchymal transition. Clin. Cancer Res. 20, 1323–1334 (2014).

    Article  PubMed  CAS  Google Scholar 

  25. Kanamori-Katayama, M. et al. LRRN4 and UPK3B are markers of primary mesothelial cells. PLoS One 6, e25391 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Lamouille, S., Xu, J. & Derynck, R. Molecular mechanisms of epithelial-mesenchymal transition. Nat. Rev. Mol. Cell Biol. 15, 178–196 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Moon, H.J. et al. MCF-7 cells expressing nuclear associated lysyl oxidase–like 2 (LOXL2) exhibit an epithelial-to-mesenchymal transition (EMT) phenotype and are highly invasive in vitro. J. Biol. Chem. 288, 30000–30008 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Durinck, S. et al. Spectrum of diverse genomic alterations define non–clear cell renal carcinoma subtypes. Nat. Genet. 47, 13–21 (2015).

    Article  CAS  PubMed  Google Scholar 

  29. 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  PubMed  Google Scholar 

  30. Cancer Genome Atlas Research Network. Integrated genomic characterization of papillary thyroid carcinoma. Cell 159, 676–690 (2014).

  31. Cancer Genome Atlas Research Network. Genomic and epigenomic landscapes of adult de novo acute myeloid leukemia. N. Engl. J. Med. 368, 2059–2074 (2013).

  32. Nik-Zainal, S. et al. Mutational processes molding the genomes of 21 breast cancers. Cell 149, 979–993 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Benedetti, S., Nuvoli, B., Catalani, S. & Galati, R. Reactive oxygen species a double-edged sword for mesothelioma. Oncotarget 6, 16848–16865 (2015).

    PubMed  PubMed Central  Google Scholar 

  34. Pfeifer, G.P. Mutagenesis at methylated CpG sequences. Curr. Top. Microbiol. Immunol. 301, 259–281 (2006).

    CAS  PubMed  Google Scholar 

  35. Ng, P.C. & Henikoff, S. Accounting for human polymorphisms predicted to affect protein function. Genome Res. 12, 436–446 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Ramensky, V., Bork, P. & Sunyaev, S. Human non-synonymous SNPs: server and survey. Nucleic Acids Res. 30, 3894–3900 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. González-Pérez, A. & López-Bigas, N. Improving the assessment of the outcome of nonsynonymous SNVs with a consensus deleteriousness score, Condel. Am. J. Hum. Genet. 88, 440–449 (2011).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  38. Kan, Z. et al. Diverse somatic mutation patterns and pathway alterations in human cancers. Nature 466, 869–873 (2010).

    Article  CAS  PubMed  Google Scholar 

  39. Altomare, D.A. et al. A mouse model recapitulating molecular features of human mesothelioma. Cancer Res. 65, 8090–8095 (2005).

    Article  CAS  PubMed  Google Scholar 

  40. Shukla, S. et al. Methylation silencing of ULK2, an autophagy gene, is essential for astrocyte transformation and tumor growth. J. Biol. Chem. 289, 22306–22318 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Qian, C. & Zhou, M.M. SET domain protein lysine methyltransferases: structure, specificity and catalysis. Cell. Mol. Life Sci. 63, 2755–2763 (2006).

    Article  CAS  PubMed  Google Scholar 

  42. 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 

  43. Robinson, G. et al. Novel mutations target distinct subgroups of medulloblastoma. Nature 488, 43–48 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Pugh, T.J. et al. Medulloblastoma exome sequencing uncovers subtype-specific somatic mutations. Nature 488, 106–110 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Jiang, L. et al. Exome sequencing identifies somatic mutations of DDX3X in natural killer/T-cell lymphoma. Nat. Genet. 47, 1061–1066 (2015).

    Article  CAS  PubMed  Google Scholar 

  47. Van Petegem, F. Ryanodine receptors: allosteric ion channel giants. J. Mol. Biol. 427, 31–53 (2015).

    Article  CAS  PubMed  Google Scholar 

  48. Liu, Z. et al. Candidate tumour suppressor CCDC19 regulates miR-184 direct targeting of c-Myc thereby suppressing cell growth in non–small cell lung cancers. J. Cell. Mol. Med. 18, 1667–1679 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. 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 

  50. Yu, W. et al. First somatic mutation of E2F1 in a critical DNA binding residue discovered in well-differentiated papillary mesothelioma of the peritoneum. Genome Biol. 12, R96 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Jean, D., Daubriac, J., Le Pimpec-Barthes, F., Galateau-Salle, F. & Jaurand, M.C. Molecular changes in mesothelioma with an impact on prognosis and treatment. Arch. Pathol. Lab. Med. 136, 277–293 (2012).

    Article  CAS  PubMed  Google Scholar 

  52. Wolff, S. et al. SMK-1, an essential regulator of DAF-16–mediated longevity. Cell 124, 1039–1053 (2006).

    Article  CAS  PubMed  Google Scholar 

  53. Durinck, S. et al. Temporal dissection of tumorigenesis in primary cancers. Cancer Discov. 1, 137–143 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Klijn, C. et al. A comprehensive transcriptional portrait of human cancer cell lines. Nat. Biotechnol. 33, 306–312 (2015).

    Article  CAS  PubMed  Google Scholar 

  55. Oltean, S. & Bates, D.O. Hallmarks of alternative splicing in cancer. Oncogene 33, 5311–5318 (2014).

    Article  CAS  PubMed  Google Scholar 

  56. GTEx Consortium. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science 348, 648–660 (2015).

  57. Franz, W.M., Berger, P. & Wang, J.Y. Deletion of an N-terminal regulatory domain of the c-Abl tyrosine kinase activates its oncogenic potential. EMBO J. 8, 137–147 (1989).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Yoshida, K. & Ogawa, S. Splicing factor mutations and cancer. Wiley Interdiscip. Rev. RNA 5, 445–459 (2014).

    Article  CAS  PubMed  Google Scholar 

  59. DeBoever, C. et al. Transcriptome sequencing reveals potential mechanism of cryptic 3′ splice site selection in SF3B1-mutated cancers. PLoS Comput. Biol. 11, e1004105 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  60. Furney, S.J. et al. SF3B1 mutations are associated with alternative splicing in uveal melanoma. Cancer Discov. 3, 1122–1129 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Rodrigues, L.U. et al. Coordinate loss of MAP3K7 and CHD1 promotes aggressive prostate cancer. Cancer Res. 75, 1021–1034 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Bonnal, S., Vigevani, L. & Valcárcel, J. The spliceosome as a target of novel antitumour drugs. Nat. Rev. Drug Discov. 11, 847–859 (2012).

    Article  CAS  PubMed  Google Scholar 

  63. Cazzola, M., Rossi, M. & Malcovati, L. Biologic and clinical significance of somatic mutations of SF3B1 in myeloid and lymphoid neoplasms. Blood 121, 260–269 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Ujiie, H. et al. The tumoral and stromal immune microenvironment in malignant pleural mesothelioma: a comprehensive analysis reveals prognostic immune markers. OncoImmunology 4, e1009285 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  65. Cornelissen, R. et al. Ratio of intratumoral macrophage phenotypes is a prognostic factor in epithelioid malignant pleural mesothelioma. PLoS One 9, e106742 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  66. Toung, J.M., Morley, M., Li, M. & Cheung, V.G. RNA-sequence analysis of human B-cells. Genome Res. 21, 991–998 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. King, J.E., Thatcher, N., Pickering, C.A. & Hasleton, P.S. Sensitivity and specificity of immunohistochemical markers used in the diagnosis of epithelioid mesothelioma: a detailed systematic analysis using published data. Histopathology 48, 223–232 (2006).

    Article  CAS  PubMed  Google Scholar 

  68. Chaouche-Mazouni, S. et al. Claudin 3, 4, and 15 expression in solid tumors of lung adenocarcinoma versus malignant pleural mesothelioma. Ann. Diagn. Pathol. 19, 193–197 (2015).

    Article  PubMed  Google Scholar 

  69. Miyanaga, A. et al. Hippo pathway gene mutations in malignant mesothelioma: revealed by RNA and targeted exon sequencing. J. Thorac. Oncol. 10, 844–851 (2015).

    Article  CAS  PubMed  Google Scholar 

  70. Richards, W.G. et al. A microaliquoting technique for precise histological annotation and optimization of cell content in frozen tissue specimens. Biotech. Histochem. 82, 189–197 (2007).

    Article  CAS  PubMed  Google Scholar 

  71. Morgan, M. et al. ShortRead: a Bioconductor package for input, quality assessment and exploration of high-throughput sequence data. Bioinformatics 25, 2607–2608 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Saunders, C.T. et al. Strelka: accurate somatic small-variant calling from sequenced tumor-normal sample pairs. Bioinformatics 28, 1811–1817 (2012).

    Article  CAS  PubMed  Google Scholar 

  75. Sherry, S.T. et al. dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 29, 308–311 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

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

    PubMed  Google Scholar 

  78. Gao, J. et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci. Signal. 6, pl1 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  79. Rudin, C.M. et al. Comprehensive genomic analysis identifies SOX2 as a frequently amplified gene in small-cell lung cancer. Nat. Genet. 44, 1111–1116 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Peifer, M. et al. Integrative genome analyses identify key somatic driver mutations of small-cell lung cancer. Nat. Genet. 44, 1104–1110 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Brunet, J.P., Tamayo, P., Golub, T.R. & Mesirov, J.P. Metagenes and molecular pattern discovery using matrix factorization. Proc. Natl. Acad. Sci. USA 101, 4164–4169 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Dees, N.D. et al. MuSiC: identifying mutational significance in cancer genomes. Genome Res. 22, 1589–1598 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. 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 

  84. Wu, T.D. & Nacu, S. Fast and SNP-tolerant detection of complex variants and splicing in short reads. Bioinformatics 26, 873–881 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Robinson, M.D., McCarthy, D.J. & Smyth, G.K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).

    Article  CAS  PubMed  Google Scholar 

  86. Anders, S. & Huber, W. Differential expression analysis for sequence count data. Genome Biol. 11, R106 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Ritchie, M.E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  89. Wu, T.D. & Watanabe, C.K. GMAP: a genomic mapping and alignment program for mRNA and EST sequences. Bioinformatics 21, 1859–1875 (2005).

    Article  CAS  PubMed  Google Scholar 

  90. Greenman, C.D. et al. PICNIC: an algorithm to predict absolute allelic copy number variation with microarray cancer data. Biostatistics 11, 164–175 (2010).

    Article  PubMed  Google Scholar 

  91. Tibshirani, R. & Wang, P. Spatial smoothing and hot spot detection for CGH data using the fused lasso. Biostatistics 9, 18–29 (2008).

    Article  PubMed  Google Scholar 

  92. 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 

  93. Boegel, S. et al. HLA typing from RNA-Seq sequence reads. Genome Med. 4, 102 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  94. Vita, R. et al. The immune epitope database (IEDB) 3.0. Nucleic Acids Res. 43, D405–D412 (2015).

    Article  CAS  PubMed  Google Scholar 

  95. Johnston, R.J. et al. The immunoreceptor TIGIT regulates antitumor and antiviral CD8+ T cell effector function. Cancer Cell 26, 923–937 (2014).

    Article  CAS  PubMed  Google Scholar 

  96. Love, M.I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  97. Croft, D. et al. Reactome: a database of reactions, pathways and biological processes. Nucleic Acids Res. 39, D691–D697 (2011).

    Article  CAS  PubMed  Google Scholar 

  98. Milacic, M. et al. Annotating cancer variants and anti-cancer therapeutics in reactome. Cancers (Basel) 4, 1180–1211 (2012).

    Article  CAS  Google Scholar 

  99. 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 

  100. Anders, S., Reyes, A. & Huber, W. Detecting differential usage of exons from RNA-seq data. Genome Res. 22, 2008–2017 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We acknowledge Genentech DNA Sequencing, Oligo and Bioinformatics groups for their help with the project. We also acknowledge the personnel of the tumor bank at the Brigham and Women's Hospital. We thank C.S. Rivers and C.J. Harris for the NGS library support. We thank Z. Zhang, P. George, K.V. Paul, P.M. Haverty, S. Jhunjhunwala, S. Sharma, B. Chow, J. Reeder and S. Lipscomb for the bioinformatics and computational support. This research was supported partly by grants to R.B. from the National Cancer Institute (2R01CA120528), The International Mesothelioma Program at Brigham and Women's Hospital and Genentech, Inc.

Author information

Author notes

  1. Raphael Bueno, Eric W Stawiski, Leonard D Goldstein, Steffen Durinck, Assunta De Rienzo, Zora Modrusan, Florian Gnad, Thong T Nguyen and Bijay S Jaiswal: These authors contributed equally to this work.

Authors and Affiliations

  1. Division of Thoracic Surgery, The Lung Center and the International Mesothelioma Program, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA

    Raphael Bueno, Assunta De Rienzo, Daniele Sciaranghella, Nhien Dao, Corinne E Gustafson, Kiara J Munir & William G Richards

  2. Bioinformatics and Computational Biology Department, Genentech, Inc., South San Francisco, California, USA

    Eric W Stawiski, Leonard D Goldstein, Steffen Durinck, Florian Gnad, Jason A Hackney & Thomas D Wu

  3. Molecular Biology Department, Genentech, Inc., South San Francisco, California, USA

    Eric W Stawiski, Leonard D Goldstein, Steffen Durinck, Zora Modrusan, Thong T Nguyen, Bijay S Jaiswal, Joseph Guillory, Karen Toy, Connie Ha, Ying-Jiun Chen, Jeremy Stinson, Subhra Chaudhuri, Na Zhang & Somasekar Seshagiri

  4. Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA

    Lucian R Chirieac

  5. Bioinformatics Department, MedGenome Labs, Pvt., Ltd., Narayana Health City, Bangalore, India

    Amitabha Chaudhuri & Ravi Gupta

  6. Division of Thoracic Surgery, Baylor College of Medicine, Houston, Texas, USA

    David J Sugarbaker

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

    Frederic J de Sauvage

Authors
  1. Raphael Bueno
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Eric W Stawiski
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Leonard D Goldstein
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Steffen Durinck
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Assunta De Rienzo
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Zora Modrusan
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Florian Gnad
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Thong T Nguyen
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Bijay S Jaiswal
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Lucian R Chirieac
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Daniele Sciaranghella
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Nhien Dao
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Corinne E Gustafson
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Kiara J Munir
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Jason A Hackney
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Amitabha Chaudhuri
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Ravi Gupta
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Joseph Guillory
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Karen Toy
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Connie Ha
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Ying-Jiun Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. Jeremy Stinson
    View author publications

    You can also search for this author in PubMed Google Scholar

  23. Subhra Chaudhuri
    View author publications

    You can also search for this author in PubMed Google Scholar

  24. Na Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  25. Thomas D Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  26. David J Sugarbaker
    View author publications

    You can also search for this author in PubMed Google Scholar

  27. Frederic J de Sauvage
    View author publications

    You can also search for this author in PubMed Google Scholar

  28. William G Richards
    View author publications

    You can also search for this author in PubMed Google Scholar

  29. Somasekar Seshagiri
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

R.B. and S.S. conceived the study. R.B., E.W.S. and S.S. designed the experiments. E.W.S. oversaw the bioinformatics analysis and performed mutation and pathway analysis. L.D.G. performed splice variant analysis. S.D. performed gene expression and copy number analysis. F.G. performed whole-genome analysis. T.T.N. performed gene fusion analysis and neoantigen prediction. A.D.R., D.S. and N.D. were responsible for the samples and nucleic acid extractions. L.R.C. performed histological analysis. K.J.M. and W.G.R. managed the tissue repository and clinical annotation that supported the study. C.E.G. provided administrative, technical and material support. Z.M. oversaw collection of genomics data. Z.M. and Y.-J.C. performed validation of the fusions. T.D.W. supported gene fusion predictions. K.T. and C.H. prepared the sequencing libraries. B.S.J., S.C. and N.Z. performed biological validation studies. J.A.H. analyzed immune signatures. A.C. and R.G. were responsible for OncoMD. J.G. and J.S. collected sequencing data. D.J.S. and F.J.d.S. provided scientific and technical support. R.B., E.W.S., L.D.G., S.D., Z.M. and S.S. wrote the manuscript, which was reviewed and edited by the other coauthors.

Corresponding authors

Correspondence to Raphael Bueno, Eric W Stawiski or Somasekar Seshagiri.

Ethics declarations

Competing interests

E.W.S., L.D.G., S.D., Z.M., F.G., T.T.N., B.S.J., J.A.H., A.C., R.G., J.G., K.T., C.H., Y.-J.C., J.S., S.C., N.Z., T.D.W., F.J.d.S. and S.S. are employees of Genentech Inc. or MedGenome Labs Pvt. Ltd. E.W.S., S.D., Z.M., F.G., B.J.S., J.A.H., J.G., K.T., C.H., Y.-J.C., J.S., S.C., N.Z., T.D.W., F.J.d.S. and S.S. hold shares in Roche. A.C. and R.G. hold stock options in MedGenome.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–21 (PDF 14921 kb)

Supplementary Table 1

Sample summary and information. (XLS 54 kb)

Supplementary Table 2

Differentially expressed genes in sarcomatoid versus epithelioid clusters. (XLS 1281 kb)

Supplementary Table 3

Sample-level exome coverage statistics. (XLS 18 kb)

Supplementary Table 4

Targeted gene panel. (XLS 17 kb)

Supplementary Table 5

Somatic mutations and germline variants of interest. (XLS 673 kb)

Supplementary Table 6

Mutation consequences. (XLS 8 kb)

Supplementary Table 7

Significantly mutated genes. (XLS 8 kb)

Supplementary Table 8

Hotspot mutations. (XLS 8 kb)

Supplementary Table 9

Meta-analysis–identified hotspot mutations. (XLS 18 kb)

Supplementary Table 10

Gene fusions. (XLS 103 kb)

Supplementary Table 11

Aberrant splice variants. (XLS 10 kb)

Supplementary Table 12

Mutant SF3B1–associated splice variants. (XLS 41 kb)

Supplementary Table 13

Tumor-infiltrating immune cell gene list and neoantigen prediction. (XLS 62 kb)

Supplementary Table 14

Significantly mutated pathways and pathway gene mutation frequencies. (XLS 13 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bueno, R., Stawiski, E., Goldstein, L. et al. Comprehensive genomic analysis of malignant pleural mesothelioma identifies recurrent mutations, gene fusions and splicing alterations. Nat Genet 48, 407–416 (2016). https://doi.org/10.1038/ng.3520

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/ng.3520

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