- Integrated analysis of germline and somatic variants in ovarian cancer

http://www.nature.com/ncomms/2014/140122/ncomms4156/full/ncomms4156.html
:


  • Krishna L. Kanchi,
  • Kimberly J. Johnson,
  • Charles Lu,
  • Michael D. McLellan,
  • Mark D. M. Leiserson,
  • Michael C. Wendl,
  • Qunyuan Zhang,
  • Daniel C. Koboldt,
  • Mingchao Xie,
  • Cyriac Kandoth,
  • Joshua F. McMichael,
  • Matthew A. Wyczalkowski,
  • David E. Larson,
  • Heather K. Schmidt,
  • Christopher A. Miller,
  • Robert S. Fulton,
  • Paul T. Spellman,
  • Elaine R. Mardis,
  • Todd E. Druley,
  • Timothy A. Graubert

    • Nature Communications
     
    5,
     
    Article number:
     
    3156
     
    doi:10.1038/ncomms4156
    Received
     
    Accepted
     
    Published
     

    Abstract



    We report the first large-scale exome-wide analysis of the combined germline–somatic landscape in ovarian cancer. Here we analyse germline and somatic alterations in 429 ovarian carcinoma cases and 557 controls. We identify 3,635 high confidence, rare truncation and 22,953 missense variants with predicted functional impact. We find germline truncation variants and large deletions across Fanconi pathway genes in 20% of cases. Enrichment of rare truncations is shown in BRCA1, BRCA2 and PALB2. In addition, we observe germline truncation variants in genes not previously associated with ovarian cancer susceptibility (NF1, MAP3K4, CDKN2B and MLL3). Evidence for loss of heterozygosity was found in 100 and 76% of cases with germline BRCA1 and BRCA2truncations, respectively. Germline–somatic interaction analysis combined with extensive bioinformatics annotation identifies 222 candidate functional germline truncation and missense variants, including two pathogenic BRCA1 and 1 TP53 deleterious variants. Finally, integrated analyses of germline and somatic variants identify significantly altered pathways, including the Fanconi, MAPK and MLL pathways.

    Subject terms:

    At a glance

    Figures

    First | 1-4 of 5 | Last
    left
    1. Overview of the integrated analysis of germline and somatic variants in 429 TCGA serous ovarian cases.
      Figure 1
    2. Germline copy-number variants in BRCA1.
      Figure 2
    3. Lolliplots showing the distribution of germline truncation variants and somatic mutations.
      Figure 3
    4. LOH analysis in tumour samples.
      Figure 4
    5. Significant pathways and subnetworks in ovarian cancer.
      Figure 5
    right

    References



    1. Howlader N.et al. (eds).SEER Cancer Statistics Review 1975–2010 (National Cancer Institute, Bethesda, MD, 2013) http://seer.cancer.gov/csr/1975_2010/ , based on November 2012 SEER data submission, posted to the SEER web site, April 2013.
    2. Weissman, S. M., Weiss, S. M. & Newlin, A. C. Genetic testing by cancer site: ovary. Cancer J. 18, 320–327 (2012).
    3. Walsh, T. et al. Mutations in 12 genes for inherited ovarian, fallopian tube, and peritoneal carcinoma identified by massively parallel sequencing. Proc. Natl Acad. Sci. USA 108,18032–18037 (2011).
    4. Cancer Genome Atlas Research Network. Integrated genomic analyses of ovarian carcinoma.Nature 474, 609–615 (2011).
    5. Dees, N. D. et al. MuSiC: Identifying mutational significance in cancer genomes. Genome Res. 22, 1589–1598 (2012).
    6. Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25,2078–2079 (2009).
    7. Koboldt, D. C. et al. VarScan 2: somatic mutation and copy number alteration discovery in cancer by exome sequencing. Genome Res 22, 568–576 (2012).
    8. McKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).
    9. Futreal, P. A. et al. A census of human cancer genes. Nat. Rev. Cancer 4, 177–183 (2004).
    10. Gonzalez-Perez, A. & Lopez-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).
    11. Morgenthaler, S. & Thilly, W. G. A strategy to discover genes that carry multi-allelic or mono-allelic risk for common diseases: a cohort allelic sums test (CAST). Mutat. Res. 615, 28–56(2007).
    12. Kandoth, C. et al. Integrated genomic characterization of endometrial carcinoma. Nature 497,67–73 (2013).
    13. Ding, L. et al. Somatic mutations affect key pathways in lung adenocarcinoma. Nature 455,1069–1075 (2008).
    14. Cancer Genome Atlas Research Network. Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature 455, 1061–1068 (2008).
    15. Thompson, E. R. et al. Exome sequencing identifies rare deleterious mutations in DNA repair genes FANCC and BLM as potential breast cancer susceptibility alleles. PLoS Genet. 8,e1002894 (2012).
    16. Thomas, G. et al. A multistage genome-wide association study in breast cancer identifies two new risk alleles at 1p11.2 and 14q24.1 (RAD51L1). Nat. Genet. 41, 579–584 (2009).
    17. Wickramanyake, A. et al. Loss of function germline mutations in RAD51D in women with ovarian carcinoma. Gynecol. Oncol. 127, 552–555 (2012).
    18. Catucci, I. et al. Germline mutations in BRIP1 and PALB2 in Jewish high cancer risk families.Fam. Cancer 11, 483–491 (2012).
    19. Seminog, O. O. & Goldacre, M. J. Risk of benign tumours of nervous system, and of malignant neoplasms, in people with neurofibromatosis: population-based record-linkage study. Br. J. Cancer 108, 193–198 (2013).
    20. Thol, F. et al. Prognostic significance of ASXL1 mutations in patients with myelodysplastic syndromes. J. Clin. Oncol. 29, 2499–2506 (2011).
    21. Carbuccia, N. et al. Mutations of ASXL1 gene in myeloproliferative neoplasms. Leukemia 23,2183–2186 (2009).
    22. Schnittger, S. et al. ASXL1 exon 12 mutations are frequent in AML with intermediate risk karyotype and are independently associated with an adverse outcome. Leukemia 27, 82–91(2013).
    23. Cancer Genome Atlas Research Network. Comprehensive molecular characterization of clear cell renal cell carcinoma. Nature 499, 43–49 (2013).
    24. Cancer Genome Atlas Network. Comprehensive molecular portraits of human breast tumours.Nature 490, 61–70 (2012).
    25. Ellis, M. J. et al. Whole-genome analysis informs breast cancer response to aromatase inhibition. Nature 486, 353–360 (2012).
    26. Patnaik, M. M. et al. Mayo prognostic model for WHO-defined chronic myelomonocytic leukemia: ASXL1 and spliceosome component mutations and outcomes. Leukemia 27,1504–1510 (2013).
    27. Mian, S. A. et al. Spliceosome mutations exhibit specific associations with epigenetic modifiers and proto-oncogenes mutated in myelodysplastic syndrome. Haematologica 98, 1058–1066(2013).
    28. Metzeler, K. H. et al. TET2 mutations improve the new European LeukemiaNet risk classification of acute myeloid leukemia: a Cancer and Leukemia Group B study. J. Clin. Oncol. 29, 1373–1381 (2011).
    29. Penzel, R. et al. EGFR mutation detection in NSCLC--assessment of diagnostic application and recommendations of the German Panel for Mutation Testing in NSCLC. Virchows Arch.458, 95–98 (2011).
    30. Fearnhead, N. S., Wilding, J. L. & Bodmer, W. F. Genetics of colorectal cancer: hereditary aspects and overview of colorectal tumorigenesis. Br. Med. Bull. 64, 27–43 (2002).
    31. Szabo, C., Masiello, A., Ryan, J. F. & Brody, L. C. The breast cancer information core: database design, structure, and scope. Hum. Mutat. 16, 123–131 (2000).
    32. Easton, D. F. et al. A systematic genetic assessment of 1,433 sequence variants of unknown clinical significance in the BRCA1 and BRCA2 breast cancer-predisposition genes. Am. J. Hum. Genet. 81, 873–883 (2007).
    33. National Human Genome Research Institute. Breast Cancer Information Core, An Open Access On-Line Breast Cancer Mutation Data Base, Vol 2013.http://research.nhgri.nih.gov/bic/ (accessed 16 May 2013).
    34. Offit, K. et al. Rare variants of ATM and risk for Hodgkin's disease and radiation-associated breast cancers. Clin. Cancer Res. 8, 3813–3819 (2002).
    35. Hellebrand, H. et al. Germline mutations in the PALB2 gene are population specific and occur with low frequencies in familial breast cancer. Hum. Mutat. 32, E2176–E2188 (2011).
    36. Wang, X. D. et al. Mutations in the hedgehog pathway genes SMO and PTCH1 in human gastric tumors. PLoS One 8, e54415 (2013).
    37. Jozwiak, J., Jozwiak, S., Grzela, T. & Lazarczyk, M. Positive and negative regulation of TSC2 activity and its effects on downstream effectors of the mTOR pathway. Neuromol. Med. 7,287–296 (2005).
    38. Nellist, M. et al. Distinct effects of single amino-acid changes to tuberin on the function of the tuberin–hamartin complex. Eur. J. Hum. Genet. 13, 59–68 (2004).
    39. Rath, M. G. et al. Prevalence of germline TP53 mutations in HER2+ breast cancer patients.Breast Cancer Res. Treat. 139, 193–198 (2013).
    40. Wendl, M. C. et al. PathScan: a tool for discerning mutational significance in groups of putative cancer genes. Bioinformatics 27, 1595–1602 (2011).
    41. Vandin, F., Upfal, E. & Raphael, B. J. De novo discovery of mutated driver pathways in cancer. Genome Res. 22, 375–385 (2012).
    42. Thirman, M. J. et al. Rearrangement of the MLL gene in acute lymphoblastic and acute myeloid leukemias with 11q23 chromosomal translocations. N. Engl. J. Med. 329, 909–914(1993).
    43. Duns, G. et al. Histone methyltransferase gene SETD2 is a novel tumor suppressor gene in clear cell renal cell carcinoma. Cancer Res. 70, 4287–4291 (2010).
    44. Kandoth, C. et al. Mutational landscape and significance across 12 major cancer types.Nature 502, 333–339 (2013).
    45. Barroso, E. et al. FANCD2 associated with sporadic breast cancer risk. Carcinogenesis 27,1930–1937 (2006).
    46. Seminog, O. O. & Goldacre, M. J. Risk of benign tumours of nervous system, and of malignant neoplasms, in people with neurofibromatosis: population-based record-linkage study. Br. J. Cancer 108, 193–198 (2013).
    47. Golmard, L. et al. Germline mutation in the RAD51B gene confers predisposition to breast cancer. BMC Cancer 13, 484 (2013).
    48. Wickramanyake, A. et al. Loss of function germline mutations in RAD51D in women with ovarian carcinoma. Gynecol. Oncol. 127, 552–555 (2012).
    49. Solyom, S. et al. Screening for large genomic rearrangements in the FANCA gene reveals extensive deletion in a Finnish breast cancer family. Cancer Lett. 302, 113–118 (2011).
    50. Durocher, F. et al. Mutation analysis and characterization of ATR sequence variants in breast cancer cases from high-risk French Canadian breast/ovarian cancer families. BMC Cancer 6,230 (2006).
    51. Pennington, K. P. & Swisher, E. M. Hereditary ovarian cancer: beyond the usual suspects.Gynecol. Oncol. 124, 347–353 (2012).
    52. Rzepecka, I. K. et al. High frequency of allelic loss at the BRCA1 locus in ovarian cancers: clinicopathologic and molecular associations. Cancer Genet. 205, 94–100 (2012).
    53. Easton, D. F. et al. Genome-wide association study identifies novel breast cancer susceptibility loci. Nature 447, 1087–1093 (2007).
    54. Abecasis, G. R. et al. An integrated map of genetic variation from 1,092 human genomes.Nature 491, 56–65 (2012).
    55. Hays, J. et al. The Women's Health Initiative recruitment methods and results. Ann. Epidemiol.13, S18–S77 (2003).
    56. Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform.Bioinformatics 25, 1754–1760 (2009).
    57. Koboldt, D. C. et al. VarScan 2: Somatic mutation and copy number alteration discovery in cancer by exome sequencing. Genome Res. 22, 568–576 (2012).
    58. McKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).
    59. McLaren, W. et al. Deriving the consequences of genomic variants with the Ensembl API and SNP Effect Predictor. Bioinformatics 26, 2069–2070 (2010).
    60. Thorvaldsdottir, H., Robinson, J. T. & Mesirov, J. P. Integrative Genomics Viewer (IGV): high-performance genomics data visualization and exploration. Brief Bioinform. 14, 178–192(2012).
    61. Chen, K. et al. PolyScan: an automatic indel and SNP detection approach to the analysis of human resequencing data. Genome Res. 17, 659–666 (2007).
    62. Nickerson, D. A., Tobe, V. O. & Taylor, S. L. PolyPhred: automating the detection and genotyping of single nucleotide substitutions using fluorescence-based resequencing.Nucleic Acids Res. 25, 2745–2751 (1997).
    63. Ng, P. C. & Henikoff, S. SIFT: Predicting amino acid changes that affect protein function.Nucleic Acids Res. 31, 3812–3814 (2003).
    64. Nakken, S., Alseth, I. & Rognes, T. Computational prediction of the effects of non-synonymous single nucleotide polymorphisms in human DNA repair genes. Neuroscience145, 1273–1279 (2007).
    65. Vandin, F., Upfal, E. & Raphael, B. J. Algorithms for detecting significantly mutated pathways in cancer. J. Comput. Biol. 18, 507–522 (2011).
    66. Adzhubei, I. A. et al. A method and server for predicting damaging missense mutations. Nat. Methods 7, 248–249 (2010).
    67. Fokkema, I. F. et al. LOVD v.2.0: the next generation in gene variant databases. Hum. Mutat.32, 557–563 (2011).
    68. Stenson, P. D. et al. The Human Gene Mutation Database: building a comprehensive mutation repository for clinical and molecular genetics, diagnostic testing and personalized genomic medicine. Hum. Genet http://www.ncbi.nlm.nih.gov/pubmed/24077912 (2013).
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    Author information



    1. These authors contributed equally to this work

      • Krishna L. Kanchi, 
      • Kimberly J. Johnson & 
      • Charles Lu

    Affiliations

    1. The Genome Institute, Washington University, St. Louis, Missouri 63108, USA

      • Krishna L. Kanchi,
        •  
      • Kimberly J. Johnson,
        •  
      • Charles Lu,
        •  
      • Michael D. McLellan,
        •  
      • Michael C. Wendl,
      • Qunyuan Zhang,
        •  
      • Daniel C. Koboldt,
        •  
      • Mingchao Xie,
        •  
      • Cyriac Kandoth,
        •  
      • Joshua F. McMichael,
        •  
      • Matthew A. Wyczalkowski,
        •  
      • David E. Larson,
        •  
      • Heather K. Schmidt,
        •  
      • Christopher A. Miller,
        •  
      • Robert S. Fulton,
      • Elaine R. Mardis,
        •  
      • Richard K. Wilson &
        •  
      • Li Ding

    2. Brown School, Washington University, St. Louis, Missouri 63130, USA

      • Kimberly J. Johnson

    3. Oregon Health and Science University, Portland, Oregon 97239, USA

      • Kimberly J. Johnson &
        •  
      • Paul T. Spellman

    4. Department of Computer Science, Brown University, Providence, Rhode Island 02912, USA

      • Mark D. M. Leiserson &
        •  
      • Benjamin J. Raphael

    5. Department of Genetics, Washington University, St. Louis, Missouri 63108, USA

      • Michael C. Wendl,
        •  
      • Qunyuan Zhang,
        •  
      • David E. Larson,
        •  
      • Robert S. Fulton,
        •  
      • Elaine R. Mardis,
        •  
      • Todd E. Druley,
        •  
      • Richard K. Wilson &
        •  
      • Li Ding

    6. Department of Mathematics, Washington University, St. Louis, Missouri 63108, USA

      • Michael C. Wendl

    7. Siteman Cancer Center, Washington University, St. Louis, Missouri 63108, USA

      • Elaine R. Mardis,
        •  
      • Timothy A. Graubert,
        •  
      • Richard K. Wilson &
        •  
      • Li Ding

    8. Department of Pediatrics, Washington University, St. Louis, Missouri 63108, USA

      • Todd E. Druley

    9. Department of Medicine, Washington University, St. Louis, Missouri 63108, USA

      • Timothy A. Graubert &
        •  
      • Li Ding

    10. The Ohio State University Comprehensive Cancer Center, The Ohio State University, Columbus, Ohio 43210, USA

      • Paul J. Goodfellow

    Contributions

    L.D. and R.K.W. jointly supervised research. L.D., K.L.K., K.J.J., C.L., M.D.M., M.D.M.L., C.K., M.A.W., J.F.M., D.C.K., C.A.M., P.T.S. and B.J.R. analysed the data. M.C.W. and Q.Z. performed statistical analysis. K.L.K., C.L., J.F.M., M.D.M., M.A.W. and L.D. prepared figures and tables. R.S.F. performed experiments. E.R.M. and D.E.L. contributed analysis tools. L.D., K.J.J., T.A.G., P.J.G., T.E.D. and B.J.R conceived and designed the experiments. L.D. and K.J.J. wrote the manuscript. K.L.K., K.J.J., C.L. and M.D.M. contributed equally but due to restrictions on the number of first authors only K.L.K., K.J.J. and C.L. are denoted as such.

    Competing financial interests

    The authors declare no competing financial interests.

    Corresponding author

    Correspondence to: 

    Supplementary information



    PDF files

    1. Supplementary Figures and Tables (660 KB)
      Supplementary Figures S1-S5 and Supplementary Tables S1-S5

    Excel files

    1. Supplementary Data 1 (39 KB)
      Clinical Information For 557 WHI Cases
    2. Supplementary Data 2 (21 KB)
      429 TCGA Ovarian Cases Data Types and Clinical Information
    3. Supplementary Data 3 (3,642 KB)
      Somatic Mutations in 429 TCGA Ovarian cases
    4. Supplementary Data 4 (466 KB)
      All 3,635 high confidence, rare (<1% population variant allele frequency) germline truncations including 115 validated germline truncations in cancer
    5. Supplementary Data 5 (9 KB)
      Validated Truncation Variants in Cancer Genes
    6. Supplementary Data 6 (2,691 KB)
      All 22,953 missense variants (<1% population variant allele frequency), predicted to be functional by Condel in 387 Caucasians
    7. Supplementary Data 7 (575 KB)
      All truncation variants (<1% population variant allele frequency), in 557 Caucasians
    8. Supplementary Data 8 (2,434 KB)
      All 30335 missense variants (<1% population variant allele frequency), predicted to be functional by Condel in 557 Caucasians
    9. Supplementary Data 9 (331 KB)
      Burden Analysis results for the Missense variants
    10. Supplementary Data 10 (16 KB)
      Burden Analysis Results for the Missense and Truncation Variants in Cancer Genes
    11. Supplementary Data 11 (13 KB)
      Germline truncation and missense within close proximity (5 amino acid) to COSMIC/OMIM variants
    12. Supplementary Data 12 (67 KB)
      Germline truncations display LOH in corresponding tumor
    13. Supplementary Data 13 (14 KB)
      Germline missense variants in cancer genes display LOH in corresponding tumor
    14. Supplementary Data 14 (21 KB)
      High confidence, functional truncation and missense variants identified by integrated approaches
    15. Supplementary Data 15 (21 KB)
      Significant pathways identified by PathScan using germline truncations and somatic mutations
    16. Supplementary Data 16 (8 KB)
      Four significant subnetworks identified by HotNet using germline truncations and somatic mutations (P = 0.17)
    17. Supplementary Data 17 (17 KB)
      672 cancer genes
    18. Supplementary Data 18 (36 KB)
      Validated Missense Variants using 11 Whole Genome Sequencing BAMs
    19. Supplementary Data 19 (12 KB)
      Primers for TCGA Germline Validation using 3730 sequencing platform
    20. Supplementary Data 20 (49 KB)
      Primers for TCGA Germline Validation using miseq
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