Genomics & Informatics

Korea Genome Organization (한국유전체학회)
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Somatic Mutaome Profile in Human Cancer Tissues

  • Kim, Nayoung (Department of Biological Sciences, Center for Advanced Bioinformatics and Systems Medicine, Sookmyung Women's University) ;
  • Hong, Yourae (Department of Biological Sciences, Center for Advanced Bioinformatics and Systems Medicine, Sookmyung Women's University) ;
  • Kwon, Doyoung (Department of Biological Sciences, Center for Advanced Bioinformatics and Systems Medicine, Sookmyung Women's University) ;
  • Yoon, Sukjoon (Department of Biological Sciences, Center for Advanced Bioinformatics and Systems Medicine, Sookmyung Women's University)
  • Received : 2013.10.29
  • Accepted : 2013.11.21
  • Published : 2013.12.31
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Abstract

Somatic mutation is a major cause of cancer progression and varied responses of tumors against anticancer agents. Thus, we must obtain and characterize genome-wide mutational profiles in individual cancer subtypes. The Cancer Genome Atlas database includes large amounts of sequencing and omics data generated from diverse human cancer tissues. In the present study, we integrated and analyzed the exome sequencing data from ~3,000 tissue samples and summarized the major mutant genes in each of the diverse cancer subtypes and stages. Mutations were observed in most human genes (~23,000 genes) with low frequency from an analysis of 11 major cancer subtypes. The majority of tissue samples harbored 20-80 different mutant genes, on average. Lung cancer samples showed a greater number of mutations in diverse genes than other cancer subtypes. Only a few genes were mutated with over 5% frequency in tissue samples. Interestingly, mutation frequency was generally similar between non-metastatic and metastastic samples in most cancer subtypes. Among the 12 major mutations, the TP53, USH2A, TTN, and MUC16 genes were found to be frequent in most cancer types, while BRAF, FRG1B, PBRM1, and VHL showed lineage-specific mutation patterns. The present study provides a useful resource to understand the broad spectrum of mutation frequencies in various cancer types.

Keywords

References

  1. Pleasance ED, Cheetham RK, Stephens PJ, McBride DJ, Humphray SJ, Greenman CD, et al. A comprehensive catalogue of somatic mutations from a human cancer genome. Nature 2010;463:191-196. https://doi.org/10.1038/nature08658
  2. Greenman C, Stephens P, Smith R, Dalgliesh GL, Hunter C, Bignell G, et al. Patterns of somatic mutation in human cancer genomes. Nature 2007;446:153-158. https://doi.org/10.1038/nature05610
  3. Barretina J, Caponigro G, Stransky N, Venkatesan K, Margolin AA, Kim S, et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 2012;483:603-607. https://doi.org/10.1038/nature11003
  4. Greshock J, Bachman KE, Degenhardt YY, Jing J, Wen YH, Eastman S, et al. Molecular target class is predictive of in vitro response profile. Cancer Res 2010;70:3677-3686. https://doi.org/10.1158/0008-5472.CAN-09-3788
  5. McDermott U, Sharma SV, Dowell L, Greninger P, Montagut C, Lamb J, et al. Identification of genotype-correlated sensitivity to selective kinase inhibitors by using high-throughput tumor cell line profiling. Proc Natl Acad Sci U S A 2007;104: 19936-19941. https://doi.org/10.1073/pnas.0707498104
  6. Kim N, He N, Kim C, Zhang F, Lu Y, Yu Q, et al. Systematic analysis of genotype-specific drug responses in cancer. Int J Cancer 2012;131:2456-2464. https://doi.org/10.1002/ijc.27529
  7. Kim N, Park H, He N, Lee HY, Yoon S. QCanvas: an advanced tool for data clustering and visualization of genomics data. Genomics Inform 2012;10:263-265. https://doi.org/10.5808/GI.2012.10.4.263
  8. Forbes SA, Bindal N, Bamford S, Cole C, Kok CY, Beare D, et al. COSMIC: mining complete cancer genomes in the catalogue of somatic mutations in cancer. Nucleic Acids Res 2011; 39:D945-D950. https://doi.org/10.1093/nar/gkq929
  9. Loeb LA, Loeb KR, Anderson JP. Multiple mutations and cancer. Proc Natl Acad Sci U S A 2003;100:776-781. https://doi.org/10.1073/pnas.0334858100
  10. Loeb KR, Loeb LA. Significance of multiple mutations in cancer. Carcinogenesis 2000;21:379-385. https://doi.org/10.1093/carcin/21.3.379
  11. Lawrence MS, Stojanov P, Polak P, Kryukov GV, Cibulskis K, Sivachenko A, et al. Mutational heterogeneity in cancer and the search for new cancer-associated genes. Nature 2013;499: 214-218. https://doi.org/10.1038/nature12213
  12. Hainaut P, Hollstein M. p53 and human cancer: the first ten thousand mutations. Adv Cancer Res 2000;77:81-137.
  13. Petitjean A, Achatz MI, Borresen-Dale AL, Hainaut P, Olivier M. TP53 mutations in human cancers: functional selection and impact on cancer prognosis and outcomes. Oncogene 2007;26:2157-2165. https://doi.org/10.1038/sj.onc.1210302
  14. Cowey CL, Rathmell WK. VHL gene mutations in renal cell carcinoma: role as a biomarker of disease outcome and drug efficacy. Curr Oncol Rep 2009;11:94-101. https://doi.org/10.1007/s11912-009-0015-5
  15. Varela I, Tarpey P, Raine K, Huang D, Ong CK, Stephens P, et al. Exome sequencing identifies frequent mutation of the SWI/SNF complex gene PBRM1 in renal carcinoma. Nature 2011;469:539-542. https://doi.org/10.1038/nature09639
  16. Pawlowski R, Mühl SM, Sulser T, Krek W, Moch H, Schraml P. Loss of PBRM1 expression is associated with renal cell carcinoma progression. Int J Cancer 2013;132:E11-E17. https://doi.org/10.1002/ijc.27822
  17. Lambert SR, Harwood CA, Purdie KJ, Gulati A, Matin RN, Romanowska M, et al. Metastatic cutaneous squamous cell carcinoma shows frequent deletion in the protein tyrosine phosphatase receptor Type D gene. Int J Cancer 2012;131: E216-E226. https://doi.org/10.1002/ijc.27333

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