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BioMed Research International
Volume 2015, Article ID 491502, 9 pages
http://dx.doi.org/10.1155/2015/491502
Research Article

Classification of Cancer Primary Sites Using Machine Learning and Somatic Mutations

1Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN 37203, USA
2School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
3Department of Psychiatry, Vanderbilt University School of Medicine, Nashville, TN 37212, USA
4Department of Cancer Biology, Vanderbilt University School of Medicine, Nashville, TN 37232, USA

Received 21 October 2014; Revised 5 February 2015; Accepted 19 February 2015

Academic Editor: Federico Ambrogi

Copyright © 2015 Yukun Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. L. A. Hindorff, E. M. Gillanders, and T. A. Manolio, “Genetic architecture of cancer and other complex diseases: lessons learned and future directions,” Carcinogenesis, vol. 32, no. 7, pp. 945–954, 2011. View at Publisher · View at Google Scholar · View at Scopus
  2. Cancer Genome Atlas Research Network, “Comprehensive genomic characterization defines human glioblastoma genes and core pathways,” Nature, vol. 455, no. 7216, pp. 1061–1068, 2008. View at Publisher · View at Google Scholar
  3. E. D. Pleasance, R. Keira Cheetham, P. J. Stephens et al., “A comprehensive catalogue of somatic mutations from a human cancer genome,” Nature, vol. 463, pp. 191–196, 2010. View at Publisher · View at Google Scholar
  4. The International Cancer Genome Consortium, “International network of cancer genome projects,” Nature, vol. 464, pp. 993–998, 2010. View at Publisher · View at Google Scholar
  5. L. Chin, W. C. Hahn, G. Getz, and M. Meyerson, “Making sense of cancer genomic data,” Genes and Development, vol. 25, no. 6, pp. 534–555, 2011. View at Publisher · View at Google Scholar · View at Scopus
  6. M. S. Lawrence, P. Stojanov, P. Polak et al., “Mutational heterogeneity in cancer and the search for new cancer-associated genes,” Nature, vol. 499, pp. 214–218, 2013. View at Google Scholar
  7. R. L. Milne and A. C. Antoniou, “Genetic modifiers of cancer risk for BRCA1 and BRCA2 mutation carriers,” Annals of Oncology, vol. 22, supplement 1, pp. i11–i17, 2011. View at Publisher · View at Google Scholar · View at Scopus
  8. K. E. Malone, J. R. Daling, D. R. Doody et al., “Prevalence and predictors of BRCA1 and BRCA2 mutations in a population-based study of breast cancer in White and Black American women ages 35 to 64 years,” Cancer Research, vol. 66, no. 16, pp. 8297–8308, 2006. View at Publisher · View at Google Scholar · View at Scopus
  9. V. M. Basham, J. M. Lipscombe, J. M. Ward et al., “BRCA1 and BRCA2 mutations in a population-based study of male breast cancer,” Breast Cancer Research, vol. 4, article R2, 2002. View at Publisher · View at Google Scholar · View at Scopus
  10. A. H. Trainer, C. R. Lewis, K. Tucker, B. Meiser, M. Friedlander, and R. L. Ward, “The role of BRCA mutation testing in determining breast cancer therapy,” Nature Reviews Clinical Oncology, vol. 7, no. 12, pp. 708–717, 2010. View at Publisher · View at Google Scholar
  11. K. P. Garnock-Jones, G. M. Keating, and L. J. Scott, “Trastuzumab: a review of its use as adjuvant treatment in human epidermal growth factor receptor 2 (HER2)-positive early breast cancer,” Drugs, vol. 70, pp. 215–239, 2010. View at Google Scholar
  12. S. Y. Kong, D. H. Lee, E. S. Lee, S. Park, K. S. Lee, and J. Ro, “Serum HER2 as a response indicator to various chemotherapeutic agents in tissue HER2 positive metastatic breast cancer,” Cancer Research and Treatment, vol. 38, no. 1, pp. 35–39, 2006. View at Publisher · View at Google Scholar
  13. B. S. Sorensen, L. S. Mortensen, J. Andersen, and E. Nexo, “Circulating HER2 DNA after trastuzumab treatment predicts survival and response in breast cancer,” Anticancer Research, vol. 30, no. 6, pp. 2463–2468, 2010. View at Google Scholar · View at Scopus
  14. H. M. Kvasnicka, “WHO classification of myeloproliferative neoplasms (MPN): a critical update,” Current Hematologic Malignancy Reports, vol. 8, no. 4, pp. 333–341, 2013. View at Publisher · View at Google Scholar · View at Scopus
  15. E. Bair and R. Tibshirani, “Semi-supervised methods to predict patient survival from gene expression data,” PLoS Biology, vol. 2, no. 4, article e108, 2004. View at Publisher · View at Google Scholar · View at Scopus
  16. C. M. Perou, T. Sørile, M. B. Eisen et al., “Molecular portraits of human breast tumours,” Nature, vol. 406, no. 6797, pp. 747–752, 2000. View at Publisher · View at Google Scholar · View at Scopus
  17. T. Sørlie, C. M. Perou, R. Tibshirani et al., “Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications,” Proceedings of the National Academy of Sciences of the United States of America, vol. 98, pp. 10869–10874, 2001. View at Google Scholar
  18. B. D. Lehmann, J. A. Bauer, X. Chen et al., “Identification of human triple-negative breast cancer subtypes and preclinical models for selection of targeted therapies,” Journal of Clinical Investigation, vol. 121, no. 7, pp. 2750–2767, 2011. View at Publisher · View at Google Scholar · View at Scopus
  19. H. A. Idikio, “Human cancer classification: a systems biology-based model integrating morphology, cancer stem cells, proteomics, and genomics,” Journal of Cancer, vol. 2, no. 1, pp. 107–115, 2011. View at Publisher · View at Google Scholar · View at Scopus
  20. B. Vogelstein, N. Papadopoulos, V. E. Velculescu, S. Zhou, L. A. Diaz Jr., and K. W. Kinzler, “Cancer genome landscapes,” Science, vol. 340, no. 6127, pp. 1546–1558, 2013. View at Publisher · View at Google Scholar · View at Scopus
  21. M. S. Lawrence, P. Stojanov, C. H. Mermel et al., “Discovery and saturation analysis of cancer genes across 21 tumour types,” Nature, vol. 505, no. 7484, pp. 495–501, 2014. View at Publisher · View at Google Scholar · View at Scopus
  22. P. Jia, W. Pao, and Z. Zhao, “Patterns and processes of somatic mutations in nine major cancers,” BMC Medical Genomics, vol. 7, article 11, 2014. View at Google Scholar
  23. P. Jia, Q. Wang, Q. Chen, K. E. Hutchinson, W. Pao, and Z. Zhao, “MSEA: detection and quantification of mutation hotspots through mutation set enrichment analysis,” Genome Biology, vol. 15, article 489, 2014. View at Publisher · View at Google Scholar
  24. C. Kandoth, M. D. McLellan, F. Vandin et al., “Mutational landscape and significance across 12 major cancer types,” Nature, vol. 502, no. 7471, pp. 333–339, 2013. View at Publisher · View at Google Scholar · View at Scopus
  25. F. Cheng, P. Jia, Q. Wang, C.-C. Lin, W.-H. Li, and Z. Zhao, “Studying tumorigenesis through network evolution and somatic mutational perturbations in the cancer interactome,” Molecular Biology and Evolution, vol. 31, no. 8, pp. 2156–2169, 2014. View at Publisher · View at Google Scholar
  26. S. A. Forbes, N. Bindal, S. Bamford et al., “COSMIC: mining complete cancer genomes in the catalogue of somatic mutations in cancer,” Nucleic Acids Research, vol. 39, no. 1, pp. D945–D950, 2011. View at Publisher · View at Google Scholar · View at Scopus
  27. M. Kanehisa and S. Goto, “KEGG: kyoto encyclopedia of genes and genomes,” Nucleic Acids Research, vol. 28, no. 1, pp. 27–30, 2000. View at Publisher · View at Google Scholar · View at Scopus
  28. W. J. Kent, C. W. Sugnet, T. S. Furey et al., “The human genome browser at UCSC,” Genome Research, vol. 12, no. 6, pp. 996–1006, 2002. View at Publisher · View at Google Scholar · View at Scopus
  29. D. Maglott, J. Ostell, K. D. Pruitt, and T. Tatusova, “Entrez gene: gene-centered information at NCBI,” Nucleic Acids Research, vol. 39, no. 1, pp. D52–D57, 2011. View at Publisher · View at Google Scholar · View at Scopus
  30. G. Fudenberg, G. Getz, M. Meyerson, and L. A. Mirny, “High order chromatin architecture shapes the landscape of chromosomal alterations in cancer,” Nature Biotechnology, vol. 29, no. 12, pp. 1109–1113, 2011. View at Publisher · View at Google Scholar · View at Scopus
  31. R.-E. Fan, K.-W. Chang, C.-J. Hsieh, X.-R. Wang, and C.-J. Lin, “LIBLINEAR: a library for large linear classification,” Journal of Machine Learning Research, vol. 9, pp. 1871–1874, 2008. View at Google Scholar · View at Scopus
  32. J. A. Cruz and D. S. Wishart, “Applications of machine learning in cancer prediction and prognosis,” Cancer Informatics, vol. 2, pp. 59–77, 2006. View at Google Scholar · View at Scopus
  33. P. Jia and Z. Zhao, “VarWalker: personalized mutation network analysis of putative cancer genes from next-generation sequencing data,” PLoS Computational Biology, vol. 10, no. 2, Article ID e1003460, 2014. View at Publisher · View at Google Scholar
  34. J. Xia, P. Jia, K. E. Hutchinson et al., “A meta-analysis of somatic mutations from next generation sequencing of 241 melanomas: a road map for the study of genes with potential clinical relevance,” Molecular Cancer Therapeutics, vol. 13, no. 7, pp. 1918–1928, 2014. View at Publisher · View at Google Scholar
  35. C.-X. Liu, S. Musco, N. M. Lisitsina, S. Y. Yaklichkin, and N. A. Lisitsyn, “Genomic organization of a new candidate tumor suppressor gene, LRP1B,” Genomics, vol. 69, no. 2, pp. 271–274, 2000. View at Publisher · View at Google Scholar · View at Scopus
  36. I. T. Jolliffe, Principal Component Analysis, Springer Series in Statistics, Springer, 1986. View at Publisher · View at Google Scholar · View at MathSciNet
  37. D. M. Blei, A. Y. Ng, and M. I. Jordan, “Latent dirichlet allocation,” Journal of Machine Learning Research, vol. 3, no. 4-5, pp. 993–1022, 2003. View at Google Scholar · View at Scopus
  38. A. Globerson and N. Tishby, “Sufficient dimensionality reduction,” Journal of Machine Learning Research, vol. 3, pp. 1307–1331, 2003. View at Google Scholar · View at Scopus
  39. C. F. Aliferis, A. Statnikov, I. Tsamardinos, S. Mani, and X. D. Koutsoukos, “Local causal and markov blanket induction for causal discovery and feature selection for classification part I: algorithms and empirical evaluation,” Journal of Machine Learning Research, vol. 11, pp. 171–234, 2010. View at Google Scholar · View at Scopus
  40. M. Dettling, “BagBoosting for tumor classification with gene expression data,” Bioinformatics, vol. 20, no. 18, pp. 3583–3593, 2004. View at Publisher · View at Google Scholar · View at Scopus
  41. Q. Liu, A. H. Sung, Z. Chen et al., “Gene selection and classification for cancer microarray data based on machine learning and similarity measures,” BMC Genomics, vol. 12, supplement 5, article S1, 2011. View at Publisher · View at Google Scholar · View at Scopus
  42. M. Hofree, J. P. Shen, H. Carter, A. Gross, and T. Ideker, “Network-based stratification of tumor mutations,” Nature Methods, vol. 10, no. 11, pp. 1108–1118, 2013. View at Publisher · View at Google Scholar · View at Scopus
  43. Y. Bengio, A. Courville, and P. Vincent, “Representation learning: a review and new perspectives,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 8, pp. 1798–1828, 2013. View at Publisher · View at Google Scholar · View at Scopus