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Chapter 14: Cancer Genome Analysis

DOI: 10.1371/journal.pcbi.1002824

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Abstract:

Although there is great promise in the benefits to be obtained by analyzing cancer genomes, numerous challenges hinder different stages of the process, from the problem of sample preparation and the validation of the experimental techniques, to the interpretation of the results. This chapter specifically focuses on the technical issues associated with the bioinformatics analysis of cancer genome data. The main issues addressed are the use of database and software resources, the use of analysis workflows and the presentation of clinically relevant action items. We attempt to aid new developers in the field by describing the different stages of analysis and discussing current approaches, as well as by providing practical advice on how to access and use resources, and how to implement recommendations. Real cases from cancer genome projects are used as examples.

References

[1]  Hudson TJ, Anderson W, Artez A, Barker AD, Bell C, et al. (2010) International network of cancer genome projects. Nature 464: 993–998 doi:10.1038/nature08987.
[2]  Roychowdhury S, Iyer MK, Robinson DR, Lonigro RJ, Wu Y-M, et al. (2011) Personalized oncology through integrative high-throughput sequencing: a pilot study. Sci Transl Med 3: 111ra121 doi:10.1126/scitranslmed.3003161.
[3]  Villarroel MC, Rajeshkumar NV, Garrido-Laguna I, De Jesus-Acosta A, Jones S, et al. (2011) Personalizing cancer treatment in the age of global genomic analyses: PALB2 gene mutations and the response to DNA damaging agents in pancreatic cancer. Mol Cancer Ther 10: 3–8 doi:10.1158/1535-7163.MCT-10-0893.
[4]  Valencia A, Hidalgo M (2012) Getting personalized cancer genome analysis into the clinic: the challenges in bioinformatics. Genome Med 4: 61 doi:10.1186/gm362.
[5]  Baudot A, Real FX, Izarzugaza JMG, Valencia A (2009) From cancer genomes to cancer models: bridging the gaps. EMBO Rep 10: 359–366 doi:10.1038/embor.2009.46.
[6]  Andrewes C (1964) Tumour-viruses and Virus-tumours. Br Med J 1: 653–658. doi: 10.1136/bmj.1.5384.653
[7]  Nielsen R, Paul JS, Albrechtsen A, Song YS (2011) Genotype and SNP calling from next-generation sequencing data. Nature Reviews Genetics 12: 443–451 doi:10.1038/nrg2986.
[8]  Metzker ML (2010) Sequencing technologies - the next generation. Nat Rev Genet 11: 31–46 doi:10.1038/nrg2626.
[9]  Quesada V, Conde L, Villamor N, Ordó?ez GR, Jares P, et al. (2011) Exome sequencing identifies recurrent mutations of the splicing factor SF3B1 gene in chronic lymphocytic leukemia. Nat Genet 44: 47–52 doi:10.1038/ng.1032.
[10]  Kulis M, Heath S, Bibikova M, Queirós AC, Navarro A, et al. (2012) Epigenomic analysis detects widespread gene-body DNA hypomethylation in chronic lymphocytic leukemia. Nat Genet 44): 1236–1242 doi:10.1038/ng.2443.
[11]  Chuang H-Y, Rassenti L, Salcedo M, Licon K, Kohlmann A, et al. (2012) Subnetwork-based analysis of chronic lymphocytic leukemia identifies pathways that associate with disease progression. Blood 120: 2639–2649 doi:10.1182/blood-2012-03-416461.
[12]  The Cancer Genome Atlas Network (2012) Comprehensive molecular portraits of human breast tumours. Nature 490: 61–70 doi:10.1038/nature11412.
[13]  Chen R, Mias GI, Li-Pook-Than J, Jiang L, Lam HYK, et al. (2012) Personal omics profiling reveals dynamic molecular and medical phenotypes. Cell 148: 1293–1307 doi:10.1016/j.cell.2012.02.009.
[14]  Fr?hling S, Scholl C, Levine RL, Loriaux M, Boggon TJ, et al. (2007) Identification of driver and passenger mutations of FLT3 by high-throughput DNA sequence analysis and functional assessment of candidate alleles. Cancer Cell 12: 501–513 doi:10.1016/j.ccr.2007.11.005.
[15]  Boris Reva YACS (2011) Predicting the functional impact of protein mutations: application to cancer genomics. Nucleic Acids Res 39: e118 doi:10.1093/nar/gkr407.
[16]  Bernstein BE, Birney E, Dunham I, Green ED, Gunter C, et al. (2012) An integrated encyclopedia of DNA elements in the human genome. Nature 489: 57–74 doi:10.1038/nature11247.
[17]  Wang L, Lawrence MS, Wan Y, Stojanov P, Sougnez C, et al. (2011) SF3B1 and other novel cancer genes in chronic lymphocytic leukemia. N Engl J Med 365: 2497–2506 doi:10.1056/NEJMoa1109016.
[18]  Damm F, Nguyen-Khac F, Fontenay M, Bernard OA (2012) Spliceosome and other novel mutations in chronic lymphocytic leukemia, and myeloid malignancies. Leukemia 26: 2027–2031. doi: 10.1038/leu.2012.86
[19]  Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, et al. (2005) Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA 102: 15545–15550 doi:10.1073/pnas.0506580102.
[20]  Glaab E, Baudot A, Krasnogor N, Schneider R, Valencia A (2012) EnrichNet: network-based gene set enrichment analysis. Bioinformatics 28: i451–i457 doi:10.1093/bioinformatics/bts389.
[21]  Hull D, Wolstencroft K, Stevens R, Goble C, Pocock MR, et al. (2006) Taverna: a tool for building and running workflows of services. Nucleic Acids Res 34: W729–W732 doi:10.1093/nar/gkl320.
[22]  Giardine B, Riemer C, Hardison RC, Burhans R, Elnitski L, et al. (2005) Galaxy: a platform for interactive large-scale genome analysis. Genome Res 15: 1451–1455 doi:10.1101/gr.4086505.

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