Skip to main content

Advertisement

Log in

Bioinformatics and cancer: an essential alliance

  • Educational Series
  • Red Series
  • Published:
Clinical and Translational Oncology Aims and scope Submit manuscript

Abstract

Modern research in cancer has been revolutionized by the introduction of new high-through put methodologies such as DNAmicroarrays. Keeping the pace with these technologies, the bioinformatics offer new solutions for data analysis and, what is more important, it permits to formulate a new class of hypothesis inspired in systems biology, more oriented to blocks of functionally-related genes. Although software implementations for this new methodologies is new there are some options already available. Bioinformatic solutions for other high-throughput techniques such asarray-CGH of large-scale genotyping is also revised.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Hood L, Heath JR, Phelps ME, Lin B. Systems biology and new technologies enable predictive and preventative medicine. Science. 2004;306:640–3

    Article  PubMed  CAS  Google Scholar 

  2. Khalil IG, Hill C. Systems biology for cancer. Curr Opin Oncol. 2005;17:44–8.

    Article  PubMed  CAS  Google Scholar 

  3. Kitano H. Cancer as a robust system: implications for anticancer therapy. Nat Rev Cancer. 2004;4:227–35.

    Article  PubMed  CAS  Google Scholar 

  4. Searls DB. Data integration: challenges for drug discovery. Nat Rev Drug Discov. 2005;4:45–58.

    Article  PubMed  CAS  Google Scholar 

  5. Butcher EC. Can cell systems biology rescue drug discovery? nat Rev Drug Discov. 2005;4:461–7.

    Article  PubMed  CAS  Google Scholar 

  6. Searls DB. Using bioinformatics in gene and drug discovery. Drug Discov Today. 2000;5:135–43

    Article  PubMed  CAS  Google Scholar 

  7. Kitano, H. Computational systems biology. Nature. 2002;420:206–10.

    Article  PubMed  CAS  Google Scholar 

  8. Westerhoff HV, Palsson BO. The evolution of molecular biology into systems biology. Nat Biotechnol. 2004;22:1249–52.

    Article  PubMed  CAS  Google Scholar 

  9. Hallikas O, Palin K, Sinjushina N, et al. Genome-wide prediction of mammalian enhancers based on analysis of transcription-factor binding affinity. Cell. 2006;124: 47–59.

    Article  PubMed  CAS  Google Scholar 

  10. Rual JF, Venkatesan K, Hao T, et al. Towards a proteome-scale map of the human protein-protein interaction network. Nature. 2005;437:1173–8.

    Article  PubMed  CAS  Google Scholar 

  11. Stelzl U, Worm U, Lalowski M, et al. A human protein-protein interaction network: a resource for annotating the proteome. Cell. 2005;122:957–68.

    Article  PubMed  CAS  Google Scholar 

  12. Lee HK, Hsu AK, Sajdak J, Qin J, Pavlidis P. Coexpression analysis of human genes across manymicroarray data sets. Genome Res. 2004;14:1085–94.

    Article  PubMed  CAS  Google Scholar 

  13. Stuart JM, Segal E, Koller D, Kim SK. A gene-coexpression network for global discovery of conserved genetic modules. Science. 2003;302:249–55.

    Article  PubMed  CAS  Google Scholar 

  14. Mateos A, Dopazo J, Jansen R, Tu Y, Gerstein M, Stolovitzky G. Systematic learning of gene functional classes from DNAarray expression data by using multilayer perceptrons. Genome Res. 2002; 12:1703–15.

    Article  PubMed  CAS  Google Scholar 

  15. van Noort V, Snel B, Huynen MA. Predicting gene function by conserved co-expression. Trends Genet. 2003;19:238–42.

    Article  PubMed  CAS  Google Scholar 

  16. Allison DB, Cui X, Page GP, Sabripour M.Microarray data analysis: from disarray to consolidation and consensus. Nat Rev Genet. 2006;7:55–65.

    Article  PubMed  CAS  Google Scholar 

  17. Golub TR, Slonim DK, Tamayo P, et al. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science. 1999;286: 531–7.

    Article  PubMed  CAS  Google Scholar 

  18. Quackenbush J. Computational analysis ofmicroarray data. Nat Rev Genet. 2001; 2:418–27.

    Article  PubMed  CAS  Google Scholar 

  19. Simon R, Radmacher MD, Dobbin K, McShane LM. Pitfalls in the use of DNAmicroarray data for diagnostic and prognostic classification. J Natl Cancer Inst. 2003;95:14–8.

    Article  PubMed  CAS  Google Scholar 

  20. van't Veer LJ, Dai H, van de Vijver MJ, et al. Gene expression profiling predicts clinical outcome of breast cancer. Nature. 2002;415:530–6.

    Article  Google Scholar 

  21. Simon R. Roadmap for developing and validating therapeutically relevant genomic classifiers. J Clin Oncol. 2005;23: 7332–41.

    Article  PubMed  CAS  Google Scholar 

  22. Moreau Y, Aerts S, De Moor B, De Strooper B, Dabrowski M. Comparison and meta-analysis ofmicroarray data: from the bench to the computer desk. Trends Genet. 2005;19:570–7

    Article  CAS  Google Scholar 

  23. Bammler T, Beyer RP, Bhattacharya S, et al. Standardizing global gene expression analysis between laboratories and across platforms. Nat Methods. 2005;2:351–6.

    Article  PubMed  CAS  Google Scholar 

  24. Al-Shahrour F, Dopazo J. Ontologies and functional genomics. In: Azuaje, F, Dopazo J. (eds.). Data analysis and visualization in genomics and proteomics. Siley: 2005; p. 99–112.

  25. Al-Shahrour F, Minguez P, Vaquerizas JM, Conde L, Dopazo J. BABELOMICS: a suite of web tools for functional annotation and analysis of groups of genes in high-throughput experiments. Nucleic Acids Res. 2005;33:W460–4.

    Article  PubMed  CAS  Google Scholar 

  26. Gentleman RC, Carey VJ, Bates DM, et al. Bioconductor: open software development for computational biology and bioinformatics. Genome Biol. 2004;5:R80.

    Article  PubMed  Google Scholar 

  27. Saeed AI, Sharov V, White J, et al. TM4: a free, open-source system formicroarray data management and analysis. Biotechniques. 2003;34:374–8.

    PubMed  CAS  Google Scholar 

  28. Saal LH, Troein C, Vallon-Christersson J, Gruvberger S, Borg A, Peterson C, Bio-Array Software Environment (BASE): a platform for comprehensive management and analysis ofmicroarray data. Genome Biol. 2002;3:SOFTWARE0003.

  29. Herrero J, Al-Shahrour F, Díaz-Uriarte R, et al. GEPAS: A web-based resource formicroarray gene expression data analysis. Nucleic Acids Res. 2003;31:3461–7.

    Article  PubMed  CAS  Google Scholar 

  30. Herrero J, Vaquerizas JM, Al-Shahrour F, et al. New challenges in gene expression data analysis and the extended GEPAS. Nucleic Acids Res. 2004;32:W485–91.

    Article  PubMed  CAS  Google Scholar 

  31. Montaner D, Tarraga J, Huerta-Cepas J, et al. Next station inmicroarray data analysis: GEPAS. Nucleic Acids Res. 2004;32: W485–91.

    Article  CAS  Google Scholar 

  32. Vaquerizas JM, Conde L, Yankilevich P, et al. GEPAS, an experiment-oriented pipeline for the analysis ofmicroarray gene expression data. Nucleic Acids Res. 2005;33: W616–20.

    Article  PubMed  CAS  Google Scholar 

  33. Herrero J, Valencia A, Dopazo J. A hierar-chical unsupervised growing neural network for clustering gene expression patterns. Bioinformatics. 2001;17:126–36.

    Article  PubMed  CAS  Google Scholar 

  34. Kohonen T. Self-organizing maps. Berlin: Springer-Verlag; 1997.

    Google Scholar 

  35. Dudoit S, Fridlyand J, Speed T. Comparison of discrimination methods for the classification of tumors using gene expression data. J Am Stat Assoc. 2002;97: 77–87.

    Article  CAS  Google Scholar 

  36. Albertson DG, Pinkel D. Genomicmicroarrays in human genetic disease and cancer. Hum Mol Genet. 2003;12:R145–52.

    Article  PubMed  CAS  Google Scholar 

  37. Gebhart E. Comparative genomic hybridization (CGH): ten years of substantial progress in human solid tumor molecular cytogenetics. Cytogenet Genome Res. 2004;104:352–8.

    Article  PubMed  CAS  Google Scholar 

  38. Monni O, Barlund M, Mousses S et al. Comprehensive copy number and gene expression profiling of the 17q23 amplicon in human breast cancer. Proc Natl Acad Sci USA. 2001;98:5711–6.

    Article  PubMed  CAS  Google Scholar 

  39. Kallioniemi A, Kallioniemi OP, Sudar D, et al. Comparative genomic hybridization for molecular cytogenetic analysis of solid tumors. Science. 1992;258:818–21.

    Article  PubMed  CAS  Google Scholar 

  40. Goldsmith ZG, Dhanasekaran N. The microrevolution: applications and impacts ofmicroarray technology on molecular biology and medicine (review). Int J Mol Med. 2004;13:483–95.

    PubMed  CAS  Google Scholar 

  41. Mantripragada KK, Buckley PG, de Stahl TD Dumanski JP. Genomicmicroarrays in the spotlight. Trends Genet. 2004;20:87–94.

    Article  PubMed  CAS  Google Scholar 

  42. Pinkel D, Albertson DG.Array comparative genomic hybridization and its applications in cancer. Nat Genet. 2005;37 Suppl: S11–7.

    Article  PubMed  CAS  Google Scholar 

  43. Pollack JR, Perou CM, Alizadeh AA, et al. Genome-wide analysis of DNA copy-number changes using cDNAmicroarrays. Nat Genet. 1999;23:41–6.

    Article  PubMed  CAS  Google Scholar 

  44. Carvalho B, Ouwerkerk E, Meijer GA, Ylstra B. High resolutionmicroarray comparative genomic hybridisation analysis using spotted oligonucleotides. J Clin Pathol. 2004;57:644–6.

    Article  PubMed  CAS  Google Scholar 

  45. Zhou X, Mok SC, Chen Z, Li Y, Wong DT. Concurrent analysis of loss of heterozygosity (LOH) and copy number abnormality (CNA) for oral premalignancy progression using the Affymetrix 10K SNP mappingarray. Hum Genet. 2004;115: 327–30.

    Article  PubMed  CAS  Google Scholar 

  46. Hyman E, Kauraniemi P, Hautaniemi S, et al. Impact of DNa amplification on gene expression patterns in breast cancer. Cancer Res. 2002;62:6240–5.

    PubMed  CAS  Google Scholar 

  47. Mahlamaki EH, Kauraniemi P, Monni O, Wolf M, Hautaniemi S, Kallioniemi A. High-resolution genomic and expression profiling reveals 105 putative amplification target genes in pancreatic cancer. Neoplasia. 2004;6:432–9.

    Article  PubMed  CAS  Google Scholar 

  48. Chi B, DeLeeuw RJ, Coe BP, MacAulay C, Lam WL. SeeGH-a software tool for visualization of whole genomearray comparative genomic hybridization data. BMC Bioinformatics. 2004;5:13.

    Article  PubMed  Google Scholar 

  49. Li C, Wong WH. DNA-Chip Analyzer (dChip) In: Parmigiani G, Garrett ES, Irizarry R, Zeger SL (eds.). The analysis of gene expression data: methods and software. NY; Springer: 2003.

    Google Scholar 

  50. Hubbard T, Andrews D, Caccamo M, et al. Ensembl 2005. Nucleic Acids Res. 2005; 33:D447–53.

    Article  PubMed  CAS  Google Scholar 

  51. Kim SY, Nam SW, Lee SH, et al.Array-CyGHt: a web application for analysis and visualization ofarray-CGH data. Bioinformatics. 2005;21:2554–5.

    Article  PubMed  CAS  Google Scholar 

  52. Lingjaerde OC, Baumbusch LO, Liestol K, Glad IK, Borresen-Dale AL. CGH-Explorer: a program for analysis ofarray-CGH data. Bioinformatics. 2005;21:821–2.

    Article  PubMed  CAS  Google Scholar 

  53. Vaquerizas JM, Dopazo J, Díaz-Uriarte R. DNMAD: web-based diagnosis and normalization formicroarray data. Bioinformatics. 2004;20:3656–8.

    Article  PubMed  CAS  Google Scholar 

  54. Herrero J, Díaz-Uriarte R, Dopazo J. Gene expression data preprocessing. Bioinformatics. 2003;19:655–6.

    Article  PubMed  CAS  Google Scholar 

  55. Al-Shahrour F, Díaz-Uriarte R, Dopazo J. FatiGO: a web tool for finding significant associations of Gene Ontology terms with groups of genes. Bioinformatics. 2004;20: 578–80.

    Article  PubMed  CAS  Google Scholar 

  56. Al-Shahrour F, Mínguez P, Tarraga J, et al. BABELOMICS: a systems biology perspective in the functional annotation of genome-scale experiments. Nucleic Acids Res. In press: 2006.

  57. Collins FS, Green ED, Guttmacher AE, Guyer MS. A vision for the future of genomics research. Nature. 2003;422:835–47.

    Article  PubMed  CAS  Google Scholar 

  58. Risch NJ. Searching for genetic determinants in the new millennium. Nature. 2000;405:847–56.

    Article  PubMed  CAS  Google Scholar 

  59. Botstein D, Risch N. Discovering genotypes underlying human phenotypes: past successes for mendelian disease, future approaches for complex disease. Nat Genet. 2003;33 Suppl:S228–37.

    Article  CAS  Google Scholar 

  60. Badano JL, Katsanis N. Beyond Mendel: an evolving view of human genetic disease transmission. Nat Rev Genet. 2002;3: 779–89.

    Article  PubMed  CAS  Google Scholar 

  61. Neale BM, Sham PC. The future of association studies: gene-based analysis and replication. Am J Hum Genet. 2004;75:353–62.

    Article  PubMed  CAS  Google Scholar 

  62. Carlson CS, Eberle MA, Kruglyak L, Nickerson DA. Mapping complex disease loci in whole-genome association studies. Nature. 2004;429:446–52.

    Article  PubMed  CAS  Google Scholar 

  63. Ng PC, Henikoff S. Predicting deleterious amino acid substitutions. Genome Res. 2001;11:863–74.

    Article  PubMed  CAS  Google Scholar 

  64. Miller MP, Kumar S. Understanding human disease mutations through the use of interspecific genetic variation. Hum Mol Genet. 2001;10:2319–28.

    Article  PubMed  CAS  Google Scholar 

  65. Arbiza L, Duchi S, Montaner D, et al. Selective pressures at a codon-level predict deleterious mutations in human disease genes. J Mol Biol. 2006;358:1390–404.

    Article  PubMed  CAS  Google Scholar 

  66. Chasman D, Adams RM. Predicting the functional consequences of non-synonymous single nucleotide polymorphisms: structure-based assessment of amino acid variation. J Mol Biol. 2001;307:683–706.

    Article  PubMed  CAS  Google Scholar 

  67. Guerois R, Nielsen JE, Serrano L. Predicting changes in the stability of proteins and protein complexes: a study of more than 1000 mutations. J Mol Biol. 2002; 320:369–87.

    Article  PubMed  CAS  Google Scholar 

  68. Ferrer-Costa C, Orozco M, de la Cruz X. characterization fo disease-associated single amino acid polymorphisms in terms of sequence and structure properties. J Mol Biol. 2002;315:771–86.

    Article  PubMed  CAS  Google Scholar 

  69. Hudson TJ. Wanted: regulatory SNPs. Nat Genet. 2003;33:439–40.

    Article  PubMed  CAS  Google Scholar 

  70. Krawczak M, Reiss J, Cooper DN. The mutational spectrum of single base-pair substitutions in mRNA splice junctions of human genes: causes and consequences. Hum Genet. 1992;90:41–54.

    Article  PubMed  CAS  Google Scholar 

  71. Yan H, Yuan W, Velculescu VE, Vogelstein B, Kinzler KW. Allelic variation in human gene expression. Science. 2002;297:1143.

    Article  PubMed  CAS  Google Scholar 

  72. Hoogendoorn B, Coleman SL, Guy CA, et al. Functional analysis of human promoter polymorphisms. Hum Mol Genet. 2003; 12:2249–54.

    Article  PubMed  CAS  Google Scholar 

  73. Conde L, Vaquerizas JM, Ferrer-Costa C, de la Cruz X, Orozco M, Dopazo J. Pupas-View: a visual tool for selecting suitable SNPs, with putative pathological effect in genes, for genotyping purposes. Nucleic Acids Res. 2005;33:W501–5.

    Article  PubMed  CAS  Google Scholar 

  74. Conde L, Vaquerizas JM, Santoyo J, et al. PupaSNP Finder: a web tool for finding SNPs with putative effect at transcriptional level. Nucleic Acids Res. 2004;32:W242–8.

    Article  PubMed  CAS  Google Scholar 

  75. Conde L, Vaquerizas J, Dopazo H, et al. PupaSuite: finding functional SNPs for large-scale genotyping purposes. Nucleic Acids Res. 2006.

  76. Khatri P, Draghici S. Ontological analysis of gene expression data: current tools, limitations, and open problems. Bioinformatics. 2005;21:3587–95.

    Article  PubMed  CAS  Google Scholar 

  77. Ashburner M, Ball CA, Blake JA, et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 2000;25:25–9.

    Article  PubMed  CAS  Google Scholar 

  78. Kanehisa M, Goto S, Kawashima S, Okuno Y, Hattori M. The KEGG resource for deciphering the genome. Nucleic Acids Res. 2004;32:D277–80.

    Article  PubMed  CAS  Google Scholar 

  79. Al-Shahrour F, Díaz-Uriarte R, Dopazo J. Discovering molecular functions significantly related to phenotypes by combining gene expression data and biological information. Bioinformatics. 2005;21:2988–93.

    Article  PubMed  CAS  Google Scholar 

  80. Subramanian A, Tamayo P, Mootha VK, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA. 2005;102:15545–50.

    Article  PubMed  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Joaquín Dopazo.

Additional information

Supported by an unrestricted educational grant from AstraZeneca.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Dopazo, J. Bioinformatics and cancer: an essential alliance. Clin Transl Oncol 8, 409–415 (2006). https://doi.org/10.1007/s12094-006-0194-6

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12094-006-0194-6

Key words

Navigation