Skip to main content

Clinical Uses of Microarrays in Cancer Research

  • Protocol
Clinical Bioinformatics

Part of the book series: Methods in Molecular Medicineā„¢ ((MIMM,volume 141))

Summary

Perturbations in genes play a key role in the pathogenesis of cancer. Microarray-based technology is an ideal way in which to study the effects and interactions of multiple genes in cancer. There are many technologic challenges in running a microarray study, including annotation of genes likely to be involved, designing the appropriate experiment, and ensuring adequate quality assurance steps are implemented. Once data are normalized, they need to be analyzed; and for this, there are numerous software packages and approaches.

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

Access this chapter

Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.00
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Abbreviations

GO:

gene ontology

RT-PCR:

reverse transcriptase PCR

References

  1. Balmain, A. (2001) Cancer genetics: from boveri and mendel to microarrays. Nat. Rev. Cancer 1, 77ā€“82.

    ArticleĀ  CASĀ  PubMedĀ  Google ScholarĀ 

  2. Watson, J. D., and Crick, F. H. (1953) Genetical implications of the structure of deoxyribonucleic acid. Nature 171, 964ā€“967.

    ArticleĀ  CASĀ  PubMedĀ  Google ScholarĀ 

  3. Stehelin, D., Varmus, H. E., Bishop, J. M., and Vogt, P. K. (1976) DNA related to the transforming gene(s) of avian sarcoma viruses is present in normal avian DNA. Nature 260, 170ā€“173.

    ArticleĀ  CASĀ  PubMedĀ  Google ScholarĀ 

  4. Friend, S. H., Bernards, R., Rogelj, S., Weinberg, R. A., Rapaport, J. M., Albert, D. M., et al. (1986) A human DNA segment with properties of the gene that predisposes to retinoblastoma and osteosarcoma. Nature 323, 643ā€“646.

    ArticleĀ  CASĀ  PubMedĀ  Google ScholarĀ 

  5. Hanahan, D., and Weinberg, R. A. (2000) The Hallmarks of Cancer. Cell 100, 57ā€“70.

    ArticleĀ  CASĀ  PubMedĀ  Google ScholarĀ 

  6. Nowell, P. C. (2002). Tumor progression: a brief historical perspective. Semin. Cancer Biol. 12, 261ā€“266.

    ArticleĀ  CASĀ  PubMedĀ  Google ScholarĀ 

  7. Lander, E. S., Linton, L. M., Birren, B., Nusbaum, C., Zody, M. C., Baldwin, J., et al. (2001) Initial sequencing and analysis of the human genome. Nature 409, 860ā€“921.

    ArticleĀ  CASĀ  PubMedĀ  Google ScholarĀ 

  8. International Human Genome Sequencing Consortium. (2004) Human genome sequencing, C. Finishing the euchromatic sequence of the human genome. Nature 431, 931ā€“945.

    ArticleĀ  Google ScholarĀ 

  9. Pennisi, E. (2003) Bioinformatics: gene counters struggle to get the right answer. Science 301, 1040ā€“1041.

    ArticleĀ  CASĀ  PubMedĀ  Google ScholarĀ 

  10. Stein, L. D. (2003) Integrating biological databases. Nat. Rev. Genet. 4, 337ā€“345.

    ArticleĀ  CASĀ  PubMedĀ  Google ScholarĀ 

  11. Strausberg, R. L., Simpson, A. J. G., and Wooster, R. (2003) Sequence-based cancer genomics: progress, lessons and opportunities. Nat. Rev. Genet. 4, 409ā€“418.

    ArticleĀ  CASĀ  PubMedĀ  Google ScholarĀ 

  12. Schena, M., Shalon, D., Davis, R. W., and Brown, P. O. (1995) Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science 270, 467ā€“470.

    ArticleĀ  CASĀ  PubMedĀ  Google ScholarĀ 

  13. Fodor, S. P., Read, J. L., Pirrung, M. C., Stryer, L., Lu, A. T., and Solas, D. (1991) Light-directed, spatially addressable parallel chemical synthesis. Science 251, 767ā€“773.

    ArticleĀ  CASĀ  PubMedĀ  Google ScholarĀ 

  14. Fodor, S. P., Rava, R. P., Huang, X. C., Pease, A. C., Holmes, C. P., and Adams, C. L. (1993) Multiplexed biochemical assays with biological chips. Nature 364, 555ā€“556.

    ArticleĀ  CASĀ  PubMedĀ  Google ScholarĀ 

  15. Ludwig, J. A., and Weinstein, J. N. (2005) Biomarkers in cancer staging, prognosis and treatment selection. Nat. Rev. Cancer 5, 845ā€“856.

    ArticleĀ  CASĀ  PubMedĀ  Google ScholarĀ 

  16. Golub, T. R., Slonim, D. K., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, J. P., et al. (1999) Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286, 531ā€“537.

    ArticleĀ  CASĀ  PubMedĀ  Google ScholarĀ 

  17. Alizadeh, A. A. (2000) Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature 403, 503ā€“511.

    ArticleĀ  CASĀ  PubMedĀ  Google ScholarĀ 

  18. Ramaswamy, S., Ross, K., Lander, E., and Golub, T. (2003) A molecular signature of metastasis in primary solid tumors. Nat. Genet. 33, 49ā€“54.

    ArticleĀ  CASĀ  PubMedĀ  Google ScholarĀ 

  19. Rhodes, D. R., and Chinnaiyan, A. M. (2005) Integrative analysis of the cancer transcriptome. Nat. Genet. 37(Suppl), S31ā€“S37.

    ArticleĀ  CASĀ  PubMedĀ  Google ScholarĀ 

  20. Pittman, J., Huang, E., Dressman, H., Horng, C. F., Cheng, S. H., Tsou, M. H., et al. (2004). Integrated modeling of clinical and gene expression information for personalized prediction of disease outcomes. Proc. Natl. Acad. Sci. U S A 101, 8431ā€“8436.

    ArticleĀ  CASĀ  PubMedĀ  Google ScholarĀ 

  21. Paik, S., Shak, S., Tang, G., Kim, C., Baker, J., Cronin, M., et al. (2004) A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N. Engl. J. Med. 351, 2817ā€“2826.

    ArticleĀ  CASĀ  PubMedĀ  Google ScholarĀ 

  22. Segal, E., Friedman, N., Kaminski, N., Regev, A., and Koller, D. (2005) From signatures to models: understanding cancer using microarrays. Nat. Genet. 37(Suppl), S38ā€“S45.

    ArticleĀ  CASĀ  PubMedĀ  Google ScholarĀ 

  23. Glinsky, G. V., Berezovska, O., and Glinskii, A. B. (2005) Microarray analysis identifies a death-from-cancer signature predicting therapy failure in patients with multiple types of cancer. J. Clin. Invest. 115, 1503ā€“1521.

    ArticleĀ  CASĀ  PubMedĀ  Google ScholarĀ 

  24. Dhanasekaran, S. M., Barrette, T. R., Ghosh, D., Shah, R., Varambally, S., Kurachi, K., et al. (2001) Delineation of prognostic biomarkers in prostate cancer. Nature 412, 822ā€“826.

    ArticleĀ  CASĀ  PubMedĀ  Google ScholarĀ 

  25. Lapointe, J., Li, C., Higgins, J. P., van de Rijn, M., Bair, E., Montgomery, K., et al. (2004) Gene expression profiling identifies clinically relevant subtypes of prostate cancer. Proc. Natl. Acad. Sci. U S A 101, 811ā€“816.

    ArticleĀ  CASĀ  PubMedĀ  Google ScholarĀ 

  26. Schaner, M. E., Ross, D. T., Ciaravino, G., Sorlie, T., Troyanskaya, O., Diehn, M., et al. (2003) Gene expression patterns in ovarian carcinomas. Mol. Biol. Cell 14, 4376ā€“4386.

    ArticleĀ  CASĀ  PubMedĀ  Google ScholarĀ 

  27. Vasselli, J. R., Shih, J. H., Iyengar, S. R., Maranchie, J., Riss, J., Worrell, R., et al. (2003) Predicting survival in patients with metastatic kidney cancer by gene-expression profiling in the primary tumor. Proc. Natl. Acad. Sci. U S A 100, 6958ā€“6963.

    ArticleĀ  CASĀ  PubMedĀ  Google ScholarĀ 

  28. Sorlie, T., Perou, C., Brown, P., Botstein, D., and Borresen-Dale, A. (2001) Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc. Natl Acad. Sci. U S A 98, 10869ā€“10874.

    ArticleĀ  CASĀ  PubMedĀ  Google ScholarĀ 

  29. Sotiriou, C. (2003) Breast cancer classification and prognosis based on gene expression profiles from a population-based study. Proc. Natl. Acad. Sci. U S A 100, 10393ā€“10398.

    ArticleĀ  CASĀ  PubMedĀ  Google ScholarĀ 

  30. Garber, M. E. (2001) Diversity of gene expression in adenocarcinoma of the lung. Proc. Natl Acad. Sci. U S A 98, 13784ā€“13789.

    ArticleĀ  CASĀ  PubMedĀ  Google ScholarĀ 

  31. Jones, M. H., Virtanen, C., Honjoh, D., Miyoshi, T., Satoh, Y., Okumura, S., et al. (2004) Two prognostically significant subtypes of high-grade lung neuroendocrine tumours independent of small-cell and large-cell neuroendocrine carcinomas identified by gene expression profiles. Lancet 363, 775ā€“781.

    ArticleĀ  CASĀ  PubMedĀ  Google ScholarĀ 

  32. West, R. B., and van de Rijn, M. (2006) The role of microarray technologies in the study of soft tissue tumours. Histopathology 48, 22ā€“31.

    ArticleĀ  CASĀ  PubMedĀ  Google ScholarĀ 

  33. Tinker, A. V., Boussioutas, A., and Bowtell, D. D. L. (2006) The challenges of gene expression microarrays for the study of human cancer. Cancer Cell 9, 333ā€“339.

    ArticleĀ  CASĀ  PubMedĀ  Google ScholarĀ 

  34. Wadlow, R., and Ramaswamy, S. (2005) DNA microarrays in clinical cancer research. Curr. Mol. Med. 5, 111ā€“120.

    ArticleĀ  CASĀ  PubMedĀ  Google ScholarĀ 

  35. The Tumor Analysis Best Practices Working Group. (2004) Expression profilingā€”Best practices for data generation and interpretation in clinical trials. Nat. Rev. Genet. 5, 229ā€“237.

    Google ScholarĀ 

  36. Dai, M., Wang, P., Boyd, A. D., Kostov, G., Athey, B., Jones, E. G., et al. (2005) Evolving gene/transcript definitions significantly alter the interpretation of GeneChip data. Nucleic Acids Res. 33, e175.

    ArticleĀ  PubMedĀ  Google ScholarĀ 

  37. Diehn, M., Sherlock, G., Binkley, G., Jin, H., Matese, J. C., Hernandez-Boussard, T., et al. (2003) Source: a unified genomic resource of functional annotations, ontologies, and gene expression data. Nucleic Acids Res. 31, 219ā€“223.

    ArticleĀ  CASĀ  PubMedĀ  Google ScholarĀ 

  38. Kent, W. J. (2002) BLATā€”The BLAST-like alignment tool. Genome Res. 12, 656ā€“664.

    CASĀ  PubMedĀ  Google ScholarĀ 

  39. Smit, A., Hubley, R., and Green, P. (1996ā€“2004) RepeatMasker Open 3.0. http://www.repeatmasker.org/.

    Google ScholarĀ 

  40. Bairoch, A., Apweiler, R., Wu, C. H., Barker, W. C., Boeckmann, B., Ferro, S., et al. (2005) The Universal Protein Resource (UniProt). Nucleic Acids Res. 33, D154ā€“D159.

    ArticleĀ  CASĀ  PubMedĀ  Google ScholarĀ 

  41. Pruitt, K. D., Katz, K. S., Sicotte, H., and Maglott, D. R. (2000) Introducing RefSeq and LocusLink: curated human genome resources at the NCBI. Trends Genet. 16, 44ā€“47.

    ArticleĀ  CASĀ  PubMedĀ  Google ScholarĀ 

  42. Wheeler, D. L., Barrett, T., Benson, D. A., Bryant, S. H., Canese, K., Chetvernin, V., et al. (2006) Database resources of the National Center for Biotechnology Information. Nucleic Acids Res. 34, D173ā€“D180.

    ArticleĀ  CASĀ  PubMedĀ  Google ScholarĀ 

  43. Murray, C. G., Larsson, T. P., Hill, T., Bjorklind, R., Fredriksson, R., and Schioth, H. B. (2005) Evaluation of EST-data using the genome assembly. Biochem. Biophys. Res. Commun. 331, 1566ā€“1576.

    ArticleĀ  CASĀ  PubMedĀ  Google ScholarĀ 

  44. Maglott, D., Ostell, J., Pruitt, K. D., and Tatusova, T. (2005) Entrez Gene: gene-centered information at NCBI. Nucleic Acids Res. 33, D54ā€“D58.

    ArticleĀ  CASĀ  PubMedĀ  Google ScholarĀ 

  45. Hamosh, A., Scott, A. F., Amberger, J. S., Bocchini, C. A., and McKusick, V. A. (2005) Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders. Nucleic Acids Res. 33, D514ā€“D517.

    ArticleĀ  CASĀ  PubMedĀ  Google ScholarĀ 

  46. Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., et al. (2000) Gene ontology: tool for the unification of biology. Nat. Genet. 25, 25ā€“29.

    ArticleĀ  CASĀ  PubMedĀ  Google ScholarĀ 

  47. Bammler, T., Beyer, R. P., Bhattacharya, S., Boorman, G. A., Boyles, A., Bradford, B. U., et al. (2005) Standardizing global gene expression analysis between laboratories and across platforms. Nat. Methods 2, 351ā€“356.

    ArticleĀ  PubMedĀ  Google ScholarĀ 

  48. Larkin, J. E., Frank, B. C., Gavras, H., Sultana, R., and Quackenbush, J. (2005) Independence and reproducibility across microarray platforms. Nat. Methods 2, 337ā€“344.

    ArticleĀ  CASĀ  PubMedĀ  Google ScholarĀ 

  49. Irizarry, R. A., Warren, D., Spencer, F., Kim, I. F., Biswal, S., Frank, B. C., et al. (2005) Multiple-laboratory comparison of microarray platforms. Nat. Methods 2, 345ā€“350.

    ArticleĀ  CASĀ  PubMedĀ  Google ScholarĀ 

  50. Simon, R. M., and Dobbin, K. (2003) Experimental design of DNA microarray experiments. Biotechniques Suppl, 16ā€“21.

    Google ScholarĀ 

  51. Kerr, M. K., and Churchill, G. A. (2001) Experimental design for gene expression microarrays. Biostatistics 2, 183ā€“201.

    ArticleĀ  PubMedĀ  Google ScholarĀ 

  52. Dobbin, K., Shih, J. H., and Simon, R. (2003) Questions and answers on design of dual-label microarrays for identifying differentially expressed genes J. Natl. Cancer Inst. 95, 1362ā€“1369.

    PubMedĀ  Google ScholarĀ 

  53. Cox, W. G., and Singer, V. L. (2004) Fluorescent DNA hybridization probe preparation using amine modification and reactive dye coupling. Biotechniques 36, 114ā€“122.

    CASĀ  PubMedĀ  Google ScholarĀ 

  54. Virtanen, C., Ishikawa, Y., Honjoh, D., Kimura, M., Shimane, M., Miyoshi, T., et al. (2002) Integrated classification of lung tumors and cell lines by expression profiling. Proc. Natl. Acad. Sci. U S A 99, 12357ā€“12362.

    ArticleĀ  CASĀ  PubMedĀ  Google ScholarĀ 

  55. Mukherjee, S., Tamayo, P., Rogers, S., Rifkin, R., Engle, A., Campbell, C., et al. (2003) Estimating dataset size requirements for classifying DNA microarray data. J. Comput. Biol. 10, 119ā€“142.

    ArticleĀ  CASĀ  PubMedĀ  Google ScholarĀ 

  56. Tibshirani, R. (2006). A simple method for assessing sample sizes in microarray experiments. BMC Bioinformatics 7, 106.

    ArticleĀ  PubMedĀ  Google ScholarĀ 

  57. Tsai, C.-A., Wang, S.-J., Chen, D.-T., and Chen, J. J. (2005) Sample size for gene expression microarray experiments. Bioinformatics 21, 1502ā€“1508.

    ArticleĀ  CASĀ  PubMedĀ  Google ScholarĀ 

  58. Gentleman, R., Carey, V., Bates, D., Bolstad, B., Dettling, M., Dudoit, S., et al. (2004) Bioconductor: open software development for computational biology and bioinformatics. Genome Biol. 5, R80.

    ArticleĀ  PubMedĀ  Google ScholarĀ 

  59. Quackenbush, J. (2002) Microarray data normalization and transformation. Nat. Genet. 32(Suppl), 496ā€“501.

    ArticleĀ  CASĀ  PubMedĀ  Google ScholarĀ 

  60. Smyth, G. K., Yang, Y. H., and Speed, T. (2003) Statistical issues in cDNA microarray data analysis. Methods Mol. Biol. 224, 111ā€“136.

    CASĀ  PubMedĀ  Google ScholarĀ 

  61. Weiner, A. M. (2002) SINEs and LINEs: the art of biting the hand that feeds you. Curr. Opin. Cell Biol. 14, 343ā€“350.

    ArticleĀ  CASĀ  PubMedĀ  Google ScholarĀ 

  62. DeRisi, J., Penland, L., Brown, P. O., Bittner, M. L., Meltzer, P. S., Ray, M., et al. (1996) Use of a cDNA microarray to analyse gene expression patterns in human cancer. Nat. Genet. 14, 457ā€“460.

    ArticleĀ  CASĀ  PubMedĀ  Google ScholarĀ 

  63. Yang, I., Chen, E., Hasseman, J., Liang, W., Frank, B., Wang, S., et al. (2002) Within the fold: assessing differential expression measures and reproducibility in microarray assays. Genome Biol. 3, R0062.

    Google ScholarĀ 

  64. Eisen, M. B., Spellman, P. T., Brown, P. O., and Botstein, D. (1998) Cluster analysis and display of genome-wide expression patterns. Proc. Natl. Acad. Sci. U S A 95, 14863ā€“14868.

    ArticleĀ  CASĀ  PubMedĀ  Google ScholarĀ 

  65. Yeung, K. Y., Haynor, D. R., and Ruzzo, W. L. (2001) Validating clustering for gene expression data. Bioinformatics 17, 309ā€“318.

    ArticleĀ  CASĀ  PubMedĀ  Google ScholarĀ 

  66. Raychaudhuri, S., Stuart, J. M., and Altman, R. B. (2000) Principal components analysis to summarize microarray experiments: application to sporulation time series. Pac. Symp. Biocomput. 455ā€“466.

    Google ScholarĀ 

  67. Cui, X., and Churchill, G. (2003) Statistical tests for differential expression in cDNA microarray experiments. Genome Biol. 4, 210.

    ArticleĀ  PubMedĀ  Google ScholarĀ 

  68. Benjamini, Y., and Hochberg, Y. (1995) Controlling the false discover rate: a practical and powerful approach to multiple testing. J. Royal Stats. Soc. 57, 289ā€“300.

    Google ScholarĀ 

  69. Tusher, V. G., Tibshirani, R., and Chu, G. (2001) Significance analysis of microarrays applied to the ionizing radiation response. Proc. Natl. Acad. Sci. U S A 98, 5116ā€“5121.

    ArticleĀ  CASĀ  PubMedĀ  Google ScholarĀ 

  70. Hastie, T., Tibshirani, R., Eisen, M., Alizadeh, A., Levy, R., Staudt, L., et al. (2000) ā€˜Gene shavingā€™ as a method for identifying distinct sets of genes with similar expression patterns. Genome Biol. 1, R0003.

    ArticleĀ  Google ScholarĀ 

  71. Rajeevan, M. S., Vernon, S. D., Taysavang, N., and Unger, E. R. (2001) Validation of Array-based gene expression profiles by real-time (Kinetic) RT-PCR. J. Mol. Diagn. 3, 26ā€“31.

    ArticleĀ  CASĀ  PubMedĀ  Google ScholarĀ 

  72. Beissbarth, T., and Speed, T. P. (2004) GOstat: find statistically overrepresented gene ontologies within a group of genes. Bioinformatics 20, 1464ā€“1465.

    ArticleĀ  CASĀ  PubMedĀ  Google ScholarĀ 

  73. Zhong, S., Tian, L., Li, C., Storch, K. F., and Wong, W. H. (2004) Comparative analysis of gene sets in the gene ontology space under the multiple hypothesis testing framework. Proc. IEEE. Comput. Syst. Bioinform Conf. 425ā€“435.

    Google ScholarĀ 

  74. Bussey, K. J., Chin, K., Lababidi, S., Reimers, M., Reinhold, W. C., Kuo, W. L., et al. (2006) Integrating data on DNA copy number with gene expression levels and drug sensitivities in the NCI-60 cell line panel. Mol. Cancer Ther. 5, 853ā€“867.

    ArticleĀ  CASĀ  PubMedĀ  Google ScholarĀ 

  75. Mootha, V. K., Lepage, P., Miller, K., Bunkenborg, J., Reich, M., Hjerrild, M., et al. (2003) From the cover: identification of a gene causing human cytochrome c oxidase deficiency by integrative genomics. Proc. Natl. Acad. Sci. U S A 100, 605ā€“610.

    ArticleĀ  CASĀ  PubMedĀ  Google ScholarĀ 

  76. Segal, E., Wang, H., and Koller, D. (2003) Discovering molecular pathways from protein interaction and gene expression data. Bioinformatics 19, i264ā€“i272.

    ArticleĀ  PubMedĀ  Google ScholarĀ 

  77. van Noort, V., Snel, B., and Huynen, M. A. (2003) Predicting gene function by conserved co-expression. Trends Genet. 19, 238ā€“242.

    ArticleĀ  PubMedĀ  Google ScholarĀ 

  78. Semon, M., and Duret, L. (2006) Evolutionary origin and maintenance of coexpressed gene clusters in mammals. Mol. Biol. Evol. 23, 1715ā€“1723.

    ArticleĀ  CASĀ  PubMedĀ  Google ScholarĀ 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

Ā© 2008 Humana Press, a part of Springer Science+Business Media, LLC

About this protocol

Cite this protocol

Virtanen, C., Woodgett, J. (2008). Clinical Uses of Microarrays in Cancer Research. In: Trent, R.J. (eds) Clinical Bioinformatics. Methods in Molecular Medicineā„¢, vol 141. Humana Press. https://doi.org/10.1007/978-1-60327-148-6_6

Download citation

  • DOI: https://doi.org/10.1007/978-1-60327-148-6_6

  • Publisher Name: Humana Press

  • Print ISBN: 978-1-58829-791-4

  • Online ISBN: 978-1-60327-148-6

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics