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From signatures to models: understanding cancer using microarrays

Nature Genetics volume 37, pages S38–S45 (2005)Cite this article

Abstract

Genomics has the potential to revolutionize the diagnosis and management of cancer by offering an unprecedented comprehensive view of the molecular underpinnings of pathology. Computational analysis is essential to transform the masses of generated data into a mechanistic understanding of disease. Here we review current research aimed at uncovering the modular organization and function of transcriptional networks and responses in cancer. We first describe how methods that analyze biological processes in terms of higher-level modules can identify robust signatures of disease mechanisms. We then discuss methods that aim to identify the regulatory mechanisms underlying these modules and processes. Finally, we show how comparative analysis, combining human data with model organisms, can lead to more robust findings. We conclude by discussing the challenges of generalizing these methods from cells to tissues and the opportunities they offer to improve cancer diagnosis and management.

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Figure 1: Module-level analysis.
Figure 2: Computational prediction of cis-regulatory networks.
Figure 3: Computational prediction of trans-regulatory networks.
Figure 4: Multispecies analysis of gene expression data.

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References

  1. Ren, B. et al. Genome-wide location and function of DNA binding proteins. Science 290, 2306–2309 (2000).

    Article  CAS  PubMed  Google Scholar 

  2. Iyer, V.R. et al. Genomic binding sites of the yeast cell-cycle transcription factors SBF and MBF. Nature 409, 533–538 (2001).

    Article  CAS  PubMed  Google Scholar 

  3. Kononen, J. et al. Tissue microarrays for high-throughput molecular profiling of tumor specimens. Nat. Med. 4, 844–847 (1998).

    Article  CAS  PubMed  Google Scholar 

  4. Lander, E.S. Array of hope. Nat. Genet. 21, 3–4 (1999).

    Article  CAS  PubMed  Google Scholar 

  5. Khan, J. et al. Expression profiling in cancer using cDNA microarrays. Electrophoresis 20, 223–239 (1999).

    Article  CAS  PubMed  Google Scholar 

  6. Garber, K. Genomic medicine. Gene expression tests foretell breast cancer's future. Science 303, 1754–1755 (2004).

    Article  PubMed  Google Scholar 

  7. Segal, E., Friedman, N., Koller, D. & Regev, A. A module map showing conditional activity of expression modules in cancer. Nat. Genet. 36, 1090–1098 (2004).

    Article  CAS  PubMed  Google Scholar 

  8. Mootha, V.K. et al. PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat. Genet. 34, 267–273 (2003).

    Article  CAS  PubMed  Google Scholar 

  9. Lamb, J. et al. A mechanism of cyclin D1 action encoded in the patterns of gene expression in human cancer. Cell 114, 323–334 (2003).

    Article  CAS  PubMed  Google Scholar 

  10. Huang, E. et al. Gene expression phenotypic models that predict the activity of oncogenic pathways. Nat. Genet. 34, 226–230 (2003).

    Article  CAS  PubMed  Google Scholar 

  11. Rhodes, D.R. et al. Large-scale meta-analysis of cancer microarray data identifies common transcriptional profiles of neoplastic transformation and progression. Proc. Natl. Acad. Sci. USA 101, 9309–9314 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Chang, C.F., Wai, K.M. & Patterton, H.G. Calculating the statistical significance of physical clusters of co-regulated genes in the genome: the role of chromatin in domain-wide gene regulation. Nucleic Acids Res. 32, 1798–1807 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Desai, K.V. et al. Initiating oncogenic event determines gene-expression patterns of human breast cancer models. Proc. Natl. Acad. Sci. USA 99, 6967–6972 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Odom, D.T. et al. Control of pancreas and liver gene expression by HNF transcription factors. Science 303, 1378–1381 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Li, Z. et al. A global transcriptional regulatory role for c-Myc in Burkitt's lymphoma cells. Proc. Natl. Acad. Sci. USA 100, 8164–8169 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Wingender, E. et al. The TRANSFAC system on gene expression regulation. Nucleic Acids Res. 29, 281–283 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Kellis, M., Patterson, N., Endrizzi, M., Birren, B. & Lander, E.S. Sequencing and comparison of yeast species to identify genes and regulatory elements. Nature 423, 241–254 (2003).

    Article  CAS  PubMed  Google Scholar 

  18. Cliften, P. et al. Finding functional features in Saccharomyces genomes by phylogenetic footprinting. Science 301, 71–76 (2003).

    Article  CAS  PubMed  Google Scholar 

  19. Tavazoie, S., Hughes, J.D., Campbell, M.J., Cho, R.J. & Church, G.M. Systematic determination of genetic network architecture. Nat. Genet. 22, 281–285 (1999).

    Article  CAS  PubMed  Google Scholar 

  20. Shen-Orr, S.S., Milo, R., Mangan, S. & Alon, U. Network motifs in the transcriptional regulation network of Escherichia coli. Nat. Genet. 31, 64–68 (2002).

    Article  CAS  PubMed  Google Scholar 

  21. Lee, T.I. et al. Transcriptional regulatory networks in Saccharomyces cerevisiae. Science 298, 799–804 (2002).

    Article  CAS  PubMed  Google Scholar 

  22. Pilpel, Y., Sudarsanam, P. & Church, G.M. Identifying regulatory networks by combinatorial analysis of promoter elements. Nat. Genet. 29, 153–159 (2001).

    Article  CAS  PubMed  Google Scholar 

  23. Harbison, C.T. et al. Transcriptional regulatory code of a eukaryotic genome. Nature 431, 99–104 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Pritsker, M., Liu, Y.C., Beer, M.A. & Tavazoie, S. Whole-genome discovery of transcription factor binding sites by network-level conservation. Genome Res. 14, 99–108 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Segal, E., Barash Y., Simon I., Friedman N. & Koller D. From promoter sequence to expression: a probabilistic framework. Proceedings of the 6th International Conference on Research in Computational Molecular Biology 263–272 (ACM Press, Washington, DC, 2002).

    Google Scholar 

  26. Segal, E., Yelensky, R. & Koller, D. Genome-wide discovery of transcriptional modules from DNA sequence and gene expression. Bioinformatics 19 Suppl. 1, i273–i282 (2003).

    Article  PubMed  Google Scholar 

  27. Beer, M.A. & Tavazoie, S. Predicting gene expression from sequence. Cell 117, 185–198 (2004).

    Article  CAS  PubMed  Google Scholar 

  28. Bar-Joseph, Z. et al. Computational discovery of gene modules and regulatory networks. Nat. Biotechnol. 21, 1337–1342 (2003).

    Article  CAS  PubMed  Google Scholar 

  29. Elkon, R., Linhart, C., Sharan, R., Shamir, R. & Shiloh, Y. Genome-wide in silico identification of transcriptional regulators controlling the cell cycle in human cells. Genome Res. 13, 773–780 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Sharan, R., Ben-Hur, A., Loots, G.G. & Ovcharenko, I. CREME: cis-regulatory module explorer for the human genome. Nucleic Acids Res. 32, W253–W256 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Schroeder, M.D. et al. Transcriptional control in the segmentation gene network of Drosophila. PLoS Biol. 2, E271 (2004).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  32. Segal, E. & Sharan, R. A discriminative model for identifying spatial cis-regulatory modules. Research in Computational Molecular Biology 141–149 (ACM Press, San Diego, 2004).

    Google Scholar 

  33. Sinha, S., van Nimwegen, E. & Siggia, E.D. A probabilistic method to detect regulatory modules. Bioinformatics 19 Suppl. 1, i292–i301 (2003).

    Article  PubMed  Google Scholar 

  34. Berman, B.P. et al. Exploiting transcription factor binding site clustering to identify cis-regulatory modules involved in pattern formation in the Drosophila genome. Proc. Natl. Acad. Sci. USA 99, 757–762 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Pe'er, D., Regev, A. & Tanay, A. Minreg: Inferring an active regulator set. Bioinformatics 18 Suppl. 1, S258–S267 (2002).

    Article  PubMed  Google Scholar 

  36. Friedman, N., Linial, M., Nachman, I. & Pe'er, D. Using Bayesian networks to analyze expression data. J. Comput. Biol. 7, 601–620 (2000).

    Article  CAS  PubMed  Google Scholar 

  37. Hartemink, A.J., Gifford, D.K., Jaakkola, T.S. & Young, R.A. Combining location and expression data for principled discovery of genetic regulatory networks. Pacific Symposium on Biocomputing, 437–439 (World Scientific, Lihue, Hawaii, 2002).

    Google Scholar 

  38. Nachman, I., Regev, A. & Friedman, N. Inferring quantitative models of regulatory networks from expression data. Bioinformatics 20 Suppl. 1, I248–I256 (2004).

    Article  CAS  PubMed  Google Scholar 

  39. Kalir, S. & Alon, U. Using a quantitative blueprint to reprogram the dynamics of the flagella gene network. Cell 117, 713–720 (2004).

    Article  CAS  PubMed  Google Scholar 

  40. Ronen, M., Rosenberg, R., Shraiman, B.I. & Alon, U. Assigning numbers to the arrows: parameterizing a gene regulation network by using accurate expression kinetics. Proc. Natl. Acad. Sci. USA 99, 10555–10560 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Segal, E. et al. Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data. Nat. Genet. 34, 166–176 (2003).

    Article  CAS  PubMed  Google Scholar 

  42. Lossos, I.S. et al. Transformation of follicular lymphoma to diffuse large-cell lymphoma: alternative patterns with increased or decreased expression of c-myc and its regulated genes. Proc. Natl. Acad. Sci. USA 99, 8886–8891 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Beer, D.G. et al. Gene-expression profiles predict survival of patients with lung adenocarcinoma. Nat. Med. 8, 816–824 (2002).

    Article  CAS  PubMed  Google Scholar 

  44. Wiseman, B.S. & Werb, Z. Stromal effects on mammary gland development and breast cancer. Science 296, 1046–1049 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Chang, H.Y. et al. Robustness, scalability, and integration of a wound-response gene expression signature in predicting breast cancer survival. Proc. Natl. Acad. Sci. USA 102, 3738–3743 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Stuart, J.M., Segal, E., Koller, D. & Kim, S.K. A gene-coexpression network for global discovery of conserved genetic modules. Science 302, 249–255 (2003).

    Article  CAS  PubMed  Google Scholar 

  47. Bergmann, S., Ihmels, J. & Barkai, N. Similarities and differences in genome-wide expression data of six organisms. PLoS Biol. 2, E9 (2004).

    Article  PubMed  CAS  Google Scholar 

  48. McCarroll, S.A. et al. Comparing genomic expression patterns across species identifies shared transcriptional profile in aging. Nat. Genet. 36, 197–204 (2004).

    Article  CAS  PubMed  Google Scholar 

  49. Sweet-Cordero, A. et al. An oncogenic KRAS2 expression signature identified by cross-species gene-expression analysis. Nat. Genet. 37, 48–55 (2005).

    Article  CAS  PubMed  Google Scholar 

  50. Segal, E. Rich Probabilistic Models for Genomic Data PhD thesis, Stanford Univ. (2004).

    Google Scholar 

  51. Mecham, B.H. et al. Increased measurement accuracy for sequence-verified microarray probes. Physiol. Genomics 18, 308–315 (2004).

    Article  CAS  PubMed  Google Scholar 

  52. Michiels, S., Koscielny, S. & Hill, C. Prediction of cancer outcome with microarrays: a multiple random validation strategy. Lancet 365, 488–492 (2005).

    Article  CAS  PubMed  Google Scholar 

  53. Cluzel, P., Surette, M. & Leibler, S. An ultrasensitive bacterial motor revealed by monitoring signaling proteins in single cells. Science 287, 1652–1655 (2000).

    Article  CAS  PubMed  Google Scholar 

  54. Lahav, G. et al. Dynamics of the p53-Mdm2 feedback loop in individual cells. Nat. Genet. 36, 147–150 (2004).

    Article  CAS  PubMed  Google Scholar 

  55. Irish, J.M. et al. Single cell profiling of potentiated phospho-protein networks in cancer cells. Cell 118, 217–228 (2004).

    Article  CAS  PubMed  Google Scholar 

  56. Stuart, R.O. et al. In silico dissection of cell-type-associated patterns of gene expression in prostate cancer. Proc. Natl. Acad. Sci. USA 101, 615–620 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Lu, P., Nakorchevskiy, A. & Marcotte, E.M. Expression deconvolution: a reinterpretation of DNA microarray data reveals dynamic changes in cell populations. Proc. Natl. Acad. Sci. USA 100, 10370–10375 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Chang, H.Y. et al. Gene expression signature of fibroblast serum response predicts human cancer progression: similarities between tumors and wounds. PLoS Biol. 2, E7 (2004).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  59. Kang, Y. et al. A multigenic program mediating breast cancer metastasis to bone. Cancer Cell 3, 537–549 (2003).

    Article  CAS  PubMed  Google Scholar 

  60. Fuller, A.P., Palmer-Toy, D., Erlander, M.G. & Sgroi, D.C. Laser capture microdissection and advanced molecular analysis of human breast cancer. J. Mammary Gland Biol. Neoplasia 8, 335–345 (2003).

    Article  PubMed  Google Scholar 

  61. Kobayashi, K. et al. Identification of genes whose expression is upregulated in lung adenocarcinoma cells in comparison with type II alveolar cells and bronchiolar epithelial cells in vivo. Oncogene 23, 3089–3096 (2004).

    Article  CAS  PubMed  Google Scholar 

  62. Whitfield, M.L. et al. Identification of genes periodically expressed in the human cell cycle and their expression in tumors. Mol. Biol. Cell. 13, 1977–2000 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Caetano, M.S. et al. NFATC2 transcription factor regulates cell cycle progression during lymphocyte activation: evidence of its involvement in the control of cyclin gene expression. FASEB J. 16, 1940–1942 (2002).

    Article  CAS  PubMed  Google Scholar 

  64. Baksh, S. et al. NFATc2-mediated repression of cyclin-dependent kinase 4 expression. Mol. Cell. 10, 1071–1081 (2002).

    Article  CAS  PubMed  Google Scholar 

  65. Behrens, J. & Lustig, B. The Wnt connection to tumorigenesis. Int. J. Dev. Biol. 48, 477–487 (2004).

    Article  CAS  PubMed  Google Scholar 

  66. Hulboy, D.L., Matrisian, L.M. & Crawford, H.C. Loss of JunB activity enhances stromelysin 1 expression in a model of the epithelial-to-mesenchymal transition of mouse skin tumors. Mol. Cell. Biol. 21, 5478–5487 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Pomeroy, S.L. et al. Prediction of central nervous system embryonal tumour outcome based on gene expression. Nature 415, 436–442 (2002).

    Article  CAS  PubMed  Google Scholar 

  68. Su, A.I. et al. A gene atlas of the mouse and human protein-encoding transcriptomes. Proc. Natl. Acad. Sci. USA 101, 6062–6067 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Rostomily, R.C. et al. Expression of neurogenic basic helix-loop-helix genes in primitive neuroectodermal tumors. Cancer Res. 57, 3526–3531 (1997).

    CAS  PubMed  Google Scholar 

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Acknowledgements

All authors contributed equally to this work. We thank M. Scott and T. Raveh for making available to us their mouse brain microarrays for the multispecies module network analysis. E.S. was supported by a Fellowship from the Center for Studies in Physics and Biology at Rockefeller University. N.F. was supported by the Harry & Abe Sherman Senior Lectureship in Computer Science, by the United States-Israel Bi-National Science Foundation grant and by grants from the US National Institutes of Health. N.K. was partly supported by grants from the US National Institutes of Health, by the Tel-Aviv Chapter of the Israeli Lung Association and by a donation from the Simmons family. A.R. was supported by a grant from the US National Institutes of Health and by the Bauer Center. D.K. was supported by a grant from the US National Science Foundation and by a BioX Center grant.

Author information

Authors and Affiliations

  1. the Center for Studies in Physics and Biology, Rockefeller University, New York, USA

    Eran Segal

  2. the School of Computer Science and Engineering, Hebrew University, Jerusalem, Israel

    Nir Friedman

  3. the Dorothy P. and Richard P. Simmons for Interstitial Lung Diseases, Pulmonary Allergy and Critical Care Medicine, University of Pittsburgh, USA

    Naftali Kaminski

  4. the Bauer Center for Genomics Research, Harvard University, Cambridge, Massachusetts, USA

    Aviv Regev

  5. the Computer Science Department, Stanford University, Stanford, California, USA

    Daphne Koller

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  1. Eran Segal
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Correspondence to Daphne Koller.

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Segal, E., Friedman, N., Kaminski, N. et al. From signatures to models: understanding cancer using microarrays. Nat Genet 37 (Suppl 6), S38–S45 (2005). https://doi.org/10.1038/ng1561

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