Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Review Article
  • Published:

Mining gene expression profiles: expression signatures as cancer phenotypes

Nature Reviews Genetics volume 8, pages 601–609 (2007)Cite this article

Key Points

  • Gene expression data derived from DNA microarray studies have been shown to provide enormous potential for dissecting the complexity of cancer. Particularly useful has been the ability to derive profiles, or signatures, that define specific cancer phenotypes.

  • Gene expression signatures can be developed that reflect phenotypes that range from cancer recurrence to more precise conditions such as drug sensitivity or the capacity of cancer cells to grow in an anchorage-independent fashion.

  • Importantly, these gene expression signatures can serve as surrogate phenotypes that link diverse biological states, ranging from patients to experimental animals to in vitro conditions.

  • Gene expression signatures can be developed to link biological states with therapeutic opportunities.

  • Gene expression signatures can serve as phenotypes for genetic association studies, and as guides for interpreting DNA sequence variation.

Abstract

Many examples highlight the power of gene expression profiles, or signatures, to inform an understanding of biological phenotypes. This is perhaps best seen in the context of cancer, where expression signatures have tremendous power to identify new subtypes and to predict clinical outcomes. Although the ability to interpret the meaning of the individual genes in these signatures remains a challenge, this does not diminish the power of the signature to characterize biological states. The use of these signatures as surrogate phenotypes has been particularly important, linking diverse experimental systems that dissect the complexity of biological systems with the in vivo setting in a way that was not previously feasible.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Generation of an expression signature.
Figure 2: Identification of patterns of signatures.
Figure 3: Stepwise linkage analysis of microarray signatures (SLAMS).
Figure 4: Use of signature patterns to identify complex cancer-relevant phenotypes to guide sequence analysis.

Similar content being viewed by others

References

  1. Golub, T. R. et al. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286, 531–537 (1999). The paradigm for use of both unsupervised and supervised methods of gene expression analysis to define new classes of tumours and predict these classes in new samples.

    Article  CAS  Google Scholar 

  2. Alizadeh, A. A. et al. Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature 403, 503–511 (2000).

    Article  CAS  Google Scholar 

  3. Hanahan, D. & Weinberg, R. A. The hallmarks of cancer. Cell 100, 57–70 (2000).

    Article  CAS  Google Scholar 

  4. Rhodes, D. R. et al. Mining for regulatory programs in the cancer transcriptome. Nature Genet. 37, 579–583 (2005).

    Article  CAS  Google Scholar 

  5. Rhodes, D. R. & Chinnaiyan, A. M. Integrative analysis of the cancer transcriptome. Nature Genet. 37, S31–S37 (2005).

    Article  CAS  Google Scholar 

  6. 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  Google Scholar 

  7. Eisen, M. B., Spellman, P. T., Brown, P. O. & Botstein, D. Cluster analysis and display of genome-wide expression patterns. Proc. Natl Acad. Sci. USA 95, 14863–14868 (1998).

    Article  CAS  Google Scholar 

  8. Sorlie, T. et al. Repeated observation of breast tumor subtypes in independent gene expression data sets. Proc. Natl Acad. Sci. USA 100, 8418–8423 (2003).

    Article  CAS  Google Scholar 

  9. Perou, C. M. et al. Molecular portraits of human breast tumors. Nature 406, 747–752 (2000).

    Article  CAS  Google Scholar 

  10. Ramaswamy, S. et al. Multiclass cancer diagnosis using tumor gene expression signatures. Proc. Natl Acad. Sci. USA 98, 15149–15154 (2001).

    Article  CAS  Google Scholar 

  11. Lapointe, J. et al. Gene expression profiling identifies clinically relevant subtypes of prostate cancer. Proc. Natl Acad. Sci. USA 101, 811–816 (2004).

    Article  CAS  Google Scholar 

  12. Hayes, D. N. et al. Gene expression profiling reveals reproducible human lung adenocarcinoma subtypes in multiple independent patient cohorts. J. Clin. Oncol. 24, 5079–5090 (2006).

    Article  CAS  Google Scholar 

  13. Dave, S. S. et al. Molecular diagnosis of Burkitt's lymphoma. N. Engl. J. Med. 354, 2431–2442 (2006).

    Article  CAS  Google Scholar 

  14. Dave, S. S. et al. Prediction of survival in follicular lymphoma based on molecular features of tumor-infiltrating immune cells. N. Engl. J. Med. 351, 2159–2169 (2004).

    Article  CAS  Google Scholar 

  15. He, L. et al. A microRNA polycistron as a potential human oncogene. Nature 435, 828–833 (2005).

    Article  CAS  Google Scholar 

  16. Yanaihara, N. et al. Unique microRNA molecular profiles in lung cancer diagnosis and prognosis. Cancer Cell 9, 189–198 (2006).

    Article  CAS  Google Scholar 

  17. Lu, J. et al. MicroRNA expression profiles classify human cancers. Nature 435, 834–838 (2005).

    Article  CAS  Google Scholar 

  18. Iorio, M. V. et al. MicroRNA gene expression deregulation in human breast cancer. Cancer Res. 65, 7065–7070 (2005).

    Article  CAS  Google Scholar 

  19. Volinia, S. et al. A microRNA expression signature of human solid tumors defines cancer gene targets. Proc. Natl Acad. Sci. USA 103, 2257–2261 (2006).

    Article  CAS  Google Scholar 

  20. Calin, G. A. & Croce, C. M. MicroRNA signatures in human cancers. Nature Rev. Cancer 6, 857–866 (2006).

    Article  CAS  Google Scholar 

  21. Ramaswamy, S., Ross, K. N., Lander, E. S. & Golub, T. R. A molecular signature of metastasis in primary solid tumors. Nature Genet. 33, 59–54 (2003).

    Article  Google Scholar 

  22. Berchuck, A. et al. Patterns of gene expression that characterize long term survival in advanced serous ovarian cancers. Clin. Cancer Res. 11, 3686–3696 (2005).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  24. Potti, A. et al. A genomic strategy to refine prognosis in non-small cell lung carcinoma. N. Engl. J. Med. 355, 570–580 (2006).

    Article  CAS  Google Scholar 

  25. Paik, S. et al. A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N. Engl. J. Med. 351, 2817–2826 (2004).

    Article  CAS  Google Scholar 

  26. Huang, E. et al. Gene expression predictors of breast cancer outcomes. Lancet 361, 1590–1596 (2003).

    Article  CAS  Google Scholar 

  27. Shipp, M. A. et al. Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning. Nature Med. 8, 68–74 (2002).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  29. van de Vijver, M. J. et al. A gene-expression signature as a predictor of survival in breast cancer. N. Engl. J. Med. 347, 1999–2009 (2002).

    Article  CAS  Google Scholar 

  30. van'T Veer, L. J. et al. Gene expression profiling predicts clinical outcome of breast cancer. Nature 415, 530–536 (2002). The initial example of a gene expression profile developed to refine and improve clinical prognosis.

    Article  CAS  Google Scholar 

  31. Singh, D. et al. Gene expression correlates of clinical prostate cancer behavior. Cancer Cell 1, 203–209 (2002).

    Article  CAS  Google Scholar 

  32. Pittman, J. et al. Integrated modeling of clinical and gene expression information for personalized prediction of disease outcomes. Proc. Natl Acad. Sci. USA 101, 8431–8436 (2004).

    Article  CAS  Google Scholar 

  33. West, M. et al. Predicting the clinical status of human breast cancer by using gene expression profiles. Proc. Natl Acad. Sci. USA 98, 11462–11467 (2001).

    Article  CAS  Google Scholar 

  34. Ein-Dor, L., Kela, I., Getz, G., Givol, D. & Domany, E. Outcome signature genes in breast cancer: is there a unique set? Bioinformatics 21, 171–178 (2005).

    Article  CAS  Google Scholar 

  35. Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005). A description of GSEA methodology as a tool to identify biological context in gene expression profiles.

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  37. Lamb, J. et al. The Connectivity Map: using gene expression signatures to connect small molecules, genes, and disease. Science 313, 1929–1935 (2006). A description of a novel approach to connect two biological states, using gene expression as the intermediary.

    Article  CAS  Google Scholar 

  38. Wei, G. et al. Gene expression based chemical genomics identifies rapamycin as a modulator of MCL1 and glucocorticoid resistance. Cancer Cell 10, 331–342 (2006).

    Article  CAS  Google Scholar 

  39. Hieronymus, H. et al. Gene expression signature-based chemical genomic prediction identifies a novel class of HSP90 pathway modulators. Cancer Cell 10, 321–330 (2006).

    Article  CAS  Google Scholar 

  40. 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  Google Scholar 

  41. Ferrando, A. A. et al. Gene expression signatures define novel oncogenic pathways in T cell acute lymphoblastic leukemia. Cancer Cell 1, 75–87 (2002).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  43. Black, E. P. et al. Distinct gene expression phenotypes of cells lacking RB and RB family members. Cancer Res. 63, 3716–3723 (2003).

    CAS  PubMed  Google Scholar 

  44. Sweet-Cordero, A. et al. An oncogenic KRAS2 expression signature identified by cross-species gene expression analysis. Nature Genet. 37, 48–54 (2005).

    Article  CAS  Google Scholar 

  45. Bild, A. et al. Oncogenic pathway signatures in human cancers as a guide to targeted therapies. Nature 439, 353–357 (2006). The development of gene expression signatures that reflect the activation or deregulation of various oncogenic signalling pathways, together with the utilization of these signatures to predict sensitivity to drugs that target the pathways.

    Article  CAS  Google Scholar 

  46. 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  Google Scholar 

  47. Fearon, E. R. & Vogelstein, B. A genetic model for colorectal tumorigenesis. Cell 17, 671–674 (1990). The first comprehensive description of genetic events involved in colon carcinogenesis.

    Google Scholar 

  48. Dressman, H. K. et al. An integrated genomic-based approach to individualized treatment of patients with advanced stage ovarian cancer. J. Clin. Oncol. 25, 517–525 (2007).

    Article  CAS  Google Scholar 

  49. Solit, D. B. et al. BRAF mutation predicts sensitivity to MEK inhibition. Nature 439, 274–275 (2006).

    Article  Google Scholar 

  50. Chang, J. C. et al. Gene expression profiling for the prediction of therapeutic response to docetaxel in patients with breast cancer. Lancet 362, 362–369 (2003).

    Article  CAS  Google Scholar 

  51. Ayers, M. et al. Gene expression profiles predict complete pathologic response to neoadjuvant paclitaxel and fluorouracil, doxorubicin, and cyclophosphamide chemotherapy in breast cancer. J. Clin. Oncol. 22, 2284–2293 (2004).

    Article  CAS  Google Scholar 

  52. Huang, F. et al. Identification of candidate molecular markers predicting sensitivity in solid tumors to dasatinib: rationale for patient selection. Cancer Res. 67, 2226–2238 (2007).

    Article  CAS  Google Scholar 

  53. Coldren, C. D. et al. Baseline gene expression predicts sensitivity to gefitinib in non-small cell lung cancer cell lines. Mol. Cancer Res. 4, 521–528 (2006).

    Article  CAS  Google Scholar 

  54. Balko, J. M. et al. Gene expression patterns that predict sensitivity to epidermal growth factor receptor tyrosine kinase inhibitors in lung cancer cell lines and human lung tumors. BMC Genomics 7, 289 (2006).

    Article  Google Scholar 

  55. Staunton, J. E. et al. Chemosensitivity prediction by transcriptional profiling. Proc. Natl Acad. Sci. USA 98, 10787–19792 (2001).

    Article  CAS  Google Scholar 

  56. Potti, A. et al. A genomic strategy to guide the use of chemotherapeutic drugs in solid tumors. Nature Med. 12, 1294–1300 (2006).

    Article  CAS  Google Scholar 

  57. Carter, S. L., Eklund, A. C., Kohane, I. S., Harris, L. N. & Szallasi, Z. A signature of chromosomal instability inferred from gene expression profiles predicts clinical outcome in multiple human cancers. Nature Genet. 38, 973–974 (2006).

    Article  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, 206–214 (2004).

    Article  CAS  Google Scholar 

  59. Adler, A. S. et al. Genetic regulators of large-scale transcriptional signatures in cancer. Nature Genet. 38, 421–430 (2006). A demonstration of the use of an expression signature as a cancer phenotype in a genetic association study to identify chromosomal alterations that associate with the phenotype.

    Article  CAS  Google Scholar 

  60. Garraway, L. A. et al. Integrative genomic analyses identify MITF as a lineage survival oncogene amplified in malignant melanoma. Nature 436, 117–122 (2005).

    Article  CAS  Google Scholar 

  61. Fan, C. et al. Different gene expression based predictors for breast cancer patients are concordant. N. Engl. J. Med. 355, 560–569 (2006).

    Article  CAS  Google Scholar 

  62. The MicroArray Quality Control Consortium. The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements. Nature Biotechnol. 24, 1151–1161 (2006).

  63. 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  Google Scholar 

  64. Davies, H. et al. Somatic mutations of the protein kinase gene family in human lung cancer. Cancer Res. 65, 7591–7595 (2005).

    Article  CAS  Google Scholar 

  65. Greenman, C. et al. Patterns of somatic mutation in human cancer genomes. Nature 446, 153–158 (2007).

    Article  CAS  Google Scholar 

  66. Parsons, D. W. et al. Colorectal cancer: mutations in a signalling pathway. Nature 436, 792 (2005).

    Article  CAS  Google Scholar 

  67. Sjoblom, T. et al. The consensus coding sequences of human breast and colorectal cancers. Science 314, 268–274 (2006).

    Article  Google Scholar 

Download references

Acknowledgements

We thank A. Bild for many helpful discussions and K. Culler for assistance with the preparation of the manuscript.

Author information

Authors and Affiliations

  1. Duke Institute for Genome Sciences and Policy, Duke University Medical Center, Durham, 27710, North Carolina, USA

    Joseph R. Nevins & Anil Potti

Authors
  1. Joseph R. Nevins
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Anil Potti
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Joseph R. Nevins.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Related links

Related links

FURTHER INFORMATION

Oncomine

Cancer Genome Atlas

Glossary

Classification

Use of an unsupervised analysis approach to identify classes of biological states, including clinical states, that often were not previously recognized.

Supervised analysis

An approach to gene expression analysis in which some aspects of the experimental samples are used to drive the analysis to identify a pattern (signature) of gene expression that characterizes the difference in the samples.

Prediction

The use of a signature to predict the state of an unknown sample, either through cross-validation procedures that use the training samples to evaluate the robustness of the signature, or by predicting an independent data set.

Unsupervised analysis

A form of gene expression analysis that involves discovery of empirical structure (patterns) in a given data set without regard to prior knowledge of the underlying biology. Gene expression patterns that are discovered in this manner then organize the biological samples.

NCI-60 cancer cell line panel

A collection of 59 human cancer cell lines derived from tumours of diverse origin and used for extensive analysis of drug sensitivity, using over 100,000 compounds. More recently, various genomic data sets have been generated using these cell lines, including chromosomal copy number and gene expression.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Nevins, J., Potti, A. Mining gene expression profiles: expression signatures as cancer phenotypes. Nat Rev Genet 8, 601–609 (2007). https://doi.org/10.1038/nrg2137

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nrg2137

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing