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.

  • Opinion
  • Published:

Linking oncogenic pathways with therapeutic opportunities

Nature Reviews Cancer volume 6, pages 735–741 (2006)Cite this article

Abstract

The accumulation of multiple mutations and alterations in the cancer genome underlies the complexity of cancer phenotypes. A consequence of these alterations is the deregulation of various cell-signalling pathways that control cell function. Molecular-profiling studies, particularly DNA microarray analyses, have the potential to describe this complexity. These studies also provide an opportunity to link pathway deregulation with potential therapeutic strategies. This approach, when coupled with other methods for identifying pathway activation, provides an opportunity to both match individual patients with the most appropriate therapeutic strategy and identify potential options for combination therapy.

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: Gene-set-enrichment analysis.
Figure 2: Expression signatures of oncogenic signalling pathways.
Figure 3: Patterns of pathway deregulation in human cancers.
Figure 4: Strategy for linking pathway status with drug sensitivity.

Similar content being viewed by others

References

  1. Ramaswamy, S. & Golub, T. R. DNA microarrays in clinical oncology. J. Clin. Oncol. 20, 1932–1941 (2002).

    Article  CAS  Google Scholar 

  2. Golub, T. R. Genome-wide views of cancer. N. Engl. J. Med. 3, 601–602 (2001).

    Article  Google Scholar 

  3. Golub, T. R. et al. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286, 531–537 (1999).

    Article  CAS  Google Scholar 

  4. 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 

  5. Rosenwald, A. et al. The use of molecular profiling to predict survival after chemotherapy for diffuse large-B-cell lymphoma. N. Engl. J. Med. 346, 1937–1947 (2006).

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

  7. Sorlie, T. et al. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc. Natl. Acad. Sci. USA 98, 10869–10874 (2001).

    Article  CAS  Google Scholar 

  8. van'T Veer, L. J. et al. Gene expression profiling predicts clinical outcome of breast cancer. Nature 415, 530–536 (2002).

    Article  CAS  Google Scholar 

  9. 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 

  10. 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 

  11. 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 

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

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

    Article  CAS  Google Scholar 

  16. 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 

  17. Dahlquist, K. D., Salomonis, N., Vranizan, K., Lawlor, S. C. & Conklin, B. R. GenMAPP, a new tool for viewing and analyzing microarray data on biological pathways. 31, 19–20 (2002).

  18. Al-Shahrour, F., Diaz-Uriarte, R. & Dopazo, J. FatiGO: a web tool for finding significant associations of Gene Ontology terms with groups of genes. Bioinformatics 20, 578–580 (2004).

    Article  CAS  Google Scholar 

  19. 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 

  20. 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 

  21. Bild, A. et al. Oncogenic pathway signatures in human cancers as a guide to targeted therapies. Nature 439, 353–357 (2006).

    Article  CAS  Google Scholar 

  22. 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 

  23. Adler, A. S. et al. Genetic regulators of large-scale transcriptional signatures in cancer. Nature Genet. 38, 421–430 (2006).

    Article  CAS  Google Scholar 

  24. Zuber, J. et al. A genome-wide survey of RAS transformation targets. Nature Genet. 24, 144–152 (2000).

    Article  CAS  Google Scholar 

  25. Tchernitsa, O. I. et al. Gene expression profiling of fibroblasts resistant toward oncogene-mediated transformation reveals preferential transcription of negative growth regulators. Oncogene 18, 5448–5454 (1999).

    Article  CAS  Google Scholar 

  26. Vasseur, S. et al. Gene expression profiling by DNA microarray analysis in mouse embryonic fibroblasts transformed by rasV12 mutated protein and the E1A oncogene. Mol. Cancer 2, 19 (2003).

    Article  Google Scholar 

  27. 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 

  28. 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 

  29. Segal, E., Friedman, N., Kaminski, N., Regev, A. & Koller, D. From signatures to models: understanding cancer using microarrays. Nature Genet. 37 (Suppl.), S38–S45 (2005).

    Article  CAS  Google Scholar 

  30. 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 

  31. Segal, E. et al. Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data. Nature Genet. 34, 66–176 (2003).

    Article  Google Scholar 

  32. 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  Google Scholar 

  33. Segal, E., Wang, H. & Koller, D. Discovering molecular pathways from protein interaction and gene expression data. Bioinformatics 19 (Suppl. 1), i264–i271 (2003).

    Article  Google Scholar 

  34. 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).

    Article  CAS  Google Scholar 

  35. Barry, W. T., Nobel, A. B. & Wright, F. A. Significance analysis of functional categories in gene expression studies: a structure permutation approach. Bioinformatics 21, 1943–1949 (2005).

    Article  CAS  Google Scholar 

  36. Tian, L. et al. Discovering statistically significant pathways in expression profiling studies. Proc. Natl. Acad. Sci. USA 102, 13544–13549 (2005).

    Article  CAS  Google Scholar 

  37. Edelman, E. et al. Analysis of sample set enrichment scores: assaying the enrichment of sets of genes for individual samples in genome-wide expression profiles. Bioinformatics (in the press).

  38. Alvarez, J. V. et al. Identification of a genetic signature of activated signal transducer and activator of transcription 3 in human tumors. Cancer Res. 65, 5054–5062 (2005).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  40. Druker, B. J. Imatinib as a paradigm of targeted therapies. Adv. Cancer Res. 91, 1–30 (2004).

    Article  CAS  Google Scholar 

  41. Sawyers, C. Targeted cancer therapy. Nature 432, 294–297 (2004).

    Article  CAS  Google Scholar 

  42. Romond, E. H. et al. Trastuzumab plus adjuvant chemotherapy for operable HER2-positive breast cancer. N. Engl. J. Med. 353, 1673–1684 (2005).

    Article  CAS  Google Scholar 

  43. Piccart-Gebhart, M. J. et al. Trastuzumab after adjuvant chmotherapy in HER2-positive breast cancer. N. Engl. J. Med. 353, 1659–1672 (2005).

    Article  CAS  Google Scholar 

  44. Hortobagyi, G. N. Trastuzumab in the treatment of breast cancer. N. Engl. J. Med. 353, 17334–1736 (2005).

    Article  Google Scholar 

  45. Druker, B. J. & Lydon, N. B. Lessons learned from the development of an abl tyrosine kinase inhibitor for chronic myelogenous leukemia. J. Clin. Invest. 105, 3–7 (2000).

    Article  CAS  Google Scholar 

  46. Cory, A. H. Use of an aqueous soluble tetrazolium/formazan assay for cell growth assays in culture. Cancer Commun. 3, 207–212 (1991).

    Article  CAS  Google Scholar 

  47. Riss, T. L. & A., M. R. Comparison of MTT, Xtt, and a novel tetrazolium compound for MTS for in vitro proliferation and chemosensitivity assays. Mol. Biol. Cell 3, 184a (1993).

    Google Scholar 

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

    Article  CAS  Google Scholar 

  49. Kantarjian, H. et al. Nilotinib in imatinib-resistant CML and Philadelphia chromosome-positive ALL. N. Engl. J. Med. 354, 2542–2551 (2006).

    Article  Google Scholar 

  50. Weisberg, E. et al. AMN107 (nilotinib): a novel and selective inhibitor of BCR-ABL. Br. J. Cancer 94, 1765–1769 (2006).

    Article  CAS  Google Scholar 

  51. Fischer, P. M. & Gianella-Borradori, A. Recent progress in the discovery and development of cyclin-dependent kinase inhibitors. Expert Opin. Investig. Drugs 14, 457–477 (2005).

    Article  CAS  Google Scholar 

  52. de la Motte, S. & Gianella-Borradori, A. Pharmacokinetic model of R-roscovitine and its metabolite in healthy male subjects. Int. J. Clin. Pharmacol. Ther. 42, 232–239 (2004).

    Article  CAS  Google Scholar 

  53. Senderowicz, A. M. Novel small molecular cyclin-dependent kinases modulators in human clinical trials. Cancer Biol. Ther. 2, S84–S95 (2003).

    Article  CAS  Google Scholar 

  54. Tyagi, P. Recent results and ongoing trials with panitumumab (ABX-EGF), a fully human anti-epidermal growth factor receptor antibody in metastatic colorectal cancer. Clin. Colorectal Cancer 5, 21–23 (2005).

    Article  Google Scholar 

  55. Nelson, M. H. & Dolder, C. R. Lapatinib: a novel dual tyrosine kinase inhibitor with activity in solid tumors. Ann. Pharmacother. 40, 261–269 (2006).

    Article  CAS  Google Scholar 

  56. Burris, I., H. A. et al. Phase II safety, pharmacokinetics, and clinical activity study of lapatinib (GW572016), a reversible dual inhibitor of epidermal growth factor receptor tyrosine kinases, in heavily pretreated patients with metastatic carcinomas. J. Clin. Oncol. 23, 5305–5313 (2005).

    Article  CAS  Google Scholar 

  57. Blum, R. & Kloog, Y. Tailoring Ras-pathway-inhibitor combinations for cancer therapy. Drug Resist. Update 8, 369–380 (2005).

    Article  CAS  Google Scholar 

  58. Sparano, J. A. et al. Targeted inhibition of farnesyltransferase in locally advanced breast cancer: a phase I and II trial of tipifarnib plus dose-dense doxorubicin and cyclophosphamide. J. Clin. Oncol. 24, 3013–3018 (2006).

    Article  CAS  Google Scholar 

  59. Siegel-Lakhai, W. S. et al. Phase I and pharmacological study of the farnesyltransferase inhibitor tipifarnib (Zarnestra, R117555) in combination with gemcitabine and cisplatin in patients with advanced solid tumours. Br. J. Cancer 93, 1222–1229 (2005).

    Article  CAS  Google Scholar 

  60. Racher, C., Dos Santos, C., Demur, C. & Payrastre, B. mTOR, a new therapeutic target in acute myeloid leukemia. Cell Cycle 4, 1540–1549 (2005).

    Article  Google Scholar 

  61. Huang, S. & Houghton, P. J. Targeting mTOR signaling for cancer therapy. Curr. Opin. Pharmacol. 3, 371–377 (2003).

    Article  CAS  Google Scholar 

  62. Chang, S. M. et al. North American Brain Tumor Consortium and the National Cancer Institute. Phase II study of CCI-779 in patients with recurrent glioblastoma multiforme. Invest. New Drugs 23, 357–361 (2005).

    Article  CAS  Google Scholar 

  63. Galanis, E. et al. North Central Cancer Treatment Group. Phase II trail of temsirolimus (CCI-779) in recurrent glioblastoma multiforme: a North Central Cancer Treatment Group Study. J. Clin. Oncol. 23, 5294–5304 (2005).

    Article  CAS  Google Scholar 

  64. Witzig, T. E. et al. Phase II trial of single-agent temsirolimus (CCI-779) for relapsed mantle cell lymphoma. J. Clin. Oncol. 23, 5357–5356 (2005).

    Article  Google Scholar 

  65. Chan, S. et al. Phase II study of temsirolimus (CCI-779), a novel inhibitor of mTOR, in heavily pretreated patients with locally advanced or metastatic breast cancer. J. Clin. Oncol. 23, 5314–5322 (2005).

    Article  CAS  Google Scholar 

  66. Tsurutani, J., West, K. A., Sayyah, J., Gills, J. J. & Dennis, P. A. Inhibition of the phosphatidylinositol 3-kinase/Akt/mammalian target of rapamycin pathway but not the MEK-ERK pathway attenuates laminin-mediated small cell lung cancer cellular survival and resistance to imatinib mesylate or chemotherapy. Cancer Res. 65, 8423–8432 (2005).

    Article  CAS  Google Scholar 

  67. Mills, G. B. et al. Linking molecular diagnostics to molecular therapeutics: targeting the PI3K pathway in breast cancer. Semin. Oncol. 30, 93–104 (2003).

    Article  CAS  Google Scholar 

  68. Talpaz, M. et al. Dasatinib in imatinib-resistant Philadelphia chromosome-positive leukemias. N. Engl. J. Med. 354, 2531–2541 (2006).

    Article  CAS  Google Scholar 

  69. Shah, N. P. et al. Dasatinib (BMS-354825) inhibits KITD816V, an imatinib-resistant activating mutation that triggers neoplastic growth in most patients with systemic mastocytosis. Blood 108, 286–291 (2006).

    Article  CAS  Google Scholar 

  70. Wakeling, A. E. Inhibitors of growth factor signalling. Endocr. Relat. Cancer (12 Suppl. 1), S183–S187 (2005).

    Article  CAS  Google Scholar 

  71. Lee, D. Phase II data with ZD6474, a small-molecule kinase inhibitor of epidermal growth factor receptor and vascular endothelial growth factor receptor, in previously treated advanced non-small-cell lung cancer. Clin. Lung Cancer 7, 89–91 (2005).

    Article  Google Scholar 

  72. Kovacs, M. J. et al. A phase II study of ZD6474 (Zactimatrade mark), a selective inhibitor of VEGFR and EGFR tyrosine kinase in patients with relapsed multiple myeloma-NCIC CTG IND. 145. Invest. New Drugs (in the press).

  73. Motzer, R. J. et al. Sunitinib in patients with metastatic renal cell carcinoma. JAMA 295, 2516–2524 (2006).

    Article  CAS  Google Scholar 

  74. Rini, B. I. SU11248 and AG013736: current data and future trials in renal call carcinoma. Clin. Genitourin. Cancer 4, 175–180 (2005).

    Article  CAS  Google Scholar 

  75. Jain, R. K., Duda, D. G., Clark, J. W. & Loeffler, J. S. Lessons from phase III clinical trials on anti-VEGF therapy for cancer. Nature Clin. Pract. Oncol. 3, 24–40 (2006).

    Article  CAS  Google Scholar 

  76. Mross, K. et al. Phase I clinical and pharmacokinetic study of PTK/ZK, a multiple VEGF receptor inhibitor, in patients with liver metastases from solid tumours. Eur. J. Cancer 41, 1291–1299 (2005).

    Article  CAS  Google Scholar 

  77. Tyagi, P. Vatalanib (PTK787/ZK 222584) in combination with FOLFOX4 versus FOLFOX4 alone as first-line treatment for colorectal cancer: preliminary results from the CONFIRM-1 trial. Clin. Colorectal Cancer 5, 24–26 (2005).

    Article  Google Scholar 

  78. Thatcher, N. et al. Gefitinib plus best supportive care in previously treated patients with refractory advanced non-small-cell lung cancer: results from a randomised, placebo-controlled, multicentre study (Iressa Survival Evaluation in Lung Cancer). Lancet 366, 1527–1537 (2005).

    Article  CAS  Google Scholar 

  79. Heymach, J. V., Nilsson, M., Blumenschein, G., Papadimitrakopoulou, V. & Herbst, R. Epidermal growth factor receptor inhibitors in development for the treatment of non-small cell lung cancer. Clin. Cancer Res. 12, 4441s–4445s (2006).

    Article  CAS  Google Scholar 

  80. Blackhall, F., Ranson, M. & Thatcher, N. Where next for gefitinib in patients with lung cancer? Lancet Oncol. 7, 499–507 (2006).

    Article  CAS  Google Scholar 

  81. West, H. L. et al. Gefitinib therapy in advanced bronchiolaolveolar carcinoma: Southwest Oncology Group Study S0126. J. Clin. Oncol. 24, 1807–1813 (2006).

    Article  CAS  Google Scholar 

  82. de marinis, F., De Santis, S. & De Petris, L. Second-line treatment options in non-small cell lung cancer: a comparison of cytotoxic agents and targeted therapies. Semin. Oncol. 33, S17–S24 (2006).

    Article  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

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

    Andrea H. Bild, Anil Potti & Joseph R. Nevins

Authors
  1. Andrea H. Bild
    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

  3. Joseph R. Nevins
    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

Joseph R. Nevins' homepage

Kyoto encyclopedia of genes and genomes

GenMAPP

Rights and permissions

Reprints and permissions

About this article

Cite this article

Bild, A., Potti, A. & Nevins, J. Linking oncogenic pathways with therapeutic opportunities. Nat Rev Cancer 6, 735–741 (2006). https://doi.org/10.1038/nrc1976

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nrc1976

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