Oncogenic pathway signatures in human cancers as a guide to targeted therapies (original) (raw)

Nature volume 439, pages 353–357 (2006)Cite this article

Abstract

The development of an oncogenic state is a complex process involving the accumulation of multiple independent mutations that lead to deregulation of cell signalling pathways central to the control of cell growth and cell fate1,2,3. The ability to define cancer subtypes, recurrence of disease and response to specific therapies using DNA microarray-based gene expression signatures has been demonstrated in multiple studies4. Various studies have also demonstrated the potential for using gene expression profiles for the analysis of oncogenic pathways5,6,7,8,9,10,11. Here we show that gene expression signatures can be identified that reflect the activation status of several oncogenic pathways. When evaluated in several large collections of human cancers, these gene expression signatures identify patterns of pathway deregulation in tumours and clinically relevant associations with disease outcomes. Combining signature-based predictions across several pathways identifies coordinated patterns of pathway deregulation that distinguish between specific cancers and tumour subtypes. Clustering tumours based on pathway signatures further defines prognosis in respective patient subsets, demonstrating that patterns of oncogenic pathway deregulation underlie the development of the oncogenic phenotype and reflect the biology and outcome of specific cancers. Predictions of pathway deregulation in cancer cell lines are also shown to predict the sensitivity to therapeutic agents that target components of the pathway. Linking pathway deregulation with sensitivity to therapeutics that target components of the pathway provides an opportunity to make use of these oncogenic pathway signatures to guide the use of targeted therapeutics.

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Acknowledgements

We are grateful to P. Bild for the inspiration to pursue this research. We also thank K. Shianna, J. Freedman, S. Mori, L. Jakoi and K. Culler for their assistance. A.H.B. has been supported by an AACR-Anna D. Barker Fellowship and an ACS Postdoctoral Fellowship. This work was supported by grants from the NIH (to J.R.N.). Author Contributions A.H.B. was responsible for all experimental work and computational data analysis. A.H.B. and J.R.N. were responsible for project planning and data analysis. G.Y., J.T.C. and Q.W. were responsible for generation of specialized computer programs used in these studies. H.K.D. and A.P. provided intellectual input and data management support. D.C. and M.-B.J. provided technical support for experiments. M.W. was responsible for conception of the statistical approach and intellectual input. A.B., J.M.L., J.R.M., J.A.O. and D.H. were responsible for the development of clinical resources used in the study.

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Authors and Affiliations

  1. Institute for Genome Sciences and Policy, Duke University, North Carolina, 27708, Durham, USA
    Andrea H. Bild, Guang Yao, Jeffrey T. Chang, Quanli Wang, Anil Potti, Dawn Chasse, John A. Olson Jr, Holly K. Dressman & Joseph R. Nevins
  2. Department of Molecular Genetics and Microbiology,
    Andrea H. Bild, Guang Yao, Jeffrey T. Chang, Dawn Chasse, Holly K. Dressman & Joseph R. Nevins
  3. Department of Surgery,
    Mary-Beth Joshi, David Harpole, John A. Olson Jr & Jeffrey R. Marks
  4. Department of Medicine,
    Anil Potti
  5. Department of Obstetrics & Gynecology, Duke University Medical Center, North Carolina, 27710, Durham, USA
    Andrew Berchuck
  6. Gynecology, Duke University Medical Center,
    Andrew Berchuck
  7. Institute of Statistics and Decision Sciences, Duke University, North Carolina, 27708, Durham, USA
    Mike West
  8. H. Lee Moffitt Cancer Center & Research Institute, University of South Florida, Florida, 33612, Tampa, USA
    Johnathan M. Lancaster
  9. Research Institute, University of South Florida,
    Johnathan M. Lancaster

Authors

  1. Andrea H. Bild
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  2. Guang Yao
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  3. Jeffrey T. Chang
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  4. Quanli Wang
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  5. Anil Potti
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  6. Dawn Chasse
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  7. Mary-Beth Joshi
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  8. David Harpole
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  9. Johnathan M. Lancaster
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  10. Andrew Berchuck
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  11. John A. Olson Jr
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  12. Jeffrey R. Marks
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  13. Holly K. Dressman
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  14. Mike West
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  15. Joseph R. Nevins
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Corresponding author

Correspondence toJoseph R. Nevins.

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Competing interests

The GEO accession numbers for the datasets are: GSE3156, breast cancer cell lines; GSE3158, mouse tumour data set; GSE3151, oncogene signature data set; GSE3141, lung cancer data set; GSE3143, breast cancer data set; GSE3149, ovarian cancer data set. Reprints and permissions information is available at npg.nature.com/reprintsandpermissions. The authors declare no competing financial interests.

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Bild, A., Yao, G., Chang, J. et al. Oncogenic pathway signatures in human cancers as a guide to targeted therapies.Nature 439, 353–357 (2006). https://doi.org/10.1038/nature04296

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Editorial Summary

Tumour profiling advances

Molecular tumour profiling is one way in which effective targeted cancer treatment regimes might be developed. Two groups report significant developments in this direction. Bild et al. studied gene expression patterns that reflect the activation of various oncogenic (cancer-causing) signal transduction pathways. Using combinations of these pathway signatures, they predict which patients with breast, lung or ovarian cancer have a particularly poor prognosis. The ability to identify molecular pathways that are deregulated in a particular cancer in this way might be used to predict its sensitivity to specific therapeutic drugs. Solit et al. studied tumour cells with mutations in the RAS and BRAF genes, thought to cause cancer at least in part by activating the MEK/ERK signalling pathway. They show that tumours with the BRAF mutation, but not RAS, are highly sensitive to PD0325901, an MEK inhibitor that is in early-stage clinical trials in patients with melanoma, colon, breast and lung cancers. So by testing for the presence of BRAF mutations it may be possible to identify those patients most likely to benefit from this type of drug.

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