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Oncogenic pathway signatures in human cancers as a guide to targeted therapies


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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Figure 1: Gene expression patterns that predict oncogenic pathway deregulation.
Figure 2: Validation of pathway predictions in tumours.
Figure 3: Patterns of pathway deregulation in human cancers.
Figure 4: Pathway deregulation in breast cancer cell lines predicts drug sensitivity.


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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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Correspondence to Joseph 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 The authors declare no competing financial interests.

Supplementary information

Supplementary Table 1

Genes that predict pathway deregulation. (PDF 89 kb)

Supplementary Table 2

Ras mutation status in NSCLC samples. (PDF 39 kb)

Supplementary Table 3

Characteristics of breast cancer cell lines. (PDF 53 kb)

Supplementary Figure 1

Biochemical assays of pathway activation. (PDF 190 kb)

Supplementary Figure 2

Gene expression patterns that predict oncogenic pathway deregulation. (PDF 506 kb)

Supplementary Figure 3

Validation of pathway predictions in tumors. (PDF 300 kb)

Supplementary Figure 4

Kaplan-Meier survival analysis for cancer patients based on individual pathway predictions for the tumor dataset. (PDF 2243 kb)

Supplementary Figure 5

Assays for pathway activities in breast cancer cell lines. (PDF 206 kb)

Supplementary Figure 6

Relationship of drug sensitivity to predictions of untargeted pathways. (PDF 551 kb)

Supplementary Methods

This file contains a more detailed materials and methods description. (DOC 38 kb)

Supplementary Legends

Legends to accompany the above Supplementary Figures and Supplementary Tables. (DOC 28 kb)

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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).

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