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.

  • Letter
  • Published:

A signature of chromosomal instability inferred from gene expression profiles predicts clinical outcome in multiple human cancers

Nature Genetics volume 38, pages 1043–1048 (2006)Cite this article

Abstract

We developed a computational method to characterize aneuploidy in tumor samples based on coordinated aberrations in expression of genes localized to each chromosomal region. We summarized the total level of chromosomal aberration in a given tumor in a univariate measure termed total functional aneuploidy. We identified a signature of chromosomal instability from specific genes whose expression was consistently correlated with total functional aneuploidy in several cancer types. Net overexpression of this signature was predictive of poor clinical outcome in 12 cancer data sets1,2,3,4,5,6,7,8,9,10,11,12 representing six cancer types. Also, the signature of chromosomal instability was higher in metastasis samples than in primary tumors and was able to stratify grade 1 and grade 2 breast tumors according to clinical outcome. These results provide a means to assess the potential role of chromosomal instability in determining malignant potential over a broad range of tumors.

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: Schematic overview of the process used to derive and apply a gene expression signature of chromosomal instability.
Figure 2: The CIN25 signature predicts survival in 12 independent cohorts representing six cancer types.
Figure 3: Metastatic foci expressed higher levels of the net CIN25 signature than primary tumors of diverse origin.
Figure 4: The CIN25 signature stratifies three independent cohorts of grade 2 breast tumors according to clinical outcome, and one cohort of grade 1 breast tumors.
Figure 5: The ability of CIN genes to predict clinical outcome is independent of their cell cycle score.

Similar content being viewed by others

References

  1. Sotiriou, C. et al. Gene expression profiling in breast cancer: understanding the molecular basis of histologic grade to improve prognosis. J. Natl. Cancer Inst. 98, 262–272 (2006).

    Article  CAS  Google Scholar 

  2. 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 

  3. 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 

  4. Wang, Y. et al. Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer. Lancet 365, 671–679 (2005).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  6. Bhattacharjee, A. et al. Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses. Proc. Natl. Acad. Sci. USA 98, 13790–13795 (2001).

    Article  CAS  Google Scholar 

  7. Phillips, H.S. et al. Molecular subclasses of high-grade glioma predict prognosis, delineate a pattern of disease progression, and resemble stages in neurogenesis. Cancer Cell 9, 157–173 (2006).

    Article  CAS  Google Scholar 

  8. Lopez-Rios, F. et al. Global gene expression profiling of pleural mesotheliomas: overexpression of aurora kinases and P16/CDKN2A deletion as prognostic factors and critical evaluation of microarray-based prognostic prediction. Cancer Res. 66, 2970–2979 (2006).

    Article  CAS  Google Scholar 

  9. Freije, W.A. et al. Gene expression profiling of gliomas strongly predicts survival. Cancer Res. 64, 6503–6510 (2004).

    Article  CAS  Google Scholar 

  10. 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 

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

    Article  CAS  Google Scholar 

  12. Nutt, C.L. et al. Gene expression-based classification of malignant gliomas correlates better with survival than histological classification. Cancer Res. 63, 1602–1607 (2003).

    CAS  PubMed  Google Scholar 

  13. Lengauer, C., Kinzler, K.W. & Vogelstein, B. Genetic instabilities in human cancers. Nature 396, 643–649 (1998).

    Article  CAS  Google Scholar 

  14. Gollin, S.M. Mechanisms leading to chromosomal instability. Semin. Cancer Biol. 15, 33–42 (2005).

    Article  CAS  Google Scholar 

  15. Draviam, V.M., Xie, S. & Sorger, P.K. Chromosome segregation and genomic stability. Curr. Opin. Genet. Dev. 14, 120–125 (2004).

    Article  CAS  Google Scholar 

  16. Diaz, L.A., Jr. The current clinical value of genomic instability. Semin. Cancer Biol. 15, 67–71 (2005).

    Article  CAS  Google Scholar 

  17. Pollack, J.R. et al. Microarray analysis reveals a major direct role of DNA copy number alteration in the transcriptional program of human breast tumors. Proc. Natl. Acad. Sci. USA 99, 12963–12968 (2002).

    Article  CAS  Google Scholar 

  18. Tonon, G. et al. High-resolution genomic profiles of human lung cancer. Proc. Natl. Acad. Sci. USA 102, 9625–9630 (2005).

    Article  CAS  Google Scholar 

  19. 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 

  20. Roschke, A.V. et al. Karyotypic complexity of the NCI-60 drug-screening panel. Cancer Res. 63, 8634–8647 (2003).

    CAS  PubMed  Google Scholar 

  21. Heidebrecht, H.J. et al. Repp86: a human protein associated in the progression of mitosis. Mol. Cancer Res. 1, 271–279 (2003).

    CAS  PubMed  Google Scholar 

  22. Kurasawa, Y., Earnshaw, W.C., Mochizuki, Y., Dohmae, N. & Todokoro, K. Essential roles of KIF4 and its binding partner PRC1 in organized central spindle midzone formation. EMBO J. 23, 3237–3248 (2004).

    Article  CAS  Google Scholar 

  23. Mollinari, C. et al. PRC1 is a microtubule binding and bundling protein essential to maintain the mitotic spindle midzone. J. Cell Biol. 157, 1175–1186 (2002).

    Article  CAS  Google Scholar 

  24. Costa, R.H. FoxM1 dances with mitosis. Nat. Cell Biol. 7, 108–110 (2005).

    Article  CAS  Google Scholar 

  25. Wonsey, D.R. & Follettie, M.T. Loss of the forkhead transcription factor FoxM1 causes centrosome amplification and mitotic catastrophe. Cancer Res. 65, 5181–5189 (2005).

    Article  CAS  Google Scholar 

  26. Ramaswamy, S., Ross, K.N., Lander, E.S. & Golub, T.R. A molecular signature of metastasis in primary solid tumors. Nat. Genet. 33, 49–54 (2003).

    Article  CAS  Google Scholar 

  27. 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  Google Scholar 

  28. Gorgoulis, V.G. et al. Activation of the DNA damage checkpoint and genomic instability in human precancerous lesions. Nature 434, 907–913 (2005).

    Article  CAS  Google Scholar 

  29. Whitfield, M.L., George, L.K., Grant, G.D. & Perou, C.M. Common markers of proliferation. Nat. Rev. Cancer 6, 99–106 (2006).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We thank A. Amon, D. Botstein, M. Meyerson and T. Ried for helpful suggestions, and Novartis for the NCI60 expression data. This work was supported in part by the US National Institutes of Health through grant 1PO1CA-092644-01 and the Department of Defense through grant W81XWH-04-1-0549. I.S.K. was supported in part by National Institutes of Health National Center for Biomedical Computing grant 5U54LM008748-02.

Author information

Authors and Affiliations

  1. Children's Hospital Informatics Program at the Harvard-MIT Division of Health Sciences and Technology (CHIP@HST), Harvard Medical School, Boston, 02115, Massachusetts, USA

    Scott L Carter, Aron C Eklund, Isaac S Kohane & Zoltan Szallasi

  2. Laboratory of Functional Genomics, Brigham and Women's Hospital, Cambridge, 02139, Massachusetts, USA

    Aron C Eklund

  3. Breast Disease Unit, Yale School of Medicine, New Haven, 06520-8032, Connecticut, USA

    Lyndsay N Harris

  4. Center for Biological Sequence Analysis, Technical University of Denmark, Lyngby, DK-2800, Denmark

    Zoltan Szallasi

Authors
  1. Scott L Carter
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Aron C Eklund
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Isaac S Kohane
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lyndsay N Harris
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Zoltan Szallasi
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

S.L.C. and Z.S. conceived of and designed the study. S.L.C. carried out all the analysis. S.L.C. and Z.S. wrote the manuscript. A.C.E., I.S.K. and L.N.H. provided guidance and participated in the preparation of the manuscript.

Corresponding author

Correspondence to Zoltan Szallasi.

Ethics declarations

Competing interests

S.L.C., A.C.E. and Z.S. have applied for a patent on the diagnostic use of the chromosomal instability signature described in the manuscript.

Supplementary information

Supplementary Fig. 1

Relationship between functional aneuploidy and DNA-based measures of chromosomal abberations. (PDF 218 kb)

Supplementary Fig. 2

Total functional aneuploidy (tFA) is a significant predictor of clinical outcome in the four breast, lung and brain cancer data sets of 18 data sets evaluated. (PDF 48 kb)

Supplementary Fig. 3

Correlation between gene expression profiles and tFA is conserved in diverse human cancer data sets. (PDF 192 kb)

Supplementary Fig. 4

The CIN signature does not generate significant predictions of clinical outcome for 6 of 18 data sets evaluated. (PDF 47 kb)

Supplementary Fig. 5

The prognostic ability of cell cycle–regulated genes is dependent on CIN score. (PDF 130 kb)

Supplementary Fig. 6

Multivariate analysis of the CIN25 and proliferation signatures revealed that CIN25 was generally more relevant for risk stratification of cancer cohorts. (PDF 27 kb)

Supplementary Fig. 7

Removal of proliferation-associated genes from the CIN signature does not impair its predictive ability for clinical outcome. (PDF 591 kb)

Supplementary Table 1

Top 70 genes with the highest levels of consistent correlation with tFA in three cancer-associated data sets. (PDF 112 kb)

Supplementary Methods (PDF 107 kb)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Carter, S., Eklund, A., Kohane, I. et al. A signature of chromosomal instability inferred from gene expression profiles predicts clinical outcome in multiple human cancers. Nat Genet 38, 1043–1048 (2006). https://doi.org/10.1038/ng1861

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/ng1861

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