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

Article metrics

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.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

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.

References

  1. 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).

  2. 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).

  3. 3

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

  4. 4

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

  5. 5

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

  6. 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).

  7. 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).

  8. 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).

  9. 9

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

  10. 10

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

  11. 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).

  12. 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).

  13. 13

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

  14. 14

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

  15. 15

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

  16. 16

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

  17. 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).

  18. 18

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

  19. 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).

  20. 20

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

  21. 21

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

  22. 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).

  23. 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).

  24. 24

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

  25. 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).

  26. 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).

  27. 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).

  28. 28

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

  29. 29

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

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

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.

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) doi:10.1038/ng1861

Download citation

Further reading