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Application of comparative functional genomics to identify best-fit mouse models to study human cancer

Nature Genetics volume 36, pages 1306–1311 (2004)Cite this article

Abstract

Genetically modified mice have been extensively used for analyzing the molecular events that occur during tumor development. In many, if not all, cases, however, it is uncertain to what extent the mouse models reproduce features observed in the corresponding human conditions1,2,3. This is due largely to lack of precise methods for direct and comprehensive comparison at the molecular level of the mouse and human tumors. Here we use global gene expression patterns of 68 hepatocellular carcinomas (HCCs) from seven different mouse models and 91 human HCCs from predefined subclasses4 to obtain direct comparison of the molecular features of mouse and human HCCs. Gene expression patterns in HCCs from Myc, E2f1 and Myc E2f1 transgenic mice were most similar to those of the better survival group of human HCCs, whereas the expression patterns in HCCs from Myc Tgfa transgenic mice and in diethylnitrosamine-induced mouse HCCs were most similar to those of the poorer survival group of human HCCs. Gene expression patterns in HCCs from Acox1−/− mice and in ciprofibrate-induced HCCs were least similar to those observed in human HCCs. We conclude that our approach can effectively identify appropriate mouse models to study human cancers.

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Figure 1: Cluster analysis of mouse HCCs.
Figure 2: Cluster analysis of integrated human and mouse HCC.
Figure 3: Comparison of measurable phenotypes between two subclasses in human and mouse HCC.

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References

  1. Hann, B. & Balmain, A. Building 'validated' mouse models of human cancer. Curr. Opin. Cell Biol. 13, 778–784 (2001).

    Article  CAS  Google Scholar 

  2. Klausner, R.D. Studying cancer in the mouse. Oncogene 18, 5249–5252 (1999).

    Article  CAS  Google Scholar 

  3. Rangarajan, A. & Weinberg, R.A. Opinion: Comparative biology of mouse versus human cells: modelling human cancer in mice. Nat. Rev. Cancer 3, 952–959 (2003).

    Article  CAS  Google Scholar 

  4. Lee, J.S. et al. Classification and prediction of survival in hepatocellular carcinoma by gene expression profiling. Hepatology 40, 667–676 (2004).

    Article  CAS  Google Scholar 

  5. Kimura, M. Evolutionary rate at the molecular level. Nature 217, 624–626 (1968).

    Article  CAS  Google Scholar 

  6. King, J.L. & Jukes, T.H. Non-Darwinian evolution. Science 164, 788–798 (1969).

    Article  CAS  Google Scholar 

  7. Ureta-Vidal, A., Ettwiller, L. & Birney, E. Comparative genomics: genome-wide analysis in metazoan eukaryotes. Nat. Rev. Genet. 4, 251–262 (2003).

    Article  CAS  Google Scholar 

  8. Cooper, G.M. & Sidow, A. Genomic regulatory regions: insights from comparative sequence analysis. Curr. Opin. Genet. Dev. 13, 604–610 (2003).

    Article  CAS  Google Scholar 

  9. Eddy, S.R. Computational genomics of noncoding RNA genes. Cell 109, 137–140 (2002).

    Article  CAS  Google Scholar 

  10. Hardison, R.C. Conserved noncoding sequences are reliable guides to regulatory elements. Trends Genet. 16, 369–372 (2000).

    Article  CAS  Google Scholar 

  11. Rao, M.S., Lalwani, N.D., Watanabe, T.K. & Reddy, J.K. Inhibitory effect of antioxidants ethoxyquin and 2(3)-tert-butyl-4-hydroxyanisole on hepatic tumorigenesis in rats fed ciprofibrate, a peroxisome proliferator. Cancer Res. 44, 1072–1076 (1984).

    CAS  PubMed  Google Scholar 

  12. Reddy, J.K. & Lalwai, N.D. Carcinogenesis by hepatic peroxisome proliferators: evaluation of the risk of hypolipidemic drugs and industrial plasticizers to humans. Crit. Rev. Toxicol. 12, 1–58 (1983).

    Article  CAS  Google Scholar 

  13. Poirier, L.A. Hepatocarcinogenesis by diethylnitrosamine in rats fed high dietary levels of lipotropes. J. Natl. Cancer Inst. 54, 137–140 (1975).

    Article  CAS  Google Scholar 

  14. Conner, E.A. et al. Dual functions of E2F-1 in a transgenic mouse model of liver carcinogenesis. Oncogene 19, 5054–5062 (2000).

    Article  CAS  Google Scholar 

  15. Conner, E.A., Lemmer, E.R., Sanchez, A., Factor, V.M. & Thorgeirsson, S.S. E2F1 blocks and c-Myc accelerates hepatic ploidy in transgenic mouse models. Biochem. Biophys. Res. Commun. 302, 114–120 (2003).

    Article  CAS  Google Scholar 

  16. Murakami, H. et al. Transgenic mouse model for synergistic effects of nuclear oncogenes and growth factors in tumorigenesis: interaction of c-myc and transforming growth factor alpha in hepatic oncogenesis. Cancer Res. 53, 1719–1723 (1993).

    CAS  PubMed  Google Scholar 

  17. Fan, C.Y. et al. Steatohepatitis, spontaneous peroxisome proliferation and liver tumors in mice lacking peroxisomal fatty acyl-CoA oxidase. Implications for peroxisome proliferator-activated receptor alpha natural ligand metabolism. J. Biol. Chem. 273, 15639–15645 (1998).

    Article  CAS  Google Scholar 

  18. Calvisi, D.F., Factor, V.M., Ladu, S., Conner, E.A. & Thorgeirsson, S.S. Disruption of beta-catenin pathway or genomic instability define two distinct categories of liver cancer in transgenic mice. Gastroenterology 126, 1374–1386 (2004).

    Article  CAS  Google Scholar 

  19. Bentley, P. et al. Hepatic peroxisome proliferation in rodents and its significance for humans. Food Chem. Toxicol. 31, 857–907 (1993).

    Article  CAS  Google Scholar 

  20. Gonzalez, F.J., Peters, J.M. & Cattley, R.C. Mechanism of action of the nongenotoxic peroxisome proliferators: role of the peroxisome proliferator-activator receptor alpha. J. Natl. Cancer Inst. 90, 1702–1709 (1998).

    Article  CAS  Google Scholar 

  21. Rosenwald, A. et al. The proliferation gene expression signature is a quantitative integrator of oncogenic events that predicts survival in mantle cell lymphoma. Cancer Cell 3, 185–197 (2003).

    Article  CAS  Google Scholar 

  22. Shirahashi, H. et al. Ubiquitin is a possible new predictive marker for the recurrence of human hepatocellular carcinoma. Liver 22, 413–418 (2002).

    Article  CAS  Google Scholar 

  23. 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 

  24. Schaner, M.E. et al. Gene expression patterns in ovarian carcinomas. Mol. Biol. Cell 14, 4376–4386 (2003).

    Article  CAS  Google Scholar 

  25. Sargent, L.M. et al. Nonrandom cytogenetic alterations in hepatocellular carcinoma from transgenic mice overexpressing c-Myc and transforming growth factor-alpha in the liver. Am. J. Pathol. 154, 1047–1055 (1999).

    Article  CAS  Google Scholar 

  26. Laurent-Puig, P. et al. Genetic alterations associated with hepatocellular carcinomas define distinct pathways of hepatocarcinogenesis. Gastroenterology 120, 1763–1773 (2001).

    Article  CAS  Google Scholar 

  27. Sambrook, J., Fritsch, E. & Maniatis, T. Molecular Cloning 7.19–7.22 (Cold Spring Harbor Laboratory Press, Cold Spring Harbor, 1989).

    Google Scholar 

  28. Meyer, K. et al. Molecular profiling of hepatocellular carcinomas developing spontaneously in acyl-CoA oxidase deficient mice: comparison with liver tumors induced in wild-type mice by a peroxisome proliferator and a genotoxic carcinogen. Carcinogenesis 24, 975–984 (2003).

    Article  CAS  Google Scholar 

  29. Ellwood-Yen, K. et al. Myc-driven murine prostate cancer shares molecular features with human prostate tumors. Cancer Cell 4, 223–238 (2003).

    Article  CAS  Google Scholar 

  30. Tusher, V.G., Tibshirani, R. & Chu, G. Significance analysis of microarrays applied to the ionizing radiation response. Proc. Natl. Acad. Sci. USA. 98, 5116–5121 (2001).

    Article  CAS  Google Scholar 

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Acknowledgements

We thank R. Simon for discussions and advice on statistical analysis, J.W. Grisham for critical reading of the manuscript, E. Asaki for managing gene expression database and V.M. Factor and E.A. Conner for help with the mouse colonies.

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Authors and Affiliations

  1. Laboratory of Experimental Carcinogenesis, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, 20892-4262, Maryland, USA

    Ju-Seog Lee, In-Sun Chu, Arsen Mikaelyan, Diego F Calvisi, Jeonghoon Heo & Snorri S Thorgeirsson

  2. Department of Pathology, Northwestern University, the Feinberg School of Medicine, Chicago, 60611-3008, Illinois, USA

    Janardan K Reddy

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Correspondence to Snorri S Thorgeirsson.

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Supplementary information

Supplementary Fig. 1

Correlation heat map. (PDF 101 kb)

Supplementary Fig. 2

Hierarchical clustering analysis of survival genes. (PDF 517 kb)

Supplementary Fig. 3

Comparison of predicted outcome. (PDF 38 kb)

Supplementary Fig. 4

Direct comparisons of top 500 orthologous genes. (PDF 43 kb)

Supplementary Fig. 5

Comparison of real-time RT-PCR and microarray experiments. (PDF 25 kb)

Supplementary Table 1

Complete list of 329 orthologous genes. (PDF 31 kb)

Supplementary Table 2

Comparison of mouse HCC and human DLBCL. (PDF 12 kb)

Supplementary Table 3

Comparison of mouse HCC and human ovarian cancer. (PDF 11 kb)

Supplementary Note (PDF 8 kb)

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Lee, JS., Chu, IS., Mikaelyan, A. et al. Application of comparative functional genomics to identify best-fit mouse models to study human cancer. Nat Genet 36, 1306–1311 (2004). https://doi.org/10.1038/ng1481

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