Bioinformatics Approaches to the Analysis of the Transcriptome of Animal Models of Cancer

  • Mark J. Hoenerhoff
  • Aleksandra M. Michalowski
  • Ting-Hu Qiu
  • Jeffery E. Green
Chapter
First Online:
Part of the Cancer Drug Discovery and Development book series (CDD&D)

Abstract

The development of genetically engineered mouse (GEM) models of human disease have played an integral role in understanding the mechanisms of action of many classes of genes involved in cancer development and progression. Their development has been critical in exploring the complexity of interactions of biological processes occurring in the entire organism, particularly when combined with recent global genomic approaches and bioinformatics. It has become apparent that breast cancer is a heterogeneous disease and multiple GEM models must be incorporated to represent the various forms of the human disease. Undoubtedly, these models and methods will be invaluable in the establishment of biomarkers and novel therapeutic approaches for patients with various subtypes of breast cancer.

Keywords

Mammary cancer Genetically engineered mouse models Gene expression profiling Bioinformatics 
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References

  1. Abe, K., Hazama, M., Katoh, H., Yamamura, K., and Suzuki, M. 2004. Establishment of an efficient BAC transgenesis protocol and its application to functional characterization of the mouse Brachyury locus. Exp Anim 53: 311–320PubMedCrossRefGoogle Scholar
  2. Aubele, M., Werner, M., and Hofler, H. 2002. Genetic alterations in presumptive precursor lesions of breast carcinomas. Anal Cell Pathol 24: 69–76PubMedGoogle Scholar
  3. Balmain, A., and Nagase, H. 1998. Cancer resistance genes in mice: models for the study of tumour modifiers. Trends Genet 14: 139–144PubMedCrossRefGoogle Scholar
  4. Barkan, D., Montagna, C., Ried, T., Green, J. E. 2004. Mammary gland cancer. In: Mouse Models of Human Cancer, ed. E. C. Holland, pp. 103–131. Hoboken: WileyGoogle Scholar
  5. Barnes, D., Dublin, E., Fisher, C., Levison, D., and Millis, R. 1993. Immunohistochemical detection of p53 protein in mammary carcinoma: an important new independent indicator of prognosis? Human Pathol 24: 469–476CrossRefGoogle Scholar
  6. Bernards, R., and Weinberg, R. A. 2002. A progression puzzle. Nature 418: 823PubMedCrossRefGoogle Scholar
  7. Bild, A. H., Yao, G., Chang, J. T., Wang, Q., Potti, A., Chasse, D., Joshi, M. B., Harpole, D., Lancaster, J. M., Berchuck, A., Olson, J. A., Jr., Marks, J. R., Dressman, H. K., West, M., and Nevins, J. R. 2006. Oncogenic pathway signatures in human cancers as a guide to targeted therapies. Nature 439: 353–357PubMedCrossRefGoogle Scholar
  8. Cailleau, R., Young, R., Olive, M., and Reeves, W. J., Jr. 1974. Breast tumor cell lines from pleural effusions. J Natl Cancer Inst 53: 661–674PubMedGoogle Scholar
  9. Callahan, R., and Smith, G. H. 2000. MMTV-induced mammary tumorigenesis: gene discovery, progression to malignancy and cellular pathways. Oncogene 19: 992–1001PubMedCrossRefGoogle Scholar
  10. Callow, M. J., Dudoit, S., Gong, E. L., Speed, T. P., and Rubin, E. M. 2000. Microarray expression profiling identifies genes with altered expression in HDL-deficient mice. Genome Res 10: 2022–2029PubMedCrossRefGoogle Scholar
  11. Cardiff, R. 2001. Validity of mouse mammary tumour models for human breast cancer: comparative pathology. Microsc Res Tech 52: 224–230PubMedCrossRefGoogle Scholar
  12. Cardiff, R. D. 2003. Mouse models of human breast cancer. Comp Med 53: 250–253PubMedGoogle Scholar
  13. Cardiff, R. D., and Wellings, S. R. 1999. The comparative pathology of human and mouse mammary glands. J Mammary Gland Biol Neoplasia 4: 105–122PubMedCrossRefGoogle Scholar
  14. Cardiff, R. D., Anver, M. R., Gusterson, B. A., Hennighausen, L., Jensen, R. A., Merino, M. J., Rehm, S., Russo, J., Tavassoli, F. A., Wakefield, L. M., Ward, J. M., and Green, J. E. 2000. The mammary pathology of genetically engineered mice: the consensus report and recommendations from the Annapolis meeting. Oncogene 19: 968–988PubMedCrossRefGoogle Scholar
  15. Cleator, S., and Ashworth, A. 2004. Molecular profiling of breast cancer: clinical implications. Br J Cancer 90: 1120–1124PubMedCrossRefGoogle Scholar
  16. Cooper, G. M., and Sidow, A. 2003. Genomic regulatory regions: insights from comparative sequence analysis. Curr Opin Genet Dev 13: 604–610PubMedCrossRefGoogle Scholar
  17. Copeland, N. G., Jenkins, N. A., and Court, D. L. 2001. Recombineering: a powerful new tool for mouse functional genomics. Nat Rev Genet 2: 769–779PubMedCrossRefGoogle Scholar
  18. Desai, K. V., Kavanaugh, C. J., Calvo, A., and Green, J. E. 2002a. Chipping away at breast cancer: insights from microarray studies of human and mouse mammary cancer. Endocr Relat Cancer 9: 207–220CrossRefGoogle Scholar
  19. Desai, K. V., Xiao, N., Wang, W., Gangi, L., Greene, J., Powell, J. I., Dickson, R., Furth, P., Hunter, K., Kucherlapati, R., Simon, R., Liu, E. T., and Green, J. E. 2002b. Initiating oncogenic event determines gene-expression patterns of human breast cancer models. Proc Natl Acad Sci USA 99: 6967–6972CrossRefGoogle Scholar
  20. Eddy, S. 2002. Computational genomics of noncoding RNA genes. Cell 217: 137–140CrossRefGoogle Scholar
  21. Fargiano, A. A., Desai, K. V., and Green, J. E. 2003. Interrogating mouse mammary cancer models: insights from gene expression profiling. J Mammary Gland Biol Neoplasia 8: 321–334PubMedCrossRefGoogle Scholar
  22. Fuller, A. P., Palmer-Toy, D., Erlander, M. G., and Sgroi, D. C. 2003. Laser capture microdissection and advanced molecular analysis of human breast cancer. J Mammary Gland Biol Neoplasia 8: 335–345PubMedCrossRefGoogle Scholar
  23. Gadbury, G. L., Page, G. P., Edwards, J., Kayo, T., Weindruch, R., Permana, P. A., Mountz, J., Allison, D. B. 2004. Power analysis and sample size estimation in the age of high dimensional biology. Stat Methods Med Res 13: 325–338CrossRefGoogle Scholar
  24. Gossen, M., Freundlieb, S., Bender, G., Muller, G., Hillen, W., and Bujard, H. 1995. Transcriptional activation by tetracyclines in mammalian cells. Science 268: 1766–1769PubMedCrossRefGoogle Scholar
  25. Green, J. E., and Hudson, T. 2005. The promise of genetically engineered mice for cancer prevention studies. Nat Rev Cancer 5: 184–198PubMedCrossRefGoogle Scholar
  26. Green, J. E., Desai, K., Ye, Y., Kavanaugh, C., Calvo, A., and Huh, J. I. 2004. Genomic approaches to understanding mammary tumor progression in transgenic mice and responses to therapy. Clin Cancer Res 10: 385S–390SPubMedCrossRefGoogle Scholar
  27. Grisendi, S., and Pandolfi, P. P. 2004. Germline modification strategies. In: Mouse Models of Human Cancer, ed. E. C. Holland, pp. 43–65. Hoboken: WileyGoogle Scholar
  28. Gruvberger, S., Ringner, M., Chen, Y., Panavally, S., Saal, L. H., Borg, A., Ferno, M., Peterson, C., and Meltzer, P. S. 2001. Estrogen receptor status in breast cancer is associated with remarkably distinct gene expression patterns. Cancer Res 61: 5979–5984PubMedGoogle Scholar
  29. Gunther, E. J., Belka, G. K., Wertheim, G. B., Wang, J., Hartman, J. L., Boxer, R. B., and Chodosh, L. A. 2002. A novel doxycycline-inducible system for the transgenic analysis of mammary gland biology. FASEB J 16: 283–292PubMedCrossRefGoogle Scholar
  30. Hao, X., Sun, B., Hu, L., Lahdesmaki, H., Dunmire, V., Feng, Y., Zhang, S. W., Wang, H., Wu, C., Wang, H., Fuller, G. N., Symmans, W. F., Shmulevich, I., and Zhang, W. 2004. Differential gene and protein expression in primary breast malignancies and their lymph node metastases as revealed by combined cDNA microarray and tissue microarray analysis. Cancer 100: 1110–1122PubMedCrossRefGoogle Scholar
  31. Hennighausen, L. 2000. Mouse models for breast cancer. Oncogene 19: 966–967PubMedCrossRefGoogle Scholar
  32. Herschkowitz, J. I., Simin, K., Weigman, V. J., Mikaelian, I., Usary, J., Hu, Z., Rasmussen, K. E., Jones, L. P., Assefnia, S., Chandrasekharan, S., Backlund, M. G., Yin, Y., Khramtsov, A. I., Bastein, R., Quackenbush, J., Glazer, R. I., Brown, P. H., Green, J. E., Kopelovich, L., Furth, P. A., Palazzo, J. P., Olopade, O. I., Bernard, P. S., Churchill, G. A., Van Dyke, T., and Perou, C. M. 2007. Identification of conserved gene expression features between murine mammary carcinoma models and human breast tumors. Genome Biol 8: R76PubMedCrossRefGoogle Scholar
  33. Huang, E., Ishida, S., Pittman, J., Dressman, H., Bild, A., Kloos, M., D’Amico, M., Pestell, R. G., West, M., and Nevins, J. R. 2003a. Gene expression phenotypic models that predict the activity of oncogenic pathways. Nat Genet 34: 226–230CrossRefGoogle Scholar
  34. Huang, E., West, M., and Nevins, J. R. 2003b. Gene expression profiling for prediction of clinical characteristics of breast cancer. Recent Prog Horm Res 58: 55–73CrossRefGoogle Scholar
  35. Huang, J., Li, X., Hilf, R., Bambara, R. A., and Muyan, M. 2005. Molecular basis of therapeutic strategies for breast cancer. Curr Drug Targets Immune Endocr Metabol Disord 5: 379–396PubMedCrossRefGoogle Scholar
  36. Hunter, K., Welch, D. R., and Liu, E. T. 2003. Genetic background is an important determinant of metastatic potential. Nat Genet 34: 23–24; author reply 25PubMedCrossRefGoogle Scholar
  37. Hutchinson, J. N., and Muller, W. J. 2000. Transgenic mouse models of human breast cancer. Oncogene 19: 6130–6137PubMedCrossRefGoogle Scholar
  38. Irizarry, R. A., Hobbs, B., Collin, F., Beazer-Barclay, Y. D., Antonellis, K. J., Scherf, U., and Speed, T. P. 2003. Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics 4: 249–264PubMedCrossRefGoogle Scholar
  39. Jager, R., Friedrichs, N., Heim, I., Buttner, R., and Schorle, H. 2005. Dual role of AP-2gamma in ErbB-2-induced mammary tumorigenesis. Breast Cancer Res Treat 90: 273–280PubMedCrossRefGoogle Scholar
  40. Jain, A. K., Murty, M. N, and Flynn, P. J. 1999. Data Clustering: A Review, Vol. 3, pp. 265–322Google Scholar
  41. Jeffrey, S. S., Lonning, P. E., and Hillner, B. E. 2005. Genomics-based prognosis and therapeutic prediction in breast cancer. J Natl Compr Canc Netw 3: 291–300PubMedGoogle Scholar
  42. Jerry, D. J., Kittrell, F. S., Kuperwasser, C., Laucirica, R., Dickinson, E. S., Bonilla, P. J., Butel, J. S., and Medina, D. 2000. A mammary-specific model demonstrates the role of the p53 tumor suppressor gene in tumor development. Oncogene 19: 1052–1058PubMedCrossRefGoogle Scholar
  43. Jolicoeur, P., Bouchard, L., Guimond, A., Ste-Marie, M., Hanna, Z., and Dievart, A. 1998. Use of mouse mammary tumour virus (MMTV)/neu transgenic mice to identify genes collaborating with the c-erbB-2 oncogene in mammary tumour development. Biochem Soc Symp 63: 159–165PubMedGoogle Scholar
  44. Kang, Y., Siegel, P. M., Shu, W., Drobnjak, M., Kakonen, S. M., Cordon-Cardo, C., Guise, T. A., and Massague, J. 2003. A multigenic program mediating breast cancer metastasis to bone. Cancer Cell 3: 537–549PubMedCrossRefGoogle Scholar
  45. Kavanaugh, C., and Green, J. E. 2003. The use of genetically altered mice for breast cancer prevention studies. J Nutr 133: 2404S–2409SPubMedGoogle Scholar
  46. Kavanaugh, C. J., Desai, K. V., Calvo, A., Brown, P. H., Couldrey, C., Lubet, R., and Green, J. E. 2002. Pre-clinical applications of transgenic mouse mammary cancer models. Transgenic Res 11: 617–633PubMedCrossRefGoogle Scholar
  47. Khan, J., Wei, J. S., Ringner, M., Saal, L. H., Ladanyi, M., Westermann, F., Berthold, F., Schwab, M., Antonescu, C. R., Peterson, C., and Meltzer, P. S. 2001. Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks. Nat Med 7: 673–679PubMedCrossRefGoogle Scholar
  48. Korn, E. L., Troendle, J. F., McShane, L. M., and Simon, R. 2004. Controlling the number of false discoveries: application to high-dimensional genomic data. J Stat Plan Infer 124: 379–398CrossRefGoogle Scholar
  49. Lahdesmaki, H., Hao, X., Sun, B., Hu, L., Yli-Harja, O., Shmulevich, I., and Zhang, W. 2004. Distinguishing key biological pathways between primary breast cancers and their lymph node metastases by gene function-based clustering analysis. Int J Oncol 24: 1589–1596PubMedGoogle Scholar
  50. Lakhani, S. R., Jacquemier, J., Sloane, J. P., Gusterson, B. A., Anderson, T. J., van de Vijver, M. J., Farid, L. M., Venter, D., Antoniou, A., Storfer-Isser, A., Smyth, E., Steel, C. M., Haites, N., Scott, R. J., Goldgar, D., Neuhausen, S., Daly, P. A., Ormiston, W., McManus, R., Scherneck, S., Ponder, B. A., Ford, D., Peto, J., Stoppa-Lyonnet, D., Bignon, Y. J., Struewing, J. P., Spurr, N. K., Bishop, D. T., Klijn, J. G., Devilee, P., Cornelisse, C. J., Lasset, C., Lenoir, G., Barkardottir, R. B., Egilsson, V., Hamann, U., Chang-Claude, J., Sobol, H., Weber, B., Stratton, M. R., and Easton, D. F. 1998. Multifactorial analysis of differences between sporadic breast cancers and cancers involving BRCA1 and BRCA2 mutations. J Natl Cancer Inst 90: 1138–1145PubMedCrossRefGoogle Scholar
  51. Lee, J. S., Grisham, J. W., and Thorgeirsson, S. S. 2005. Comparative functional genomics for identifying models of human cancer. Carcinogenesis 26: 1013–1020PubMedCrossRefGoogle Scholar
  52. Lin, S. C., Lee, K. F., Nikitin, A. Y., Hilsenbeck, S. G., Cardiff, R. D., Li, A., Kang, K. W., Frank, S. A., Lee, W. H., and Lee, E. Y. 2004. Somatic mutation of p53 leads to estrogen receptor alpha-positive and -negative mouse mammary tumors with high frequency of metastasis. Cancer Res 64: 3525–3532PubMedCrossRefGoogle Scholar
  53. Liu, E. T. 2003. Classification of cancers by expression profiling. Curr Opin Genet Dev 13: 97–103PubMedCrossRefGoogle Scholar
  54. Luzzi, V., Holtschlag, V., and Watson, M. A. 2001. Expression profiling of ductal carcinoma in situ by laser capture microdissection and high-density oligonucleotide arrays. Am J Pathol 158: 2005–2010PubMedCrossRefGoogle Scholar
  55. Ma, X. J., Salunga, R., Tuggle, J. T., Gaudet, J., Enright, E., McQuary, P., Payette, T., Pistone, M., Stecker, K., Zhang, B. M., Zhou, Y. X., Varnholt, H., Smith, B., Gadd, M., Chatfield, E., Kessler, J., Baer, T. M., Erlander, M. G., and Sgroi, D. C. 2003. Gene expression profiles of human breast cancer progression. Proc Natl Acad Sci USA 100: 5974–5979PubMedCrossRefGoogle Scholar
  56. McShane, L. M., Shih, J. H., and Michalowska, A. M. 2003. Statistical issues in the design and analysis of gene expression microarray studies of animal models. J Mammary Gland Biol Neoplasia 8: 359–374PubMedCrossRefGoogle Scholar
  57. Medina, D., Sivaraman, L., Hilsenbeck, S. G., Conneely, O., Ginger, M., Rosen, J., and Omalle, B. W. 2001. Mechanisms of hormonal prevention of breast cancer. Ann NY Acad Sci 952: 23–35PubMedCrossRefGoogle Scholar
  58. Minn, A. J., Gupta, G. P., Siegel, P. M., Bos, P. D., Shu, W., Giri, D. D., Viale, A., Olshen, A. B., Gerald, W. L., and Massague, J. 2005a. Genes that mediate breast cancer metastasis to lung. Nature 436: 518–524CrossRefGoogle Scholar
  59. Minn, A. J., Kang, Y., Serganova, I., Gupta, G. P., Giri, D. D., Doubrovin, M., Ponomarev, V., Gerald, W. L., Blasberg, R., and Massague, J. 2005b. Distinct organ-specific metastatic potential of individual breast cancer cells and primary tumors. J Clin Invest 115: 44–55Google Scholar
  60. Nandi, S., Guzman, R. C., and Yang, J. 1995. Hormones and mammary carcinogenesis in mice, rats, and humans: a unifying hypothesis. Proc Natl Acad Sci USA 92: 3650–3657PubMedCrossRefGoogle Scholar
  61. Nevins, J. R., Huang, E. S., Dressman, H., Pittman, J., Huang, A. T., and West, M. 2003. Towards integrated clinico-genomic models for personalized medicine: combining gene expression signatures and clinical factors in breast cancer outcomes prediction. Hum Mol Genet 12(Spec No. 2): R153–R157PubMedCrossRefGoogle Scholar
  62. Nikitin, A. Y., Alcaraz, A., Anver, M. R., Bronson, R. T., Cardiff, R. D., Dixon, D., Fraire, A. E., Gabrielson, E. W., Gunning, W. T., Haines, D. C., Kaufman, M. H., Linnoila, R. I., Maronpot, R. R., Rabson, A. S., Reddick, R. L., Rehm, S., Rozengurt, N., Schuller, H. M., Shmidt, E. N., Travis, W. D., Ward, J. M., and Jacks, T. 2004. Classification of proliferative pulmonary lesions of the mouse: recommendations of the mouse models of human cancers consortium. Cancer Res 64: 2307–2316PubMedCrossRefGoogle Scholar
  63. Pawitan, Y., Michiels, S., Koscielny, S., Gusnanto, A., and Ploner, A. 2005. False discovery rate, sensitivity and sample size for microarray studies. Bioinformatics 21: 3017–3024PubMedCrossRefGoogle Scholar
  64. Perou, C. M., Jeffrey, S. S., van de Rijn, M., Rees, C. A., Eisen, M. B., Ross, D. T., Pergamenschikov, A., Williams, C. F., Zhu, S. X., Lee, J. C., Lashkari, D., Shalon, D., Brown, P. O., and Botstein, D. 1999. Distinctive gene expression patterns in human mammary epithelial cells and breast cancers. Proc Natl Acad Sci USA 96: 9212–9217PubMedCrossRefGoogle Scholar
  65. Porter, D. A., Krop, I. E., Nasser, S., Sgroi, D., Kaelin, C. M., Marks, J. R., Riggins, G., and Polyak, K. 2001. A SAGE (serial analysis of gene expression) view of breast tumor progression. Cancer Res 61: 5697–5702PubMedGoogle Scholar
  66. van der Pouw Kraan, C. T., Dijkstra, C. D., and Verweij, C. L. 2005. Molecular unraveling of disease by means of DNA-microarrays. Ned Tijdschr Geneeskd 149: 626–631PubMedGoogle Scholar
  67. Qiu, T. H., Chandramouli, G. V., Hunter, K. W., Alkharouf, N. W., Green, J. E., and Liu, E. T. 2004. Global expression profiling identifies signatures of tumor virulence in MMTV-PyMT-transgenic mice: correlation to human disease. Cancer Res 64: 5973–5981PubMedCrossRefGoogle Scholar
  68. Ramaswamy, S., Ross, K. N., Lander, E. S., and Golub, T. R. 2003. A molecular signature of metastasis in primary solid tumors. Nat Genet 33: 49–54PubMedCrossRefGoogle Scholar
  69. Ried, T., Just, K. E., Holtgreve-Grez, H., du Manoir, S., Speicher, M. R., Schrock, E., Latham, C., Blegen, H., Zetterberg, A., Cremer, T., et al. 1995. Comparative genomic hybridization of formalin-fixed, paraffin-embedded breast tumors reveals different patterns of chromosomal gains and losses in fibroadenomas and diploid and aneuploid carcinomas. Cancer Res 55: 5415–5423PubMedGoogle Scholar
  70. Roth, V., and Lange, T. 2004. Bayesian class discovery in microarray datasets. IEEE Trans Biomed Eng 51: 707–718PubMedCrossRefGoogle Scholar
  71. Sauer, B. 1998. Inducible gene targeting in mice using the Cre/lox system. Methods 14: 381–392PubMedCrossRefGoogle Scholar
  72. Seth, A., Kitching, R., Landberg, G., Xu, J., Zubovits, J., and Burger, A. M. 2003. Gene expression profiling of ductal carcinomas in situ and invasive breast tumors. Anticancer Res 23: 2043–2051PubMedGoogle Scholar
  73. Sgroi, D. C., Teng, S., Robinson, G., LeVangie, R., Hudson, J. R., Jr., and Elkahloun, A. G. 1999. In vivo gene expression profile analysis of human breast cancer progression. Cancer Res 59: 5656–5661PubMedGoogle Scholar
  74. Shappell, S. B., Thomas, G. V., Roberts, R. L., Herbert, R., Ittmann, M. M., Rubin, M. A., Humphrey, P. A., Sundberg, J. P., Rozengurt, N., Barrios, R., Ward, J. M., and Cardiff, R. D. 2004. Prostate pathology of genetically engineered mice: definitions and classification. The consensus report from the Bar Harbor meeting of the Mouse Models of Human Cancer Consortium Prostate Pathology Committee. Cancer Res 64: 2270–2305PubMedCrossRefGoogle Scholar
  75. Simon, R. 2003. Using DNA microarrays for diagnostic and prognostic prediction. Expert Rev Mol Diagn 3: 587–595PubMedCrossRefGoogle Scholar
  76. Simon, R. 2004. When is a genomic classifier ready for prime time? Nat Clin Pract Oncol 1: 4–5PubMedCrossRefGoogle Scholar
  77. Simon, R. M., Korn, E. L., McShane, L. M., Radmacher, M. D., Wright, G. W., Zhao, Y. 2003. Design and Analysis of DNA Microarray Investigations. New York: SpringerGoogle Scholar
  78. Sorlie, T., Tibshirani, R., Parker, J., Hastie, T., Marron, J. S., Nobel, A., Deng, S., Johnsen, H., Pesich, R., Geisler, S., Demeter, J., Perou, C. M., Lonning, P. E., Brown, P. O., Borresen-Dale, A. L., and Botstein, D. 2003. Repeated observation of breast tumor subtypes in independent gene expression data sets. Proc Natl Acad Sci USA 100: 8418–8423PubMedCrossRefGoogle Scholar
  79. Sotiriou, C., Neo, S. Y., McShane, L. M., Korn, E. L., Long, P. M., Jazaeri, A., Martiat, P., Fox, S. B., Harris, A. L., and Liu, E. T. 2003. Breast cancer classification and prognosis based on gene expression profiles from a population-based study. Proc Natl Acad Sci USA 100: 10393–10398PubMedCrossRefGoogle Scholar
  80. Sweet-Cordero, A., Mukherjee, S., Subramanian, A., You, H., Roix, J. J., Ladd-Acosta, C., Mesirov, J., Golub, T. R., and Jacks, T. 2005. An oncogenic KRAS2 expression signature identified by cross-species gene-expression analysis. Nat Genet 37: 48–55PubMedGoogle Scholar
  81. Tusher, V. G., Tibshirani, R., and Chu, G. 2001. Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci USA 98: 5116–5121PubMedCrossRefGoogle Scholar
  82. Ureta-Vidal, A., Ettwiller, L., and Birney, E. 2003. Comparative genomics: genome-wide analysis in metazoan eukaryotes. Nat Rev Genet 4: 251–262PubMedCrossRefGoogle Scholar
  83. Van Laere, S., Van der Auwera, I., Van den Eynden, G. G., Fox, S. B., Bianchi, F., Harris, A. L., van Dam, P., Van Marck, E. A., Vermeulen, P. B., and Dirix, L. Y. 2005. Distinct molecular signature of inflammatory breast cancer by cDNA microarray analysis. Breast Cancer Res Treat 93: 237–246PubMedCrossRefGoogle Scholar
  84. van't Veer, L. J., Dai, H., van de Vijver, M. J., He, Y. D., Hart, A. A., Mao, M., Peterse, H. L., van der Kooy, K., Marton, M. J., Witteveen, A. T., Schreiber, G. J., Kerkhoven, R. M., Roberts, C., Linsley, P. S., Bernards, R., and Friend, S. H. 2002. Gene expression profiling predicts clinical outcome of breast cancer. Nature 415: 530–536CrossRefGoogle Scholar
  85. van de Vijver, M. J., He, Y. D., van't Veer, L. J., Dai, H., Hart, A. A., Voskuil, D. W., Schreiber, G. J., Peterse, J. L., Roberts, C., Marton, M. J., Parrish, M., Atsma, D., Witteveen, A., Glas, A., Delahaye, L., van der Velde, T., Bartelink, H., Rodenhuis, S., Rutgers, E. T., Friend, S. H., and Bernards, R. 2002. A gene-expression signature as a predictor of survival in breast cancer. N Engl J Med 347: 1999–2009PubMedCrossRefGoogle Scholar
  86. Wajapeyee, N., and Somasundaram, K. 2004. Pharmacogenomics in breast cancer: current trends and future directions. Curr Opin Mol Ther 6: 296–301PubMedGoogle Scholar
  87. Weaver, Z., Montagna, C., Xu, X., Howard, T., Gadina, M., Brodie, S. G., Deng, C. X., and Ried, T. 2002. Mammary tumors in mice conditionally mutant for Brca1 exhibit gross genomic instability and centrosome amplification yet display a recurring distribution of genomic imbalances that is similar to human breast cancer. Oncogene 21: 5097–5107PubMedCrossRefGoogle Scholar
  88. Weigelt, B., and van't Veer, L. J. 2004. Hard-wired genotype in metastatic breast cancer. Cell Cycle 3: 756–757PubMedCrossRefGoogle Scholar
  89. Weigelt, B., Glas, A. M., Wessels, L. F., Witteveen, A. T., Peterse, J. L., and van't Veer, L. J. 2003. Gene expression profiles of primary breast tumors maintained in distant metastases. Proc Natl Acad Sci USA 100: 15901–15905PubMedCrossRefGoogle Scholar
  90. Weiss, W. A., Israel, M., Cobbs, C., Holland, E., James, C. D., Louis, D. N., Marks, C., McClatchey, A. I., Roberts, T., Van Dyke, T., Wetmore, C., Chiu, I. M., Giovannini, M., Guha, A., Higgins, R. J., Marino, S., Radovanovic, I., Reilly, K., and Aldape, K. 2002. Neuropathology of genetically engineered mice: consensus report and recommendations from an international forum. Oncogene 21: 7453–7463PubMedCrossRefGoogle Scholar
  91. West, M., Blanchette, C., Dressman, H., Huang, E., Ishida, S., Spang, R., Zuzan, H., Olson, J. A., Jr., Marks, J. R., and Nevins, J. R. 2001. Predicting the clinical status of human breast cancer by using gene expression profiles. Proc Natl Acad Sci USA 98: 11462–11467PubMedCrossRefGoogle Scholar
  92. Xu, X., Wagner, K. U., Larson, D., Weaver, Z., Li, C., Ried, T., Hennighausen, L., Wynshaw-Boris, A., and Deng, C. X. 1999. Conditional mutation of Brca1 in mammary epithelial cells results in blunted ductal morphogenesis and tumour formation. Nat Genet 22: 37–43PubMedCrossRefGoogle Scholar
  93. Ye, Y., Qiu, T. H., Kavanaugh, C., and Green, J. E. 2004. Molecular mechanisms of breast cancer progression: lessons from mouse mammary cancer models and gene expression profiling. Breast Dis 19: 69–82PubMedGoogle Scholar

Copyright information

© Humana Press, a part of Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Mark J. Hoenerhoff
    • 1
  • Aleksandra M. Michalowski
    • 1
  • Ting-Hu Qiu
    • 1
  • Jeffery E. Green
    • 1
  1. 1.Laboratory of Cell Regulation and Carcinogenesis, National Cancer InstituteNational Institutes of HealthBethesdaUSA

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