Microarray Profiling in Breast Cancer Patients

  • Yong Qian
  • Xianglin Shi
  • Vincent Castranova
  • Nancy L. Guo
Part of the Cancer Drug Discovery and Development™ book series (CDD&D)

Summary

Breast cancer is the most common cancer among women. It arises from a variety of genetic, epigenetic, and chromosomal alterations. The traditional prognostic and predictive factors in breast cancer mainly focus on the clinical–pathological parameters, which are unable to reveal the diverse molecular alterations of breast cancer and are imprecise in predicting breast cancer progression and clinical outcomes. In recent years, the advances in microarray profiling, including both DNA microarrays and tumor tissue microarrays, provide an unprecedented screen technique to systemically study the pathogenesis of breast cancer.

Disclaimer: The findings and conclusions in this report are those of the author(s) and do not necessarily represent the views of the National Institute for Occupational Safety and Health.

In this chapter, we mainly summarize the progress of our group in microarray profiling for breast cancer. In the first project, we present a population-based study to predict recurrence and metastases of breast cancer using the public gene expression profiles and associated clinical data. In the second project, we develop an integrative model for breast cancer survival and treatment response predictions, which is composed of the expression profiles of several major activated protein kinases as well as traditional clinical–pathological parameters. In the third project, we create a predictive model system to explore proteomic contributions to drug sensitivity, including breast cancer drugs, based on the NCI-60 cell line-related databases. Finally, we discuss the new guidelines for reporting tumor biomarkers in cancer prognostic studies. We believe that an integrated approach combining gene expression profiles, protein expression profiles, as well as clinical information will lead to more informed clinical decision making in breast cancer intervention.

Key Words

Breast cancer prognosis transcriptional profiling proteomic profiling tissue array chemosensitivity prediction 

References

  1. 1.
    Cancer Facts & Figures 2007. Atlanta, GA: American Cancer Society, 2007.Google Scholar
  2. 2.
    Peto R, Boreham J, Clarke M et al. UK and USA breast cancer deaths down 25% in year 2000 at ages 20–69 years. Lancet 2000;355:1822.CrossRefPubMedGoogle Scholar
  3. 3.
    Giordano SH, Buzdar AU, Smith TL et al. Is breast cancer survival improving? Cancer 2004;100: 44–52.CrossRefPubMedGoogle Scholar
  4. 4.
    Kallioniemi A. Molecular signatures of breast cancer: predicting the future. N Engl J Med 2002;347:2067–2068.CrossRefPubMedGoogle Scholar
  5. 5.
    Hanahan D, Weinberg RA. The hallmarks of cancer. Cell 2000;100:57–70.CrossRefPubMedGoogle Scholar
  6. 6.
    Abramovitz M, Leyland-Jones B. A systems approach to clinical oncology: focus on breast cancer. Proteome Sci 2006;4:5.CrossRefPubMedGoogle Scholar
  7. 7.
    Cleator S, Ashworth A. Molecular profiling of breast cancer: clinical implications. Br J Cancer 2004;90:1120–1124.CrossRefPubMedGoogle Scholar
  8. 8.
    Gruvberger-Saal SK, Cunliffe HE, Carr KM et al. Microarrays in breast cancer research and clinical practice: the future lies ahead. Endocr Relat Cancer2006;13:1017–1031.CrossRefPubMedGoogle Scholar
  9. 9.
    Paik S. Molecular profiling of breast cancer. Curr Opin Obstet Gynecol 2006;18:59–63.CrossRefPubMedGoogle Scholar
  10. 10.
    Schnitt SJ. Traditional and newer pathologic factors. J Natl Cancer Inst Monogr 2001;22–26.Google Scholar
  11. 11.
    Hayes DF, Isaacs C, Stearns V. Prognostic factors in breast cancer: current and new predictors of metastasis. J Mammary Gland Biol Neoplasia 2001;6:375–392.CrossRefPubMedGoogle Scholar
  12. 12.
    Ludwig JA, Weinstein JN. Biomarkers in cancer staging, prognosis, and treatment selection. Nat Rev Cancer 2005;5:845–856.CrossRefPubMedGoogle Scholar
  13. 13.
    Olivotto IA, Bajdik CD, Ravdin PM et al. Population-based validation of the prognostic model ADJUVANT! for early breast cancer. J Clin Oncol 2005;23:2716–2725.CrossRefPubMedGoogle Scholar
  14. 14.
    Singletary SE, Allred C, Ashley P et al. Revision of the American Joint Committee on Cancer staging system for breast cancer. J Clin Oncol 2002;20:3628–3636.CrossRefPubMedGoogle Scholar
  15. 15.
    Chin K, de Solorzano CO, Knowles D et al. In situ analyses of genome instability in breast cancer. Nat Genet 2004;36:984–988.CrossRefPubMedGoogle Scholar
  16. 16.
    Pinkel D, Albertson DG. Array comparative genomic hybridization and its applications in cancer. Nat Genet 2005;37 Suppl:S11–S17.Google Scholar
  17. 17.
    Rodriguez V, Chen Y, Elkahloun A et al. Chromosome 8 BAC array comparative genomic hybridization and expression analysis identify amplification and overexpression of TRMT12 in breast cancer. Genes Chromosomes Cancer 2007;46:694–670.CrossRefPubMedGoogle Scholar
  18. 18.
    van't Veer LJ, Dai H, van de Vijver MJ et al. Gene expression profiling predicts clinical outcome of breast cancer. Nature 2002;415:530–536.CrossRefGoogle Scholar
  19. 19.
    Bild AH, Yao G, Chang JT et al. Oncogenic pathway signatures in human cancers as a guide to targeted therapies. Nature 2006;439:353–357.CrossRefPubMedGoogle Scholar
  20. 20.
    Glinsky GV, Higashiyama T, Glinskii AB. Classification of human breast cancer using gene expression profiling as a component of the survival predictor algorithm. Clin Cancer Res 2004;10:2272–2283.CrossRefPubMedGoogle Scholar
  21. 21.
    Huang E, Cheng SH, Dressman H et al. Gene expression predictors of breast cancer outcomes. Lancet 361;2003:1590–1596.CrossRefPubMedGoogle Scholar
  22. 22.
    Murphy N, Millar E, Lee CS. Gene expression profiling in breast cancer: towards individualising patient management. Pathology 2005;37:271–277.CrossRefPubMedGoogle Scholar
  23. 23.
    Sorlie T, Perou CM, Tibshirani R et al. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci USA 2001;98:10869–10874.CrossRefPubMedGoogle Scholar
  24. 24.
    Sotiriou C, Neo SY, McShane LM et al. Breast cancer classification and prognosis based on gene expression profiles from a population-based study. Proc Natl Acad Sci USA 2003;100:10393–10398.CrossRefPubMedGoogle Scholar
  25. 25.
    van de Vijver MJ, He YD, van't Veer LJ et al. A gene-expression signature as a predictor of survival in breast cancer. N Engl J Med 2002;347:1999–2009.CrossRefPubMedGoogle Scholar
  26. 26.
    West M, Blanchette C, Dressman H et al. Predicting the clinical status of human breast cancer by using gene expression profiles. Proc Natl Acad Sci USA 2001;98:11462–11467.CrossRefPubMedGoogle Scholar
  27. 27.
    Zhao H, Langerod A, Ji Y et al. Different gene expression patterns in invasive lobular and ductal carcinomas of the breast. Mol Biol Cell 2004;15:2523–2536.CrossRefPubMedGoogle Scholar
  28. 28.
    Paik S, Shak S, Tang G et al. A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N Engl J Med 2004;351:2817–2826.CrossRefPubMedGoogle Scholar
  29. 29.
    Fan C, Oh DS, Wessels L et al. Concordance among gene-expression–based predictors for breast cancer. N Engl J Med 2006;355:560–569.CrossRefPubMedGoogle Scholar
  30. 30.
    Chang HY, Nuyten DS, Sneddon JB et al. Robustness, scalability, and integration of a wound–response gene expression signature in predicting breast cancer survival. Proc Natl Acad Sci USA 2005;102:3738–3743.CrossRefPubMedGoogle Scholar
  31. 31.
    Ma XJ, Wang Z, Ryan PD et al. A two-gene expression ratio predicts clinical outcome in breast cancer patients treated with tamoxifen. Cancer Cell 2004;5:607–616.CrossRefPubMedGoogle Scholar
  32. 32.
    Perou CM, Sorlie T, Eisen MB et al. Molecular portraits of human breast tumours. Nature 2000;406:747–752.CrossRefPubMedGoogle Scholar
  33. 33.
    Sorlie T, Tibshirani R, Parker J et al. Repeated observation of breast tumor subtypes in independent gene expression data sets. Proc Natl Acad Sci USA 2003;100:8418–8423.CrossRefPubMedGoogle Scholar
  34. 34.
    Naderi A, Teschendorff AE, Barbosa-Morais NL et al. A gene-expression signature to predict survival in breast cancer across independent data sets. Oncogene 2007;26:1507–1516.CrossRefPubMedGoogle Scholar
  35. 35.
    Baker SG, Kramer BS, Srivastava S. Markers for early detection of cancer: statistical guidelines for nested case-control studies. BMC Med Res Methodol 2002;2:4.CrossRefPubMedGoogle Scholar
  36. 36.
    Ma Y, Qian Y, Wei L et al. Population-based molecular prognosis of breast cancer by transcriptional profiling. Clin Cancer Res 2007;13:2014–2022.CrossRefPubMedGoogle Scholar
  37. 37.
    Espina V, Geho D, Mehta AI et al. Pathology of the future: molecular profiling for targeted therapy. Cancer Invest 2005;23:36–46.PubMedGoogle Scholar
  38. 38.
    Rosen JM. Hormone receptor patterning plays a critical role in normal lobuloalveolar development and breast cancer progression. Breast Dis 2003;18:3–9.PubMedGoogle Scholar
  39. 39.
    Guo L, Abraham J, Flynn DC et al. Individualized survival and treatment response predictions for breast cancers using phospho-EGFR, phospho-ER, phospho-HER2/neu, phospho-IGF–IR/In, phospho-MAPK, and phospho–p70S6 K proteins. Int J Biol Markers 2007;22:1–11.PubMedGoogle Scholar
  40. 40.
    Bussey KJ, Chin K, Lababidi S et al. Integrating data on DNA copy number with gene expression levels and drug sensitivities in the NCI-60 cell line panel. Mol Cancer Ther 2006;5:853–867.CrossRefPubMedGoogle Scholar
  41. 41.
    Ikediobi ON, Davies H, Bignell G et al. Mutation analysis of 24 known cancer genes in the NCI-60 cell line set. Mol Cancer Ther 2006;5:2606–2612.CrossRefPubMedGoogle Scholar
  42. 42.
    Scherf U, Ross DT, Waltham M et al. A gene expression database for the molecular pharmacology of cancer. Nat Genet 2000;24:236–244.CrossRefPubMedGoogle Scholar
  43. 43.
    Staunton JE, Slonim DK, Coller HA et al. Chemosensitivity prediction by transcriptional profiling. Proc Natl Acad Sci USA 2001;98:10787–10792.CrossRefPubMedGoogle Scholar
  44. 44.
    Szakacs G, Annereau JP, Lababidi S et al. Predicting drug sensitivity and resistance: profiling ABC transporter genes in cancer cells. Cancer Cell 2004;6:129–137.CrossRefPubMedGoogle Scholar
  45. 45.
    Solit DB, Garraway LA, Pratilas CA et al. BRAF mutation predicts sensitivity to MEK inhibition. Nature 2006;439:358–362.CrossRefPubMedGoogle Scholar
  46. 46.
    Ma Y, Ding Z, Qian Y et al. Predicting cancer drug response by proteomic profiling. Clin Cancer Res 2006;12:4583–4589.CrossRefPubMedGoogle Scholar
  47. 47.
    Nishizuka S, Charboneau L, Young L et al. Proteomic profiling of the NCI-60 cancer cell lines using new high-density reverse-phase lysate microarrays. Proc Natl Acad Sci USA 2003;100:14229–14234.CrossRefPubMedGoogle Scholar
  48. 48.
    Paweletz CP, Charboneau L, Bichsel VE et al. Reverse phase protein microarrays which capture disease progression show activation of pro-survival pathways at the cancer invasion front. Oncogene 2001;20:1981–1989.CrossRefPubMedGoogle Scholar
  49. 49.
    Jiang H, Deng Y, Chen HS et al. Joint analysis of two microarray gene-expression data sets to select lung adenocarcinoma marker genes. BMC Bioinformatics 2004;5:81.CrossRefPubMedGoogle Scholar
  50. 50.
    Nimgaonkar A, Sanoudou D, Butte AJ et al. Reproducibility of gene expression across generations of Affymetrix microarrays. BMC Bioinformatics 2003;4:27.CrossRefPubMedGoogle Scholar
  51. 51.
    Emir B, Wieand S, Su JQ et al. Analysis of repeated markers used to predict progression of cancer. Stat Med 1998;17:2563–2578.CrossRefPubMedGoogle Scholar
  52. 52.
    Pepe MS, Janes H, Longton G et al. Limitations of the odds ratio in gauging the performance of a diagnostic, prognostic, or screening marker. Am J Epidemiol 2004;159:882–890.CrossRefPubMedGoogle Scholar
  53. 53.
    Heagerty PJ, Lumley T, Pepe MS. Time-dependent ROC curves for censored survival data and a diagnostic marker. Biometrics 2000;56:337–344.CrossRefPubMedGoogle Scholar
  54. 54.
    Heagerty PJ, Zheng Y. Survival model predictive accuracy and ROC curves. Biometrics 2005;61: 92–105.CrossRefPubMedGoogle Scholar
  55. 55.
    McShane LM, Altman DG, Sauerbrei W et al. Reporting recommendations for tumor MARKer prognostic studies (REMARK). Breast Cancer Res Treat 2006;100:229–235.CrossRefPubMedGoogle Scholar
  56. 56.
    McShane LM, Altman DG, Sauerbrei W et al. Reporting recommendations for tumor marker prognostic studies (remark). Exp Oncol 2006;28:99–105.PubMedGoogle Scholar
  57. 57.
    McShane LM, Altman DG, Sauerbrei W et al. REporting recommendations for tumor MARKer prognostic studies (REMARK). Nat Clin Pract Urol 2005;2:416–422.CrossRefPubMedGoogle Scholar
  58. 58.
    McShane LM, Altman DG, Sauerbrei W et al. Reporting recommendations for tumor marker prognostic studies. J Clin Oncol 2005;23:9067–9072.CrossRefPubMedGoogle Scholar
  59. 59.
    McShane LM, Altman DG, Sauerbrei W et al. REporting recommendations for tumor MARKer prognostic studies (REMARK). Nat Clin Pract Oncol 2005;2:416–422.CrossRefPubMedGoogle Scholar
  60. 60.
    McShane LM, Altman DG, Sauerbrei W et al. REporting recommendations for tumour MARKer prognostic studies (REMARK). Br J Cancer 2005;93:387–391.CrossRefPubMedGoogle Scholar
  61. 61.
    McShane LM, Altman DG, Sauerbrei W et al. Reporting recommendations for tumor marker prognostic studies (REMARK). J Natl Cancer Inst 2005;97:1180–1184.CrossRefPubMedGoogle Scholar
  62. 62.
    McShane LM, Altman DG, Sauerbrei W et al. REporting recommendations for tumour MARKer prognostic studies (REMARK). Eur J Cancer 2005;41:1690–1696.CrossRefPubMedGoogle Scholar
  63. 63.
    Hayes DF, Ethier S, Lippman ME. New guidelines for reporting of tumor marker studies in breast cancer research and treatment: REMARK. Breast Cancer Res Treat 2006;100:237–238.CrossRefPubMedGoogle Scholar

Copyright information

© Humana Press, a part of Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Yong Qian
    • 1
  • Xianglin Shi
    • 1
  • Vincent Castranova
  • Nancy L. Guo
    • 2
  1. 1.The Pathology and Physiology Research Branch, Health Effects Laboratory DivisionNational Institute for Occupational Safety and HealthMorgantownUSA
  2. 2.Mary Babb Randolph Cancer Center/Department of Community MedicineWest Virginia UniversityMorgantownUSA

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