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Genomics in breast cancer—therapeutic implications

Nature Clinical Practice Oncology volume 2, pages 26–33 (2005)Cite this article

Abstract

The introduction of DNA microarray techniques has had dramatic implications on cancer research, allowing researchers to analyze expression of multiple genes in concert and relate the findings to clinical parameters. The main discoveries in breast cancer, as well as in other malignancies, have so far been with respect to two key issues. First, individual tumors arising from the same organ may be grouped into distinct classes based on their gene expression profiles, independent of stage and grade. Second, the biologic relevance of such classification is corroborated by significant prognostic impact. We review how the use of microarray technologies can provide unique possibilities to explore the mechanisms of tumor behavior in vivo that will allow evaluation of prognosis and, potentially, drug resistance. However, in spite of recent advances, we are not yet at a stage where the use of these techniques should be implemented for routine clinical use, whether to define prognostic factors or to predict sensitivity to therapy.

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Figure 1: Strategy for a typical reference-based DNA microarray experiment.
Figure 2: Hierarchical clustering of primary breast tumors using the “intrinsic” subset of genes.
Figure 3: Overall survival analysis of breast cancer patients stratified by gene expression-based subtypes.
Figure 4: The risk for relapse (or 'prognosis') should be considered a compound vector influenced by a multiple of different biological factors.

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References

  1. Levi F et al. (2001) The fall in breast cancer mortality in Europe. Eur J Cancer 37: 1409–1412

    Article  CAS  Google Scholar 

  2. Abe O et al. (1998) Polychemotherapy for early breast cancer: an overview of the randomised trials. Lancet 352: 930–942

    Article  Google Scholar 

  3. Early Breast Cancer Trialist Group (1998) Tamoxifen for early breast cancer: An overview of the randomised trials. Lancet 351: 1451–1467

  4. Citron ML et al. (2003) Randomized trial of dose-dense versus conventionally scheduled and sequential versus concurrent combination chemotherapy as postoperative adjuvant treatment of node-positive primary breast cancer: first report of Intergroup Trial C9741/Cancer and Leukemia Group B Trial 9741. J Clin Oncol 21: 1431–1439

    Article  CAS  Google Scholar 

  5. Lønning PE (2003) Study of suboptimum treatment response: lessons from breast cancer. Lancet Oncol 4: 177–185

    Article  Google Scholar 

  6. Muss HB et al. (1994) c-erbB-2 expression and response to adjuvant therapy in women with node-positive early Breast cancer. N Engl J Med 330: 1260–1266

    Article  CAS  Google Scholar 

  7. Aas T et al. (1996) Specific P53 mutations are associated with de novo resistance to doxorubicin in breast cancer patients. Nat Med 2: 811–814

    Article  CAS  Google Scholar 

  8. Holloway AJ et al. (2002) Options available–from start to finish–for obtaining data from DNA microarrays. Nat Genet 32: 481–489

    Article  CAS  Google Scholar 

  9. Quackenbush J (2001) Computational analysis of microarray data. Nat Rev Genet 2: 418–427

    Article  CAS  Google Scholar 

  10. Ball CA et al. (2002) Standards for Microarray data. Science 298: 539–539

    Article  CAS  Google Scholar 

  11. Perou CM et al. (2000) Molecular portraits of human breast tumours. Nature 406: 747–752

    Article  CAS  Google Scholar 

  12. Sorlie T et al. (2001) Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci U S A 98: 10869–10874

    Article  CAS  Google Scholar 

  13. Sorlie T et al. (2003) Repeated observation of breast tumor subtypes in independent gene expression data sets. Proc Natl Acad Sci U S A 100: 8418–8423

    Article  CAS  Google Scholar 

  14. Sotiriou C et al. (2003) Breast cancer classification and prognosis based on gene expression profiles from a population-based study. Proc Natl Acad Sci U S A 100: 10393–10398

    Article  CAS  Google Scholar 

  15. Zhao HJ et al. (2004) Different gene expression patterns in invasive lobular and ductal carcinomas of the breast. Mol Biol Cell 15: 2523–2536

    Article  CAS  Google Scholar 

  16. Geisler S et al. (2001) Influence of TP53 gene alterations and c-erbB-2 expression on the response to treatment with doxorubicin in locally advanced breast cancer. Cancer Res 61: 2505–2512

    CAS  PubMed  Google Scholar 

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

    Article  CAS  Google Scholar 

  18. Ma XJ et al. (2004) A two-gene expression ratio predicts clinical outcome in breast cancer patients treated with tamoxifen. Cancer Cell 5: 607–616

    Article  CAS  Google Scholar 

  19. Beer D et al. (2002) Gene-expression profiles predict survival of patients with lung adenocarcinoma. Nat Med 8: 816–824

    Article  CAS  Google Scholar 

  20. Glinsky GV et al. (2004). Gene expression profiling predicts clinical outcome of prostate cancer. J Clin Invest 113: 913–923

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  22. Chung CH et al. (2004) Molecular classification of head and neck squamous cell carcinomas using patterns of gene expression. Cancer Cell 5: 489–500

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  24. Nakamura T et al. (2004) Genome-wide cDNA microarray analysis of gene expression profiles in pancreatic cancers using populations of tumor cells and normal ductal epithelial cells selected for purity by laser microdissection. Oncogene 23: 2385–2400

    Article  CAS  Google Scholar 

  25. Weiss MM et al. (2003) Genomic profiling of gastric cancer predicts lymph node status and survival. Oncogene 22: 1872–1879

    Article  CAS  Google Scholar 

  26. Bertucci F et al. (2004) Gene expression profiling of colon cancer by DNA microarrays and correlation with histoclinical parameters. Oncogene 23: 1377–1391

    Article  CAS  Google Scholar 

  27. Yeoh E et al. (2002) Classification, subtype discovery, and prediction of outcome in pediatric acute lymphoblastic leukemia by gene expression profiling. Cancer Cell 1: 133–143

    Article  CAS  Google Scholar 

  28. Rosenwald A et al. (2003) Molecular diagnosis of primary mediastinal B cell lymphoma identifies a clinically favorable subgroup of diffuse large B cell lymphoma related to Hodgkin lymphoma. J Exp Med 198: 851–862

    Article  CAS  Google Scholar 

  29. van de Vijver MJ et al. (2002) A gene-expression signature as a predictor of survival in breast cancer. N Engl J Med 347: 1999–2009

    Article  CAS  Google Scholar 

  30. Ahr A et al. (2002) Identification of high risk breast-cancer patients by gene expression profiling. Lancet 359: 131–132

    Article  Google Scholar 

  31. Onda M et al. (2004) Gene expression patterns as marker for 5-year postoperative prognosis of primary breast cancer. J Cancer Res Clin Oncol 130: 537–545

    Article  CAS  Google Scholar 

  32. Huang E et al. (2003) Gene expression predictors of breast cancer outcomes. Lancet 361: 1590–1596

    Article  CAS  Google Scholar 

  33. Nagahata T et al. (2004) Expression profiling to predict postoperative prognosis for estrogen receptor-negative breast cancers by analysis of 25,344 genes on a cDNA microarray. Cancer Sci 95: 218–225

    Article  CAS  Google Scholar 

  34. Huang E et al. (2003) Gene expression profiling for prediction of clinical characteristics of breast cancer. Recent Prog Horm Res 58: 55–73

    Article  CAS  Google Scholar 

  35. Pittman J et al. (2004) Integrated modeling of clinical and gene expression information for personalized prediction of disease outcomes. Proc Natl Acad Sci U S A 101: 8431–8436

    Article  CAS  Google Scholar 

  36. Eden P et al. (2004) “Good Old” clinical markers have similar power in breast cancer prognosis as microarray gene expression profilers. Eur J Cancer 40: 1837–1841

    Article  CAS  Google Scholar 

  37. Ein-Dor L et al. (2004) Outcome signature genes in breast cancer: is there a unique set? Bioinformatics [doi: 10.1093/bioinformatics/bth469]

  38. Chang J et al. (2003) Gene expression profiling for the prediction of therapeutic response to docetaxel in patients with breast cancer. Lancet 362: 362–369

    Article  CAS  Google Scholar 

  39. Ayers M et al. (2004) Gene expression profiles predict complete pathologic response to neoadjuvant paclitaxel and fluorouracil, doxorubicin, and cyclophosphamide chemotherapy in breast cancer. J Clin Oncol 22: 2284–2293

    Article  CAS  Google Scholar 

  40. Brenton JD and Caldas C (2003) Predictive cancer genomics–what do we need? Lancet 362: 340–341

    Article  Google Scholar 

  41. Ellis M and Ballman K (2004) Trawling for genes that predict response to breast cancer adjuvant therapy. J Clin Oncol 22: 2267–2269

    Article  CAS  Google Scholar 

  42. Therasse P et al. (2000) New guidelines to evaluate the response to treatment in solid tumors. J Natl Cancer Inst 92: 205–216

    Article  CAS  Google Scholar 

  43. Kannan K et al. (2001) DNA microarrays identification of primary and secondary target genes regulated by p53. Oncogene 20: 2225–2234

    Article  CAS  Google Scholar 

  44. Yoon H et al. (2002) Gene expression profiling of isogenic cells with different TP53 gene dosage reveals numerous genes that are affected by TP53 dosage and identifies CSPG2 as a direct target of p53. Proc Natl Acad Sci U S A 99: 15632–15637

    Article  CAS  Google Scholar 

  45. Muller A et al. (2001) Involvement of chemokine receptors in breast cancer metastasis. Nature 410: 50

    Article  CAS  Google Scholar 

  46. Vousden KH and Lu X (2002) Live or let die: The cell's response to p53. Nat Rev Cancer 2: 594–604

    Article  CAS  Google Scholar 

  47. Lønning PE (2004) Genes causing inherited cancer as beacons identifying the mechanisms of chemoresistance. Trends Mol Med 10: 113–118

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Breast Cancer Unit at the Section of Oncology, Institute of Medicine, Haukeland University Hospital, Bergen

    Per Eystein Lønning

  2. Haukeland University Hospital, Norway

    Per Eystein Lønning

  3. Postdoctoral Fellow of the in the Department of Genetics, Norwegian Cancer Society, at the Norwegian Radium Hospital, Oslo, Norway

    Therese Sørlie

  4. Chairman and Head of the Department of Genetics, Institute for Cancer Research, The University Hospital the Norwegian Radium Hospital, Oslo, Norway

    Anne-Lise Børresen-Dale

Authors
  1. Per Eystein Lønning
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  2. Therese Sørlie
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  3. Anne-Lise Børresen-Dale
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Correspondence to Anne-Lise Børresen-Dale.

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Lønning, P., Sørlie, T. & Børresen-Dale, AL. Genomics in breast cancer—therapeutic implications. Nat Rev Clin Oncol 2, 26–33 (2005). https://doi.org/10.1038/ncponc0072

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