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Cancer and Metastasis Reviews

, Volume 31, Issue 1–2, pp 41–46 | Cite as

The present and future of gene profiling in breast cancer

  • E. EspinosaEmail author
  • A. Gámez-Pozo
  • I. Sánchez-Navarro
  • A. Pinto
  • C. A. Castañeda
  • E. Ciruelos
  • J. Feliu
  • J. A. Fresno Vara
NON-THEMATIC REVIEW

Abstract

Gene signatures can provide prognostic and predictive information to help in the treatment of early-stage breast cancer. Although many of these signatures have been described, only a few have been properly validated. MammaPrint and OncoType offer prognostic information and identify low-risk patients who do not benefit from adjuvant chemotherapy. With regard to prediction of response, molecular subtypes of breast cancer differ in their sensitivity to chemotherapy, although further studies are needed in this field. Cost, small sample size, and the need to use central laboratories are common limitations to the widespread use of these tools.

Keywords

Gene expression profiling Breast cancer Chemotherapy, adjuvant Prognosis 

References

  1. 1.
    Olivotto, I. A., Bajdik, C. D., Ravdin, P. M., Speers, C. H., Coldman, A. J., Norris, B. D., et al. (2005). Population-based validation of the prognostic model ADJUVANT! for early breast cancer. Journal of Clinical Oncology, 23, 2716–2725.PubMedCrossRefGoogle Scholar
  2. 2.
    Elston, C. W., & Ellis, I. O. (1991). Pathological prognostic factors in breast cancer. I. The value of histological grade in breast cancer: experience from a large study with long-term follow-up. Histopathology, 19, 403–410.PubMedCrossRefGoogle Scholar
  3. 3.
    Robbins, P., Pinder, S., de Klerk, N., Dawkins, H., Harvey, J., Sterrett, G., et al. (1995). Histological grading of breast carcinomas: a study of interobserver agreement. Human Pathology, 26, 873–879.PubMedCrossRefGoogle Scholar
  4. 4.
    Martin, M., Mahillo, E., Llombart-Cussac, A., Lluch, A., Munarriz, B., Pastor, M., et al. (2006). The “El Alamo” project (1990–1997): two consecutive hospital-based studies of breast cancer outcomes in Spain. Clinical and Translational Oncology, 8, 508–518.PubMedCrossRefGoogle Scholar
  5. 5.
    Ross, J. S., Hatzis, C., Symmans, W. F., Pusztai, L., & Hortobagyi, G. N. (2008). Commercialized multigene predictors of clinical outcome for breast cancer. The Oncologist, 13, 477–493.PubMedCrossRefGoogle Scholar
  6. 6.
    Sanchez-Navarro, I., Gamez-Pozo, A., Pinto, A., Hardisson, D., Madero, R., Lopez, R., et al. (2010). An 8-gene qRT-PCR-based gene expression score that has prognostic value in early breast cancer. BMC Cancer, 10, 336.PubMedCrossRefGoogle Scholar
  7. 7.
    Ma, X. J., Salunga, R., Dahiya, S., Wang, W., Carney, E., Durbecq, V., et al. (2008). A five-gene molecular grade index and HOXB13:IL17BR are complementary prognostic factors in early stage breast cancer. Clinical Cancer Research, 14, 2601–2608.PubMedCrossRefGoogle Scholar
  8. 8.
    Jankowitz, R., Chivukula, M., Ma, X., et al. (2010). Predictive value of the Theros Breast Cancer Index for distant recurrence and overall survival in comparison to Adjuvant! Online and clinicopathologic characteristics in women with lymph node-negative, ER-positive breast cancer. In: Proceedings of the American Society for Clinical Oncology, abs 10582.Google Scholar
  9. 9.
    van de Vijver, M. J., He, Y. D., van't Veer, L. J., Dai, H., Hart, A. A., Voskuil, D. W., et al. (2002). A gene-expression signature as a predictor of survival in breast cancer. The New England Journal of Medicine, 347, 1999–2009.PubMedCrossRefGoogle Scholar
  10. 10.
    Espinosa, E., Sanchez-Navarro, I., Gamez-Pozo, A., Marin, A. P., Hardisson, D., Madero, R., et al. (2009). Comparison of prognostic gene profiles using qRT-PCR in paraffin samples: a retrospective study in patients with early breast cancer. PLoS One, 4, e5911.PubMedCrossRefGoogle Scholar
  11. 11.
    Espinosa, E., Vara, J. A., Redondo, A., Sanchez, J. J., Hardisson, D., Zamora, P., et al. (2005). Breast cancer prognosis determined by gene expression profiling: a quantitative reverse transcriptase polymerase chain reaction study. Journal of Clinical Oncology, 23, 7278–7285.PubMedCrossRefGoogle Scholar
  12. 12.
    Paik, S., Shak, S., Tang, G., Kim, C., Baker, J., Cronin, M., et al. (2004). A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. The New England Journal of Medicine, 351, 2817–2826.PubMedCrossRefGoogle Scholar
  13. 13.
    Albain, K. S., Barlow, W. E., Shak, S., Hortobagyi, G. N., Livingston, R. B., Yeh, I. T., et al. (2010). Prognostic and predictive value of the 21-gene recurrence score assay in postmenopausal women with node-positive, oestrogen-receptor-positive breast cancer on chemotherapy: a retrospective analysis of a randomised trial. The Lancet Oncology, 11, 55–65.PubMedCrossRefGoogle Scholar
  14. 14.
    Mook, S., Schmidt, M. K., Viale, G., Pruneri, G., Eekhout, I., Floore, A., et al. (2009). The 70-gene prognosis-signature predicts disease outcome in breast cancer patients with 1–3 positive lymph nodes in an independent validation study. Breast Cancer Research and Treatment, 116, 295–302.PubMedCrossRefGoogle Scholar
  15. 15.
    Teschendorff, A. E., & Caldas, C. (2008). A robust classifier of high predictive value to identify good prognosis patients in ER-negative breast cancer. Breast Cancer Research, 10, R73.PubMedCrossRefGoogle Scholar
  16. 16.
    Yau, C., Esserman, L., Moore, D. H., Waldman, F., Sninsky, J., & Benz, C. C. (2010). A multigene predictor of metastatic outcome in early stage hormone receptor-negative and triple-negative breast cancer. Breast Cancer Research, 12, R85.PubMedCrossRefGoogle Scholar
  17. 17.
    Ein-Dor, L., Zuk, O., & Domany, E. (2006). Thousands of samples are needed to generate a robust gene list for predicting outcome in cancer. Proceedings of the National Academy of Sciences of the United States of America, 103, 5923–5928.PubMedCrossRefGoogle Scholar
  18. 18.
    Simon, R. (2005). Roadmap for developing and validating therapeutically relevant genomic classifiers. Journal of Clinical Oncology, 23, 7332–7341.PubMedCrossRefGoogle Scholar
  19. 19.
    Simon, R. M., Paik, S., & Hayes, D. F. (2009). Use of archived specimens in evaluation of prognostic and predictive biomarkers. Journal of the National Cancer Institute, 101, 1446–1452.PubMedCrossRefGoogle Scholar
  20. 20.
    Bogaerts, J., Cardoso, F., Buyse, M., Braga, S., Loi, S., Harrison, J. A., et al. (2006). Gene signature evaluation as a prognostic tool: challenges in the design of the MINDACT trial. Nature Clinical Practice Oncology, 3, 540–551.PubMedCrossRefGoogle Scholar
  21. 21.
    Sparano, J. A. (2006). TAILORx: trial assigning individualized options for treatment (Rx). Clinical Breast Cancer, 7, 347–350.PubMedCrossRefGoogle Scholar
  22. 22.
    Peppercorn, J., Perou, C. M., & Carey, L. A. (2008). Molecular subtypes in breast cancer evaluation and management: divide and conquer. Cancer Investigation, 26, 1–10.PubMedCrossRefGoogle Scholar
  23. 23.
    Perou, C. M., & Borresen, A. L. (2011). Systems biology and genomics of breast cancer. Cold Spring Harbor Perspectives in Biology, 3(2), pii:a003293.CrossRefGoogle Scholar
  24. 24.
    Sorlie, T., Tibshirani, R., Parker, J., Hastie, T., Marron, J. S., Nobel, A., et al. (2003). Repeated observation of breast tumor subtypes in independent gene expression data sets. Proceedings of the National Academy of Sciences of the United States of America, 100, 8418–8423.PubMedCrossRefGoogle Scholar
  25. 25.
    Herschkowitz, J. I., Simin, K., Weigman, V. J., Mikaelian, I., Usary, J., Hu, Z., et al. (2007). Identification of conserved gene expression features between murine mammary carcinoma models and human breast tumors. Genome Biology, 8, R76.PubMedCrossRefGoogle Scholar
  26. 26.
    Hu, Z., Fan, C., Oh, D. S., Marron, J. S., He, X., Qaqish, B. F., et al. (2006). The molecular portraits of breast tumors are conserved across microarray platforms. BMC Genomics, 7, 96.PubMedCrossRefGoogle Scholar
  27. 27.
    Parker, J. S., Mullins, M., Cheang, M. C., Leung, S., Voduc, D., Vickery, T., et al. (2009). Supervised risk predictor of breast cancer based on intrinsic subtypes. Journal of Clinical Oncology, 27, 1160–1167.PubMedCrossRefGoogle Scholar
  28. 28.
    Esserman, L. J., Perou, C., Cheang, M., DeMichele, A., Carey, L., van't Veer, L., et al. (2009). Breast cancer molecular profiles and tumor response of neoadjuvant doxorubicin and paclitaxel: the I-SPY TRIAL. In: Proceedings of the American Society for Clinical Oncology, LBA 515.Google Scholar
  29. 29.
    Parker, J. S., Prat, A., Cheang, M., Lenburg, M. E., Paik, S., & Perou, C. (2009). Breast cancer molecular subtypes predict response to anthracycline/taxane based chemotherapy. In: San Antonio Breast Cancer Symposium, abs 2019.Google Scholar
  30. 30.
    Martin, M., Romero, A., Lopez Garcia-Asenjo, L., Cheang, M., Oliva, B., Garcia Saenz, J., et al. (2010). Molecular and genomic predictors of response to single-agent doxorubicin versus single-agent docetaxel in primary breast cancer. In: Proceedings of the American Society for Clinical Oncology, abs 502; Chicago.Google Scholar
  31. 31.
    Liedtke, C., Mazouni, C., Hess, K. R., Andre, F., Tordai, A., Mejia, J. A., et al. (2008). Response to neoadjuvant therapy and long-term survival in patients with triple-negative breast cancer. Journal of Clinical Oncology, 26, 1275–1281.PubMedCrossRefGoogle Scholar
  32. 32.
    Annunziata, C. M., & O'Shaughnessy, J. (2010). Poly (adp-ribose) polymerase as a novel therapeutic target in cancer. Clinical Cancer Research, 16, 4517–4526.PubMedCrossRefGoogle Scholar
  33. 33.
    Sotiriou, C., Wirapati, P., Loi, S., Harris, A., Fox, S., Smeds, J., et al. (2006). Gene expression profiling in breast cancer: understanding the molecular basis of histologic grade to improve prognosis. Journal of the National Cancer Institute, 98, 262–272.PubMedCrossRefGoogle Scholar
  34. 34.
    Paik, S., Tang, G., Shak, S., Kim, C., Baker, J., Kim, W., et al. (2006). Gene expression and benefit of chemotherapy in women with node-negative, estrogen receptor-positive breast cancer. Journal of Clinical Oncology, 24, 3726–3734.PubMedCrossRefGoogle Scholar
  35. 35.
    Fan, C., Oh, D. S., Wessels, L., Weigelt, B., Nuyten, D. S., Nobel, A. B., et al. (2006). Concordance among gene-expression-based predictors for breast cancer. The New England Journal of Medicine, 355, 560–569.PubMedCrossRefGoogle Scholar
  36. 36.
    Straver, M. E., Glas, A. M., Hannemann, J., Wesseling, J., van de Vijver, M. J., Rutgers, E. J., et al. (2010). The 70-gene signature as a response predictor for neoadjuvant chemotherapy in breast cancer. Breast Cancer Research and Treatment, 119, 551–558.PubMedCrossRefGoogle Scholar
  37. 37.
    Ayers, M., Symmans, W. F., Stec, J., Damokosh, A. I., Clark, E., Hess, K., et al. (2004). Gene expression profiles predict complete pathologic response to neoadjuvant paclitaxel and fluorouracil, doxorubicin, and cyclophosphamide chemotherapy in breast cancer. Journal of Clinical Oncology, 22, 2284–2293.PubMedCrossRefGoogle Scholar
  38. 38.
    Iwao-Koizumi, K., Matoba, R., Ueno, N., Kim, S. J., Ando, A., Miyoshi, Y., et al. (2005). Prediction of docetaxel response in human breast cancer by gene expression profiling. Journal of Clinical Oncology, 23, 422–431.PubMedCrossRefGoogle Scholar
  39. 39.
    Rouzier, R., Pusztai, L., Delaloge, S., Gonzalez-Angulo, A. M., Andre, F., Hess, K. R., et al. (2005). Nomograms to predict pathologic complete response and metastasis-free survival after preoperative chemotherapy for breast cancer. Journal of Clinical Oncology, 23, 8331–8339.PubMedCrossRefGoogle Scholar
  40. 40.
    Lyman, G. H., Cosler, L. E., Kuderer, N. M., & Hornberger, J. (2007). Impact of a 21-gene RT-PCR assay on treatment decisions in early-stage breast cancer: an economic analysis based on prognostic and predictive validation studies. Cancer, 109, 1011–1018.PubMedCrossRefGoogle Scholar
  41. 41.
    Roepman, P., Horlings, H. M., Krijgsman, O., Kok, M., Bueno-de-Mesquita, J. M., Bender, R., et al. (2009). Microarray-based determination of estrogen receptor, progesterone receptor, and HER2 receptor status in breast cancer. Clinical Cancer Research, 15, 7003–7011.PubMedCrossRefGoogle Scholar
  42. 42.
    Baehner, F. L., Achacoso, N., Maddala, T., Shak, S., Quesenberry, C. P., Jr., Goldstein, L. C., et al. (2010). Human epidermal growth factor receptor 2 assessment in a case–control study: comparison of fluorescence in situ hybridization and quantitative reverse transcription polymerase chain reaction performed by central laboratories. Journal of Clinical Oncology, 28, 4300–4306.PubMedCrossRefGoogle Scholar
  43. 43.
    Iverson, A. A., Gillett, C., Cane, P., Santini, C. D., Vess, T. M., Kam-Morgan, L., et al. (2009). A single-tube quantitative assay for mRNA levels of hormonal and growth factor receptors in breast cancer specimens. Journal of Molecular Diagnostics, 11, 117–130.PubMedCrossRefGoogle Scholar
  44. 44.
    Pentheroudakis, G., Kalogeras, K. T., Wirtz, R. M., Grimani, I., Zografos, G., Gogas, H., et al. (2009). Gene expression of estrogen receptor, progesterone receptor and microtubule-associated protein Tau in high-risk early breast cancer: a quest for molecular predictors of treatment benefit in the context of a Hellenic Cooperative Oncology Group trial. Breast Cancer Research and Treatment, 116, 131–143.PubMedCrossRefGoogle Scholar
  45. 45.
    Minn, A. J., Gupta, G. P., Siegel, P. M., Bos, P. D., Shu, W., Giri, D. D., et al. (2005). Genes that mediate breast cancer metastasis to lung. Nature, 436, 518–524.PubMedCrossRefGoogle Scholar
  46. 46.
    Bos, P. D., Zhang, X. H., Nadal, C., Shu, W., Gomis, R. R., Nguyen, D. X., et al. (2009). Genes that mediate breast cancer metastasis to the brain. Nature, 459, 1005–1009.PubMedCrossRefGoogle Scholar
  47. 47.
    Smid, M., Wang, Y., Zhang, Y., Sieuwerts, A. M., Yu, J., Klijn, J. G., et al. (2008). Subtypes of breast cancer show preferential site of relapse. Cancer Research, 68, 3108–3114.PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • E. Espinosa
    • 1
    • 5
    Email author
  • A. Gámez-Pozo
    • 2
  • I. Sánchez-Navarro
    • 2
  • A. Pinto
    • 1
  • C. A. Castañeda
    • 3
    • 4
  • E. Ciruelos
    • 3
  • J. Feliu
    • 1
  • J. A. Fresno Vara
    • 2
  1. 1.Service of Medical OncologyHospital Universitario La PazMadridSpain
  2. 2.Research Unit - INGEMMHospital Universitario La Paz—IdiPAZMadridSpain
  3. 3.Service of Medical OncologyHospital Universitario Doce de OctubreMadridSpain
  4. 4.Instituto Nacional de Enfermedades NeoplásicasLimaPeru
  5. 5.Hospital Universitario La Paz—Hospital De Día 1ª PlantaMadridSpain

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