Skip to main content

Advertisement

SpringerLink
Log in

The present and future of gene profiling in breast cancer

  • NON-THEMATIC REVIEW
  • Published:
Cancer and Metastasis Reviews Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  CAS  Google Scholar 

  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.

    Article  PubMed  CAS  Google Scholar 

  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.

    Article  PubMed  CAS  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  CAS  Google Scholar 

  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.

    Article  PubMed  CAS  Google Scholar 

  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.

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  CAS  Google Scholar 

  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.

    Article  PubMed  CAS  Google Scholar 

  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.

    Article  PubMed  CAS  Google Scholar 

  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.

    Article  PubMed  CAS  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  CAS  Google Scholar 

  18. Simon, R. (2005). Roadmap for developing and validating therapeutically relevant genomic classifiers. Journal of Clinical Oncology, 23, 7332–7341.

    Article  PubMed  CAS  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  CAS  Google Scholar 

  21. Sparano, J. A. (2006). TAILORx: trial assigning individualized options for treatment (Rx). Clinical Breast Cancer, 7, 347–350.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  CAS  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  PubMed  CAS  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

  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.

  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.

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  CAS  Google Scholar 

  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.

    Article  PubMed  CAS  Google Scholar 

  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.

    Article  PubMed  CAS  Google Scholar 

  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.

    Article  PubMed  CAS  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  CAS  Google Scholar 

  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.

    Article  PubMed  CAS  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  CAS  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  CAS  Google Scholar 

  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.

    Article  PubMed  CAS  Google Scholar 

  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.

    Article  PubMed  CAS  Google Scholar 

  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.

    Article  PubMed  CAS  Google Scholar 

  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.

    Article  PubMed  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Service of Medical Oncology, Hospital Universitario La Paz, Paseo de la castellana 261, 28046, Madrid, Spain

    E. Espinosa, A. Pinto & J. Feliu

  2. Research Unit - INGEMM, Hospital Universitario La Paz—IdiPAZ, Paseo de la castellana 261, 28046, Madrid, Spain

    A. Gámez-Pozo, I. Sánchez-Navarro & J. A. Fresno Vara

  3. Service of Medical Oncology, Hospital Universitario Doce de Octubre, Avenida de Córdoba s/n., 28041, Madrid, Spain

    C. A. Castañeda & E. Ciruelos

  4. Instituto Nacional de Enfermedades Neoplásicas, Av. Angamos Este, 2520, Surquillo, Lima, Peru

    C. A. Castañeda

  5. Hospital Universitario La Paz—Hospital De Día 1ª Planta, Paseo de la Castellana, 261, 28046, Madrid, Spain

    E. Espinosa

Authors
  1. E. Espinosa
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. A. Gámez-Pozo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. I. Sánchez-Navarro
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. A. Pinto
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. C. A. Castañeda
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. E. Ciruelos
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. J. Feliu
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. J. A. Fresno Vara
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to E. Espinosa.

Additional information

La verdadera grandeza de la ciencia acaba valorándose por su utilidad (The true greatness of science is eventually determined by its utility). Gregorio Marañón.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Espinosa, E., Gámez-Pozo, A., Sánchez-Navarro, I. et al. The present and future of gene profiling in breast cancer. Cancer Metastasis Rev 31, 41–46 (2012). https://doi.org/10.1007/s10555-011-9327-7

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10555-011-9327-7

Keywords

Search

Navigation