Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Review Article
  • Published:

Clinical utility of gene-expression signatures in early stage breast cancer

Nature Reviews Clinical Oncology volume 14, pages 595–610 (2017)Cite this article

Subjects

Key Points

  • Breast cancer is not a single, homogenous disease, but a heterogeneous group of different disease subtypes, each with its own biological and clinical characteristics

  • Methods to enhance the identification of individuals with high-risk disease, who would benefit from more-intensive treatment are an area of expanding research interest

  • The development of multigene assays has led to improvements in predicting the risk of recurrence in patients with early stage oestrogen receptor (ER)-positive, HER2-negative breast cancer, compared with use of standard prognostic criteria

  • Several multigene assays, including Oncotype DX, Prosigna, and MammaPrint have emerged as well-studied technologies and have already been incorporated into clinical practice, to some extent

  • In 2016, the ASCO Breast Cancer Guidelines Advisory Group and Clinical Practice Guidelines Committee published updated guidelines for the use of biomarkers, including gene-expression assays, to help guide the use of adjuvant systemic therapy

  • Several large, randomized trials are currently ongoing and aim to prospectively evaluate and further validate the performance of these assays, in order to provide the highest level of evidence of clinical utility

Abstract

Breast cancer is a heterogeneous disease, with different subtypes having a distinct biological, molecular, and clinical course. Assessments of standard clinical and pathological features have traditionally been used to determine the use of adjuvant systemic therapy in patients with early stage breast cancer; however, the ability to identify those who will benefit from adjuvant chemotherapy remains a challenge, leading to the overtreatment of some patients. Advances in molecular medicine have substantially improved the accuracy of gene-expression profiling of breast tumours, resulting in improvements in the ability to predict a patient's risk of breast cancer recurrence and likely response to endocrine therapy and/or chemotherapy. These genomic assays, several of which are commercially available, have aided physicians in tailoring treatment decisions for patients at the individual level. Herein, we describe the available data on the clinical validity of the most widely available assays in patients with early stage breast cancer, with a focus on the development, validation, and clinical application of these assays, in addition to the anticipated outcomes of ongoing prospective trials. We also review data from comparative studies of these assays and from cost-effectiveness analyses relating to their clinical use.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Gene-expression quantification methods.
Figure 2: Genes evaluated by multigene assays to calculate a recurrence risk score.

Similar content being viewed by others

References

  1. Siegel, R. L., Miller, K. D. & Jemal, A. Cancer statistics, 2016. CA Cancer J. Clin. 66, 7–30 (2016).

    Article  PubMed  Google Scholar 

  2. Cancer Genome Atlas Network. Comprehensive molecular portraits of human breast tumours. Nature 490, 61–70 (2012).

  3. Gruvberger, S. et al. Estrogen receptor status in breast cancer is associated with remarkably distinct gene expression patterns. Cancer Res. 61, 5979–5984 (2001).

    CAS  PubMed  Google Scholar 

  4. Sorlie, T. et al. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc. Natl Acad. Sci. USA 98, 10869–10874 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Esteva, F. J., Sahin, A. A., Cristofanilli, M., Arun, B. & Hortobagyi, G. N. Molecular prognostic factors for breast cancer metastasis and survival. Semin. Radiat. Oncol. 12, 319–328 (2002).

    Article  PubMed  Google Scholar 

  6. Hammond, M. E. et al. American Society of Clinical Oncology/College Of American Pathologists guideline recommendations for immunohistochemical testing of estrogen and progesterone receptors in breast cancer. J. Clin. Oncol. 28, 2784–2795 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Wolff, A. C. et al. Recommendations for human epidermal growth factor receptor 2 testing in breast cancer: American Society of Clinical Oncology/College of American Pathologists clinical practice guideline update. J. Clin. Oncol. 31, 3997–4013 (2013).

    Article  PubMed  Google Scholar 

  8. Esteva, F. J. Monoclonal antibodies, small molecules, and vaccines in the treatment of breast cancer. Oncologist 9, 4–9 (2004).

    Article  CAS  PubMed  Google Scholar 

  9. Morrow, P. K., Zambrana, F. & Esteva, F. J. Recent advances in systemic therapy: advances in systemic therapy for HER2-positive metastatic breast cancer. Breast Cancer Res. 11, 207 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  10. Fornier, M., Esteva, F. J. & Seidman, A. D. Trastuzumab in combination with chemotherapy for the treatment of metastatic breast cancer. Semin. Oncol. 27, 38–45 (2000).

    CAS  PubMed  Google Scholar 

  11. Burstein, H. J. et al. Adjuvant endocrine therapy for women with hormone receptor-positive breast cancer: American Society of Clinical Oncology clinical practice guideline focused update. J. Clin. Oncol. 32, 2255–2269 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  12. Ravdin, P. M. et al. Computer program to assist in making decisions about adjuvant therapy for women with early breast cancer. J. Clin. Oncol. 19, 980–991 (2001).

    Article  CAS  PubMed  Google Scholar 

  13. Yersal, O. & Barutca, S. Biological subtypes of breast cancer: prognostic and therapeutic implications. World J. Clin. Oncol. 5, 412–424 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  14. Early Breast Cancer Trialists' Collaborative Group. Effects of chemotherapy and hormonal therapy for early breast cancer on recurrence and 15-year survival: an overview of the randomised trials. Lancet 365, 1687–1717 (2005).

  15. Early Breast Cancer Trialists' Collaborative Group et al. Relevance of breast cancer hormone receptors and other factors to the efficacy of adjuvant tamoxifen: patient-level meta-analysis of randomised trials. Lancet 378, 771–784 (2011).

  16. Early Breast Cancer Trialists' Collaborative Group et al. Comparisons between different polychemotherapy regimens for early breast cancer: meta-analyses of long-term outcome among 100,000 women in 123 randomised trials. Lancet 379, 432–444 (2012).

  17. Perou, C. M. et al. Molecular portraits of human breast tumours. Nature 406, 747–752 (2000).

    Article  CAS  PubMed  Google Scholar 

  18. Cheang, M. C. et al. Ki67 index, HER2 status, and prognosis of patients with luminal B breast cancer. J. Natl Cancer Inst. 101, 736–750 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Sotiriou, C. et al. Breast cancer classification and prognosis based on gene expression profiles from a population-based study. Proc. Natl Acad. Sci. USA 100, 10393–10398 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Rouzier, R. et al. Breast cancer molecular subtypes respond differently to preoperative chemotherapy. Clin. Cancer Res. 11, 5678–5685 (2005).

    Article  CAS  PubMed  Google Scholar 

  21. Prat, A. et al. Phenotypic and molecular characterization of the claudin-low intrinsic subtype of breast cancer. Breast Cancer Res. 12, R68 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  22. Harris, L. N. et al. Use of biomarkers to guide decisions on adjuvant systemic therapy for women with early-stage invasive breast cancer: American Society of Clinical Oncology clinical practice guideline. J. Clin. Oncol. 34, 1134–1150 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Teutsch, S. M. et al. The Evaluation of Genomic Applications in Practice and Prevention (EGAPP) initiative: methods of the EGAPP Working Group. Genet. Med. 11, 3–14 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  24. Botkin, J. R. et al. Outcomes of interest in evidence-based evaluations of genetic tests. Genet. Med. 12, 228–235 (2010).

    Article  PubMed  Google Scholar 

  25. Paik, S. et al. A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N. Engl. J. Med. 351, 2817–2826 (2004).

    Article  CAS  PubMed  Google Scholar 

  26. Esteban, J. et al. Tumor gene expression and prognosis in breast cancer: multi-gene RT-PCR assay of paraffin-embedded tissue [abstract]. Proc. Am. Soc. Clin. Oncol. 22, 850 (2003).

    Google Scholar 

  27. Cobleigh, M. A. et al. Tumor gene expression predicts distant disease- free survival (DDFS) in breast cancer patients with 10 or more positive nodes: high throughout RT-PCR assay of paraffin-embedded tumor tissues [abstract]. Proc. Am. Soc. Clin. Oncol. 22, 850 (2003).

    Google Scholar 

  28. Paik, S. et al. Multi-gene RT-PCR assay for predicting recurrence in node negative breast cancer patients — NSABP studies B-20 and B-14. Breast Cancer Res. Treat. 82, A16 (2003).

    Google Scholar 

  29. Esteva, F. J. et al. Prognostic role of a multigene reverse transcriptase-PCR assay in patients with node-negative breast cancer not receiving adjuvant systemic therapy. Clin. Cancer Res. 11, 3315–3319 (2005).

    Article  CAS  PubMed  Google Scholar 

  30. Paik, S. et al. Gene expression and benefit of chemotherapy in women with node-negative, estrogen receptor-positive breast cancer. J. Clin. Oncol. 24, 3726–3734 (2006).

    Article  CAS  PubMed  Google Scholar 

  31. Goldstein, L. J. et al. Concurrent doxorubicin plus docetaxel is not more effective than concurrent doxorubicin plus cyclophosphamide in operable breast cancer with 0 to 3 positive axillary nodes: North American Breast Cancer Intergroup Trial E 2197. J. Clin. Oncol. 26, 4092–4099 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Goldstein, L. J. et al. Prognostic utility of the 21-gene assay in hormone receptor-positive operable breast cancer compared with classical clinicopathologic features. J. Clin. Oncol. 26, 4063–4071 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  33. Dowsett, M. et al. Prediction of risk of distant recurrence using the 21-gene recurrence score in node-negative and node-positive postmenopausal patients with breast cancer treated with anastrozole or tamoxifen: a TransATAC study. J. Clin. Oncol. 28, 1829–1834 (2010).

    Article  PubMed  Google Scholar 

  34. Hayes, D. F. Biomarker validation and testing. Mol. Oncol. 9, 960–966 (2015).

    Article  PubMed  Google Scholar 

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

    Article  PubMed  Google Scholar 

  36. Sparano, J. A. et al. Prospective validation of a 21-gene expression assay in breast cancer. N. Engl. J. Med. 373, 2005–2014 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Parker, J. S. et al. Supervised risk predictor of breast cancer based on intrinsic subtypes. J. Clin. Oncol. 27, 1160–1167 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  38. Brumbaugh, C. D., Kim, H. J., Giovacchini, M. & Pourmand, N. NanoStriDE: normalization and differential expression analysis of NanoString nCounter data. BMC Bioinformatics 12, 479 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  39. Hess, K. R. et al. Pharmacogenomic predictor of sensitivity to preoperative chemotherapy with paclitaxel and fluorouracil, doxorubicin, and cyclophosphamide in breast cancer. J. Clin. Oncol. 24, 4236–4244 (2006).

    Article  CAS  PubMed  Google Scholar 

  40. Esteva, F. J. et al. Expression of erbB/HER receptors, heregulin and P38 in primary breast cancer using quantitative immunohistochemistry. Pathol. Oncol. Res. 7, 171–177 (2001).

    Article  CAS  PubMed  Google Scholar 

  41. Nielsen, T. O. et al. A comparison of PAM50 intrinsic subtyping with immunohistochemistry and clinical prognostic factors in tamoxifen-treated estrogen receptor-positive breast cancer. Clin. Cancer Res. 16, 5222–5232 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Gnant, M. et al. Predicting distant recurrence in receptor-positive breast cancer patients with limited clinicopathological risk: using the PAM50 Risk of Recurrence score in 1478 postmenopausal patients of the ABCSG-8 trial treated with adjuvant endocrine therapy alone. Ann. Oncol. 25, 339–345 (2014).

    Article  CAS  PubMed  Google Scholar 

  43. van 't Veer, L. J. et al. Gene expression profiling predicts clinical outcome of breast cancer. Nature 415, 530–536 (2002).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  45. Drukker, C. A. et al. Long-term impact of the 70-gene signature on breast cancer outcome. Breast Cancer Res. Treat. 143, 587–592 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Buyse, M. et al. Validation and clinical utility of a 70-gene prognostic signature for women with node-negative breast cancer. J. Natl Cancer Inst. 98, 1183–1192 (2006).

    Article  CAS  PubMed  Google Scholar 

  47. Drukker, C. A. et al. A prospective evaluation of a breast cancer prognosis signature in the observational RASTER study. Int. J. Cancer 133, 929–936 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Kwaliteitsinstituut voor de Gezondheidszorg CBO VvlK. Adjuvante Systemische Therapie voor het Operabel Mammacarcinoom. Richtlijn Behandeling van het Mammacarcinoom [Dutch]. 46–70 (2004).

  49. Bueno-de-Mesquita, J. M., Sonke, G. S., van de Vijver, M. J. & Linn, S. C. Additional value and potential use of the 70-gene prognosis signature in node-negative breast cancer in daily clinical practice. Ann. Oncol. 22, 2021–2030 (2011).

    Article  CAS  PubMed  Google Scholar 

  50. Wittner, B. S. et al. Analysis of the MammaPrint breast cancer assay in a predominantly postmenopausal cohort. Clin. Cancer Res. 14, 2988–2993 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Drukker, C. A. et al. Gene expression profiling to predict the risk of locoregional recurrence in breast cancer: a pooled analysis. Breast Cancer Res. Treat. 148, 599–613 (2014).

    Article  CAS  PubMed  Google Scholar 

  52. Mook, S. et al. 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 Res. Treat. 116, 295–302 (2009).

    Article  CAS  PubMed  Google Scholar 

  53. Mook, S. et al. The 70-gene prognosis signature predicts early metastasis in breast cancer patients between 55 and 70 years of age. Ann. Oncol. 21, 717–722 (2010).

    Article  CAS  PubMed  Google Scholar 

  54. Saghatchian, M. et al. Additional prognostic value of the 70-gene signature (MammaPrint®) among breast cancer patients with 4–9 positive lymph nodes. Breast 22, 682–690 (2013).

    Article  CAS  PubMed  Google Scholar 

  55. Sapino, A. et al. MammaPrint molecular diagnostics on formalin-fixed, paraffin-embedded tissue. J. Mol. Diagn. 16, 190–197 (2014).

    Article  CAS  PubMed  Google Scholar 

  56. Mittempergher, L. et al. Gene expression profiles from formalin fixed paraffin embedded breast cancer tissue are largely comparable to fresh frozen matched tissue. PLoS ONE 6, e17163 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Cardoso, F. et al. 70-gene signature as an aid to treatment decisions in early-stage breast cancer. N. Engl. J. Med. 375, 717–729 (2016).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  59. Ma, X. J. et al. A five-gene molecular grade index and HOXB13:IL17BR are complementary prognostic factors in early stage breast cancer. Clin. Cancer Res. 14, 2601–2608 (2008).

    Article  CAS  PubMed  Google Scholar 

  60. Jerevall, P. L. et al. Prognostic utility of HOXB13:IL17BR and molecular grade index in early-stage breast cancer patients from the Stockholm trial. Br. J. Cancer 104, 1762–1769 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Habel, L. A. et al. HOXB13:IL17BR and molecular grade index and risk of breast cancer death among patients with lymph node-negative invasive disease. Breast Cancer Res. 15, R24 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Goss, P. E. et al. A randomized trial of letrozole in postmenopausal women after five years of tamoxifen therapy for early-stage breast cancer. N. Engl. J. Med. 349, 1793–1802 (2003).

    Article  CAS  PubMed  Google Scholar 

  63. National Comprehensive Cancer Network. NCCN clinical practice guidelines, breast cancer, 2016. Version 2.2016 (NCCN, 2016).

  64. Sgroi, D. C. et al. Prediction of late disease recurrence and extended adjuvant letrozole benefit by the HOXB13/IL17BR biomarker. J. Natl Cancer Inst. 105, 1036–1042 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Filipits, M. et al. A new molecular predictor of distant recurrence in ER-positive, HER2-negative breast cancer adds independent information to conventional clinical risk factors. Clin. Cancer Res. 17, 6012–6020 (2011).

    Article  CAS  PubMed  Google Scholar 

  66. Kronenwett, R. et al. Decentral gene expression analysis: analytical validation of the Endopredict genomic multianalyte breast cancer prognosis test. BMC Cancer 12, 456 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Dubsky, P. et al. EndoPredict improves the prognostic classification derived from common clinical guidelines in ER-positive, HER2-negative early breast cancer. Ann. Oncol. 24, 640–647 (2013).

    Article  CAS  PubMed  Google Scholar 

  68. Dubsky, P. et al. The EndoPredict score provides prognostic information on late distant metastases in ER+/HER2- breast cancer patients. Br. J. Cancer 109, 2959–2964 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Schmid, M. et al. Randomized trial of tamoxifen versus tamoxifen plus aminoglutethimide as adjuvant treatment in postmenopausal breast cancer patients with hormone receptor-positive disease: Austrian breast and colorectal cancer study group trial 6. J. Clin. Oncol. 21, 984–990 (2003).

    Article  CAS  PubMed  Google Scholar 

  70. Jakesz, R. et al. Switching of postmenopausal women with endocrine-responsive early breast cancer to anastrozole after 2 years' adjuvant tamoxifen: combined results of ABCSG trial 8 and ARNO 95 trial. Lancet 366, 455–462 (2005).

    Article  CAS  PubMed  Google Scholar 

  71. Martin, M. et al. Clinical validation of the EndoPredict test in node-positive, chemotherapy-treated ER+/HER2- breast cancer patients: results from the GEICAM 9906 trial. Breast Cancer Res. 16, R38 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  72. Cuzick, J. et al. Prognostic value of a combined estrogen receptor, progesterone receptor, Ki-67, and human epidermal growth factor receptor 2 immunohistochemical score and comparison with the Genomic Health recurrence score in early breast cancer. J. Clin. Oncol. 29, 4273–4278 (2011).

    Article  PubMed  Google Scholar 

  73. Polley, M. Y. et al. An international Ki67 reproducibility study. J. Natl Cancer Inst. 105, 1897–1906 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  74. Sotiriou, C. et al. Gene expression profiling in breast cancer: understanding the molecular basis of histologic grade to improve prognosis. J. Natl Cancer Inst. 98, 262–272 (2006).

    Article  CAS  PubMed  Google Scholar 

  75. Loi, S. et al. Definition of clinically distinct molecular subtypes in estrogen receptor-positive breast carcinomas through genomic grade. J. Clin. Oncol. 25, 1239–1246 (2007).

    Article  CAS  PubMed  Google Scholar 

  76. Liedtke, C. et al. Genomic grade index is associated with response to chemotherapy in patients with breast cancer. J. Clin. Oncol. 27, 3185–3191 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  77. Fan, C. et al. Concordance among gene-expression-based predictors for breast cancer. N. Engl. J. Med. 355, 560–569 (2006).

    Article  CAS  PubMed  Google Scholar 

  78. Sorlie, T. et al. Repeated observation of breast tumor subtypes in independent gene expression data sets. Proc. Natl Acad. Sci. USA 100, 8418–8423 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Chang, H. Y. et al. Robustness, scalability, and integration of a wound-response gene expression signature in predicting breast cancer survival. Proc. Natl Acad. Sci. USA 102, 3738–3743 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Chang, H. Y. et al. Gene expression signature of fibroblast serum response predicts human cancer progression: similarities between tumors and wounds. PLoS Biol. 2, E7 (2004).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  81. Wirapati, P. et al. Meta-analysis of gene expression profiles in breast cancer: toward a unified understanding of breast cancer subtyping and prognosis signatures. Breast Cancer Res. 10, R65 (2008).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  82. Dowsett, M. et al. Comparison of PAM50 risk of recurrence score with oncotype DX and IHC4 for predicting risk of distant recurrence after endocrine therapy. J. Clin. Oncol. 31, 2783–2790 (2013).

    Article  PubMed  Google Scholar 

  83. Kelly, C. M. et al. Utility of oncotype DX risk estimates in clinically intermediate risk hormone receptor-positive, HER2-normal, grade II, lymph node-negative breast cancers. Cancer 116, 5161–5167 (2010).

    Article  PubMed  Google Scholar 

  84. Kelly, C. M. et al. Agreement in risk prediction between the 21-gene recurrence score assay (Oncotype DX®) and the PAM50 Breast Cancer Intrinsic Classifier in early-stage estrogen receptor-positive breast cancer. Oncologist 17, 492–498 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  85. Sgroi, D. C. et al. Prediction of late distant recurrence in patients with oestrogen-receptor-positive breast cancer: a prospective comparison of the breast-cancer index (BCI) assay, 21-gene recurrence score, and IHC4 in the TransATAC study population. Lancet Oncol. 14, 1067–1076 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  86. Ignatiadis, M. et al. The Genomic Grade assay compared with Ki67 to determine risk of distant breast cancer recurrence. JAMA Oncol. 2, 217–224 (2016).

    Article  PubMed  Google Scholar 

  87. Breast International Group (BIG) 1–98 Collaborative Group et al. A comparison of letrozole and tamoxifen in postmenopausal women with early breast cancer. N. Engl. J. Med. 353, 2747–2757 (2005).

  88. Bartlett, J. M. et al. Comparing breast cancer multiparameter tests in the OPTIMA prelim trial: no test is more equal than the others. J. Natl Cancer Inst. 108, djw050 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  89. Sestack, I. et al. Comprehensive comparison of prognostic signatures for breast cancer in TransATAC [abstract]. 2016 San Antonio Breast Cancer Symposium (SABCS). Abstr. S6-05 (2016).

  90. Bajdik, I. A. et al. Population-based validation of the prognostic model ADJUVANT! for early breast cancer. J. Clin. Oncol. 23, 2716–2725 (2005).

    Article  PubMed  Google Scholar 

  91. Retel, V. P. et al. Prospective cost-effectiveness analysis of genomic profiling in breast cancer. Eur. J. Cancer 49, 3773–3779 (2013).

    Article  CAS  PubMed  Google Scholar 

  92. Lamond, N. W., Skedgel, C., Rayson, D., Lethbridge, L. & Younis, T. Cost-utility of the 21-gene recurrence score assay in node-negative and node-positive breast cancer. Breast Cancer Res. Treat. 133, 1115–1123 (2012).

    Article  PubMed  Google Scholar 

  93. Lyman, G. H., Cosler, L. E., Kuderer, N. M. & Hornberger, J. 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 (2007).

    Article  PubMed  Google Scholar 

  94. Chen, E., Tong, K. B. & Malin, J. L. Cost-effectiveness of 70-gene MammaPrint signature in node-negative breast cancer. Am. J. Manag. Care 16, e333–e342 (2010).

    PubMed  Google Scholar 

  95. Retel, V. P. et al. Cost-effectiveness of the 70-gene signature versus St. Gallen guidelines and Adjuvant Online for early breast cancer. Eur. J. Cancer 46, 1382–1391 (2010).

    Article  PubMed  Google Scholar 

  96. Ward, S. et al. Gene expression profiling and expanded immunohistochemistry tests to guide the use of adjuvant chemotherapy in breast cancer management: a systematic review and cost-effectiveness analysis. Health Technol. Assess. 17, 1–302 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. Rouzier, R. et al. Multigene assays and molecular markers in breast cancer: systematic review of health economic analyses. Breast Cancer Res. Treat. 139, 621–637 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. Yang, M., Rajan, S. & Issa, A. M. Cost effectiveness of gene expression profiling for early stage breast cancer: a decision-analytic model. Cancer 118, 5163–5170 (2012).

    Article  PubMed  Google Scholar 

  99. Griffin, A. M. et al. On the receiving end. V: patient perceptions of the side effects of cancer chemotherapy in 1993. Ann. Oncol. 7, 189–195 (1996).

    Article  CAS  PubMed  Google Scholar 

  100. Azim, H. A. Jr et al. Utility of prognostic genomic tests in breast cancer practice: the IMPAKT 2012 Working Group consensus statement. Ann. Oncol. 24, 647–654 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  101. Harris, L. et al. American Society of Clinical Oncology 2007 update of recommendations for the use of tumor markers in breast cancer. J. Clin. Oncol. 25, 5287–5312 (2007).

    Article  CAS  PubMed  Google Scholar 

  102. Harbeck, N. et al. Ten-year analysis of the prospective multicentre Chemo-N0 trial validates American Society of Clinical Oncology (ASCO)-recommended biomarkers uPA and PAI-1 for therapy decision making in node-negative breast cancer patients. Eur. J. Cancer 49, 1825–1835 (2013).

    Article  CAS  PubMed  Google Scholar 

  103. Ross, D. T. et al. Chemosensitivity and stratification by a five monoclonal antibody immunohistochemistry test in the NSABP B14 and B20 trials. Clin. Cancer Res. 14, 6602–6609 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  104. Desmedt, C. et al. Biological processes associated with breast cancer clinical outcome depend on the molecular subtypes. Clin. Cancer Res. 14, 5158–5165 (2008).

    Article  CAS  PubMed  Google Scholar 

  105. Loi, S. et al. Prognostic and predictive value of tumor-infiltrating lymphocytes in a phase III randomized adjuvant breast cancer trial in node-positive breast cancer comparing the addition of docetaxel to doxorubicin with doxorubicin-based chemotherapy: BIG 02–98. J. Clin. Oncol. 31, 860–867 (2013).

    Article  CAS  PubMed  Google Scholar 

  106. Loi, S. et al. Tumor infiltrating lymphocytes are prognostic in triple negative breast cancer and predictive for trastuzumab benefit in early breast cancer: results from the FinHER trial. Ann. Oncol. 25, 1544–1550 (2014).

    Article  CAS  PubMed  Google Scholar 

  107. Albain, K. S. et al. Adjuvant chemotherapy and timing of tamoxifen in postmenopausal patients with endocrine-responsive, node-positive breast cancer: a phase 3, open-label, randomised controlled trial. Lancet 374, 2055–2063 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  108. DeSantis, C., Ma, J., Bryan, L. & Jemal, A. Breast cancer statistics, 2013. CA Cancer J. Clin. 64, 52–62 (2014).

    Article  PubMed  Google Scholar 

  109. Lewis, J. H. et al. Participation of patients 65 years of age or older in cancer clinical trials. J. Clin. Oncol. 21, 1383–1389 (2003).

    Article  PubMed  Google Scholar 

  110. Wildiers, H. et al. The EORTC Cancer in the Elderly Task Force, a Protostar for EORTC's future. Eur. J. Cancer 10, 34–38 (2012).

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank Mr. Gordon Cook of the NYU Langone Medical Center for graphical design assistance with the manuscript figures.

Author information

Authors and Affiliations

  1. Division of Hematology/Oncology, Perlmutter Cancer Center, New York University Langone Medical Center, 160 East 34th Street, New York, 10016, New York, USA

    Maryann Kwa & Francisco J. Esteva

  2. Breast Cancer Research Unit, Mount Vernon Cancer Centre, Rickmansworth Road, Northwood, HA6 2RN, Middlesex, UK

    Andreas Makris

Authors
  1. Maryann Kwa
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Andreas Makris
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Francisco J. Esteva
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors made a substantial contribution to all aspects of the preparation of this manuscript.

Corresponding author

Correspondence to Francisco J. Esteva.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Related links

FURTHER INFORMATION

Adjuvant! Online

PowerPoint slides

Supplementary information

Supplementary information S1 (figure)

Current prospective clinical trials incorporating gene-expression assays. (PDF 571 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kwa, M., Makris, A. & Esteva, F. Clinical utility of gene-expression signatures in early stage breast cancer. Nat Rev Clin Oncol 14, 595–610 (2017). https://doi.org/10.1038/nrclinonc.2017.74

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nrclinonc.2017.74

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Cancer

Sign up for the Nature Briefing: Cancer newsletter — what matters in cancer research, free to your inbox weekly.

Get what matters in cancer research, free to your inbox weekly. Sign up for Nature Briefing: Cancer