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  • Review Article
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Practical implications of gene-expression-based assays for breast oncologists

Nature Reviews Clinical Oncology volume 9, pages 48–57 (2012)Cite this article

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Abstract

Gene-expression profiling has had a considerable impact on our understanding of breast cancer biology, and more recently on clinical care. Two statistical approaches underlie these advancements. Supervised analyses have led to the development of gene-expression signatures designed to predict survival and/or treatment response, which has resulted in the development of new clinical assays. Unsupervised analyses have identified numerous biological signatures including signatures of cell type of origin, signaling pathways, and of cellular proliferation. Included within these biological signatures are the molecular subtypes known as the 'intrinsic' subtypes of breast cancer. This classification has expanded our appreciation of the heterogeneity of breast cancer and has provided a way to sub-classify the disease in a manner that might have clinical utility. In this Review, we discuss the clinical utility of gene-expression-based assays and their technical potential as clinical tools vis-a-vis the performance of breast cancer biomarkers that are the current standard of care.

Key Points

  • Gene-expression-based assays provide independent prognostic information beyond standard clinical-pathological variables; however, tumor and nodal stage remain important and must be taken into account in the final prognostic assessment

  • Gene-expression-based assays identify patients with ER-positive node-negative disease at low risk of relapse after treatment with hormonal therapy and who might be spared from chemotherapy

  • Clinical use of gene-expression-based assays for the prediction of chemotherapy benefit in node-positive disease, and in ER-negative disease, is currently experimental

  • Current methodologies for ER, PR and HER2 testing might benefit from additional protocol standardizations, but may still be less reproducible than standardized gene-expression-based assays

  • Non-standardized research-based identification of the intrinsic subtypes shows concordance values equivalent to current clinical testing for histological grade, ER, PR and HER2

  • For daily clinical use, we recommend the highest level of reproducibility/concordance (Level 1), which will only be achieved for pathology and gene-expression-based tests by using a single platform and standardized protocol

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Figure 1: Identification of tumor individuality using global gene-expression analyses.

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References

  1. Jemal, A., Siegel, R., Xu, J. & Ward, E. Cancer Statistics, 2010. CA Cancer J. Clin. 60, 277–300 (2010).

    Article  PubMed  Google Scholar 

  2. NCCM Clinical Practicce Guidelines in Oncology™. Breast Cancer [online], (2011).

  3. 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 

  4. Goldhirsch, A. et al. Strategies for subtypes—dealing with the diversity of breast cancer: highlights of the St. Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2011. Ann. Oncol. 22, 1736–1747 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. 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 

  6. 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 

  7. Prat, A. et al. Concordance among gene-expression-based predictors for ER-positive breast cancer treated with adjuvant tamoxifen [abstract]. J. Clin. Oncol. 29 (Suppl.), a502 (2011).

    Article  Google Scholar 

  8. Kim, C. & Paik, S. Gene-expression-based prognostic assays for breast cancer. Nat. Rev. Clin. Oncol. 7, 340–347 (2010).

    Article  CAS  PubMed  Google Scholar 

  9. Sparano, J. A. & Paik, S. Development of the 21-gene assay and its application in clinical practice and clinical trials. J. Clin. Oncol. 26, 721–728 (2008).

    Article  PubMed  Google Scholar 

  10. Fisher, B. et al. Tamoxifen and chemotherapy for lymph node-negative, estrogen receptor-positive breast cancer. J. Natl Cancer Inst. 89, 1673–1682 (1997).

    Article  CAS  PubMed  Google Scholar 

  11. 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 

  12. Habel, L. A. et al. A population-based study of tumor gene expression and risk of breast cancer death among lymph node-negative patients. Breast Cancer Res. 8, R25 (2006).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  14. 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 

  15. 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 

  16. Cardoso, F. et al. Clinical application of the 70-gene profile: the MINDACT trial. J. Clin. Oncol. 26, 729–735 (2008).

    Article  PubMed  Google Scholar 

  17. 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 

  18. 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 

  19. 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 

  20. 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 

  21. 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 

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

    Article  CAS  PubMed  Google Scholar 

  23. 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 

  24. Sørlie, 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  PubMed  PubMed Central  Google Scholar 

  25. Perou, C. M. et al. Distinctive gene expression patterns in human mammary epithelial cells and breast cancers. Proc. Natl Acad. Sci. USA 96, 9212–9217 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Millikan, R. C. et al. Epidemiology of basal-like breast cancer. Breast Cancer Res. Treat. 109, 123–139 (2008).

    Article  PubMed  Google Scholar 

  27. Fan, C. et al. Building prognostic models for breast cancer patients using clinical variables and hundreds of gene expression signatures. BMC Med. Genomics 4, 3 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  28. 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 

  29. Martin, M. et al. Genomic predictors of response to doxorubicin versus docetaxel in primary breast cancer. Breast Cancer Res. Treat. 128, 127–136 (2011).

    Article  CAS  PubMed  Google Scholar 

  30. Glück, S. et al. TP53 genomics predict higher clinical and pathologic tumor response in operable early-stage breast cancer treated with docetaxel-capecitabine ± trastuzumab. Breast Cancer Res. Treat. doi:10.1007/s10549-011-1412-7.

  31. Carey, L. A. et al. The triple negative paradox: primary tumor chemosensitivity of breast cancer subtypes. Clin. Cancer Res. 13, 2329–2334 (2007).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  33. 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 

  34. Geiss, G. K. et al. Direct multiplexed measurement of gene expression with color-coded probe pairs. Nat. Biotechnol. 26, 317–325 (2008).

    Article  CAS  PubMed  Google Scholar 

  35. Prat, A. & Perou, C. M. Deconstructing the molecular portraits of breast cancer. Mol. Oncol. 5, 5–23 (2011).

    Article  CAS  PubMed  Google Scholar 

  36. 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 

  37. Nielsen, T. O. et al. Immunohistochemical and clinical characterization of the basal-like subtype of invasive breast carcinoma. Clin. Cancer Res. 10, 5367–3574 (2004).

    Article  CAS  PubMed  Google Scholar 

  38. Cheang, M. C. et al. Basal-like breast cancer defined by five biomarkers has superior prognostic value than triple-negative phenotype. Clin. Cancer Res. 14, 1368–1368 (2008).

    Article  CAS  PubMed  Google Scholar 

  39. Alexe, G. et al. High expression of lymphocyte-associated genes in node-negative HER2+ breast cancers correlates with lower recurrence rates. Cancer Res. 67, 10669–10669 (2007).

    Article  CAS  PubMed  Google Scholar 

  40. Teschendorff, A. E. et al.: An immune response gene expression module identifies a good prognosis subtype in estrogen receptor negative breast cancer. Genome Biol. 8, R157 (2007).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  41. Lo, S. S. et al. Prospective multicenter study of the impact of the 21-gene recurrence score assay on medical oncologist and patient adjuvant breast cancer treatment selection. J. Clin. Oncol. 28, 1671–1676 (2010).

    Article  PubMed  Google Scholar 

  42. Geffen, D. B. et al. The impact of the 21-gene recurrence score assay on decision making about adjuvant chemotherapy in early-stage estrogen-receptor-positive breast cancer in an oncology practice with a unified treatment policy. Ann. Oncol. 22, 2381–2386 (2011).

    Article  CAS  PubMed  Google Scholar 

  43. Tsoi, D. T., Inoue, M., Kelly, C. M., Verma, S. & Pritchard, K. I. Cost-effectiveness analysis of recurrence score-guided treatment using a 21-gene assay in early breast cancer. Oncologist 15, 457–465 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  44. Hornberger, J., Cosler, L. & Lyman, G. Economic analysis of targeting chemotherapy using a 21-gene RT-PCR assay in lymph-node-negative, estrogen-receptor-positive, early-stage breast cancer. Am. J. Manag. Care 11, 313–324 (2005).

    PubMed  Google Scholar 

  45. Albain, K. S. et al. 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. Lancet Oncol. 11, 55–65 (2010).

    Article  CAS  PubMed  Google Scholar 

  46. 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 

  47. 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 

  48. Parker, J. et al. Breast cancer molecular subtypes predict response to anthracycline/taxane-based chemotherapy [abstract]. Cancer Res. 69 (Suppl. 3), a2019 (2009).

    Article  Google Scholar 

  49. 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 

  50. Straver, M. E. et al. The 70-gene signature as a response predictor for neoadjuvant chemotherapy in breast cancer. Breast Cancer Res. Treat. 119, 551–558 (2010).

    Article  PubMed  Google Scholar 

  51. Gianni, L. et al. Gene expression profiles in paraffin-embedded core biopsy tissue predict response to chemotherapy in women with locally advanced breast cancer. J. Clin. Oncol. 23, 7265–7277 (2005).

    Article  CAS  PubMed  Google Scholar 

  52. Ellis, M. J. et al. Outcome prediction for estrogen receptor-positive breast cancer based on postneoadjuvant endocrine therapy tumor characteristics. J. Natl Cancer Inst. 100, 1380–1388 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Ellis, M. J. et al. Letrozole is more effective neoadjuvant endocrine therapy than tamoxifen for ErbB-1- and/or ErbB-2-positive, estrogen receptor-positive primary breast cancer: evidence from a phase III randomized trial. J. Clin. Oncol. 19, 3808–3816 (2001).

    Article  CAS  PubMed  Google Scholar 

  54. Ellis, M. J. et al. Randomized phase II neoadjuvant comparison between letrozole, anastrozole, and exemestane for postmenopausal women with estrogen receptor-rich stage 2 to 3 breast cancer: clinical and biomarker outcomes and predictive value of the baseline PAM50-based intrinsic subtype--ACOSOG Z1031. J. Clin. Oncol. 29, 2342–2349 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. 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 

  56. Cronin, M. et al. Analytical validation of the Oncotype DX genomic diagnostic test for recurrence prognosis and therapeutic response prediction in node-negative, estrogen receptor-positive breast cancer. Clin. Chem. 53, 1084–1091 (2007).

    Article  CAS  PubMed  Google Scholar 

  57. Glas, A. M. et al. Converting a microarray breast cancer signature into a high throughput diagnostic test. BMC Genomics 7, 278 (2006).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  58. Wolff, A. C. et al. American Society of Clinical Oncology/College of American Pathologists guideline recommendations for human epidermal growth factor receptor 2 testing in breast cancer. J. Clin. Oncol. 25, 118–145 (2007).

    Article  CAS  PubMed  Google Scholar 

  59. Hammond, M. E., Hayes, D. F., Wolff, A. C., Mangu, P. B. & Temin, S. 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 

  60. Cohen, J. A coefficient of agreement for nominal scales. Educ. Psychol. Meas. 20, 37–46 (1960).

    Article  Google Scholar 

  61. Randolph, J. Free-marginal multirater kappa: an alternative to Fleiss' fixed-marginal multirater kappa. Joensuu University Learning and Instruction Symp. October 14–15 (2005).

    Google Scholar 

  62. Mackay, A. et al. Microarray-based class discovery for molecular classification of breast cancer: analysis of interobserver agreement. J. Natl Cancer Inst. 103, 662–673 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Weigelt, B. et al. Breast cancer molecular profiling with single sample predictors: a retrospective analysis. Lancet Oncol. 11, 339–349 (2010).

    Article  CAS  PubMed  Google Scholar 

  64. Turbin, D. A. et al. Automated quantitative analysis of estrogen receptor expression in breast carcinoma does not differ from expert pathologist scoring: a tissue microarray study of 3,484 cases. Breast Cancer Res. Treat. 110, 417–426 (2008).

    Article  CAS  PubMed  Google Scholar 

  65. Bueno-de-Mesquita, J. M. et al. The impact of inter-observer variation in pathological assessment of node-negative breast cancer on clinical risk assessment and patient selection for adjuvant systemic treatment. Ann. Oncol. 21, 40–47 (2010).

    Article  CAS  PubMed  Google Scholar 

  66. Mudduwa, L. & Liyanage, T. Immunohistochemical assessment of hormone receptor status of breast carcinoma: interobserver variation of the quick score. Indian J. Med. Sci. 63, 21–27 (2009).

    Article  PubMed  Google Scholar 

  67. Wells, C. A. et al. Consistency of staining and reporting of oestrogen receptor immunocytochemistry within the European Union—an inter-laboratory study. Virchows Arch. 445, 119–128 (2004).

    Article  CAS  PubMed  Google Scholar 

  68. Badve, S. S. et al. Estrogen- and progesterone-receptor status in ECOG 2197: comparison of immunohistochemistry by local and central laboratories and quantitative reverse transcription polymerase chain reaction by central laboratory. J. Clin. Oncol. 26, 2473–2481 (2008).

    Article  PubMed  Google Scholar 

  69. Collins, L. C., Marotti, J. D., Baer, H. J. & Tamimi, R. M. Comparison of estrogen receptor results from pathology reports with results from central laboratory testing. J. Natl Cancer Inst. 100, 218–221 (2008).

    Article  CAS  PubMed  Google Scholar 

  70. Parker, R. L. et al. Assessment of interlaboratory variation in the immunohistochemical determination of estrogen receptor status using a breast cancer tissue microarray. Am. J. Clin. Pathol. 117, 723–728 (2002).

    Article  PubMed  Google Scholar 

  71. Rydén, L. et al. Reproducibility of human epidermal growth factor receptor 2 analysis in primary breast cancer: a national survey performed at pathology departments in Sweden. Acta Oncol. 48, 860–866 (2009).

    Article  PubMed  Google Scholar 

  72. Press, M. F. et al. Diagnostic evaluation of HER-2 as a molecular target: an assessment of accuracy and reproducibility of laboratory testing in large, prospective, randomized clinical trials. Clin. Cancer Res. 11, 6598–6607 (2005).

    Article  CAS  PubMed  Google Scholar 

  73. Umemura, S. et al. What causes discrepancies in HER2 testing for breast cancer? Am. J. Clin. Pathol. 130, 883–891 (2008).

    Article  PubMed  Google Scholar 

  74. Diaz, L. K., Gupta, R., Kidwai, N., Sneige, N. & Wiley, E. L. The use of TMA for interlaboratory validation of FISH testing for detection of HER2 gene amplification in breast cancer. J. Histochem. Cytochem. 52, 501–507 (2004).

    Article  CAS  PubMed  Google Scholar 

  75. Di Palma, S. et al. A quality assurance exercise to evaluate the accuracy and reproducibility of chromogenic in situ hybridisation for HER2 analysis in breast cancer. J. Clin. Pathol. 61, 757–760 (2008).

    Article  CAS  PubMed  Google Scholar 

  76. Bartlett, J. M. et al. Evaluating HER2 amplification and overexpression in breast cancer. J. Pathol. 195, 422–428 (2001).

    Article  CAS  PubMed  Google Scholar 

  77. Turashvili, G. et al. Inter-observer reproducibility of HER2 immunohistochemical assessment and concordance with fluorescent in situ hybridization (FISH): pathologist assessment compared to quantitative image analysis. BMC Cancer 9, 165 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  78. van der Vegt, B., de Bock, G. H., Bart, J., Zwartjes, N. G. & Wesseling, J. Validation of the 4B5 rabbit monoclonal antibody in determining Her2/neu status in breast cancer. Mod. Pathol. 22, 879–886 (2009).

    Article  CAS  PubMed  Google Scholar 

  79. Tsuda, H. et al. HER2 testing on core needle biopsy specimens from primary breast cancers: interobserver reproducibility and concordance with surgically resected specimens. BMC Cancer 10, 534 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  80. Arena, V., Pennacchia, I., Monego, G., Carbone, A. & Capelli, A. Fluorescent in situ hybridization as a primary test for HER2 status in breast cancer: controversies. J. Clin. Oncol. 28, e83–e84 (2009).

    Article  Google Scholar 

  81. Sauter, G., Lee, J., Bartlett, J. M., Slamon, D. J. & Presse, M. J. Guidelines for human epidermal growth factor receptor 2 testing: biologic and methodologic considerations. J. Clin. Oncol. 27, 1323–1333 (2009).

    Article  CAS  PubMed  Google Scholar 

  82. Perez, E. A. et al. HER2 testing by local, central, and reference laboratories in specimens from the North Central Cancer Treatment Group N9831 intergroup adjuvant trial. J. Clin. Oncol. 24, 3032–3038 (2006).

    Article  PubMed  Google Scholar 

  83. Paik, S. et al. Real-world performance of HER2 testing--National Surgical Adjuvant Breast and Bowel Project experience. J. Natl Cancer Inst. 94, 852–854 (2002).

    Article  PubMed  Google Scholar 

  84. Regan, M. M. et al. Re-evaluating adjuvant breast cancer trials: assessing hormone receptor status by immunohistochemical versus extraction assays. J. Natl Cancer Inst. 98, 1571–1581 (2006).

    Article  CAS  PubMed  Google Scholar 

  85. Cheang, M. C. et al. Immunohistochemical detection using the new rabbit monoclonal antibody SP1 of estrogen receptor in breast cancer is superior to mouse monoclonal antibody 1D5 in predicting survival. J. Clin. Oncol. 24, 5637–5644 (2006).

    Article  CAS  PubMed  Google Scholar 

  86. Harvey, J. M., Clark, G. M., Osborne, C. K. & Allred, D. C. Estrogen receptor status by immunohistochemistry is superior to the ligand-binding assay for predicting response to adjuvant endocrine therapy in breast cancer. J. Clin. Oncol. 17, 1474–1481 (1999).

    Article  CAS  PubMed  Google Scholar 

  87. Nassar, A., Cohen, C. & Siddiqui, M. Estimation of hormone receptor status and HER2 in cytologic cell blocks from breast cancer using the novel rabbit monoclonal antibodies (SP1, SP2, and SP3). Diagn. Cytopathol. 37, 865–870 (2009).

    Article  PubMed  Google Scholar 

  88. Rhodes, A., Jasani, B., Barnes, D. M., Bobrow, L. G. & Miller, K. D. Reliability of immunohistochemical demonstration of oestrogen receptors in routine practice: interlaboratory variance in the sensitivity of detection and evaluation of scoring systems. J. Clin. Pathol. 53, 125–130 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Viale, G. et al. Prognostic and predictive value of centrally reviewed expression of estrogen and progesterone receptors in a randomized trial comparing letrozole and tamoxifen adjuvant therapy for postmenopausal early breast cancer: BIG 1–98. J. Clin. Oncol. 25, 3846–3852 (2007).

    Article  PubMed  Google Scholar 

  90. Regitnig, P. et al. Quality assurance for detection of estrogen and progesterone receptors by immunohistochemistry in Austrian pathology laboratories. Virchows Arch. 441, 328–334 (2002).

    Article  CAS  PubMed  Google Scholar 

  91. Anderson, T. J., Alexander, F. E., Lamb, J., Smith, A. & Forrest, A. P. Pathology characteristics that optimize outcome prediction of a breast screening trial. Br. J. Cancer 83, 487–492 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. Frierson, H. F. Jr et al. Interobserver reproducibility of the Nottingham modification of the Bloom and Richardson histologic grading scheme for infiltrating ductal carcinoma. Am. J. Clin. Pathol. 103, 195–198 (1995).

    Article  PubMed  Google Scholar 

  93. Ellis, I. O. et al. Impact of a national external quality assessment scheme for breast pathology in the UK. J. Clin. Pathol. 59, 138–145 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. Longacre, T. A. et al. Interobserver agreement and reproducibility in classification of invasive breast carcinoma: an NCI breast cancer family registry study. Mod. Pathol. 19, 195–207 (2005).

    Article  Google Scholar 

  95. Boiesen, P. et al. Histologic grading in breast cancer--reproducibility between seven pathologic departments. South Sweden Breast Cancer Group. Acta Oncol. 39, 41–45 (2000).

    Article  CAS  PubMed  Google Scholar 

  96. Sloane, J. P. et al. Consistency achieved by 23 European pathologists from 12 countries in diagnosing breast disease and reporting prognostic features of carcinomas. Virchows Arch. 434, 3–10 (1999).

    Article  CAS  PubMed  Google Scholar 

  97. Adams, A. L., Chhieng, D. C., Bell, W. C., Winokur, T. & Hameed, O. Histologic grading of invasive lobular carcinoma: does use of a 2-tiered nuclear grading system improve interobserver variability? Ann. Diagn. Pathol. 13, 223–225 (2009).

    Article  PubMed  Google Scholar 

  98. Tsuda, H. et al. Evaluation of the interobserver agreement in the number of mitotic figures of breast carcinoma as simulation of quality monitoring in the Japan National Surgical Adjuvant Study of Breast Cancer (NSAS-BC) protocol. Jpn J. Cancer Res. 91, 451–457 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  99. Reed, W. et al. The prognostic value of p53 and c-erb B-2 immunostaining is overrated for patients with lymph node negative breast carcinoma. Cancer 88, 804–813 (2000).

    Article  CAS  PubMed  Google Scholar 

  100. Rakha, E. et al. Breast cancer prognostic classification in the molecular era: the role of histological grade. Breast Cancer Res. 12, 207 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  101. Dybdal, N. et al. Determination of HER2 gene amplification by fluorescence in situ hybridization and concordance with the clinical trials immunohistochemical assay in women with metastatic breast cancer evaluated for treatment with trastuzumab. Breast Cancer Res. Treat. 93, 3–11 (2005).

    Article  CAS  PubMed  Google Scholar 

  102. Press, M. F. et al. HER-2 gene amplification, HER-2 and epidermal growth factor receptor mRNA and protein expression, and lapatinib efficacy in women with metastatic breast cancer. Clin. Cancer Res. 14, 7861–7870 (2008).

    Article  CAS  PubMed  Google Scholar 

  103. Dressler, L. G. et al. Comparison of HER2 status by fluorescence in situ hybridization and immunohistochemistry to predict benefit from dose escalation of adjuvant doxorubicin-based therapy in node-positive breast cancer patients. J. Clin. Oncol. 23, 4287–4297 (2005).

    Article  CAS  PubMed  Google Scholar 

  104. Powell, W. C. et al. A new rabbit monoclonal antibody (4B5) for the immunohistochemical (IHC) determination of the HER2 status in breast cancer: comparison with CB11, fluorescence in situ hybridization (FISH), and interlaboratory reproducibility. Appl. Immunohistochem. Mol. Morphol. 15, 94–102 (2007).

    Article  CAS  PubMed  Google Scholar 

  105. Noske, A. et al. Comparison of different approaches for assessment of HER2 expression on protein and mRNA level: prediction of chemotherapy response in the neoadjuvant GeparTrio trial (NCT00544765). Breast Cancer Res. Treat. 126, 109–117 (2011).

    Article  CAS  PubMed  Google Scholar 

  106. Baehner, F. L. et al. 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. J. Clin. Oncol. 28, 4300–4306 (2010).

    Article  PubMed  Google Scholar 

  107. Perou, C. M., Parker, J. S., Prat, A., Ellis, M. J. & Bernard, P. S. Clinical implementation of the intrinsic subtypes of breast cancer. Lancet Oncol. 11, 718–719 (2010).

    Article  PubMed  Google Scholar 

  108. Sørlie, T. et al. The importance of gene-centring microarray data. Lancet Oncol. 11, 719–720 (2010).

    Article  PubMed  Google Scholar 

  109. Dunning, M. J. et al. The importance of platform annotation in interpreting microarray data. Lancet Oncol. 11, 717–717 (2010).

    Article  PubMed  Google Scholar 

  110. Pillai, R. et al. Validation and reproducibility of a microarray-based gene expression test for tumor identification in formalin-fixed, paraffin-embedded specimens. J. Mol. Diagn. 13, 48–56 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  111. Hu, Z. et al. The molecular portraits of breast tumors are conserved across microarray platforms. BMC Genomics 7, 96 (2006).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  112. Harrell, J. C. et al. Genomic analysis identifies unique signatures predictive of brain, lung, and liver relapse. Breast Cancer Res. Treat. doi:10.1007/s10549-011-1619-7.

  113. Ding, L. et al. Genome remodelling in a basal-like breast cancer metastasis and xenograft. Nature 464, 999–1005 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  114. Shah, S. P. et al. Mutational evolution in a lobular breast tumour profiled at single nucleotide resolution. Nature 461, 809–813 (2009).

    Article  CAS  PubMed  Google Scholar 

  115. Nunes, C. B. et al. Comparative analysis of six different antibodies against Her2 including the novel rabbit monoclonal antibody (SP3) and chromogenic in situ hybridisation in breast carcinomas. J. Clin. Pathol. 61, 934–938 (2008).

    Article  CAS  PubMed  Google Scholar 

  116. Moelans, C. B. et al. Validation of a fully automated HER2 staining kit in breast cancer. Cell Oncol. 32, 149–155 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  117. Tubbs, R. R. et al. Discrepancies in clinical laboratory testing of eligibility for trastuzumab therapy: apparent immunohistochemical false-positives do not get the message. J. Clin. Oncol. 19, 2714–2721 (2001).

    Article  CAS  PubMed  Google Scholar 

  118. Arihiro, K. et al. Comparison of evaluations for hormone receptors in breast carcinoma using two manual and three automated immunohistochemical assays. Am. J. Clin. Pathol. 127, 356–365 (2007).

    Article  PubMed  Google Scholar 

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Acknowledgements

We thank Cynthia Ma for reviewing this manuscript. This work was supported by funds from the NCI Breast SPORE program (P50-CA58223-09A1), by RO1-CA138255, by the Breast Cancer Research Foundation and the V Foundation for Cancer Research. A. Prat is affiliated to the Medicine PhD program of the Autonomous University of Barcelona (UAB), Spain.

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Authors and Affiliations

  1. Department of Genetics and Pathology, Lineberger Comprehensive Cancer Center, University of North Carolina, 450 West Drive, Chapel Hill, 27599, NC, USA

    Aleix Prat & Charles M. Perou

  2. Department of Medicine, Siteman Comprehensive Cancer Center, Washington University, 660 South Euclid Avenue, Saint Louis, 63110, MI, USA

    Matthew J. Ellis

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  1. Aleix Prat
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  3. Charles M. Perou
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All authors researched the data and wrote the article, provided substantial contributions of content, and reviewed and edited the manuscript before submission.

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Correspondence to Charles M. Perou.

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Competing interests

M. J. Ellis and C. M. Perou are stockholders of BioClassifier LLC. which has licensed the PAM50 assay. CMP and MJE are inventors on a pending patent application for the PAM50 assay. A. Prat declares no competing interests.

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Prat, A., Ellis, M. & Perou, C. Practical implications of gene-expression-based assays for breast oncologists. Nat Rev Clin Oncol 9, 48–57 (2012). https://doi.org/10.1038/nrclinonc.2011.178

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