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Breast Cancer Research and Treatment

, Volume 110, Issue 3, pp 417–426 | Cite as

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

  • Dmitry A. Turbin
  • Samuel Leung
  • Maggie C. U. Cheang
  • Hagen A. Kennecke
  • Kelli D. Montgomery
  • Steven McKinney
  • Diana O. Treaba
  • Niki Boyd
  • Lynn C. Goldstein
  • Sunil Badve
  • Allen M. Gown
  • Matt van de Rijn
  • Torsten O. Nielsen
  • C. Blake Gilks
  • David G. Huntsman
Preclinical Study

Abstract

Background

Estrogen receptor (ER) expression is routinely assessed by immunohistochemistry (IHC) in breast carcinoma. Our study compares visual scoring of ER in invasive breast cancer by histopathologists to quantitation of staining using a fully automated system.

Materials and methods

A tissue microarray was constructed from 4,049 cases (3,484 included in analysis) of invasive breast carcinoma linked to treatment and outcome information. Slides were scored independently by two pathologists and scores were dichotomised, with ER positivity recognized at a cut-off of >1% positive nuclei. The slides were scanned and analyzed with an Ariol automated system.

Results

Using data dichotomised as ER positive or negative, both visual and automated scores were highly consistent: there was excellent concordance between two pathologists (kappa = 0.918 (95%CI: 0.903–0.932)) and between two Ariol machines (kappa = 0.913 (95%CI: 0.897–0.928)). The prognostic significance of ER positivity was similar whether determined by pathologist or automated scoring for both the entire patient cohort and subsets of patients treated with tamoxifen alone or receiving no systemic adjuvant therapy. The optimal cut point for the automated scores using breast cancer disease-specific survival as an endpoint was >0.4% positive nuclei. The concordance between dextran-coated charcoal ER biochemical assay data and automated scores (kappa = 0.728 (95%CI: 0.69–0.75); 0.74 (95%CI: 0.71–0.77)) was similar to the concordance between biochemical assay and pathologist scores (kappa = 0.72 (95%CI: 0.70–0.75; 0.70 (95%CI: 0.67–0.72)).

Conclusion

Fully automated quantitation of ER immunostaining yields results that do not differ from human scoring against both biochemical assay and patient outcome gold standards.

Keywords

Automated scoring Breast cancer Estrogen receptor Immunohistochemistry Pathology Tissue microarray 

Notes

Acknowledgements

The study was supported in part by an unrestricted educational grant from Sanofi-Aventis, Canada and a Translational Acceleration grant from the Canadian Breast Cancer Research Alliance. TON and DGH are scholars of Michael Smith Foundation for Health Research.

Supplementary material

10549_2007_9736_MOESM1_ESM.pdf (118 kb)
Supplementary figure (PDF 118 kb)

References

  1. 1.
    Duffy MJ (2006) Estrogen receptors: role in breast cancer. Crit Rev Clin Lab Sci 43:325–347PubMedCrossRefGoogle Scholar
  2. 2.
    Cheang MC, Treaba DO, Speers CH et al (2006) 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 24:5637–5644PubMedCrossRefGoogle Scholar
  3. 3.
    Bolger N, Heffron C, Regan I et al (2006) Implementation and evaluation of a new automated interactive image analysis system. Acta Cytol 50:483–491PubMedGoogle Scholar
  4. 4.
    Camp RL, Dolled-Filhart M, Rimm DL (2004) X-tile: a new bio-informatics tool for biomarker assessment and outcome-based cut-point optimization. Clin Cancer Res 10:7252–7259PubMedCrossRefGoogle Scholar
  5. 5.
    Taylor CR, Levenson RM (2006) Quantification of immunohistochemistry-issues concerning methods, utility and semiquantitative assessment II. Histopathology 49:411–424PubMedCrossRefGoogle Scholar
  6. 6.
    Walker RA (2006) Quantification of immunohistochemistry-issues concerning methods, utility and semiquantitative assessment I. Histopathology 49:406–410PubMedCrossRefGoogle Scholar
  7. 7.
    BVD/FOGRA (1992) Manual for standardization of the offset printing process. WiesbadenGoogle Scholar
  8. 8.
    Insight into images: principles and practices for segmentation, registration, and image analysis (2004). A.K. Peters Ltd., Wellesey, MAGoogle Scholar
  9. 9.
    Byrne A, Hilbert DR (2003) Color realism and color science. Behav Brain Sci 26:3–21; discussion 22–63PubMedCrossRefGoogle Scholar
  10. 10.
    Gegenfurtner KR (2003) Cortical mechanisms of colour vision. Nat Rev Neurosci 4:563–572PubMedCrossRefGoogle Scholar
  11. 11.
    McLelland D, Fuller LU (2005) Photoshop CS2 bible. Wiley Publishing Inc., Hoboken, NJGoogle Scholar
  12. 12.
    Rinner O, Gegenfurtner KR (2000) Time course of chromatic adaptation for color appearance and discrimination. Vision Res 40:1813–1826PubMedCrossRefGoogle Scholar
  13. 13.
    Wen C-H, Lee J-J (2000) Design and production of color calibration targets for digital input devices. In: Input/output and imaging technologies II:4080. Taipei, Taiwan, pp 148–158Google Scholar
  14. 14.
    Greene GL, Nolan C, Engler JP et al (1980) Monoclonal antibodies to human estrogen receptor. Proc Natl Acad Sci USA 77:5115–5119PubMedCrossRefGoogle Scholar
  15. 15.
    King WJ, Greene GL (1984) Monoclonal antibodies localize oestrogen receptor in the nuclei of target cells. Nature 307:745–747PubMedCrossRefGoogle Scholar
  16. 16.
    Underwood JC (1983) Oestrogen receptors in human breast cancer: review of histopathological correlations and critique of histochemical methods. Diagn Histopathol 6:1–22PubMedGoogle Scholar
  17. 17.
    Aziz DC, Barathur RB (1994) Quantitation and morphometric analysis of tumors by image analysis. J Cell Biochem Suppl 19:120–125PubMedGoogle Scholar
  18. 18.
    Esteban JM, Ahn C, Battifora H et al (1994) Quantitative immunohistochemical assay for hormonal receptors: technical aspects and biological significance. J Cell Biochem Suppl 19:138–145PubMedGoogle Scholar
  19. 19.
    Schultz DS, Katz RL, Patel S et al (1992) Comparison of visual and CAS-200 quantitation of immunocytochemical staining in breast carcinoma samples. Anal Quant Cytol Histol 14:35–40PubMedGoogle Scholar
  20. 20.
    Kononen J, Bubendorf L, Kallioniemi A et al (1998) Tissue microarrays for high-throughput molecular profiling of tumor specimens. Nat Med 4:844–847PubMedCrossRefGoogle Scholar
  21. 21.
    Makretsov N, Gilks CB, Coldman AJ et al (2003) Tissue microarray analysis of neuroendocrine differentiation and its prognostic significance in breast cancer. Hum Pathol 34:1001–1008PubMedCrossRefGoogle Scholar
  22. 22.
    Turbin DA, Cheang MC, Bajdik CD et al (2006) MDM2 protein expression is a negative prognostic marker in breast carcinoma. Mod Pathol 19:69–74PubMedCrossRefGoogle Scholar
  23. 23.
    Liu CL, Prapong W, Natkunam Y et al (2002) Software tools for high-throughput analysis and archiving of immunohistochemistry staining data obtained with tissue microarrays. Am J Pathol 161:1557–1565PubMedGoogle Scholar
  24. 24.
    Liu CL, Montgomery KD, Natkunam Y et al (2005) TMA-Combiner, a simple software tool to permit analysis of replicate cores on tissue microarrays. Mod Pathol 18:1641–1648PubMedGoogle Scholar
  25. 25.
    Ng TL, Gown AM, Barry TS et al (2005) Nuclear beta-catenin in mesenchymal tumors. Mod Pathol 18:68–74PubMedCrossRefGoogle Scholar
  26. 26.
    de las Mulas JM, van Niel M, Millan Y et al (2000) Immunohistochemical analysis of estrogen receptors in feline mammary gland benign and malignant lesions: comparison with biochemical assay. Domest Anim Endocrinol 18:111–125CrossRefGoogle Scholar
  27. 27.
    Magne N, Toillon RA, Castadot P et al (2006) Different clinical impact of estradiol receptor determination according to the analytical method: A study on 1940 breast cancer patients over a period of 16 consecutive years. Breast Cancer Res Treat 95:179–184PubMedCrossRefGoogle Scholar
  28. 28.
    Costa SD, Lange S, Klinga K et al (2002) Factors influencing the prognostic role of oestrogen and progesterone receptor levels in breast cancer – results of the analysis of 670 patients with 11 years of follow-up. Eur J Cancer 38:1329–1334PubMedCrossRefGoogle Scholar
  29. 29.
    Franklin WA, Bibbo M, Doria MI et al (1987) Quantitation of estrogen receptor content and Ki-67 staining in breast carcinoma by the microTICAS image analysis system. Anal Quant Cytol Histol 9:279–286PubMedGoogle Scholar
  30. 30.
    Gil J, Wu HS (2003) Applications of image analysis to anatomic pathology: realities and promises. Cancer Invest 21:950–959PubMedCrossRefGoogle Scholar
  31. 31.
    Rojo MG, Garcia GB, Mateos CP et al (2006) Critical comparison of 31 commercially available digital slide systems in pathology. Int J Surg Pathol 14:285–305PubMedCrossRefGoogle Scholar
  32. 32.
    Cregger M, Berger AJ, Rimm DL (2006) Immunohistochemistry and quantitative analysis of protein expression. Arch Pathol Lab Med 130:1026–1030PubMedGoogle Scholar
  33. 33.
    McCabe A, Dolled-Filhart M, Camp RL et al (2005) Automated quantitative analysis (AQUA) of in situ protein expression, antibody concentration, and prognosis. J Natl Cancer Inst 97:1808–1815PubMedCrossRefGoogle Scholar
  34. 34.
    Rimm DL (2006) What brown cannot do for you. Nat Biotechnol 24:914–916PubMedCrossRefGoogle Scholar
  35. 35.
    Harvey JM, Clark GM, Osborne CK et al (1999) 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 17:1474–1481PubMedGoogle Scholar
  36. 36.
    Diaz LK, Sahin A, Sneige N (2004) Interobserver agreement for estrogen receptor immunohistochemical analysis in breast cancer: a comparison of manual and computer-assisted scoring methods. Ann Diagn Pathol 8:23–27PubMedCrossRefGoogle Scholar
  37. 37.
    Hasegawa T, Yamamoto S, Matsuno Y (2002) Quantitative immunohistochemical evaluation of MIB-1 labeling index in adult soft-tissue sarcomas by computer-assisted image analysis. Pathol Int 52:433–437PubMedCrossRefGoogle Scholar
  38. 38.
    Kirkegaard T, Edwards J, Tovey S et al (2006) Observer variation in immunohistochemical analysis of protein expression, time for a change? Histopathology 48:787–794PubMedCrossRefGoogle Scholar
  39. 39.
    Lorinc E, Jakobsson B, Landberg G et al (2005) Ki67 and p53 immunohistochemistry reduces interobserver variation in assessment of Barrett’s oesophagus. Histopathology 46:642–648PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  • Dmitry A. Turbin
    • 1
    • 2
    • 3
  • Samuel Leung
    • 1
    • 2
  • Maggie C. U. Cheang
    • 1
    • 2
  • Hagen A. Kennecke
    • 1
    • 2
  • Kelli D. Montgomery
    • 4
  • Steven McKinney
    • 3
  • Diana O. Treaba
    • 6
  • Niki Boyd
    • 3
  • Lynn C. Goldstein
    • 6
  • Sunil Badve
    • 5
  • Allen M. Gown
    • 6
  • Matt van de Rijn
    • 7
  • Torsten O. Nielsen
    • 1
    • 2
  • C. Blake Gilks
    • 1
    • 2
  • David G. Huntsman
    • 1
    • 2
    • 3
  1. 1.Genetic Pathology Evaluation CentreVancouver Coastal Health Research InstituteVancouverCanada
  2. 2.British Columbia Cancer AgencyUniversity of British ColumbiaVancouverCanada
  3. 3.Centre for Translational and Applied GenomicsVancouverCanada
  4. 4.Stanford University Medical CenterStanfordUSA
  5. 5.Indiana University HospitalIndianapolisUSA
  6. 6.PhenoPath LaboratoriesSeattleUSA
  7. 7.Stanford University Medical CenterStanfordUSA

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