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A new tool for technical standardization of the Ki67 immunohistochemical assay

Modern Pathology volume 34, pages 1261–1270 (2021)Cite this article

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Abstract

Ki67, a nuclear proliferation-related protein, is heavily used in anatomic pathology but has not become a companion diagnostic or a standard-of-care biomarker due to analytic variability in both assay protocols and interpretation. The International Ki67 Working Group in breast cancer has published and has ongoing efforts in the standardization of the interpretation of Ki67, but they have not yet assessed technical issues of assay production representing multiple sources of variation, including antibody clones, antibody formats, staining platforms, and operators. The goal of this work is to address these issues with a new standardization tool. We have developed a cell line microarray system in which mixes of human Karpas 299 or Jurkat cells (Ki67+) with Sf9 (Spodoptera frugiperda) (Ki67-) cells are present in incremental standardized ratios. To validate the tool, six different antibodies, including both ready-to-use and concentrate formats from six vendors, were used to measure Ki67 proliferation indices using IHC protocols for manual (bench-top) and automated platforms. The assays were performed by three different laboratories at Yale and analyzed using two image analysis software packages, including QuPath and Visiopharm. Results showed statistically significant differences in Ki67 reactivity between each antibody clone. However, subsets of Ki67 assays using three clones performed in three different labs show no significant differences. This work shows the need for analytic standardization of the Ki67 assay and provides a new tool to do so. We show here how a cell line standardization system can be used to normalize the staining variability in proliferation indices between different antibody clones in a triple negative breast cancer cohort. We believe that this cell line standardization array has the potential to improve reproducibility among Ki67 assays and laboratories, which is critical for establishing Ki67 as a standard-of-care assay.

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Fig. 1: Ki67 index CMA.
Fig. 2: Comparison of antibody performance between six clones and staining performance between three different labs using three antibody clones at Yale University.
Fig. 3: Staining performance of five clones at their recommended concentrations on Ki67 index CMA.
Fig. 4: Concordance between production batches.
Fig. 5: The concordance of data analyses using two different DIA platforms.
Fig. 6: Parallel staining of CMA and TNBC-TMA (YTMA-341) using MIB-1 and 1297A.

References

  1. Gerdes J, Lemke H, Baisch H, Wacker H-H, Schwab U, Stein H. Cell cycle analysis of a cell proliferation-associated human nuclear antigen defined by the monoclonal antibody Ki-67. J Immunol. 1984;133:1710–5.

    CAS  Google Scholar 

  2. Gerdes J, Stein H, Pileri S, Rivano M, Gobbi M, Ralfkiaer E, et al. Prognostic relevance of tumour-cell growth fraction in malignant non-Hodgkin’s lymphomas. Lancet. 1987;330:448–9.

    Article  Google Scholar 

  3. Dowsett M, Nielsen TO, A’Hern R, Bartlett J, Coombes RC, Cuzick J, et al. Assessment of Ki67 in breast cancer: recommendations from the International Ki67 in Breast Cancer working group. J Natl Cancer Inst. 2011;103:1656–64.

    CAS  Article  Google Scholar 

  4. Mandard AM, Denoux Y, Herlin P, Duigou F, van de Vijver MJ, Clahsen PC, et al. Prognostic value of DNA cytometry in 281 premenopausal patients with lymph node negative breast carcinoma randomized in a control trial: multivariate analysis with Ki‐67 index, mitotic count, and microvessel density. Cancer. 2000;89:1748–57.

    CAS  Article  Google Scholar 

  5. Clahsen P, Van de Velde C, Duval C, Pallud C, Mandard A-M, Delobelle-Deroide A, et al. The utility of mitotic index, oestrogen receptor and Ki-67 measurements in the creation of novel prognostic indices fornode-negative breast cancer. Eur J Surg Oncol. 1999;25:356–63.

    CAS  Article  Google Scholar 

  6. Viale G, Giobbie-Hurder A, Regan MM, Coates AS, Mastropasqua MG, Dell’Orto P, et al. Prognostic and predictive value of centrally reviewed Ki-67 labeling index in postmenopausal women with endocrine-responsive breast cancer: results from Breast International Group Trial 1-98 comparing adjuvant tamoxifen with letrozole. J Clin Oncol. 2008;26:5569.

    Article  Google Scholar 

  7. Sahin AA, Ro JY, El‐Naggar AK, Ordonez NG, Ayala AG, Ro J, et al. Ki‐67 immunostaining in node‐negative stage I/II breast carcinoma. Significant correlation with prognosis. Cancer. 1991;68:549–57.

    CAS  Article  Google Scholar 

  8. Domagala W, Markiewski M, Harezga B, Dukowicz A, Osborn M. Prognostic significance of tumor cell proliferation rate as determined by the MIB-1 antibody in breast carcinoma: its relationship with vimentin and p53 protein. Clin Cancer Res. 1996;2:147–54.

    CAS  PubMed  Google Scholar 

  9. Brown JR, DiGiovanna MP, Killelea B, Lannin DR, Rimm DL. Quantitative assessment Ki-67 score for prediction of response to neoadjuvant chemotherapy in breast cancer. Lab Investig. 2014;94:98–106.

    CAS  Article  Google Scholar 

  10. Fasching PA, Heusinger K, Haeberle L, Niklos M, Hein A, Bayer CM, et al. Ki67, chemotherapy response, and prognosis in breast cancer patients receiving neoadjuvant treatment. BMC Cancer. 2011;11:486.

    CAS  Article  Google Scholar 

  11. Yerushalmi R, Woods R, Ravdin PM, Hayes MM, Gelmon KA. Ki67 in breast cancer: prognostic and predictive potential. Lancet Oncol. 2010;11:174–83.

    CAS  Article  Google Scholar 

  12. Hsu DW, Louis DN, Efird JT, Hedley-Whyte ET. Use of MIB-1 (Ki-67) immunoreactivity in differentiating grade II and grade III gliomas. J Neuropathol Exp Neurol. 1997;56:857–65.

    CAS  Article  Google Scholar 

  13. Goldhirsch A, Wood WC, Coates AS, Gelber RD, Thürlimann B, Senn H-J, 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. 2011;22:1736–47.

    CAS  Article  Google Scholar 

  14. Tan Q-X, Qin Q-H, Yang W-P, Mo Q-G, Wei C-Y. Prognostic value of Ki67 expression in HR-negative breast cancer before and after neoadjuvant chemotherapy. Int J Clin Exp Pathol. 2014;7:6862.

    PubMed  PubMed Central  Google Scholar 

  15. Harris L, Fritsche H, Mennel R, Norton L, Ravdin P, Taube S, et al. American Society of Clinical Oncology 2007 update of recommendations for the use of tumor markers in breast cancer. J Clin Oncol. 2007;25:5287–312.

    CAS  Article  Google Scholar 

  16. Røge R, Nielsen S, Riber-Hansen R, Vyberg M. Impact of Primary Antibody Clone, Format, and Stainer Platform on Ki67 Proliferation Indices in Breast Carcinomas. Appl Immunohistochem Mol Morphol. 2019;27:732–9.

    Article  Google Scholar 

  17. Vörös A, Csörgő E, Kővári B, Lázár P, Kelemen G, Rusz O, et al. Different methods of pretreatment Ki-67 labeling index evaluation in core biopsies of breast cancer patients treated with neoadjuvant chemotherapy and their relation to response to therapy. Pathol Oncol Res. 2015;21:147–55.

    Article  Google Scholar 

  18. Acs B, Pelekanou V, Bai Y, Martinez-Morilla S, Toki M, Leung SC, et al. Ki67 reproducibility using digital image analysis: an inter-platform and inter-operator study. Lab Investig. 2019;99:107–17.

    CAS  Article  Google Scholar 

  19. Gustavson MD, Rimm DL, Dolled-Filhart M. Tissue microarrays: leaping the gap between research and clinical adoption. Per Med. 2013;10:441–51.

    CAS  Article  Google Scholar 

  20. MacNeil T, Vathiotis IA, Martinez-Morilla S, Yaghoobi V, Zugazagoitia J, Liu Y, et al. Antibody validation for protein expression on tissue slides: a protocol for immunohistochemistry. Biotechniques. 2020;69:460–8.

    CAS  Article  Google Scholar 

  21. Bankhead P, Loughrey MB, Fernández JA, Dombrowski Y, McArt DG, Dunne PD, et al. QuPath: open source software for digital pathology image analysis. Sci Rep. 2017;7:1–7.

    CAS  Article  Google Scholar 

  22. Camp RL, Dolled-Filhart M, Rimm DL. X-tile: a new bio-informatics tool for biomarker assessment and outcome-based cut-point optimization. Clin Cancer Res. 2004;10:7252–9.

    CAS  Article  Google Scholar 

  23. Stuart-Harris R, Caldas C, Pinder S, Pharoah P. Proliferation markers and survival in early breast cancer: a systematic review and meta-analysis of 85 studies in 32,825 patients. Breast. 2008;17:323–34.

    CAS  Article  Google Scholar 

  24. Harris LN, Ismaila N, McShane LM, Andre F, Collyar DE, Gonzalez-Angulo AM, 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. 2016;34:1134.

    CAS  Article  Google Scholar 

  25. Polley MY, Leung SC, McShane LM, Gao D, Hugh JC, Mastropasqua MG, et al. An international Ki67 reproducibility study. J Natl Cancer Inst. 2013;105:1897–906.

    Article  Google Scholar 

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Acknowledgements

We would like to thank Lori Charette, Amos Brooks and the team at the Yale Pathology Tissue Service and Developmental Histology Facility for production of the high-quality tissue sections and IHC staining.

Funding

This work was supported by the Breast Cancer Research Foundation (DLR).

Author information

Affiliations

  1. Department of Pathology, Yale University School of Medicine, New Haven, CT, USA

    Thazin Nwe Aung, Yalai Bai, Patricia Gaule, Sandra Martinez-Morilla, Ioannis Vathiotis, Saba Shafi, Myrto Moutafi & David L. Rimm

  2. Department of Oncology and Pathology, CCK, Karolinska Institutet, Stockholm, Sweden

    Balazs Acs

  3. Department of Clinical Pathology and Cytology, Karolinska University Laboratory, Stockholm, Sweden

    Balazs Acs

  4. Department of Molecular Biophysics and Biochemistry, Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA

    Jonathan Warrell & Mark Gerstein

  5. Visiopharm Corp, Westminster, CO, USA

    Benjamin Freiberg

  6. Array Science, LLC, Sausalito, CA, USA

    Regan Fulton

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  1. Thazin Nwe Aung
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Corresponding author

Correspondence to David L. Rimm.

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Conflict of interest

DLR has served as an advisor for Astra Zeneca, Agendia, Amgen, BMS, Cell Signaling Technology, Cepheid, Daiichi Sankyo, Novartis, GSK, Konica Minolta, Merck, NanoString, PAIGE.AI, Perkin Elmer, Roche, Sanofi, Ventana and Ultivue. Amgen, Cepheid, Konica Minolta, NavigateBP, NextCure, and Lilly, and fund research in his lab.

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Aung, T.N., Acs, B., Warrell, J. et al. A new tool for technical standardization of the Ki67 immunohistochemical assay. Mod Pathol 34, 1261–1270 (2021). https://doi.org/10.1038/s41379-021-00745-6

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