An International Ki67 Reproducibility Study

Mei-Yin Polley

Samuel Leung

Lisa McShane

Dongxia Gao

Frédérique Penault-Llorca+10 authors

Background

In breast cancer, immunohistochemical assessment of proliferation using the marker Ki67 has potential use in both research and clinical management. However, lack of consistency across laboratories has limited Ki67’s value. A working group was assembled to devise a strategy to harmonize Ki67 analysis and increase scoring concordance. Toward that goal, we conducted a Ki67 reproducibility study.

Methods

Eight laboratories received 100 breast cancer cases arranged into 1-mm core tissue microarrays—one set stained by the participating laboratory and one set stained by the central laboratory, both using antibody MIB-1. Each laboratory scored Ki67 as percentage of positively stained invasive tumor cells using its own method. Six laboratories repeated scoring of 50 locally stained cases on 3 different days. Sources of variation were analyzed using random effects models with log2-transformed measurements. Reproducibility was quantified by intraclass correlation coefficient (ICC), and the approximate two-sided 95% confidence intervals (CIs) for the true intraclass correlation coefficients in these experiments were provided.

Results

Intralaboratory reproducibility was high (ICC = 0.94; 95% CI = 0.93 to 0.97). Interlaboratory reproducibility was only moderate (central staining: ICC = 0.71, 95% CI = 0.47 to 0.78; local staining: ICC = 0.59, 95% CI = 0.37 to 0.68). Geometric mean of Ki67 values for each laboratory across the 100 cases ranged 7.1% to 23.9% with central staining and 6.1% to 30.1% with local staining. Factors contributing to interlaboratory discordance included tumor region selection, counting method, and subjective assessment of staining positivity. Formal counting methods gave more consistent results than visual estimation.

Conclusions

Substantial variability in Ki67 scoring was observed among some of the world’s most experienced laboratories. Ki67 values and cutoffs for clinical decision-making cannot be transferred between laboratories without standardizing scoring methodology because analytical validity is limited.

Funding

This work was supported by the Breast Cancer Research Foundation. Additional funding for the UK labs was received from Breakthrough Breast Cancer and the National Institute for Health Research Biomedical Research Centre at the Royal Marsden Hospital. Funding for the Ontario Institute for Cancer Research is provided by the Government of Ontario.

Supplementary Material

Supplementary Data:

Click here to view.

Affiliations of authors:Biometric Research Branch, Division of Cancer Treatment and Diagnosis, National Cancer Institute, Bethesda, MD (MCP, LMM); Pathology and Laboratory Medicine, University of British Columbia, Vancouver, British Columbia, Canada (SCYL, DG, EM, TON); Department of Laboratory Medicine and Pathology, University of Alberta, Alberta, Canada (JCH); Division of Pathology and Laboratory Medicine, European Institute of Oncology, Milan, Italy (MGM); Division of Pathology and Laboratory Medicine, European Institute of Oncology, and University of Milan, Milan, Italy (GV); Breakthrough Breast Cancer Research Centre, The Institute of Cancer Research, London, United Kingdom (LAZ); Department of Pathology, Centre Jean Perrin, Clermont-Ferrand, and Université d’Auvergne, France (FP-L); Transformative Pathology, Ontario Institute for Cancer Research, Toronto, Ontario, Canada (JMSB); PhenoPath Laboratories, Seattle, WA (AMG); Department of Pathology, MD Anderson Cancer Center, Houston, TX (WFS); Edinburgh Cancer Research Centre, Western General Hospital, Edinburgh, United Kingdom (TP); The EMMES Corporation, Rockville, MD (RAE); Breast Oncology Program, University of Michigan Comprehensive Cancer Center, Ann Arbor, MI (DFH); Academic Department of Biochemistry, Royal Marsden Hospital, London, United Kingdom (MD); on behalf of the International Ki67 in Breast Cancer Working Group of the Breast International Group and North American Breast Cancer Group.

^{Corresponding author.}

Correspondence to: Torsten Nielsen, MD, PhD, FRCPC, University of British Columbia Pathology and Laboratory Medicine, Anatomical Pathology, JP 1401, Vancouver Hospital & Health Sciences Centre, 855 W 12th Ave, Vancouver, BC V5Z 1M9, Canada (e-mail: ac.cbu.liam@netsrot).

Received 2013 Apr 2; Revised 2013 Sep 3; Accepted 2013 Sep 16.

Abstract

Background

Methods

Results

Conclusions

Abstract

Uncontrolled proliferation is a key feature of malignancy. The nuclear proliferation marker Ki67 is of interest for various potential uses in the clinical management of breast cancer (eg, prognosis, prediction, and monitoring of response) (1–9). The most commonly used assay to assess Ki67 is immunohistochemical (IHC) staining with the MIB-1 antibody. However, interlaboratory methodology is inconsistent, and, despite the apparent prognostic utility of Ki67, routine use of this tumor biomarker has not been widely recommended by consensus guidelines panels such as that convened by the American Society of Clinical Oncology, mainly because of concerns regarding analytical validity (10).

With the goal of harmonizing Ki67 analytical methodology, Dowsett et al., on behalf of the International Ki67 in Breast Cancer Working Group of the Breast International Group and North American Breast Cancer Group, provided an overview of the current state of the art of Ki67 evaluation and proposed a set of guidelines for analysis and reporting of Ki67 (1). Although those guidelines aimed to reduce preanalytical and analytical variations, the Working Group recognized that actual scoring procedures varied substantially, contributing to a lack of consensus regarding optimal cutoffs that should be applied in various research and clinical decision-making settings. This lack of consistency has prevented direct comparisons of Ki67 across laboratories and clinical trials.

In an effort to harmonize Ki67 analysis, the Working Group studied intra- and interlaboratory reproducibility of IHC assays for Ki67 in breast cancer among a group of highly experienced pathology laboratories. A secondary aim was to identify key sources of variation, particularly those introduced by different scoring methodologies.

* EDTA = Ethylenediaminetetraacetic acid; PT = PT Link is a pre-treatment system from Dako; TFS = Thermo Fisher Scientific; TRIS = Tris(hydroxymethyl)aminomethane.

* max = maximum; min = minimum; Q1 = first quartile; Q3 = third quartile; SD = standard deviation.

Click here to view.

Notes

The study sponsors had no role in the design of the study; the collection, analysis, and interpretation of the data; the writing of the manuscript; and the decision to submit the manuscript for publication.

The authors wish to disclose the following: JC Hugh has worked as a consultant for NanoString Technologies. WF Symmans has received travel/accomodations/meeting expenses from San Antonio Breast Cancer Symposium/AACR and planned patents, royalties, and stock/stock options with Nuvera Biosciences for prognostic/predictive genomic signatures. DF Hayes has stock/stock options with InBiomotion and OncImmune; has grants/grants pending or contracts with Veridex/Janssen; and has planned patents relating to detection and characterization of circulating tumor cells. M. Dowsett has worked as a consultant for Genoptix and Nanostring. TO Nielsen has a grant or contract with Breast Cancer Research Foundation; has patents with Bioclassifier LLC related to IP in a gene expression test that is not part of the submitted work; and has worked as a consultant for Nanostring Technologies related to IP in a gene expression test that is not part of the submitted work.

We are grateful for the contributions of Drs Patricia Kandalaft, Inès Raoelfils, Nancy Davidson, Martine Piccart, and Larry Norton, and the Breast Cancer Research Foundation.

Notes

References

1. Dowsett M, Nielsen TO, A’Hern R, et al Assessment of Ki67 in breast cancer: recommendations from the International Ki67 in Breast Cancer working group. J Natl Cancer Inst. 2011;103(22):1656–1664 [Google Scholar]
2. Goldhirsch A, Wood WC, Coates AS, Gelber RD, Thurlimann B, Senn HJ. 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(8):1736–1747
3. Yerushalmi R, Woods R, Ravdin PM, Hayes MM, Gelmon KA. Ki67 in breast cancer: prognostic and predictive potential. Lancet Oncol. 2010;11(2):174–183 [[PubMed]
4. Viale G, Giobbie-Hurder A, Regan MM, 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(34):5569–5575 [Google Scholar]
5. Dowsett M, Ebbs SR, Dixon JM, et al Biomarker changes during neoadjuvant anastrozole, tamoxifen, or the combination: influence of hormonal status and HER-2 in breast cancer—a study from the IMPACT trialists. J Clin Oncol. 2005;23(11):2477–2492 [[PubMed][Google Scholar]
6. Ellis MJ, Suman VJ, Hoog 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. 2011;29(17):2342–2349 [Google Scholar]
7. Freedman OC, Amir E, Hanna W, et al A randomized trial exploring the biomarker effects of neoadjuvant sequential treatment with exemestane and anastrozole in post-menopausal women with hormone receptor-positive breast cancer. Breast Cancer Res Treat. 2010;119(1):155–161 [[PubMed][Google Scholar]
8. Murray J, Young OE, Renshaw L, et al A randomised study of the effects of letrozole and anastrozole on oestrogen receptor positive breast cancers in postmenopausal women. Breast Cancer Res Treat. 2009;114(3):495–501 [[PubMed][Google Scholar]
9. Smith IE, Walsh G, Skene A, et al A phase II placebo-controlled trial of neoadjuvant anastrozole alone or with gefitinib in early breast cancer. J Clin Oncol. 2007;25(25):3816–3822 [[PubMed][Google Scholar]
10. Harris L, Fritsche H, Mennel R, 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(33):5287–5312 [[PubMed][Google Scholar]
11. Galea MH, Blamey RW, Elston CE, et al The Nottingham Prognostic Index in primary breast cancer. Breast Cancer Res Treat. 1992;22(3):207–219 [[PubMed][Google Scholar]
12. Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet. 1986;1(8476):307–310 [[PubMed]
13. Rabe-Hesketh S, Skrondal A. Multilevel and longitudinal modeling using Stata. 2nd ed College Station, TX: Stata Press; 2008 [PubMed]
14. Hadfield J. MCMC methods for multi-response generalised linear mixed models: the MCMCglmm R Package (R package version 2–12). J Stat Softw. 2010;33(2):1–22 [PubMed]
15. Cappelleri JC, Ting N. A modified large-sample approach to approximate interval estimation for a particular intraclass correlation coefficient. Stat Med. 2003;22(11):1861–1877 [[PubMed]
16. R Foundation for Statistical Computing R: A language and environment for statistical computing. Vienna, Austria: R Development Core Team; 2011 [PubMed]
17. Bates D, Maechler M, Bolker B. Linear mixed-effects models using S4 classes. R package. Version 0.999375–39. 2011. Available at: Accessed October 25, 2013.[PubMed]
18. Carlson RW, Allred DC, Anderson BO, et al Invasive breast cancer. J Natl Compr Canc Netw. 2011;9(2):136–222 [[PubMed][Google Scholar]
19. Teutsch SM, Bradley LA, Palomaki GE, et al The Evaluation of Genomic Applications in Practice and Prevention (EGAPP) Initiative: methods of the EGAPP Working Group. Genet Med. 2009;11(1):3–14 [Google Scholar]
20. Simon RM, Paik S, Hayes DF. Use of archived specimens in evaluation of prognostic and predictive biomarkers. J Natl Cancer Inst. 2009;101(21):1446–1452
21. Varga Z, Diebold J, Dommann-Scherrer C, et al How reliable is Ki-67 immunohistochemistry in grade 2 breast carcinomas? A QA study of the Swiss Working Group of Breast- and Gynecopathologists. PLoS One. 2012;7(5):e37379. [Google Scholar]
22. Kirkegaard T, Edwards J, Tovey S, et al Observer variation in immunohistochemical analysis of protein expression, time for a change?Histopathology. 2006;48(7):787–794 [[PubMed][Google Scholar]
23. Stuart-Harris R, Caldas C, Pinder SE, 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(4):323–334 [[PubMed]
24. Urruticoechea A, Smith IE, Dowsett M. Proliferation marker Ki-67 in early breast cancer. J Clin Oncol. 2005;23(28):7212–7220 [[PubMed]
25. Fasanella S, Leonardi E, Cantaloni C, et al Proliferative activity in human breast cancer: Ki-67 automated evaluation and the influence of different Ki-67 equivalent antibodies. Diagn Pathol. 2011;6(Suppl 1):S7. [Google Scholar]
26. Konsti J, Lundin M, Joensuu H, et al Development and evaluation of a virtual microscopy application for automated assessment of Ki-67 expression in breast cancer. BMC Clin Pathol. 2011;25(11):3. 10.1186/ 1472-6890-11-3. [Google Scholar]
27. Laurinavicius A, Laurinaviciene A, Dasevicius D, et al Digital image analysis in pathology: benefits and obligation. Anal Cell Pathol (Amst). 2012;35(2):75–78 [Google Scholar]
28. Gudlaugsson E, Skaland I, Janssen EA, et al Comparison of the effect of different techniques for measurement of Ki67 proliferation on reproducibility and prognosis prediction accuracy in breast cancer. Histopathology. 2012;61(6):1134–1144 [[PubMed][Google Scholar]
29. Adsay V. Ki67 labeling index in neuroendocrine tumors of the gastrointestinal and pancreatobiliary tract: to count or not to count is not the question, but rather how to count. Am J Surg Pathol. 2012;36(12):1743–1746 [[PubMed]
30. Remes SM, Tuominen VJ, Helin H, Isola J, Arola J. Grading of neuroendocrine tumors with Ki-67 requires high-quality assessment practices. Am J Surg Pathol. 2012;36(9):1359–1363 [[PubMed]
31. Tang LH, Gonen M, Hedvat C, Modlin IM, Klimstra DS. Objective quantification of the ki67 proliferative index in neuroendocrine tumors of the gastroenteropancreatic system: a comparison of digital image analysis with manual methods. Am J Surg Pathol. 2012;36(12):1761–1770 [[PubMed]