High-throughput automated scoring of Ki67 in breast cancer tissue microarrays from the Breast Cancer Association Consortium

被引:17
作者
Abubakar, Mustapha [1 ]
Howat, William J. [2 ]
Daley, Frances [3 ]
Zabaglo, Lila [4 ]
McDuffus, Leigh-Anne [2 ]
Blows, Fiona [5 ]
Coulson, Penny [1 ]
Ali, H. Raza [2 ]
Benitez, Javier [6 ,7 ]
Milne, Roger [8 ,9 ]
Brenner, Herman [10 ,11 ,12 ,13 ]
Stegmaier, Christa [14 ]
Mannermaa, Arto [15 ,16 ]
Chang-Claude, Jenny [17 ,18 ]
Rudolph, Anja [17 ]
Sinn, Peter [19 ]
Couch, Fergus J. [20 ]
Tollenaar, Rob A. E. M. [21 ]
Devilee, Peter [22 ,23 ]
Figueroa, Jonine [24 ]
Sherman, Mark E. [25 ]
Lissowska, Jolanta [26 ]
Hewitt, Stephen [27 ]
Eccles, Diana [28 ]
Hooning, Maartje J. [29 ]
Hollestelle, Antoinette [29 ]
Martens, John W. M. [29 ]
van Deurzen, Carolien H. M. [30 ]
Bolla, Manjeet K. [32 ]
Wang, Qin [32 ]
Jones, Michael [1 ]
Schoemaker, Minouk [1 ]
Broeks, Annegien [33 ]
van Leeuwen, Flora E. [34 ]
Van't Veer, Laura [33 ]
Swerdlow, Anthony J. [1 ,35 ]
Orr, Nick [3 ]
Dowsett, Mitch [3 ,4 ]
Easton, Douglas [5 ,32 ]
Schmidt, Marjanka K. [33 ,34 ]
Pharoah, Paul D. [5 ,32 ]
Garcia-Closas, Montserrat [25 ]
机构
[1] Inst Canc Res, Div Genet & Epidemiol, London, England
[2] Univ Cambridge, Canc Res UK Cambridge Inst, Cambridge, England
[3] Inst Canc Res, Div Breast Canc Res, Breakthrough Breast Canc Res Ctr, London, England
[4] Royal Marsden Hosp, Acad Dept Biochem, Fulham Rd, London, England
[5] Univ Cambridge, Dept Oncol, Ctr Canc Genet Epidemiol, Cambridge, England
[6] Spanish Natl Canc Res Ctr CNIO, Human Genet Grp, Human Canc Genet Program, Madrid, Spain
[7] Ctr Invest Red Enfermedades Raras CIBERER, Valencia, Spain
[8] Canc Council Victoria, Canc Epidemiol Ctr, Melbourne, Vic, Australia
[9] Univ Melbourne, Melbourne Sch Populat & Global Hlth, Ctr Epidemiol & Biostat, Melbourne, Vic, Australia
[10] German Canc Res Ctr, Div Clin Epidemiol & Aging Res, Heidelberg, Germany
[11] German Canc Res Ctr, Div Prevent Oncol, Heidelberg, Germany
[12] Natl Ctr Tumor Dis NCT, Heidelberg, Germany
[13] German Canc Res Ctr, German Canc Consortium DKTK, Heidelberg, Germany
[14] Saarland Canc Registry, Saarland, Germany
[15] Univ Eastern Finland, Inst Clin Med Pathol & Forens Med, Sch Med, Canc Ctr Eastern Finland, Kuopio, Finland
[16] Kuopio Univ Hosp, Dept Clin Pathol, Imaging Ctr, Kuopio, Finland
[17] German Canc Res Ctr, Div Canc Epidemiol, Heidelberg, Germany
[18] Univ Med Ctr Hamburg Eppendorf, UCCH, Hamburg, Germany
[19] Heidelberg Univ Hosp, Inst Pathol, Dept Pathol, Heidelberg, Germany
[20] Mayo Clin, Dept Lab Med & Pathol, Rochester, MN USA
[21] Leiden Univ, Dept Surg, Med Ctr, Leiden, Netherlands
[22] Leiden Univ, Med Ctr, Dept Human Genet, Leiden, Netherlands
[23] Leiden Univ, Dept Pathol, Med Ctr, Leiden, Netherlands
[24] Univ Edinburgh, Usher Inst Populat Hlth Sci & Informat, Edinburgh, Midlothian, Scotland
[25] Natl Canc Inst, Div Canc Epidemiol & Genet, Rockville, MD USA
[26] M Sklodowska Curie Mem Canc Ctr & Inst Oncol, Dept Canc Epidemiol & Prevent, Warsaw, Poland
[27] NCI, NIH, Lab Pathol, Rockville, MD USA
[28] Southampton Gen Hosp, Fac Med, Acad Unit Canc Sci, Southampton, Hants, England
[29] Erasmus MC Canc Inst, Dept Med Oncol, Family Canc Clin, Rotterdam, Netherlands
[30] Erasmus MC Canc Inst, Dept Pathol, Rotterdam, Netherlands
[31] QIMR Berghofer Med Res Inst, Dept Genet, Brisbane, Qld, Australia
[32] Univ Cambridge, Dept Publ Hlth & Primary Care, Ctr Canc Genet Epidemiol, Cambridge, England
[33] Antoni van Leeuwenhoek Hosp, Netherlands Canc Inst, Div Mol Pathol, Amsterdam, Netherlands
[34] Netherlands Canc Inst, Antoni van Leeuwenhoek Hosp, Div Psychosocial Res & Epidemiol, Amsterdam, Netherlands
[35] Inst Canc Res, Div Breast Canc Res, London, England
来源
JOURNAL OF PATHOLOGY CLINICAL RESEARCH | 2016年 / 2卷 / 03期
基金
英国医学研究理事会; 芬兰科学院; 澳大利亚国家健康与医学研究理事会;
关键词
breast cancer; automated algorithm; tissue microarrays; Ki67; immunohistochemistry;
D O I
10.1002/cjp2.42
中图分类号
R36 [病理学];
学科分类号
100104 ;
摘要
Automated methods are needed to facilitate high-throughput and reproducible scoring of Ki67 and other markers in breast cancer tissue microarrays (TMAs) in large-scale studies. To address this need, we developed an automated protocol for Ki67 scoring and evaluated its performance in studies from the Breast Cancer Association Consortium. We utilized 166 TMAs containing 16,953 tumour cores representing 9,059 breast cancer cases, from 13 studies, with information on other clinical and pathological characteristics. TMAs were stained for Ki67 using standard immunohistochemical procedures, and scanned and digitized using the Ariol system. An automated algorithm was developed for the scoring of Ki67, and scores were compared to computer assisted visual (CAV) scores in a subset of 15 TMAs in a training set. We also assessed the correlation between automated Ki67 scores and other clinical and pathological characteristics. Overall, we observed good discriminatory accuracy (AUC=85%) and good agreement (kappa=0.64) between the automated and CAV scoring methods in the training set. The performance of the automated method varied by TMA (kappa range=0.37-0.87) and study (kappa range=0.39-0.69). The automated method performed better in satisfactory cores (kappa=0.68) than suboptimal (kappa=0.51) cores (p-value for comparison=0.005); and among cores with higher total nuclei counted by the machine (4,000-4,500 cells: kappa=0.78) than those with lower counts (50-500 cells: kappa=0.41; p-value=0.010). Among the 9,059 cases in this study, the correlations between automated Ki67 and clinical and pathological characteristics were found to be in the expected directions. Our findings indicate that automated scoring of Ki67 can be an efficient method to obtain good quality data across large numbers of TMAs from multicentre studies. However, robust algorithm development and rigorous pre- and post-analytical quality control procedures are necessary in order to ensure satisfactory performance.
引用
收藏
页码:138 / 153
页数:16
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