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

Mustapha Abubakar, William J Howat, Frances Daley, Lila Zabaglo, Leigh-Anne McDuffus, Fiona Blows, Penny Coulson, H Raza Ali, Javier Benitez, Roger Milne, Herman Brenner, Christa Stegmaier, Arto Mannermaa, Jenny Chang-Claude, Anja Rudolph, Peter Sinn, Fergus J Couch, Rob A E M Tollenaar, Peter Devilee, Jonine FigueroaMark E Sherman, Jolanta Lissowska, Stephen Hewitt, Diana Eccles, Maartje J Hooning, Antoinette Hollestelle, John Wm Martens, Carolien Hm van Deurzen, kConFab Investigators, Manjeet K Bolla, Qin Wang, Michael Jones, Minouk Schoemaker, Annegien Broeks, Flora E van Leeuwen, Laura Van't Veer, Anthony J Swerdlow, Nick Orr, Mitch Dowsett, Douglas Easton, Marjanka K Schmidt, Paul D Pharoah, Montserrat Garcia-Closas

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

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.

Original languageEnglish
Pages (from-to)138-53
Number of pages16
JournalJournal of Pathology: Clinical Research
Issue number3
Early online date2 Mar 2016
Publication statusPublished - Jul 2016


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