Abstract
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.
Highlights
Breast cancer is not a single entity but a heterogeneous disease [1,2], characterized by subtypes which differ in terms of outcome [3,4] and aetiologically [5,6]
We developed and applied an automated protocol for the scoring of Ki67 in tissue microarrays (TMAs) from multiple study centres within the Breast Cancer Association Consortium (BCAC)
Ten studies (ABCS, CNIO, ESTHER, KBCP, MCBCS, ORIGO, POSH, RBCS, UKBGS and kConFab) submitted unstained TMA slides which were centrally stained in the Breakthrough Core Pathology Laboratory at the Institute of Cancer Research (ICR) while two studies (MARIE and PBCS) submitted TMAs stained at their local laboratories
Summary
Breast cancer is not a single entity but a heterogeneous disease [1,2], characterized by subtypes which differ in terms of outcome [3,4] and aetiologically [5,6]. While visual scoring may ensure accuracy in recognition of tumour cells versus benign ductal epithelial or stromal cells and in the implementation of quality control protocols, it is often difficult to organize, slow, laborious and, for almost all of the markers, exhibits varying degrees of intra- and inter-observer reproducibility. This is even more so for Ki67 for which a number of studies have reported poor inter-observer reproducibility [11,12,13]. On the other hand, automated algorithms are highthroughput and reproducible, and several investigators have reported evidence in support of their use for the scoring of tissue markers especially oestrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2) [14,15,16,17,18,19], B-cell CLL/lymphoma 2 (BCL2) [17,20], epidermal growth factor receptor (EGFR) [18,21,22], cytokeratin (CK) 5/6 [18] and Ki67 [13,23,24,25,26,27,28]
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