Dataset Information

Ki67 expression in invasive breast cancer: the use of tissue microarrays compared with whole tissue sections.

ABSTRACT:

Background

Although the prognostic value of Ki67 in breast cancer is well documented, using optimal cut-points for patient stratification, reproducibility of the scoring and interpretation of the results remains a matter of debate particularly when using tissue microarrays (TMAs). This study aims to assess Ki67 expression assessed on TMAs and their matched whole tissue sections (WTS). Moreover, whether the cut-off used for WTS is reproducible on TMA in BC molecular classes and the association between Ki67 expression cut-off, assessed on TMAs and WTS, and clinicopathological parameters and patient outcome were tested.

Method

A large series (n = 707) of primary invasive breast tumours were immunostained for Ki67 using both TMA and WTS and assessed as percentage staining and correlated with each other, clinicopathological parameters and patient outcome. In addition, MKI67 mRNA expression was correlated with Ki67 protein levels on WTS and TMAs in a subset of cases included in the METABRIC study.

Results

There was moderate concordance in Ki67 expression between WTS and TMA when analysed as a continuous variable (Intraclass correlation coefficient = 0.61) and low concordance when dichotomised (kappa value = 0.3). TMA showed low levels of Ki67 with mean percentage of expression of 35 and 22% on WTS and TMA, respectively. MKI67 mRNA expression was significantly correlated with protein expression determined on WTS (Spearman Correlation, r = 0.52) and to a lesser extent on TMA (r = 0.34) (p < 0.001). Regarding prediction of patient outcome, statistically significant differences were detected upon stratification of patients with tumours expressing Ki67 at 10, 15, 20, 25 or 30% in TMA. Using TMA, ?20% Ki67 provided the best prognostic cut-off particularly in triple-negative and HER2-positive classes.

Conclusion

Ki67 expression in breast cancer can be evaluated using TMA although different cut-points are required to emulate results from WTS. A cut-off of ?20% for Ki67 expression in BC provides the best prognostic correlations when TMAs are used.

SUBMITTER: Muftah AA

PROVIDER: S-EPMC5487701 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature

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Publications

Ki67 expression in invasive breast cancer: the use of tissue microarrays compared with whole tissue sections.

Muftah Abir A AA Aleskandarany Mohammed A MA Al-Kaabi Methaq M MM Sonbul Sultan N SN Diez-Rodriguez Maria M Nolan Chris C CC Caldas Carlos C Ellis Ian O IO Rakha Emad A EA Green Andrew R AR

Breast cancer research and treatment 20170506 2

<h4>Background</h4>Although the prognostic value of Ki67 in breast cancer is well documented, using optimal cut-points for patient stratification, reproducibility of the scoring and interpretation of the results remains a matter of debate particularly when using tissue microarrays (TMAs). This study aims to assess Ki67 expression assessed on TMAs and their matched whole tissue sections (WTS). Moreover, whether the cut-off used for WTS is reproducible on TMA in BC molecular classes and the associ ...[more]

PMID: 28478613

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Project description: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.

Project description:BACKGROUND:The scoring of Ki-67 is highly relevant for the diagnosis, classification, prognosis, and treatment in breast invasive ductal carcinoma (IDC). Traditional scoring method of Ki-67 staining followed by manual counting, is time-consumption and inter-/intra observer variability, which may limit its clinical value. Although more and more algorithms and individual platforms have been developed for the assessment of Ki-67 stained images to improve its accuracy level, most of them lack of accurate registration of immunohistochemical (IHC) images and their matched hematoxylin-eosin (HE) images, or did not accurately labelled each positive and negative cell with Ki-67 staining based on whole tissue sections (WTS). In view of this, we introduce an accurate image registration method and an automatic identification and counting software of Ki-67 based on WTS by deep learning. METHODS:We marked 1017 breast IDC whole slide imaging (WSI), established a research workflow based on the (i) identification of IDC area, (ii) registration of HE and IHC slides from the same anatomical region, and (iii) counting of positive Ki-67 staining. RESULTS:The accuracy, sensitivity, and specificity levels of identifying breast IDC regions were 89.44, 85.05, and 95.23%, respectively, and the contiguous HE and Ki-67 stained slides perfectly registered. We counted and labelled each cell of 10 Ki-67 slides as standard for testing on WTS, the accuracy by automatic calculation of Ki-67 positive rate in attained IDC was 90.2%. In the human-machine competition of Ki-67 scoring, the average time of 1 slide was 2.3?min with 1 GPU by using this software, and the accuracy was 99.4%, which was over 90% of the results provided by participating doctors. CONCLUSIONS:Our study demonstrates the enormous potential of automated quantitative analysis of Ki-67 staining and HE images recognition and registration based on WTS, and the automated scoring of Ki67 can thus successfully address issues of consistency, reproducibility and accuracy. We will provide those labelled images as an open-free platform for researchers to assess the performance of computer algorithms for automated Ki-67 scoring on IHC stained slides.

Dataset Information

Ki67 expression in invasive breast cancer: the use of tissue microarrays compared with whole tissue sections.

Background

Method

Results

Conclusion

Publications

Ki67 expression in invasive breast cancer: the use of tissue microarrays compared with whole tissue sections.

Similar Datasets

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