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Performance of automated scoring of ER, PR, HER2, CK5/6 and EGFR in breast cancer tissue microarrays in the Breast Cancer Association Consortium.


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Authors

Howat, William J 
Blows, Fiona M 
Provenzano, Elena 
Brook, Mark N 
Morris, Lorna 

Abstract

Breast cancer risk factors and clinical outcomes vary by tumour marker expression. However, individual studies often lack the power required to assess these relationships, and large-scale analyses are limited by the need for high throughput, standardized scoring methods. To address these limitations, we assessed whether automated image analysis of immunohistochemically stained tissue microarrays can permit rapid, standardized scoring of tumour markers from multiple studies. Tissue microarray sections prepared in nine studies containing 20 263 cores from 8267 breast cancers stained for two nuclear (oestrogen receptor, progesterone receptor), two membranous (human epidermal growth factor receptor 2 and epidermal growth factor receptor) and one cytoplasmic (cytokeratin 5/6) marker were scanned as digital images. Automated algorithms were used to score markers in tumour cells using the Ariol system. We compared automated scores against visual reads, and their associations with breast cancer survival. Approximately 65-70% of tissue microarray cores were satisfactory for scoring. Among satisfactory cores, agreement between dichotomous automated and visual scores was highest for oestrogen receptor (Kappa = 0.76), followed by human epidermal growth factor receptor 2 (Kappa = 0.69) and progesterone receptor (Kappa = 0.67). Automated quantitative scores for these markers were associated with hazard ratios for breast cancer mortality in a dose-response manner. Considering visual scores of epidermal growth factor receptor or cytokeratin 5/6 as the reference, automated scoring achieved excellent negative predictive value (96-98%), but yielded many false positives (positive predictive value = 30-32%). For all markers, we observed substantial heterogeneity in automated scoring performance across tissue microarrays. Automated analysis is a potentially useful tool for large-scale, quantitative scoring of immunohistochemically stained tissue microarrays available in consortia. However, continued optimization, rigorous marker-specific quality control measures and standardization of tissue microarray designs, staining and scoring protocols is needed to enhance results.

Description

Keywords

automated scoring, breast tumours, digital pathology, immunohistochemistry, tissue microarrays

Journal Title

J Pathol Clin Res

Conference Name

Journal ISSN

2056-4538
2056-4538

Volume Title

1

Publisher

Wiley
Sponsorship
Department of Health (via National Institute for Health Research (NIHR)) (NF-SI-0515-10090)
Cancer Research UK (CB4140)
Cancer Research UK (unknown)
Cancer Research UK (60098573)
Cancer Research UK (unknown)
Department of Health (via National Institute for Health Research (NIHR)) (unknown)
European Commission (260791)
Cambridge University Hospitals NHS Foundation Trust (CUH) (RG51913)
Cancer Research Uk (None)
European Commission FP7 Network of Excellence (NoE) (260791)
Cambridge University Hospitals NHS Foundation Trust (CUH) (unknown)
Cancer Research Uk (None)
Academy of Medical Sciences (unknown)
Medical Research Council (MR/M008975/1)
Academy of Medical Sciences (ALI 01/08/14)
Pathological Society of Great Britain & Ireland (CDF 2012/01)
European Commission FP7 Collaborative projects (CP) (258967)
Cancer Research UK (C507/A16278)
European Commission (258967)
Cancer Research UK (20544)
Medical Research Council (MR/P012442/1)
European Commission and European Federation of Pharmaceutical Industries and Associations (EFPIA) FP7 Innovative Medicines Initiative (IMI) (115749)
European Commission (242006)
European Research Council (694620)
Cancer Research UK (A24622)
European Commission (223175)
Cancer Research Uk (None)
Cancer Research Uk (None)
We would like to thank Rob Sykes from Ariol who helped to retrain the SEARCH image analysis for EGFR and CK5/6. We would also like to thank Mike Irwin at the Institute of Astronomy, Cambridge for assistance with methods in the automated analysis. ABCS was supported by the Dutch Cancer Society [grants NKI 2007-3839; 2009 4363]; BBMRI-NL, which is a Research Infrastructure financed by the Dutch government (NWO 184.021.007); and the Dutch National Genomics Initiative. CNIO-BCS was supported by the Genome Spain Foundation, the Red Temática de Investigación Cooperativa en Cáncer and grants from the Asociación Española Contra el Cáncer and the Fondo de Investigación Sanitario (PI11/00923 and PI081120). The Human Genotyping-CEGEN Unit (CNIO) is supported by the Instituto de Salud Carlos III. HEBCS was financially supported by the Helsinki University Central Hospital Research Fund, Academy of Finland (132473), the Finnish Cancer Society, The Nordic Cancer Union and the Sigrid Juselius Foundation. The KBCP was financially supported by the special Government Funding (EVO) of Kuopio University Hospital grants, Cancer Fund of North Savo, the Finnish Cancer Organizations, the Academy of Finland and by the strategic funding of the University of Eastern Finland The MCBCS was supported by an NIH Specialized Program of Research Excellence (SPORE) in Breast Cancer [CA116201], the Breast Cancer Research Foundation, the Mayo Clinic Breast Cancer Registry and a generous gift from the David F. and Margaret T. Grohne Family Foundation and the Ting Tsung and Wei Fong Chao Foundation. ORIGO authors thank E. Krol-Warmerdam, and J. Blom; The contributing studies were funded by grants from the Dutch Cancer Society (UL1997-1505) and the Biobanking and Biomolecular Resources Research Infrastructure (BBMRI-NL CP16). PBCS was funded by Intramural Research Funds of the National Cancer Institute, Department of Health and Human Services, USA. SBCS was supported by Yorkshire Cancer Research S295, S299, S305PA. SEARCH is funded by programme grant from Cancer Research UK [C490/A10124. C490/A16561] and supported by the UK National Institute for Health Research Biomedical Research Centre at the University of Cambridge. Part of this work was supported by the European Community´s Seventh Framework Programme under grant agreement number 223175 (grant number HEALTH-F2-2009-223175) (COGS). We acknowledge funds from Breakthrough Breast Cancer, UK, in support of MGC and MB.