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Breast cancer subtype discrimination using standardized 4-IHC and digital image analysis

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Abstract

Breast cancer is a heterogeneous disease. Surrogate classification of intrinsic subtypes of invasive carcinomas by combined immunohistochemistry for estrogen receptor (ER), progesterone receptor (PR), HER2, and Ki67 (4-IHC) has increased steadily since the 2011 St Gallen symposium, due to its rapid subty** of tumors at a reasonable cost. An important step in improving 4-IHC reproducibility and reliability will be to provide reference values from the routine use of standardized 4-IHC followed by image analysis. The aims of the current study were (1) to analyze invasive breast carcinomas using standardized 4-IHC and quantitative image analysis and (2) to compare the results obtained in the classification of biological subtypes using current Ki67 and PR threshold values proposed by different authors to sub-classifying the luminal A-like and the luminal B-like (HER2-negative) subtypes. Five hundred twenty-one tumors were analyzed by standardized immunohistochemistry, with automatic image analysis, and HER2 FISH technique. Positivity for ER was found in 82.7% and for PR in 70.1% of cases. Using the Allred scoring system, hormone receptor results showed a bimodal distribution, particularly for ER. HER2 positivity was found in 15.7% of cases, and the mean Ki67 score was 32.3%. Using the most recently proposed surrogate definitions for the classification of luminal breast cancer subtypes, the percentages of different subtypes that we found were similar to those published with genomic platforms: 40.7% luminal A-like, 32.4% luminal B-like/HER2-negative, 9.8% luminal B-like/HER2-positive, 6.0% HER2-positive, and 11.1% triple negative. Standardized 4-IHC with automatic image analysis constitutes a low-cost method for surrogate definitions of biological subtypes of breast cancer that delivers accurate results in a day.

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Acknowledgements

The manuscript was edited for English language by Pamela V Lear PhD, Oxford University, UK.

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Author notes

  1. Marina Gándara-Cortes

    Present address: Department of Pathology, Alvaro Cunqueiro University Hospital, Vigo, Spain

Authors and Affiliations

  1. Department of Morphological Sciences, School of Medicine, University of Santiago de Compostela, Santiago de Compostela, Spain

    Marina Gándara-Cortes, Rosalía Gallego & Tomás García-Caballero

  2. Department of Pathology, University Clinical Hospital, Santiago de Compostela, Spain

    Ángel Vázquez-Boquete, Beatriz Fernández-Rodríguez, Patricia Viaño, Dora Ínsua, Alejandro Seoane-Seoane, Máximo Fraga, José R. Antúnez & Tomás García-Caballero

  3. Clinical Epidemiology Unit, University Clinical Hospital, Santiago de Compostela, Spain

    Francisco Gude

  4. Department of Oncology, University Clinical Hospital, Santiago de Compostela, Spain

    Teresa Curiel

  5. Department of Oncology, University Hospital of Ourense, Ourense, Spain

    Eva Pérez-López

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  1. Marina Gándara-Cortes
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Correspondence to Ángel Vázquez-Boquete.

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Gándara-Cortes, M., Vázquez-Boquete, Á., Fernández-Rodríguez, B. et al. Breast cancer subtype discrimination using standardized 4-IHC and digital image analysis. Virchows Arch 472, 195–203 (2018). https://doi.org/10.1007/s00428-017-2194-z

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  • DOI: https://doi.org/10.1007/s00428-017-2194-z

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