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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

  • William J. Howat
  • , Fiona M. Blows
  • , Elena Provenzano
  • , Mark N. Brook
  • , Lorna Morris
  • , Patrycja Gazinska
  • , Nicola Johnson
  • , Leigh Anne McDuffus
  • , Jodi Miller
  • , Elinor J. Sawyer
  • , Sarah Pinder
  • , Carolien H.M. van Deurzen
  • , Louise Jones
  • , Reijo Sironen
  • , Daniel Visscher
  • , Carlos Caldas
  • , Frances Daley
  • , Penny Coulson
  • , Annegien Broeks
  • , Joyce Sanders
  • Jelle Wesseling, Heli Nevanlinna, Rainer Fagerholm, Carl Blomqvist, Päivi Heikkilä, H. Raza Ali, Sarah Jane Dawson, Jonine Figueroa, Jolanta Lissowska, Louise Brinton, Arto Mannermaa, Vesa Kataja, Veli Matti Kosma, Angela Cox, Ian W. Brock, Simon S. Cross, Malcolm W. Reed, Fergus J. Couch, Janet E. Olson, Peter Devillee, Wilma E. Mesker, Caroline M. Seyaneve, Antoinette Hollestelle, Javier Benitez, Jose Ignacio Arias Perez, Primitiva Menéndez, Manjeet K. Bolla, Douglas F. Easton, Marjanka K. Schmidt, Paul D. Pharoah, Mark E. Sherman, Montserrat García-Closas*
*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

28 Scopus citations

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.

Original languageEnglish
Pages (from-to)18-32
Number of pages15
JournalJournal of Pathology: Clinical Research
Volume1
Issue number1
DOIs
StatePublished - Jan 2015
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2014 John Wiley and Sons Ltd and The Pathological Society of Great Britain and Ireland

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

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

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