Integrated tumor identification and automated scoring minimizes pathologist involvement and provides new insights to key biomarkers in breast cancer

Peter Bankhead, José A Fernández, Darragh G McArt, David P Boyle, Gerald Li, Maurice B Loughrey, Gareth W Irwin, D Paul Harkin, Jacqueline A James, Stephen McQuaid, Manuel Salto-Tellez, Peter W Hamilton

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Abstract

Digital image analysis (DIA) is becoming central to the quantitative evaluation of tissue biomarkers for discovery, diagnosis and therapeutic selection for the delivery of precision medicine. In this study, automated DIA using a new purpose-built software platform (QuPath) is applied to a cohort of 293 breast cancer patients to score five biomarkers in tissue microarrays (TMAs): ER, PR, HER2, Ki67 and p53. This software is able to measure IHC expression following fully automated tumor recognition in the same immunohistochemical (IHC)-stained tissue section, as part of a rapid workflow to ensure objectivity and accelerate biomarker analysis. The digital scores produced by QuPath were compared with manual scores by a pathologist and shown to have a good level of concordance in all cases (Cohen's κ>0.6), and almost perfect agreement for the clinically relevant biomarkers ER, PR and HER2 (κ>0.86). To assess prognostic value, cutoff thresholds could be applied to both manual and automated scores using the QuPath software, and survival analysis performed for 5-year overall survival. DIA was shown to be capable of replicating the statistically significant stratification of patients achieved using manual scoring across all biomarkers (P<0.01, log-rank test). Furthermore, the image analysis scores were shown to consistently lead to statistical significance across a wide range of potential cutoff thresholds, indicating the robustness of the method, and identify sub-populations of cases exhibiting different expression patterns within the p53 and Ki67 data sets that warrant further investigation. These findings have demonstrated QuPath's suitability for fast, reproducible, high-throughput TMA analysis across a range of important biomarkers. This was achieved using our tumor recognition algorithms for IHC-stained sections, trained interactively without the need for any additional tumor recognition markers, for example, cytokeratin, to obtain greater insight into the relationship between biomarker expression and clinical outcome applicable to a range of cancer types.Laboratory Investigation advance online publication, 18 December 2017; doi:10.1038/labinvest.2017.131.

Original languageEnglish
Pages (from-to)15-26
JournalLaboratory investigation
Volume98
Early online date18 Dec 2017
DOIs
Publication statusPublished - Jan 2018

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  • Journal Article

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