Abstract
Differences between computer-assisted image analysis (CAI) algorithms may cause discrepancies in the identification of immunohistochemically stained immune biomarkers in biopsies of breast cancer patients. These discrepancies have implications for their association with disease outcome. This study aims to compare three CAI procedures (A, B and C) to measure positive marker areas in post-neoadjuvant chemotherapy biopsies of patients with triple-negative breast cancer (TNBC) and to explore the differences in their performance in determining the potential association with relapse in these patients. A total of 3304 digital images of biopsy tissue obtained from 118 TNBC patients were stained for seven immune markers using immunohistochemistry (CD4, CD8, FOXP3, CD21, CD1a, CD83, HLA-DR) and were analyzed with procedures A, B and C. The three methods measure the positive pixel markers in the total tissue areas. The extent of agreement between paired CAI procedures, a principal component analysis (PCA) and Cox multivariate analysis was assessed. Comparisons of paired procedures showed close agreement for most of the immune markers at low concentration. The probability of differences between the paired procedures B/C and B/A was generally higher than those observed in C/A. The principal component analysis, largely based on data from CD8, CD1a and HLA-DR, identified two groups of patients with a significantly lower probability of relapse than the others. The multivariate regression models showed similarities in the factors associated with relapse for procedures A and C, as opposed to those obtained with procedure B. General agreement among the results of CAI procedures would not guarantee that the same predictive breast cancer markers were consistently identified. These results highlight the importance of developing additional strategies to improve the sensitivity of CAI procedures.
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The data sets used and analyzed in this current study are available from the corresponding author upon reasonable request.
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Acknowledgements
The authors thank the Xarxa de Bancs de Tumors de Catalunya (XBTC), sponsored by the Oncology Master Plan for Catalonia (Pla Director d’Oncologia de Catalunya), as well as the biobanks and tumor banks of the Spanish hospitals for providing the biopsies of the TNBC patients and their clinical and pathological data. The authors thank Phil Mason for his thorough work and advice, which have helped us improve this article. Abstracts of parts of this study were presented at the XXIX Congreso Nacional de la SEAP-IAP, XXIV Congreso Nacional de la (SEC), the V Congreso Nacional de la SEPAF (Granada, Spain, 22-24 May 2019), and the 162nd ICB Seminar on Computer-Aided Diagnosis Support by Digital Pathology (Warsaw, Poland, 4–6 November 2018).
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This work was supported by grants of the Institute of Health Carlos III (PI13/02501 and PI11/0488) co-financed by the European Regional Development Fund (ERDF). ML acknowledges support from the “PATH-IMAGE” project funded by the ERDF (agreement 2903/335-41).
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ML: Conceptualization, Methodology, Data curation, Writing—Original draft, Supervision. BP: Methodology, Software, Formal analysis, Investigation, Writing—Review & Editing. NE: Methodology, Software, Formal analysis, Investigation, Writing—Review & Editing. RB: Conceptualization, Writing—Review & Editing. LF: Formal analysis, Data curation, Review & Editing. IV: Formal analysis, Data curation, Review & Editing. AK: Methodology, Software, Review & Editing. AGN: Writing—Review & Editing. ESC: Data curation, Review & Editing. CL: Conceptualization, Methodology, Data curation, Writing—Original draft, Review & Editing.
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Lejeune, M., Plancoulaine, B., Elie, N. et al. How the variability between computer-assisted analysis procedures evaluating immune markers can influence patients’ outcome prediction. Histochem Cell Biol 156, 461–478 (2021). https://doi.org/10.1007/s00418-021-02022-8
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DOI: https://doi.org/10.1007/s00418-021-02022-8