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Impact of automated methods for quantitative evaluation of immunostaining: Towards digital pathology

INTRODUCTION: We sought to develop a novel method for a fully automated, robust quantification of protein biomarker expression within the epithelial component of high-grade serous ovarian tumors (HGSOC). Rather than defining thresholds for a given biomarker, the objective of this study in a small co...

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Autores principales: Elie, Nicolas, Giffard, Florence, Blanc-Fournier, Cécile, Morice, Pierre-Marie, Brachet, Pierre-Emmanuel, Dutoit, Soizic, Plancoulaine, Benoît, Poulain, Laurent
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9592864/
https://www.ncbi.nlm.nih.gov/pubmed/36303844
http://dx.doi.org/10.3389/fonc.2022.931035
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author Elie, Nicolas
Giffard, Florence
Blanc-Fournier, Cécile
Morice, Pierre-Marie
Brachet, Pierre-Emmanuel
Dutoit, Soizic
Plancoulaine, Benoît
Poulain, Laurent
author_facet Elie, Nicolas
Giffard, Florence
Blanc-Fournier, Cécile
Morice, Pierre-Marie
Brachet, Pierre-Emmanuel
Dutoit, Soizic
Plancoulaine, Benoît
Poulain, Laurent
author_sort Elie, Nicolas
collection PubMed
description INTRODUCTION: We sought to develop a novel method for a fully automated, robust quantification of protein biomarker expression within the epithelial component of high-grade serous ovarian tumors (HGSOC). Rather than defining thresholds for a given biomarker, the objective of this study in a small cohort of patients was to develop a method applicable to the many clinical situations in which immunomarkers need to be quantified. We aimed to quantify biomarker expression by correlating it with the heterogeneity of staining, using a non-subjective choice of scoring thresholds based on classical mathematical approaches. This could lead to a universal method for quantifying other immunohistochemical markers to guide pathologists in therapeutic decision-making. METHODS: We studied a cohort of 25 cases of HGSOC for which three biomarkers predictive of the response observed ex vivo to the BH3 mimetic molecule ABT-737 had been previously validated by a pathologist. We calibrated our algorithms using Stereology analyses performed by two experts to detect immunohistochemical staining and epithelial/stromal compartments. Immunostaining quantification within Stereology grids of hexagons was then performed for each histological slice. To define thresholds from the staining distribution histograms and to classify staining within each hexagon as low, medium, or high, we used the Gaussian Mixture Model (GMM). RESULTS: Stereology analysis of this calibration process produced a good correlation between the experts for both epithelium and immunostaining detection. There was also a good correlation between the experts and image processing. Image processing clearly revealed the respective proportions of low, medium, and high areas in a single tumor and showed that this parameter of heterogeneity could be included in a composite score, thus decreasing the level of discrepancy. Therefore, agreement with the pathologist was increased by taking heterogeneity into account. CONCLUSION AND DISCUSSION: This simple, robust, calibrated method using basic tools and known parameters can be used to quantify and characterize the expression of protein biomarkers within the different tumor compartments. It is based on known mathematical thresholds and takes the intratumoral heterogeneity of staining into account. Although some discrepancies need to be diminished, correlation with the pathologist’s classification was satisfactory. The method is replicable and can be used to analyze other biological and medical issues. This non-subjective technique for assessing protein biomarker expression uses a fully automated choice of thresholds (GMM) and defined composite scores that take the intra-tumor heterogeneity of immunostaining into account. It could help to avoid the misclassification of patients and its subsequent negative impact on therapeutic care.
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spelling pubmed-95928642022-10-26 Impact of automated methods for quantitative evaluation of immunostaining: Towards digital pathology Elie, Nicolas Giffard, Florence Blanc-Fournier, Cécile Morice, Pierre-Marie Brachet, Pierre-Emmanuel Dutoit, Soizic Plancoulaine, Benoît Poulain, Laurent Front Oncol Oncology INTRODUCTION: We sought to develop a novel method for a fully automated, robust quantification of protein biomarker expression within the epithelial component of high-grade serous ovarian tumors (HGSOC). Rather than defining thresholds for a given biomarker, the objective of this study in a small cohort of patients was to develop a method applicable to the many clinical situations in which immunomarkers need to be quantified. We aimed to quantify biomarker expression by correlating it with the heterogeneity of staining, using a non-subjective choice of scoring thresholds based on classical mathematical approaches. This could lead to a universal method for quantifying other immunohistochemical markers to guide pathologists in therapeutic decision-making. METHODS: We studied a cohort of 25 cases of HGSOC for which three biomarkers predictive of the response observed ex vivo to the BH3 mimetic molecule ABT-737 had been previously validated by a pathologist. We calibrated our algorithms using Stereology analyses performed by two experts to detect immunohistochemical staining and epithelial/stromal compartments. Immunostaining quantification within Stereology grids of hexagons was then performed for each histological slice. To define thresholds from the staining distribution histograms and to classify staining within each hexagon as low, medium, or high, we used the Gaussian Mixture Model (GMM). RESULTS: Stereology analysis of this calibration process produced a good correlation between the experts for both epithelium and immunostaining detection. There was also a good correlation between the experts and image processing. Image processing clearly revealed the respective proportions of low, medium, and high areas in a single tumor and showed that this parameter of heterogeneity could be included in a composite score, thus decreasing the level of discrepancy. Therefore, agreement with the pathologist was increased by taking heterogeneity into account. CONCLUSION AND DISCUSSION: This simple, robust, calibrated method using basic tools and known parameters can be used to quantify and characterize the expression of protein biomarkers within the different tumor compartments. It is based on known mathematical thresholds and takes the intratumoral heterogeneity of staining into account. Although some discrepancies need to be diminished, correlation with the pathologist’s classification was satisfactory. The method is replicable and can be used to analyze other biological and medical issues. This non-subjective technique for assessing protein biomarker expression uses a fully automated choice of thresholds (GMM) and defined composite scores that take the intra-tumor heterogeneity of immunostaining into account. It could help to avoid the misclassification of patients and its subsequent negative impact on therapeutic care. Frontiers Media S.A. 2022-10-11 /pmc/articles/PMC9592864/ /pubmed/36303844 http://dx.doi.org/10.3389/fonc.2022.931035 Text en Copyright © 2022 Elie, Giffard, Blanc-Fournier, Morice, Brachet, Dutoit, Plancoulaine and Poulain https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Oncology
Elie, Nicolas
Giffard, Florence
Blanc-Fournier, Cécile
Morice, Pierre-Marie
Brachet, Pierre-Emmanuel
Dutoit, Soizic
Plancoulaine, Benoît
Poulain, Laurent
Impact of automated methods for quantitative evaluation of immunostaining: Towards digital pathology
title Impact of automated methods for quantitative evaluation of immunostaining: Towards digital pathology
title_full Impact of automated methods for quantitative evaluation of immunostaining: Towards digital pathology
title_fullStr Impact of automated methods for quantitative evaluation of immunostaining: Towards digital pathology
title_full_unstemmed Impact of automated methods for quantitative evaluation of immunostaining: Towards digital pathology
title_short Impact of automated methods for quantitative evaluation of immunostaining: Towards digital pathology
title_sort impact of automated methods for quantitative evaluation of immunostaining: towards digital pathology
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9592864/
https://www.ncbi.nlm.nih.gov/pubmed/36303844
http://dx.doi.org/10.3389/fonc.2022.931035
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