Digital Pathology Analysis Quantifies Spatial Heterogeneity of CD3, CD4, CD8, CD20, and FoxP3 Immune Markers in Triple-Negative Breast Cancer
Overwhelming evidence has shown the significant role of the tumor microenvironment (TME) in governing the triple-negative breast cancer (TNBC) progression. Digital pathology can provide key information about the spatial heterogeneity within the TME using image analysis and spatial statistics. These...
Main Authors: | , , , , , , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Frontiers Media S.A.
2020-10-01
|
Series: | Frontiers in Physiology |
Subjects: | |
Online Access: | https://www.frontiersin.org/articles/10.3389/fphys.2020.583333/full |
id |
doaj-661ed5204e1a42009d6a3f5cc131bda4 |
---|---|
record_format |
Article |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Haoyang Mi Chang Gong Jeremias Sulam Jeremias Sulam Elana J. Fertig Elana J. Fertig Alexander S. Szalay Alexander S. Szalay Elizabeth M. Jaffee Elizabeth M. Jaffee Vered Stearns Leisha A. Emens Ashley M. Cimino-Mathews Ashley M. Cimino-Mathews Aleksander S. Popel Aleksander S. Popel |
spellingShingle |
Haoyang Mi Chang Gong Jeremias Sulam Jeremias Sulam Elana J. Fertig Elana J. Fertig Alexander S. Szalay Alexander S. Szalay Elizabeth M. Jaffee Elizabeth M. Jaffee Vered Stearns Leisha A. Emens Ashley M. Cimino-Mathews Ashley M. Cimino-Mathews Aleksander S. Popel Aleksander S. Popel Digital Pathology Analysis Quantifies Spatial Heterogeneity of CD3, CD4, CD8, CD20, and FoxP3 Immune Markers in Triple-Negative Breast Cancer Frontiers in Physiology digital pathology image informatics spatial patterns breast cancer tumor heterogeneity immuno-architecture |
author_facet |
Haoyang Mi Chang Gong Jeremias Sulam Jeremias Sulam Elana J. Fertig Elana J. Fertig Alexander S. Szalay Alexander S. Szalay Elizabeth M. Jaffee Elizabeth M. Jaffee Vered Stearns Leisha A. Emens Ashley M. Cimino-Mathews Ashley M. Cimino-Mathews Aleksander S. Popel Aleksander S. Popel |
author_sort |
Haoyang Mi |
title |
Digital Pathology Analysis Quantifies Spatial Heterogeneity of CD3, CD4, CD8, CD20, and FoxP3 Immune Markers in Triple-Negative Breast Cancer |
title_short |
Digital Pathology Analysis Quantifies Spatial Heterogeneity of CD3, CD4, CD8, CD20, and FoxP3 Immune Markers in Triple-Negative Breast Cancer |
title_full |
Digital Pathology Analysis Quantifies Spatial Heterogeneity of CD3, CD4, CD8, CD20, and FoxP3 Immune Markers in Triple-Negative Breast Cancer |
title_fullStr |
Digital Pathology Analysis Quantifies Spatial Heterogeneity of CD3, CD4, CD8, CD20, and FoxP3 Immune Markers in Triple-Negative Breast Cancer |
title_full_unstemmed |
Digital Pathology Analysis Quantifies Spatial Heterogeneity of CD3, CD4, CD8, CD20, and FoxP3 Immune Markers in Triple-Negative Breast Cancer |
title_sort |
digital pathology analysis quantifies spatial heterogeneity of cd3, cd4, cd8, cd20, and foxp3 immune markers in triple-negative breast cancer |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Physiology |
issn |
1664-042X |
publishDate |
2020-10-01 |
description |
Overwhelming evidence has shown the significant role of the tumor microenvironment (TME) in governing the triple-negative breast cancer (TNBC) progression. Digital pathology can provide key information about the spatial heterogeneity within the TME using image analysis and spatial statistics. These analyses have been applied to CD8+ T cells, but quantitative analyses of other important markers and their correlations are limited. In this study, a digital pathology computational workflow is formulated for characterizing the spatial distributions of five immune markers (CD3, CD4, CD8, CD20, and FoxP3) and then the functionality is tested on whole slide images from patients with TNBC. The workflow is initiated by digital image processing to extract and colocalize immune marker-labeled cells and then convert this information to point patterns. Afterward invasive front (IF), central tumor (CT), and normal tissue (N) are characterized. For each region, we examine the intra-tumoral heterogeneity. The workflow is then repeated for all specimens to capture inter-tumoral heterogeneity. In this study, both intra- and inter-tumoral heterogeneities are observed for all five markers across all specimens. Among all regions, IF tends to have higher densities of immune cells and overall larger variations in spatial model fitting parameters and higher density in cell clusters and hotspots compared to CT and N. Results suggest a distinct role of IF in the tumor immuno-architecture. Though the sample size is limited in the study, the computational workflow could be readily reproduced and scaled due to its automatic nature. Importantly, the value of the workflow also lies in its potential to be linked to treatment outcomes and identification of predictive biomarkers for responders/non-responders, and its application to parameterization and validation of computational immuno-oncology models. |
topic |
digital pathology image informatics spatial patterns breast cancer tumor heterogeneity immuno-architecture |
url |
https://www.frontiersin.org/articles/10.3389/fphys.2020.583333/full |
work_keys_str_mv |
AT haoyangmi digitalpathologyanalysisquantifiesspatialheterogeneityofcd3cd4cd8cd20andfoxp3immunemarkersintriplenegativebreastcancer AT changgong digitalpathologyanalysisquantifiesspatialheterogeneityofcd3cd4cd8cd20andfoxp3immunemarkersintriplenegativebreastcancer AT jeremiassulam digitalpathologyanalysisquantifiesspatialheterogeneityofcd3cd4cd8cd20andfoxp3immunemarkersintriplenegativebreastcancer AT jeremiassulam digitalpathologyanalysisquantifiesspatialheterogeneityofcd3cd4cd8cd20andfoxp3immunemarkersintriplenegativebreastcancer AT elanajfertig digitalpathologyanalysisquantifiesspatialheterogeneityofcd3cd4cd8cd20andfoxp3immunemarkersintriplenegativebreastcancer AT elanajfertig digitalpathologyanalysisquantifiesspatialheterogeneityofcd3cd4cd8cd20andfoxp3immunemarkersintriplenegativebreastcancer AT alexandersszalay digitalpathologyanalysisquantifiesspatialheterogeneityofcd3cd4cd8cd20andfoxp3immunemarkersintriplenegativebreastcancer AT alexandersszalay digitalpathologyanalysisquantifiesspatialheterogeneityofcd3cd4cd8cd20andfoxp3immunemarkersintriplenegativebreastcancer AT elizabethmjaffee digitalpathologyanalysisquantifiesspatialheterogeneityofcd3cd4cd8cd20andfoxp3immunemarkersintriplenegativebreastcancer AT elizabethmjaffee digitalpathologyanalysisquantifiesspatialheterogeneityofcd3cd4cd8cd20andfoxp3immunemarkersintriplenegativebreastcancer AT veredstearns digitalpathologyanalysisquantifiesspatialheterogeneityofcd3cd4cd8cd20andfoxp3immunemarkersintriplenegativebreastcancer AT leishaaemens digitalpathologyanalysisquantifiesspatialheterogeneityofcd3cd4cd8cd20andfoxp3immunemarkersintriplenegativebreastcancer AT ashleymciminomathews digitalpathologyanalysisquantifiesspatialheterogeneityofcd3cd4cd8cd20andfoxp3immunemarkersintriplenegativebreastcancer AT ashleymciminomathews digitalpathologyanalysisquantifiesspatialheterogeneityofcd3cd4cd8cd20andfoxp3immunemarkersintriplenegativebreastcancer AT aleksanderspopel digitalpathologyanalysisquantifiesspatialheterogeneityofcd3cd4cd8cd20andfoxp3immunemarkersintriplenegativebreastcancer AT aleksanderspopel digitalpathologyanalysisquantifiesspatialheterogeneityofcd3cd4cd8cd20andfoxp3immunemarkersintriplenegativebreastcancer |
_version_ |
1725045025653391360 |
spelling |
doaj-661ed5204e1a42009d6a3f5cc131bda42020-11-25T01:40:33ZengFrontiers Media S.A.Frontiers in Physiology1664-042X2020-10-011110.3389/fphys.2020.583333583333Digital Pathology Analysis Quantifies Spatial Heterogeneity of CD3, CD4, CD8, CD20, and FoxP3 Immune Markers in Triple-Negative Breast CancerHaoyang Mi0Chang Gong1Jeremias Sulam2Jeremias Sulam3Elana J. Fertig4Elana J. Fertig5Alexander S. Szalay6Alexander S. Szalay7Elizabeth M. Jaffee8Elizabeth M. Jaffee9Vered Stearns10Leisha A. Emens11Ashley M. Cimino-Mathews12Ashley M. Cimino-Mathews13Aleksander S. Popel14Aleksander S. Popel15Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, United StatesDepartment of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, United StatesDepartment of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, United StatesJohns Hopkins Mathematical Institute for Data Science, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, United StatesDepartment of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, United StatesDepartment of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD, United StatesHenry A. Rowland Department of Physics and Astronomy, Krieger School of Arts and Sciences, Johns Hopkins University, Baltimore, MD, United StatesDepartment of Computer Science, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, United StatesDepartment of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD, United StatesThe Bloomberg∼Kimmel Institute for Cancer Immunotherapy, Johns Hopkins School of Medicine, Baltimore, MD, United StatesDepartment of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD, United StatesDepartment of Medicine/Hematology-Oncology, Hillman Cancer Center, University of Pittsburgh Medical Center, Pittsburgh, PA, United StatesDepartment of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD, United StatesDepartment of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, United StatesDepartment of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, United StatesDepartment of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD, United StatesOverwhelming evidence has shown the significant role of the tumor microenvironment (TME) in governing the triple-negative breast cancer (TNBC) progression. Digital pathology can provide key information about the spatial heterogeneity within the TME using image analysis and spatial statistics. These analyses have been applied to CD8+ T cells, but quantitative analyses of other important markers and their correlations are limited. In this study, a digital pathology computational workflow is formulated for characterizing the spatial distributions of five immune markers (CD3, CD4, CD8, CD20, and FoxP3) and then the functionality is tested on whole slide images from patients with TNBC. The workflow is initiated by digital image processing to extract and colocalize immune marker-labeled cells and then convert this information to point patterns. Afterward invasive front (IF), central tumor (CT), and normal tissue (N) are characterized. For each region, we examine the intra-tumoral heterogeneity. The workflow is then repeated for all specimens to capture inter-tumoral heterogeneity. In this study, both intra- and inter-tumoral heterogeneities are observed for all five markers across all specimens. Among all regions, IF tends to have higher densities of immune cells and overall larger variations in spatial model fitting parameters and higher density in cell clusters and hotspots compared to CT and N. Results suggest a distinct role of IF in the tumor immuno-architecture. Though the sample size is limited in the study, the computational workflow could be readily reproduced and scaled due to its automatic nature. Importantly, the value of the workflow also lies in its potential to be linked to treatment outcomes and identification of predictive biomarkers for responders/non-responders, and its application to parameterization and validation of computational immuno-oncology models.https://www.frontiersin.org/articles/10.3389/fphys.2020.583333/fulldigital pathologyimage informaticsspatial patternsbreast cancertumor heterogeneityimmuno-architecture |