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Deciphering the Immune Complexity in Esophageal Adenocarcinoma and Pre-Cancerous Lesions With Sequential Multiplex Immunohistochemistry and Sparse Subspace Clustering Approach
Esophageal adenocarcinoma (EAC) develops from a chronic inflammatory environment across four stages: intestinal metaplasia, known as Barrett’s esophagus, low- and high-grade dysplasia, and adenocarcinoma. Although the genomic characteristics of this progression have been well defined via large-scale...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9161782/ https://www.ncbi.nlm.nih.gov/pubmed/35663986 http://dx.doi.org/10.3389/fimmu.2022.874255 |
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author | Sundaram, Srinand Kim, Eun Na Jones, Georgina M. Sivagnanam, Shamilene Tripathi, Monika Miremadi, Ahmad Di Pietro, Massimiliano Coussens, Lisa M. Fitzgerald, Rebecca C. Chang, Young Hwan Zhuang, Lizhe |
author_facet | Sundaram, Srinand Kim, Eun Na Jones, Georgina M. Sivagnanam, Shamilene Tripathi, Monika Miremadi, Ahmad Di Pietro, Massimiliano Coussens, Lisa M. Fitzgerald, Rebecca C. Chang, Young Hwan Zhuang, Lizhe |
author_sort | Sundaram, Srinand |
collection | PubMed |
description | Esophageal adenocarcinoma (EAC) develops from a chronic inflammatory environment across four stages: intestinal metaplasia, known as Barrett’s esophagus, low- and high-grade dysplasia, and adenocarcinoma. Although the genomic characteristics of this progression have been well defined via large-scale DNA sequencing, the dynamics of various immune cell subsets and their spatial interactions in their tumor microenvironment remain unclear. Here, we applied a sequential multiplex immunohistochemistry (mIHC) platform with computational image analysis pipelines that allow for the detection of 10 biomarkers in one formalin-fixed paraffin-embedded (FFPE) tissue section. Using this platform and quantitative image analytics, we studied changes in the immune landscape during disease progression based on 40 normal and diseased areas from endoscopic mucosal resection specimens of chemotherapy treatment- naïve patients, including normal esophagus, metaplasia, low- and high-grade dysplasia, and adenocarcinoma. The results revealed a steady increase of FOXP3(+) T regulatory cells and a CD163(+) myelomonocytic cell subset. In parallel to the manual gating strategy applied for cell phenotyping, we also adopted a sparse subspace clustering (SSC) algorithm allowing the automated cell phenotyping of mIHC-based single-cell data. The algorithm successfully identified comparable cell types, along with significantly enriched FOXP3 T regulatory cells and CD163(+) myelomonocytic cells as found in manual gating. In addition, SCC identified a new CSF1R(+)CD1C(+) myeloid lineage, which not only was previously unknown in this disease but also increases with advancing disease stages. This study revealed immune dynamics in EAC progression and highlighted the potential application of a new multiplex imaging platform, combined with computational image analysis on routine clinical FFPE sections, to investigate complex immune populations in tumor ecosystems. |
format | Online Article Text |
id | pubmed-9161782 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-91617822022-06-03 Deciphering the Immune Complexity in Esophageal Adenocarcinoma and Pre-Cancerous Lesions With Sequential Multiplex Immunohistochemistry and Sparse Subspace Clustering Approach Sundaram, Srinand Kim, Eun Na Jones, Georgina M. Sivagnanam, Shamilene Tripathi, Monika Miremadi, Ahmad Di Pietro, Massimiliano Coussens, Lisa M. Fitzgerald, Rebecca C. Chang, Young Hwan Zhuang, Lizhe Front Immunol Immunology Esophageal adenocarcinoma (EAC) develops from a chronic inflammatory environment across four stages: intestinal metaplasia, known as Barrett’s esophagus, low- and high-grade dysplasia, and adenocarcinoma. Although the genomic characteristics of this progression have been well defined via large-scale DNA sequencing, the dynamics of various immune cell subsets and their spatial interactions in their tumor microenvironment remain unclear. Here, we applied a sequential multiplex immunohistochemistry (mIHC) platform with computational image analysis pipelines that allow for the detection of 10 biomarkers in one formalin-fixed paraffin-embedded (FFPE) tissue section. Using this platform and quantitative image analytics, we studied changes in the immune landscape during disease progression based on 40 normal and diseased areas from endoscopic mucosal resection specimens of chemotherapy treatment- naïve patients, including normal esophagus, metaplasia, low- and high-grade dysplasia, and adenocarcinoma. The results revealed a steady increase of FOXP3(+) T regulatory cells and a CD163(+) myelomonocytic cell subset. In parallel to the manual gating strategy applied for cell phenotyping, we also adopted a sparse subspace clustering (SSC) algorithm allowing the automated cell phenotyping of mIHC-based single-cell data. The algorithm successfully identified comparable cell types, along with significantly enriched FOXP3 T regulatory cells and CD163(+) myelomonocytic cells as found in manual gating. In addition, SCC identified a new CSF1R(+)CD1C(+) myeloid lineage, which not only was previously unknown in this disease but also increases with advancing disease stages. This study revealed immune dynamics in EAC progression and highlighted the potential application of a new multiplex imaging platform, combined with computational image analysis on routine clinical FFPE sections, to investigate complex immune populations in tumor ecosystems. Frontiers Media S.A. 2022-05-19 /pmc/articles/PMC9161782/ /pubmed/35663986 http://dx.doi.org/10.3389/fimmu.2022.874255 Text en Copyright © 2022 Sundaram, Kim, Jones, Sivagnanam, Tripathi, Miremadi, Di Pietro, Coussens, Fitzgerald, Chang and Zhuang 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 | Immunology Sundaram, Srinand Kim, Eun Na Jones, Georgina M. Sivagnanam, Shamilene Tripathi, Monika Miremadi, Ahmad Di Pietro, Massimiliano Coussens, Lisa M. Fitzgerald, Rebecca C. Chang, Young Hwan Zhuang, Lizhe Deciphering the Immune Complexity in Esophageal Adenocarcinoma and Pre-Cancerous Lesions With Sequential Multiplex Immunohistochemistry and Sparse Subspace Clustering Approach |
title | Deciphering the Immune Complexity in Esophageal Adenocarcinoma and Pre-Cancerous Lesions With Sequential Multiplex Immunohistochemistry and Sparse Subspace Clustering Approach |
title_full | Deciphering the Immune Complexity in Esophageal Adenocarcinoma and Pre-Cancerous Lesions With Sequential Multiplex Immunohistochemistry and Sparse Subspace Clustering Approach |
title_fullStr | Deciphering the Immune Complexity in Esophageal Adenocarcinoma and Pre-Cancerous Lesions With Sequential Multiplex Immunohistochemistry and Sparse Subspace Clustering Approach |
title_full_unstemmed | Deciphering the Immune Complexity in Esophageal Adenocarcinoma and Pre-Cancerous Lesions With Sequential Multiplex Immunohistochemistry and Sparse Subspace Clustering Approach |
title_short | Deciphering the Immune Complexity in Esophageal Adenocarcinoma and Pre-Cancerous Lesions With Sequential Multiplex Immunohistochemistry and Sparse Subspace Clustering Approach |
title_sort | deciphering the immune complexity in esophageal adenocarcinoma and pre-cancerous lesions with sequential multiplex immunohistochemistry and sparse subspace clustering approach |
topic | Immunology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9161782/ https://www.ncbi.nlm.nih.gov/pubmed/35663986 http://dx.doi.org/10.3389/fimmu.2022.874255 |
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