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Tumor Niche Network-Defined Subtypes Predict Immunotherapy Response of Esophageal Squamous Cell Cancer
Despite the promising outcomes of immune checkpoint blockade (ICB), resistance to ICB presents a new challenge. Therefore, selecting patients for specific ICB applications is crucial for maximizing therapeutic efficacy. Herein we curated 69 human esophageal squamous cell cancer (ESCC) patients’ tumo...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cold Spring Harbor Laboratory
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9949073/ https://www.ncbi.nlm.nih.gov/pubmed/36824935 http://dx.doi.org/10.1101/2023.02.15.528539 |
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author | Ko, Kyung-Pil Zhang, Shengzhe Huang, Yuanjian Kim, Bongjun Zou, Gengyi Jun, Sohee Zhang, Jie Martin, Cecilia Dunbar, Karen J. Efe, Gizem Rustgi, Anil K. Zhang, Haiyang Nakagawa, Hiroshi Park, Jae-Il |
author_facet | Ko, Kyung-Pil Zhang, Shengzhe Huang, Yuanjian Kim, Bongjun Zou, Gengyi Jun, Sohee Zhang, Jie Martin, Cecilia Dunbar, Karen J. Efe, Gizem Rustgi, Anil K. Zhang, Haiyang Nakagawa, Hiroshi Park, Jae-Il |
author_sort | Ko, Kyung-Pil |
collection | PubMed |
description | Despite the promising outcomes of immune checkpoint blockade (ICB), resistance to ICB presents a new challenge. Therefore, selecting patients for specific ICB applications is crucial for maximizing therapeutic efficacy. Herein we curated 69 human esophageal squamous cell cancer (ESCC) patients’ tumor microenvironment (TME) single-cell transcriptomic datasets to subtype ESCC. Integrative analyses of the cellular network transcriptional signatures of T cells, myeloid cells, and fibroblasts define distinct ESCC subtypes characterized by T cell exhaustion, Interferon (IFN) a/b signaling, TIGIT enrichment, and specific marker genes. Furthermore, this approach classifies ESCC patients into ICB responders and non-responders, as validated by liquid biopsy single-cell transcriptomics. Our study stratifies ESCC patients based on TME transcriptional network, providing novel insights into tumor niche remodeling and predicting ICB responses in ESCC patients. |
format | Online Article Text |
id | pubmed-9949073 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Cold Spring Harbor Laboratory |
record_format | MEDLINE/PubMed |
spelling | pubmed-99490732023-02-24 Tumor Niche Network-Defined Subtypes Predict Immunotherapy Response of Esophageal Squamous Cell Cancer Ko, Kyung-Pil Zhang, Shengzhe Huang, Yuanjian Kim, Bongjun Zou, Gengyi Jun, Sohee Zhang, Jie Martin, Cecilia Dunbar, Karen J. Efe, Gizem Rustgi, Anil K. Zhang, Haiyang Nakagawa, Hiroshi Park, Jae-Il bioRxiv Article Despite the promising outcomes of immune checkpoint blockade (ICB), resistance to ICB presents a new challenge. Therefore, selecting patients for specific ICB applications is crucial for maximizing therapeutic efficacy. Herein we curated 69 human esophageal squamous cell cancer (ESCC) patients’ tumor microenvironment (TME) single-cell transcriptomic datasets to subtype ESCC. Integrative analyses of the cellular network transcriptional signatures of T cells, myeloid cells, and fibroblasts define distinct ESCC subtypes characterized by T cell exhaustion, Interferon (IFN) a/b signaling, TIGIT enrichment, and specific marker genes. Furthermore, this approach classifies ESCC patients into ICB responders and non-responders, as validated by liquid biopsy single-cell transcriptomics. Our study stratifies ESCC patients based on TME transcriptional network, providing novel insights into tumor niche remodeling and predicting ICB responses in ESCC patients. Cold Spring Harbor Laboratory 2023-02-15 /pmc/articles/PMC9949073/ /pubmed/36824935 http://dx.doi.org/10.1101/2023.02.15.528539 Text en https://creativecommons.org/licenses/by-nc-nd/4.0/This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (https://creativecommons.org/licenses/by-nc-nd/4.0/) , which allows reusers to copy and distribute the material in any medium or format in unadapted form only, for noncommercial purposes only, and only so long as attribution is given to the creator. |
spellingShingle | Article Ko, Kyung-Pil Zhang, Shengzhe Huang, Yuanjian Kim, Bongjun Zou, Gengyi Jun, Sohee Zhang, Jie Martin, Cecilia Dunbar, Karen J. Efe, Gizem Rustgi, Anil K. Zhang, Haiyang Nakagawa, Hiroshi Park, Jae-Il Tumor Niche Network-Defined Subtypes Predict Immunotherapy Response of Esophageal Squamous Cell Cancer |
title | Tumor Niche Network-Defined Subtypes Predict Immunotherapy Response of Esophageal Squamous Cell Cancer |
title_full | Tumor Niche Network-Defined Subtypes Predict Immunotherapy Response of Esophageal Squamous Cell Cancer |
title_fullStr | Tumor Niche Network-Defined Subtypes Predict Immunotherapy Response of Esophageal Squamous Cell Cancer |
title_full_unstemmed | Tumor Niche Network-Defined Subtypes Predict Immunotherapy Response of Esophageal Squamous Cell Cancer |
title_short | Tumor Niche Network-Defined Subtypes Predict Immunotherapy Response of Esophageal Squamous Cell Cancer |
title_sort | tumor niche network-defined subtypes predict immunotherapy response of esophageal squamous cell cancer |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9949073/ https://www.ncbi.nlm.nih.gov/pubmed/36824935 http://dx.doi.org/10.1101/2023.02.15.528539 |
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