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CD8+ T cell trajectory subtypes decode tumor heterogeneity and provide treatment recommendations for hepatocellular carcinoma

INTRODUCTION: Mounting evidence has revealed that the interactions and dynamic alterations among immune cells are critical in shaping the tumor microenvironment and ultimately map onto heterogeneous clinical outcomes. Currently, the underlying clinical significance of immune cell evolutions remains...

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Autores principales: Liu, Long, Liu, Zaoqu, Gao, Jie, Liu, Xudong, Weng, Siyuan, Guo, Chunguang, Hu, Bowen, Wang, Zhihui, Zhang, Jiakai, Shi, Jihua, Guo, Wenzhi, Zhang, Shuijun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9363578/
https://www.ncbi.nlm.nih.gov/pubmed/35967384
http://dx.doi.org/10.3389/fimmu.2022.964190
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author Liu, Long
Liu, Zaoqu
Gao, Jie
Liu, Xudong
Weng, Siyuan
Guo, Chunguang
Hu, Bowen
Wang, Zhihui
Zhang, Jiakai
Shi, Jihua
Guo, Wenzhi
Zhang, Shuijun
author_facet Liu, Long
Liu, Zaoqu
Gao, Jie
Liu, Xudong
Weng, Siyuan
Guo, Chunguang
Hu, Bowen
Wang, Zhihui
Zhang, Jiakai
Shi, Jihua
Guo, Wenzhi
Zhang, Shuijun
author_sort Liu, Long
collection PubMed
description INTRODUCTION: Mounting evidence has revealed that the interactions and dynamic alterations among immune cells are critical in shaping the tumor microenvironment and ultimately map onto heterogeneous clinical outcomes. Currently, the underlying clinical significance of immune cell evolutions remains largely unexplored in hepatocellular carcinoma (HCC). METHODS: A total of 3,817 immune cells and 1,750 HCC patients of 15 independent public datasets were retrieved. The Seurat and Monocle algorithms were used to depict T cell evolution, and nonnegative matrix factorization (NMF) was further applied to identify the molecular classification. Subsequently, the prognosis, biological characteristics, genomic variations, and immune landscape among distinct clusters were decoded. The clinical efficacy of multiple treatment approaches was further investigated. RESULTS: According to trajectory gene expression, three heterogeneous clusters with different clinical outcomes were identified. C2, with a more advanced pathological stage, presented the most dismal prognosis relative to C1 and C3. Eight independent external cohorts validated the robustness and reproducibility of the three clusters. Further explorations elucidated C1 to be characterized as lipid metabolic HCC, and C2 was referred to as cell-proliferative HCC, whereas C3 was defined as immune inflammatory HCC. Moreover, C2 also displayed the most conspicuous genomic instability, and C3 was deemed as “immune-hot”, having abundant immune cells and an elevated expression of immune checkpoints. The assessments of therapeutic intervention suggested that patients in C1 were suitable for transcatheter arterial chemoembolization treatment, and patients in C2 were sensitive to tyrosine kinase inhibitors, while patients in C3 were more responsive to immunotherapy. We also identified numerous underlying therapeutic agents, which might be conducive to clinical transformation in the future. CONCLUSIONS: Our study developed three clusters with distinct characteristics based on immune cell evolutions. For specifically stratified patients, we proposed individualized treatment strategies to improve the clinical outcomes and facilitate the clinical management.
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spelling pubmed-93635782022-08-11 CD8+ T cell trajectory subtypes decode tumor heterogeneity and provide treatment recommendations for hepatocellular carcinoma Liu, Long Liu, Zaoqu Gao, Jie Liu, Xudong Weng, Siyuan Guo, Chunguang Hu, Bowen Wang, Zhihui Zhang, Jiakai Shi, Jihua Guo, Wenzhi Zhang, Shuijun Front Immunol Immunology INTRODUCTION: Mounting evidence has revealed that the interactions and dynamic alterations among immune cells are critical in shaping the tumor microenvironment and ultimately map onto heterogeneous clinical outcomes. Currently, the underlying clinical significance of immune cell evolutions remains largely unexplored in hepatocellular carcinoma (HCC). METHODS: A total of 3,817 immune cells and 1,750 HCC patients of 15 independent public datasets were retrieved. The Seurat and Monocle algorithms were used to depict T cell evolution, and nonnegative matrix factorization (NMF) was further applied to identify the molecular classification. Subsequently, the prognosis, biological characteristics, genomic variations, and immune landscape among distinct clusters were decoded. The clinical efficacy of multiple treatment approaches was further investigated. RESULTS: According to trajectory gene expression, three heterogeneous clusters with different clinical outcomes were identified. C2, with a more advanced pathological stage, presented the most dismal prognosis relative to C1 and C3. Eight independent external cohorts validated the robustness and reproducibility of the three clusters. Further explorations elucidated C1 to be characterized as lipid metabolic HCC, and C2 was referred to as cell-proliferative HCC, whereas C3 was defined as immune inflammatory HCC. Moreover, C2 also displayed the most conspicuous genomic instability, and C3 was deemed as “immune-hot”, having abundant immune cells and an elevated expression of immune checkpoints. The assessments of therapeutic intervention suggested that patients in C1 were suitable for transcatheter arterial chemoembolization treatment, and patients in C2 were sensitive to tyrosine kinase inhibitors, while patients in C3 were more responsive to immunotherapy. We also identified numerous underlying therapeutic agents, which might be conducive to clinical transformation in the future. CONCLUSIONS: Our study developed three clusters with distinct characteristics based on immune cell evolutions. For specifically stratified patients, we proposed individualized treatment strategies to improve the clinical outcomes and facilitate the clinical management. Frontiers Media S.A. 2022-07-27 /pmc/articles/PMC9363578/ /pubmed/35967384 http://dx.doi.org/10.3389/fimmu.2022.964190 Text en Copyright © 2022 Liu, Liu, Gao, Liu, Weng, Guo, Hu, Wang, Zhang, Shi, Guo and Zhang 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
Liu, Long
Liu, Zaoqu
Gao, Jie
Liu, Xudong
Weng, Siyuan
Guo, Chunguang
Hu, Bowen
Wang, Zhihui
Zhang, Jiakai
Shi, Jihua
Guo, Wenzhi
Zhang, Shuijun
CD8+ T cell trajectory subtypes decode tumor heterogeneity and provide treatment recommendations for hepatocellular carcinoma
title CD8+ T cell trajectory subtypes decode tumor heterogeneity and provide treatment recommendations for hepatocellular carcinoma
title_full CD8+ T cell trajectory subtypes decode tumor heterogeneity and provide treatment recommendations for hepatocellular carcinoma
title_fullStr CD8+ T cell trajectory subtypes decode tumor heterogeneity and provide treatment recommendations for hepatocellular carcinoma
title_full_unstemmed CD8+ T cell trajectory subtypes decode tumor heterogeneity and provide treatment recommendations for hepatocellular carcinoma
title_short CD8+ T cell trajectory subtypes decode tumor heterogeneity and provide treatment recommendations for hepatocellular carcinoma
title_sort cd8+ t cell trajectory subtypes decode tumor heterogeneity and provide treatment recommendations for hepatocellular carcinoma
topic Immunology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9363578/
https://www.ncbi.nlm.nih.gov/pubmed/35967384
http://dx.doi.org/10.3389/fimmu.2022.964190
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