Low muscle mass in lung cancer is associated with an inflammatory and immunosuppressive tumor microenvironment

Detalhes bibliográficos
Autor(a) principal: Cury, Sarah Santiloni [UNESP]
Data de Publicação: 2023
Outros Autores: de Moraes, Diogo [UNESP], Oliveira, Jakeline Santos [UNESP], Freire, Paula Paccielli, dos Reis, Patricia Pintor [UNESP], Batista, Miguel Luiz, Hasimoto, Érica Nishida [UNESP], Carvalho, Robson Francisco [UNESP]
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1186/s12967-023-03901-5
http://hdl.handle.net/11449/248341
Resumo: Background: Computed tomographies (CT) are useful for identifying muscle loss in non-small lung cancer (NSCLC) cachectic patients. However, we lack consensus on the best cutoff point for pectoralis muscle loss. We aimed to characterize NSCLC patients based on muscularity, clinical data, and the transcriptional profile from the tumor microenvironment to build a cachexia classification model. Methods: We used machine learning to generate a muscle loss prediction model, and the tumor's cellular and transcriptional profile was characterized in patients with low muscularity. First, we measured the pectoralis muscle area (PMA) of 211 treatment-naive NSCLC patients using CT available in The Cancer Imaging Archive. The cutoffs were established using machine learning algorithms (CART and Cutoff Finder) on PMA, clinical, and survival data. We evaluated the prediction model in a validation set (36 NSCLC). Tumor RNA-Seq (GSE103584) was used to profile the transcriptome and cellular composition based on digital cytometry. Results: CART demonstrated that a lower PMA was associated with a high risk of death (HR = 1.99). Cutoff Finder selected PMA cutoffs separating low-muscularity (LM) patients based on the risk of death (P-value = 0.003; discovery set). The cutoff presented 84% of success in classifying low muscle mass. The high risk of LM patients was also found in the validation set. Tumor RNA-Seq revealed 90 upregulated secretory genes in LM that potentially interact with muscle cell receptors. The LM upregulated genes enriched inflammatory biological processes. Digital cytometry revealed that LM patients presented high proportions of cytotoxic and exhausted CD8+ T cells. Conclusions: Our prediction model identified cutoffs that distinguished patients with lower PMA and survival with an inflammatory and immunosuppressive TME enriched with inflammatory factors and CD8+ T cells.
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spelling Low muscle mass in lung cancer is associated with an inflammatory and immunosuppressive tumor microenvironmentCD8+ T cellsComputed tomographyMachine learningNon-small cell lung cancerTranscriptomicsBackground: Computed tomographies (CT) are useful for identifying muscle loss in non-small lung cancer (NSCLC) cachectic patients. However, we lack consensus on the best cutoff point for pectoralis muscle loss. We aimed to characterize NSCLC patients based on muscularity, clinical data, and the transcriptional profile from the tumor microenvironment to build a cachexia classification model. Methods: We used machine learning to generate a muscle loss prediction model, and the tumor's cellular and transcriptional profile was characterized in patients with low muscularity. First, we measured the pectoralis muscle area (PMA) of 211 treatment-naive NSCLC patients using CT available in The Cancer Imaging Archive. The cutoffs were established using machine learning algorithms (CART and Cutoff Finder) on PMA, clinical, and survival data. We evaluated the prediction model in a validation set (36 NSCLC). Tumor RNA-Seq (GSE103584) was used to profile the transcriptome and cellular composition based on digital cytometry. Results: CART demonstrated that a lower PMA was associated with a high risk of death (HR = 1.99). Cutoff Finder selected PMA cutoffs separating low-muscularity (LM) patients based on the risk of death (P-value = 0.003; discovery set). The cutoff presented 84% of success in classifying low muscle mass. The high risk of LM patients was also found in the validation set. Tumor RNA-Seq revealed 90 upregulated secretory genes in LM that potentially interact with muscle cell receptors. The LM upregulated genes enriched inflammatory biological processes. Digital cytometry revealed that LM patients presented high proportions of cytotoxic and exhausted CD8+ T cells. Conclusions: Our prediction model identified cutoffs that distinguished patients with lower PMA and survival with an inflammatory and immunosuppressive TME enriched with inflammatory factors and CD8+ T cells.Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Department of Structural and Functional Biology Institute of Biosciences São Paulo State University (UNESP), São PauloDepartment of Biochemistry and Tissue Biology University of Campinas, Rua Monteiro Lobato, 255, São PauloDepartment of Immunology Institute of Biomedical Sciences University of São Paulo, SPDepartment of Surgery and Orthopedics Faculty of Medicine São Paulo State University (UNESP), São PauloDepartment of Biochemistry Boston University School of MedicineDepartment of Structural and Functional Biology Institute of Biosciences São Paulo State University (UNESP), São PauloDepartment of Surgery and Orthopedics Faculty of Medicine São Paulo State University (UNESP), São PauloCNPq: 141601/2019-1FAPESP: 2020/03854-4CNPq: 311530/2019-2Universidade Estadual Paulista (UNESP)Universidade Estadual de Campinas (UNICAMP)Universidade de São Paulo (USP)Boston University School of MedicineCury, Sarah Santiloni [UNESP]de Moraes, Diogo [UNESP]Oliveira, Jakeline Santos [UNESP]Freire, Paula Pacciellidos Reis, Patricia Pintor [UNESP]Batista, Miguel LuizHasimoto, Érica Nishida [UNESP]Carvalho, Robson Francisco [UNESP]2023-07-29T13:41:16Z2023-07-29T13:41:16Z2023-12-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1186/s12967-023-03901-5Journal of Translational Medicine, v. 21, n. 1, 2023.1479-5876http://hdl.handle.net/11449/24834110.1186/s12967-023-03901-52-s2.0-85147835896Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengJournal of Translational Medicineinfo:eu-repo/semantics/openAccess2024-08-14T14:18:55Zoai:repositorio.unesp.br:11449/248341Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-14T14:18:55Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Low muscle mass in lung cancer is associated with an inflammatory and immunosuppressive tumor microenvironment
title Low muscle mass in lung cancer is associated with an inflammatory and immunosuppressive tumor microenvironment
spellingShingle Low muscle mass in lung cancer is associated with an inflammatory and immunosuppressive tumor microenvironment
Cury, Sarah Santiloni [UNESP]
CD8+ T cells
Computed tomography
Machine learning
Non-small cell lung cancer
Transcriptomics
title_short Low muscle mass in lung cancer is associated with an inflammatory and immunosuppressive tumor microenvironment
title_full Low muscle mass in lung cancer is associated with an inflammatory and immunosuppressive tumor microenvironment
title_fullStr Low muscle mass in lung cancer is associated with an inflammatory and immunosuppressive tumor microenvironment
title_full_unstemmed Low muscle mass in lung cancer is associated with an inflammatory and immunosuppressive tumor microenvironment
title_sort Low muscle mass in lung cancer is associated with an inflammatory and immunosuppressive tumor microenvironment
author Cury, Sarah Santiloni [UNESP]
author_facet Cury, Sarah Santiloni [UNESP]
de Moraes, Diogo [UNESP]
Oliveira, Jakeline Santos [UNESP]
Freire, Paula Paccielli
dos Reis, Patricia Pintor [UNESP]
Batista, Miguel Luiz
Hasimoto, Érica Nishida [UNESP]
Carvalho, Robson Francisco [UNESP]
author_role author
author2 de Moraes, Diogo [UNESP]
Oliveira, Jakeline Santos [UNESP]
Freire, Paula Paccielli
dos Reis, Patricia Pintor [UNESP]
Batista, Miguel Luiz
Hasimoto, Érica Nishida [UNESP]
Carvalho, Robson Francisco [UNESP]
author2_role author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (UNESP)
Universidade Estadual de Campinas (UNICAMP)
Universidade de São Paulo (USP)
Boston University School of Medicine
dc.contributor.author.fl_str_mv Cury, Sarah Santiloni [UNESP]
de Moraes, Diogo [UNESP]
Oliveira, Jakeline Santos [UNESP]
Freire, Paula Paccielli
dos Reis, Patricia Pintor [UNESP]
Batista, Miguel Luiz
Hasimoto, Érica Nishida [UNESP]
Carvalho, Robson Francisco [UNESP]
dc.subject.por.fl_str_mv CD8+ T cells
Computed tomography
Machine learning
Non-small cell lung cancer
Transcriptomics
topic CD8+ T cells
Computed tomography
Machine learning
Non-small cell lung cancer
Transcriptomics
description Background: Computed tomographies (CT) are useful for identifying muscle loss in non-small lung cancer (NSCLC) cachectic patients. However, we lack consensus on the best cutoff point for pectoralis muscle loss. We aimed to characterize NSCLC patients based on muscularity, clinical data, and the transcriptional profile from the tumor microenvironment to build a cachexia classification model. Methods: We used machine learning to generate a muscle loss prediction model, and the tumor's cellular and transcriptional profile was characterized in patients with low muscularity. First, we measured the pectoralis muscle area (PMA) of 211 treatment-naive NSCLC patients using CT available in The Cancer Imaging Archive. The cutoffs were established using machine learning algorithms (CART and Cutoff Finder) on PMA, clinical, and survival data. We evaluated the prediction model in a validation set (36 NSCLC). Tumor RNA-Seq (GSE103584) was used to profile the transcriptome and cellular composition based on digital cytometry. Results: CART demonstrated that a lower PMA was associated with a high risk of death (HR = 1.99). Cutoff Finder selected PMA cutoffs separating low-muscularity (LM) patients based on the risk of death (P-value = 0.003; discovery set). The cutoff presented 84% of success in classifying low muscle mass. The high risk of LM patients was also found in the validation set. Tumor RNA-Seq revealed 90 upregulated secretory genes in LM that potentially interact with muscle cell receptors. The LM upregulated genes enriched inflammatory biological processes. Digital cytometry revealed that LM patients presented high proportions of cytotoxic and exhausted CD8+ T cells. Conclusions: Our prediction model identified cutoffs that distinguished patients with lower PMA and survival with an inflammatory and immunosuppressive TME enriched with inflammatory factors and CD8+ T cells.
publishDate 2023
dc.date.none.fl_str_mv 2023-07-29T13:41:16Z
2023-07-29T13:41:16Z
2023-12-01
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://dx.doi.org/10.1186/s12967-023-03901-5
Journal of Translational Medicine, v. 21, n. 1, 2023.
1479-5876
http://hdl.handle.net/11449/248341
10.1186/s12967-023-03901-5
2-s2.0-85147835896
url http://dx.doi.org/10.1186/s12967-023-03901-5
http://hdl.handle.net/11449/248341
identifier_str_mv Journal of Translational Medicine, v. 21, n. 1, 2023.
1479-5876
10.1186/s12967-023-03901-5
2-s2.0-85147835896
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Journal of Translational Medicine
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.source.none.fl_str_mv Scopus
reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
institution UNESP
reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
repository.mail.fl_str_mv
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