Low muscle mass in lung cancer is associated with an inflammatory and immunosuppressive tumor microenvironment
Abstract 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...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2023-02-01
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| Series: | Journal of Translational Medicine |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12967-023-03901-5 |
| _version_ | 1828035057182310400 |
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| author | Sarah Santiloni Cury Diogo de Moraes Jakeline Santos Oliveira Paula Paccielli Freire Patricia Pintor dos Reis Miguel Luiz Batista Érica Nishida Hasimoto Robson Francisco Carvalho |
| author_facet | Sarah Santiloni Cury Diogo de Moraes Jakeline Santos Oliveira Paula Paccielli Freire Patricia Pintor dos Reis Miguel Luiz Batista Érica Nishida Hasimoto Robson Francisco Carvalho |
| author_sort | Sarah Santiloni Cury |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-04-10T15:42:21Z |
| format | Article |
| id | doaj.art-b423bb2ef2b843f197d89f1c6acfba19 |
| institution | Directory Open Access Journal |
| issn | 1479-5876 |
| language | English |
| last_indexed | 2024-04-10T15:42:21Z |
| publishDate | 2023-02-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of Translational Medicine |
| spelling | doaj.art-b423bb2ef2b843f197d89f1c6acfba192023-02-12T12:21:04ZengBMCJournal of Translational Medicine1479-58762023-02-0121111410.1186/s12967-023-03901-5Low muscle mass in lung cancer is associated with an inflammatory and immunosuppressive tumor microenvironmentSarah Santiloni Cury0Diogo de Moraes1Jakeline Santos Oliveira2Paula Paccielli Freire3Patricia Pintor dos Reis4Miguel Luiz Batista5Érica Nishida Hasimoto6Robson Francisco Carvalho7Department of Structural and Functional Biology, Institute of Biosciences, São Paulo State University (UNESP)Department of Structural and Functional Biology, Institute of Biosciences, São Paulo State University (UNESP)Department of Structural and Functional Biology, Institute of Biosciences, São Paulo State University (UNESP)Department of Immunology, Institute of Biomedical Sciences, University of São PauloDepartment of Surgery and Orthopedics, Faculty of Medicine, São Paulo State University (UNESP)Department of Biochemistry, Boston University School of MedicineDepartment of Surgery and Orthopedics, Faculty of Medicine, São Paulo State University (UNESP)Department of Structural and Functional Biology, Institute of Biosciences, São Paulo State University (UNESP)Abstract 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.https://doi.org/10.1186/s12967-023-03901-5Non-small cell lung cancerMachine learningComputed tomographyTranscriptomicsCD8+ T cells |
| spellingShingle | Sarah Santiloni Cury Diogo de Moraes Jakeline Santos Oliveira Paula Paccielli Freire Patricia Pintor dos Reis Miguel Luiz Batista Érica Nishida Hasimoto Robson Francisco Carvalho Low muscle mass in lung cancer is associated with an inflammatory and immunosuppressive tumor microenvironment Journal of Translational Medicine Non-small cell lung cancer Machine learning Computed tomography Transcriptomics CD8+ T cells |
| title | 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_short | 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 |
| topic | Non-small cell lung cancer Machine learning Computed tomography Transcriptomics CD8+ T cells |
| url | https://doi.org/10.1186/s12967-023-03901-5 |
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