The prognosis and metabolite changes of NSCLC patients receiving first‐line immunotherapy combined chemotherapy in different M1c categories according to 9th edition of TNM classification
Abstract Background The 9th edition of the TNM Classification for lung cancer delineates M1c into two subcategories: M1c1 (Multiple extrathoracic lesions within a single organ system) and M1c2 (Multiple extrathoracic lesions involving multiple organ systems). Existing research indicates that patient...
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2024-09-01
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| Online Access: | https://doi.org/10.1002/cam4.70223 |
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| author | Liang Zheng Fang Hu Wei Nie Jun Lu Bo Zhang Jianlin Xu Shuyuan Wang Ying Li Xiaoxuan Zheng Wei Zhang Yinchen Shen Runbo Zhong Tianqing Chu Baohui Han Hua Zhong Xueyan Zhang |
| author_facet | Liang Zheng Fang Hu Wei Nie Jun Lu Bo Zhang Jianlin Xu Shuyuan Wang Ying Li Xiaoxuan Zheng Wei Zhang Yinchen Shen Runbo Zhong Tianqing Chu Baohui Han Hua Zhong Xueyan Zhang |
| author_sort | Liang Zheng |
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| description | Abstract Background The 9th edition of the TNM Classification for lung cancer delineates M1c into two subcategories: M1c1 (Multiple extrathoracic lesions within a single organ system) and M1c2 (Multiple extrathoracic lesions involving multiple organ systems). Existing research indicates that patients with lung cancer in stage M1c1 exhibit superior overall survival compared to those in stage M1c2. The primary frontline therapy for patients with advanced non‐small cell lung cancer (NSCLC), lacking driver gene mutations, involves the use of immune checkpoint inhibitors (ICIs) combined with chemotherapy. Nevertheless, a dearth of evidence exists regarding potential survival disparities between NSCLC patients with M1c1 and M1c2 undergoing first‐line immune‐chemotherapy, and reliable biomarkers for predicting treatment outcomes are elusive. Serum metabolic profiles may elucidate distinct prognostic mechanisms, necessitating the identification of divergent metabolites in M1c1 and M1c2 undergoing combination therapy. This study seeks to scrutinize survival discrepancies between various metastatic patterns (M1c1 and M1c2) and pinpoint metabolites associated with treatment outcomes in NSCLC patients undergoing first‐line ICIs combined with chemotherapy. Method In this study, 33 NSCLC patients lacking driver gene mutations diagnosed with M1c1, and 22 similarly diagnosed with M1c2 according to the 9th edition of TNM Classification, were enrolled. These patients received first‐line PD‐1 inhibitor plus chemotherapy. The relationship between metastatic patterns and progression‐free survival (PFS) in patients undergoing combination therapy was analyzed using univariate and multivariate Cox regression models. Serum samples were obtained from all patients before treatment initiation for untargeted metabolomics analysis, aiming to identify differential metabolites. Results In the univariate analysis of PFS, NSCLC patients in M1c1 receiving first‐line PD‐1 inhibitor plus chemotherapy exhibited an extended PFS (HR = 0.49, 95% CI, 0.27–0.88, p = 0.017). In multivariate PFS analyses, these M1c1 patients receiving first‐line PD‐1 inhibitor plus chemotherapy also demonstrated prolonged PFS (HR = 0.45, 95% CI, 0.22–0.92, p = 0.028). The serum metabolic profiles of M1c1 and M1c2 undergoing first‐line PD‐1 inhibitors plus chemotherapy displayed notable distinctions. In comparison to M1c1 patients, M1c2 patients exhibited alterations in various pathways pretreatment, including platelet activation, linoleic acid metabolism, and the VEGF signaling pathway. Diminished levels of lipid‐associated metabolites (diacylglycerol, sphingomyelin) were correlated with adverse outcomes. Conclusion NSCLC patients in M1c1, devoid of driver gene mutations, receiving first‐line PD‐1 inhibitors combined with chemotherapy, experienced superior outcomes compared to M1c2 patients. Moreover, metabolomic profiles strongly correlated with the prognosis of these patients, and M1c2 patients with unfavorable outcomes manifested distinct changes in metabolic pathways before treatment. These changes predominantly involved alterations in lipid metabolism, such as decreased diacylglycerol and sphingomyelin, which may impact tumor migration and invasion. |
| format | Article |
| id | doaj-art-5c7af439f5194ea8b098e0278cb05b37 |
| institution | Kabale University |
| issn | 2045-7634 |
| language | English |
| publishDate | 2024-09-01 |
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| series | Cancer Medicine |
| spelling | doaj-art-5c7af439f5194ea8b098e0278cb05b372025-02-07T09:08:08ZengWileyCancer Medicine2045-76342024-09-011317n/an/a10.1002/cam4.70223The prognosis and metabolite changes of NSCLC patients receiving first‐line immunotherapy combined chemotherapy in different M1c categories according to 9th edition of TNM classificationLiang Zheng0Fang Hu1Wei Nie2Jun Lu3Bo Zhang4Jianlin Xu5Shuyuan Wang6Ying Li7Xiaoxuan Zheng8Wei Zhang9Yinchen Shen10Runbo Zhong11Tianqing Chu12Baohui Han13Hua Zhong14Xueyan Zhang15Department of Respiratory and Critical Care Medicine, Shanghai Chest Hospital Shanghai Jiao Tong University School of Medicine Shanghai ChinaDepartment of Thoracic Medical Oncology The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital) Zhejiang ChinaDepartment of Respiratory and Critical Care Medicine, Shanghai Chest Hospital Shanghai Jiao Tong University School of Medicine Shanghai ChinaDepartment of Respiratory and Critical Care Medicine, Shanghai Chest Hospital Shanghai Jiao Tong University School of Medicine Shanghai ChinaDepartment of Respiratory and Critical Care Medicine, Shanghai Chest Hospital Shanghai Jiao Tong University School of Medicine Shanghai ChinaDepartment of Respiratory and Critical Care Medicine, Shanghai Chest Hospital Shanghai Jiao Tong University School of Medicine Shanghai ChinaDepartment of Respiratory and Critical Care Medicine, Shanghai Chest Hospital Shanghai Jiao Tong University School of Medicine Shanghai ChinaDepartment of Respiratory and Critical Care Medicine, Shanghai Chest Hospital Shanghai Jiao Tong University School of Medicine Shanghai ChinaDepartment of Respiratory and Critical Care Medicine, Shanghai Chest Hospital Shanghai Jiao Tong University School of Medicine Shanghai ChinaDepartment of Respiratory and Critical Care Medicine, Shanghai Chest Hospital Shanghai Jiao Tong University School of Medicine Shanghai ChinaDepartment of Respiratory and Critical Care Medicine, Shanghai Chest Hospital Shanghai Jiao Tong University School of Medicine Shanghai ChinaDepartment of Respiratory and Critical Care Medicine, Shanghai Chest Hospital Shanghai Jiao Tong University School of Medicine Shanghai ChinaDepartment of Respiratory and Critical Care Medicine, Shanghai Chest Hospital Shanghai Jiao Tong University School of Medicine Shanghai ChinaDepartment of Respiratory and Critical Care Medicine, Shanghai Chest Hospital Shanghai Jiao Tong University School of Medicine Shanghai ChinaDepartment of Respiratory and Critical Care Medicine, Shanghai Chest Hospital Shanghai Jiao Tong University School of Medicine Shanghai ChinaDepartment of Respiratory and Critical Care Medicine, Shanghai Chest Hospital Shanghai Jiao Tong University School of Medicine Shanghai ChinaAbstract Background The 9th edition of the TNM Classification for lung cancer delineates M1c into two subcategories: M1c1 (Multiple extrathoracic lesions within a single organ system) and M1c2 (Multiple extrathoracic lesions involving multiple organ systems). Existing research indicates that patients with lung cancer in stage M1c1 exhibit superior overall survival compared to those in stage M1c2. The primary frontline therapy for patients with advanced non‐small cell lung cancer (NSCLC), lacking driver gene mutations, involves the use of immune checkpoint inhibitors (ICIs) combined with chemotherapy. Nevertheless, a dearth of evidence exists regarding potential survival disparities between NSCLC patients with M1c1 and M1c2 undergoing first‐line immune‐chemotherapy, and reliable biomarkers for predicting treatment outcomes are elusive. Serum metabolic profiles may elucidate distinct prognostic mechanisms, necessitating the identification of divergent metabolites in M1c1 and M1c2 undergoing combination therapy. This study seeks to scrutinize survival discrepancies between various metastatic patterns (M1c1 and M1c2) and pinpoint metabolites associated with treatment outcomes in NSCLC patients undergoing first‐line ICIs combined with chemotherapy. Method In this study, 33 NSCLC patients lacking driver gene mutations diagnosed with M1c1, and 22 similarly diagnosed with M1c2 according to the 9th edition of TNM Classification, were enrolled. These patients received first‐line PD‐1 inhibitor plus chemotherapy. The relationship between metastatic patterns and progression‐free survival (PFS) in patients undergoing combination therapy was analyzed using univariate and multivariate Cox regression models. Serum samples were obtained from all patients before treatment initiation for untargeted metabolomics analysis, aiming to identify differential metabolites. Results In the univariate analysis of PFS, NSCLC patients in M1c1 receiving first‐line PD‐1 inhibitor plus chemotherapy exhibited an extended PFS (HR = 0.49, 95% CI, 0.27–0.88, p = 0.017). In multivariate PFS analyses, these M1c1 patients receiving first‐line PD‐1 inhibitor plus chemotherapy also demonstrated prolonged PFS (HR = 0.45, 95% CI, 0.22–0.92, p = 0.028). The serum metabolic profiles of M1c1 and M1c2 undergoing first‐line PD‐1 inhibitors plus chemotherapy displayed notable distinctions. In comparison to M1c1 patients, M1c2 patients exhibited alterations in various pathways pretreatment, including platelet activation, linoleic acid metabolism, and the VEGF signaling pathway. Diminished levels of lipid‐associated metabolites (diacylglycerol, sphingomyelin) were correlated with adverse outcomes. Conclusion NSCLC patients in M1c1, devoid of driver gene mutations, receiving first‐line PD‐1 inhibitors combined with chemotherapy, experienced superior outcomes compared to M1c2 patients. Moreover, metabolomic profiles strongly correlated with the prognosis of these patients, and M1c2 patients with unfavorable outcomes manifested distinct changes in metabolic pathways before treatment. These changes predominantly involved alterations in lipid metabolism, such as decreased diacylglycerol and sphingomyelin, which may impact tumor migration and invasion.https://doi.org/10.1002/cam4.702239th edition TNM classificationimmune‐checkpoint inhibitorsnon‐small cell lung cancerprognosisuntargeted metabolomics |
| spellingShingle | Liang Zheng Fang Hu Wei Nie Jun Lu Bo Zhang Jianlin Xu Shuyuan Wang Ying Li Xiaoxuan Zheng Wei Zhang Yinchen Shen Runbo Zhong Tianqing Chu Baohui Han Hua Zhong Xueyan Zhang The prognosis and metabolite changes of NSCLC patients receiving first‐line immunotherapy combined chemotherapy in different M1c categories according to 9th edition of TNM classification Cancer Medicine 9th edition TNM classification immune‐checkpoint inhibitors non‐small cell lung cancer prognosis untargeted metabolomics |
| title | The prognosis and metabolite changes of NSCLC patients receiving first‐line immunotherapy combined chemotherapy in different M1c categories according to 9th edition of TNM classification |
| title_full | The prognosis and metabolite changes of NSCLC patients receiving first‐line immunotherapy combined chemotherapy in different M1c categories according to 9th edition of TNM classification |
| title_fullStr | The prognosis and metabolite changes of NSCLC patients receiving first‐line immunotherapy combined chemotherapy in different M1c categories according to 9th edition of TNM classification |
| title_full_unstemmed | The prognosis and metabolite changes of NSCLC patients receiving first‐line immunotherapy combined chemotherapy in different M1c categories according to 9th edition of TNM classification |
| title_short | The prognosis and metabolite changes of NSCLC patients receiving first‐line immunotherapy combined chemotherapy in different M1c categories according to 9th edition of TNM classification |
| title_sort | prognosis and metabolite changes of nsclc patients receiving first line immunotherapy combined chemotherapy in different m1c categories according to 9th edition of tnm classification |
| topic | 9th edition TNM classification immune‐checkpoint inhibitors non‐small cell lung cancer prognosis untargeted metabolomics |
| url | https://doi.org/10.1002/cam4.70223 |
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