Machine learning-based integration develops a metabolism-derived consensus model for improving immunotherapy in pancreatic cancer
Background Pancreatic cancer (PAC) is one of the most malignant cancer types and immunotherapy has emerged as a promising treatment option. PAC cells undergo metabolic reprogramming, which is thought to modulate the tumor microenvironment (TME) and affect immunotherapy outcomes. However, the metabol...
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BMJ Publishing Group
2023-09-01
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| Series: | Journal for ImmunoTherapy of Cancer |
| Online Access: | https://jitc.bmj.com/content/11/9/e007466.full |
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| author | Yang Song Cheng Yang Jie Min Lei Hua Haichuan Su Ting Zhao Yongdong Guo Ronglin Wang Jingjie Shi Peixiang Ma Junqiang Li |
| author_facet | Yang Song Cheng Yang Jie Min Lei Hua Haichuan Su Ting Zhao Yongdong Guo Ronglin Wang Jingjie Shi Peixiang Ma Junqiang Li |
| author_sort | Yang Song |
| collection | DOAJ |
| description | Background Pancreatic cancer (PAC) is one of the most malignant cancer types and immunotherapy has emerged as a promising treatment option. PAC cells undergo metabolic reprogramming, which is thought to modulate the tumor microenvironment (TME) and affect immunotherapy outcomes. However, the metabolic landscape of PAC and its association with the TME remains largely unexplored.Methods We characterized the metabolic landscape of PAC based on 112 metabolic pathways and constructed a novel metabolism-related signature (MBS) using data from 1,188 patients with PAC. We evaluated the predictive performance of MBS for immunotherapy outcomes in 11 immunotherapy cohorts from both bulk-RNA and single-cell perspectives. We validated our results using immunohistochemistry, western blotting, colony-formation assays, and an in-house cohort.Results MBS was found to be negatively associated with antitumor immunity, while positively correlated with cancer stemness, intratumoral heterogeneity, and immune resistant pathways. Notably, MBS outperformed other acknowledged signatures for predicting immunotherapy response in multiple immunotherapy cohorts. Additionally, MBS was a powerful and robust biomarker for predicting prognosis compared with 66 published signatures. Further, we identified dasatinib and epothilone B as potential therapeutic options for MBS-high patients, which were validated through experiments.Conclusions Our study provides insights into the mechanisms of immunotherapy resistance in PAC and introduces MBS as a robust metabolism-based indicator for predicting response to immunotherapy and prognosis in patients with PAC. These findings have significant implications for the development of personalized treatment strategies in patients with PAC and highlight the importance of considering metabolic pathways and immune infiltration in TME regulation. |
| format | Article |
| id | doaj-art-e7ddd1aadb77420cae032335faf6628c |
| institution | OA Journals |
| issn | 2051-1426 |
| language | English |
| publishDate | 2023-09-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | Journal for ImmunoTherapy of Cancer |
| spelling | doaj-art-e7ddd1aadb77420cae032335faf6628c2025-08-20T02:11:29ZengBMJ Publishing GroupJournal for ImmunoTherapy of Cancer2051-14262023-09-0111910.1136/jitc-2023-007466Machine learning-based integration develops a metabolism-derived consensus model for improving immunotherapy in pancreatic cancerYang Song0Cheng Yang1Jie Min2Lei Hua3Haichuan Su4Ting Zhao5Yongdong Guo6Ronglin Wang7Jingjie Shi8Peixiang Ma9Junqiang Li10Department of Oncology, Tangdu Hospital, Air Force Medical University, Xi`an, Shaanxi, ChinaDepartment of Rehabilitation, Shenzhen Hospital of Southern Medical University, Shenzhen, Guangdong, ChinaShaanxi Eye Hospital, Xi’an People’s Hospital (Xi’an Fourth Hospital), Affiliated People’s Hospital of Northwest University, Xi`an, Shaanxi, ChinaDepartment of Oncology, Tangdu Hospital, Air Force Medical University, Xi`an, Shaanxi, ChinaDepartment of Oncology, Tangdu Hospital, Air Force Medical University, Xi`an, Shaanxi, ChinaDepartment of Oncology, Tangdu Hospital, Air Force Medical University, Xi`an, Shaanxi, ChinaDepartment of Oncology, Tangdu Hospital, Air Force Medical University, Xi`an, Shaanxi, ChinaDepartment of Oncology, Tangdu Hospital, Air Force Medical University, Xi`an, Shaanxi, ChinaDepartment of Oncology, Tangdu Hospital, Air Force Medical University, Xi`an, Shaanxi, ChinaDepartment of Oncology, Tangdu Hospital, Air Force Medical University, Xi`an, Shaanxi, ChinaDepartment of Oncology, Tangdu Hospital, Air Force Medical University, Xi`an, Shaanxi, ChinaBackground Pancreatic cancer (PAC) is one of the most malignant cancer types and immunotherapy has emerged as a promising treatment option. PAC cells undergo metabolic reprogramming, which is thought to modulate the tumor microenvironment (TME) and affect immunotherapy outcomes. However, the metabolic landscape of PAC and its association with the TME remains largely unexplored.Methods We characterized the metabolic landscape of PAC based on 112 metabolic pathways and constructed a novel metabolism-related signature (MBS) using data from 1,188 patients with PAC. We evaluated the predictive performance of MBS for immunotherapy outcomes in 11 immunotherapy cohorts from both bulk-RNA and single-cell perspectives. We validated our results using immunohistochemistry, western blotting, colony-formation assays, and an in-house cohort.Results MBS was found to be negatively associated with antitumor immunity, while positively correlated with cancer stemness, intratumoral heterogeneity, and immune resistant pathways. Notably, MBS outperformed other acknowledged signatures for predicting immunotherapy response in multiple immunotherapy cohorts. Additionally, MBS was a powerful and robust biomarker for predicting prognosis compared with 66 published signatures. Further, we identified dasatinib and epothilone B as potential therapeutic options for MBS-high patients, which were validated through experiments.Conclusions Our study provides insights into the mechanisms of immunotherapy resistance in PAC and introduces MBS as a robust metabolism-based indicator for predicting response to immunotherapy and prognosis in patients with PAC. These findings have significant implications for the development of personalized treatment strategies in patients with PAC and highlight the importance of considering metabolic pathways and immune infiltration in TME regulation.https://jitc.bmj.com/content/11/9/e007466.full |
| spellingShingle | Yang Song Cheng Yang Jie Min Lei Hua Haichuan Su Ting Zhao Yongdong Guo Ronglin Wang Jingjie Shi Peixiang Ma Junqiang Li Machine learning-based integration develops a metabolism-derived consensus model for improving immunotherapy in pancreatic cancer Journal for ImmunoTherapy of Cancer |
| title | Machine learning-based integration develops a metabolism-derived consensus model for improving immunotherapy in pancreatic cancer |
| title_full | Machine learning-based integration develops a metabolism-derived consensus model for improving immunotherapy in pancreatic cancer |
| title_fullStr | Machine learning-based integration develops a metabolism-derived consensus model for improving immunotherapy in pancreatic cancer |
| title_full_unstemmed | Machine learning-based integration develops a metabolism-derived consensus model for improving immunotherapy in pancreatic cancer |
| title_short | Machine learning-based integration develops a metabolism-derived consensus model for improving immunotherapy in pancreatic cancer |
| title_sort | machine learning based integration develops a metabolism derived consensus model for improving immunotherapy in pancreatic cancer |
| url | https://jitc.bmj.com/content/11/9/e007466.full |
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