RT Journal Article
SR Electronic
T1 Machine learning-based integration develops a metabolism-derived consensus model for improving immunotherapy in pancreatic cancer
JF Journal for ImmunoTherapy of Cancer
JO J Immunother Cancer
FD BMJ Publishing Group Ltd
SP e007466
DO 10.1136/jitc-2023-007466
VO 11
IS 9
A1 Guo, Yongdong
A1 Wang, Ronglin
A1 Shi, Jingjie
A1 Yang, Cheng
A1 Ma, Peixiang
A1 Min, Jie
A1 Zhao, Ting
A1 Hua, Lei
A1 Song, Yang
A1 Li, Junqiang
A1 Su, Haichuan
YR 2023
UL http://jitc.bmj.com/content/11/9/e007466.abstract
AB 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.