Machine learning-based integration develops a metabolism-derived consensus model for improving immunotherapy in pancreatic cancer

AI Summary

The text discusses a study that explores the metabolic landscape of pancreatic cancer and its relationship with the tumor microenvironment. The researchers constructed a metabolism-related signature (MBS) and found that it was correlated with cancer stemness, intratumoral heterogeneity, and immune resistant pathways. The MBS outperformed other signatures in predicting immunotherapy response and prognosis. The study suggests potential therapeutic options for MBS-high patients and emphasizes the importance of considering metabolic pathways and immune infiltration in personalized treatment strategies.

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.

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