Comprehensive pan‐cancer analysis of mitochondrial outer membrane permeabilisation activity reveals positive immunomodulation and assists in identifying potential therapeutic targets for immunotherapy resistance

• Published in: Clinical and translational medicine Vol. 14; no. 6; pp. e1735 - n/a
• Main Authors: Chen, Qingshan, Gao, Fenglin, Wu, Junwan, Zhang, Kaiming, Du, Tian, Chen, Yuhong, Cai, Ruizhao, Zhao, Dechang, Deng, Rong, Tang, Jun
• Format: Journal Article
• Language: English
• Published: United States John Wiley & Sons, Inc 01.06.2024; John Wiley and Sons Inc; Wiley
• Subjects: Algorithms; Apoptosis; Cancer; Cell death; Correlation analysis; Datasets; Genes; Humans; immune resistance; Immunomodulation - drug effects; Immunotherapy; Immunotherapy - methods; Mitochondrial DNA; Mitochondrial Membranes - metabolism; mitochondrial outer membrane permeabilisation; Neoplasms - drug therapy; Neoplasms - immunology; Neoplasms - therapy; Neutrophils; pan‐cancer; Research methodology; pan‐cancer; immune resistance; immunotherapy; mitochondrial outer membrane permeabilisation
• ISSN: 2001-1326; 2001-1326
• DOI: 10.1002/ctm2.1735

Background Mitochondrial outer membrane permeabilisation (MOMP) plays a pivotal role in cellular death and immune activation. A deeper understanding of the impact of tumour MOMP on immunity will aid in guiding more effective immunotherapeutic strategies. Methods A comprehensive pan‐cancer dataset comprising 30 cancer‐type transcriptomic cohorts, 20 immunotherapy transcriptomic cohorts and three immunotherapy scRNA‐seq datasets was collected and analysed to determine the influence of tumour MOMP activity on clinical prognosis, immune infiltration and immunotherapy effectiveness. Leveraging 65 scRNA‐Seq datasets, the MOMP signature (MOMP.Sig) was developed to accurately reflect tumour MOMP activity. The clinical predictive value of MOMP.Sig was explored through machine learning models. Integration of the MOMP.Sig model and a pan‐cancer immunotherapy CRISPR screen further investigated potential targets to overcome immunotherapy resistance, which subsequently underwent clinical validation. Results Our research revealed that elevated MOMP activity reduces mortality risk in cancer patients, drives the formation of an anti‐tumour immune environment and enhances the response to immunotherapy. This finding emphasises the potential clinical application value of MOMP activity in immunotherapy. MOMP.Sig, offering a more precise indicator of tumour cell MOMP activity, demonstrated outstanding predictive efficacy in machine‐learning models. Moreover, with the assistance of the MOMP.Sig model, FOXO1 was identified as a core modulator that promotes immune resistance. Finally, these findings were successfully validated in clinical immunotherapy cohorts of skin cutaneous melanoma and triple‐negative breast cancer patients. Conclusions This study enhances our understanding of MOMP activity in immune modulation, providing valuable insights for more effective immunotherapeutic strategies across diverse tumours. MOMP activity exhibits a positive role in promoting anti‐tumour TIME and immunotherapy response. The MOMP.Sig model has a better prediction efficiency than 16 other pan‐cancer ICI signatures. MOMP.Sig model integrates pan‐cancer ICI CRISPR screen to identify key molecule FOXO1 for immunotherapy resistance. Multiple ICI clinical cohorts have verified the immune resistance effect of FOXO1.