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iMLGAM: Integrated Machine Learning and Genetic Algorithm‐driven Multiomics analysis for pan‐cancer immunotherapy response prediction
Bicheng Ye (Zhongda Hospital Southeast University, Southeast University), Jun Fan (Jiangsu Province Hospital, Nanjing Medical University), Lei Xue (Jiangsu Province Hospital, Nanjing Medical University), Zhuang Yu (Nanjing Brain Hospital, Nanjing Chest Hospital), Peng Luo (Zhujiang Hospital, Southern Medical University), Aimin Jiang (Second Military Medical University, Changhai Hospital), Jiaheng Xie (Central South University, Xiangya Hospital Central South University), Qifan Li (First Affiliated Hospital of Soochow University), Xiaoqing Liang (The Affiliated Yongchuan Hospital of Chongqing Medical University, Chongqing Medical University), Jiaxiong Tan (Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University), Songyun Zhao (First Affiliated Hospital of Wenzhou Medical University), Wenhang Zhou (Second People’s Hospital of Huai’an), Chuanli Ren (Northern Jiangsu People's Hospital, Yangzhou University), Haoran Lin (Jiangsu Province Hospital, Nanjing Medical University), Pengpeng Zhang (Tianjin Medical University Cancer Institute and Hospital, Jiangsu Province Hospital)
Abstract: Abstract To address the substantial variability in immune checkpoint blockade (ICB) therapy effectiveness, we developed an innovative R package called integrated Machine Learning and Genetic Algorithm‐driven Multiomics analysis (iMLGAM), which establishes a comprehensive scoring system for predicting treatment outcomes through advanced multi‐omics data integration. Our research demonstrates that iMLGAM scores exhibit superior predictive performance across independent cohorts, with lower scores correlating significantly with enhanced therapeutic responses and outperforming existing clinical biomarkers. Detailed analysis revealed that tumors with low iMLGAM scores display distinctive immune microenvironment characteristics, including increased immune cell infiltration and amplified antitumor immune responses. Critically, through clustered regularly interspaced short palindromic repeats screening, we identified Centrosomal Protein 55 (CEP55) as a key molecule modulating tumor immune evasion, mechanistically confirming its role in regulating T cell‐mediated antitumor immune responses. These findings not only validate iMLGAM as a powerful prognostic tool but also propose CEP55 as a promising therapeutic target, offering novel strategies to enhance ICB treatment efficacy. The iMLGAM package is freely available on GitHub ( https://github.com/Yelab1994/iMLGAM ), providing researchers with an innovative approach to personalized cancer immunotherapy prediction.