iMLGAM: Integrated Machine Learning and Genetic Algorithm-driven Multiomics analysis for pan-cancer immunotherapy response prediction

IF 23.7 Q1 MICROBIOLOGY
iMeta Pub Date : 2025-03-08 DOI:10.1002/imt2.70011
Bicheng Ye, Jun Fan, Lei Xue, Yu Zhuang, Peng Luo, Aimin Jiang, Jiaheng Xie, Qifan Li, Xiaoqing Liang, Jiaxiong Tan, Songyun Zhao, Wenhang Zhou, Chuanli Ren, Haoran Lin, Pengpeng Zhang
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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.

Abstract Image

iMLGAM:综合机器学习和遗传算法驱动的多组学分析用于泛癌症免疫治疗反应预测
为了解决免疫检查点阻断(ICB)治疗效果的实质性变化,我们开发了一个创新的R包,称为集成机器学习和遗传算法驱动的多组学分析(iMLGAM),它建立了一个综合评分系统,通过先进的多组学数据集成来预测治疗结果。我们的研究表明,iMLGAM评分在独立队列中表现出优越的预测性能,较低的评分与增强的治疗反应显著相关,并且优于现有的临床生物标志物。详细分析显示,iMLGAM评分低的肿瘤表现出独特的免疫微环境特征,包括免疫细胞浸润增加和抗肿瘤免疫反应增强。至关重要的是,通过聚集规律间隔的短回文重复序列筛选,我们确定了中心体蛋白55 (CEP55)是调节肿瘤免疫逃避的关键分子,机制上证实了其在调节T细胞介导的抗肿瘤免疫反应中的作用。这些发现不仅验证了iMLGAM作为一种强大的预后工具,而且提出了CEP55作为一种有希望的治疗靶点,为提高ICB治疗效果提供了新的策略。iMLGAM包在GitHub (https://github.com/Yelab1994/iMLGAM)上免费提供,为研究人员提供了个性化癌症免疫治疗预测的创新方法。
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CiteScore
10.80
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