Zhao Xilong, Wu Jiashuo, Lai Jiyin, Pan Bingyue, Ji Miao, Li Xiangmei, He Yalan, Han Junwei
College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
Adv Sci (Weinh). 2025 Jan;12(1):e2408007. doi: 10.1002/advs.202408007. Epub 2024 Nov 5.
The tumor microenvironment (TME) cells interact with each other and play a pivotal role in tumor progression and treatment response. A comprehensive characterization of cell and intercellular crosstalk in the TME is essential for understanding tumor biology and developing effective therapies. However, current cell infiltration analysis methods only partially describe the TME's cellular landscape and overlook cell-cell crosstalk. Here, this approach, CITMIC, can infer the cell infiltration of TME by simultaneously measuring 86 different cell types, constructing an individualized cell-cell crosstalk network based on functional similarities between cells, and using only gene transcription data. This is a novel approach to estimating the relative cell infiltration levels, which are shown to be superior to the current methods. The TME cell-based features generated by analyzing melanoma data are effective in predicting prognosis and treatment response. Interestingly, these features are found to be particularly effective in assessing the prognosis of high-stage patients, and this method is applied to multiple high-stage adenocarcinomas, where more significant prognostic performance is also observed. In conclusion, CITMIC offers a more comprehensive description of TME cell composition by considering cell-cell crosstalk, providing an important reference for the discovery of predictive biomarkers and the development of new therapeutic strategies.
肿瘤微环境(TME)中的细胞相互作用,并在肿瘤进展和治疗反应中起关键作用。全面表征TME中的细胞及细胞间串扰对于理解肿瘤生物学和开发有效疗法至关重要。然而,当前的细胞浸润分析方法仅部分描述了TME的细胞格局,而忽略了细胞间串扰。在此,CITMIC这种方法能够通过同时测量86种不同细胞类型、基于细胞间功能相似性构建个性化的细胞间串扰网络且仅使用基因转录数据来推断TME的细胞浸润情况。这是一种估计相对细胞浸润水平的新方法,其表现优于当前方法。通过分析黑色素瘤数据生成的基于TME细胞的特征在预测预后和治疗反应方面是有效的。有趣的是,发现这些特征在评估晚期患者的预后方面特别有效,并且该方法应用于多种晚期腺癌时,也观察到了更显著的预后性能。总之,CITMIC通过考虑细胞间串扰对TME细胞组成提供了更全面的描述,为预测生物标志物的发现和新治疗策略的开发提供了重要参考。