Ravi Nanthini, Tye Gee Jun, Dhaliwal Satvinder Singh, Musa Muhamad Yusri, Wong Matthew Tze Jian, Lai Ngit Shin
Institute for Research in Molecular Medicine (INFORMM), Universiti Sains Malaysia, Minden, 11800, Penang, Malaysia.
Malaysian Institute of Pharmaceuticals and Nutraceuticals, National Institutes of Biotechnology Malaysia, Halaman Bukit Gambir, Gelugor, 11700, Pinang, Pulau, Malaysia.
Med Oncol. 2025 Aug 26;42(10):446. doi: 10.1007/s12032-025-03002-x.
Immune profiling has become a transformative tool in oncology, offering comprehensive information on tumor immune interactions and facilitating precision medicine. Recent advances such as mass cytometry (CyTOF), single-cell RNA sequencing (scRNA-seq), spatial transcriptomics, and liquid biopsy have greatly enhanced our ability to characterize immune heterogeneity and predict treatment responses. These innovations support the identification of new biomarkers, therapeutic targets, and resistance mechanisms, refining patient stratification and clinical results. Additionally, artificial intelligence (AI) driven models are now being employed to integrate multi-omics datasets and create predictive insights, thereby linking the gap between research and clinical decision-making. This review studies the evolution of immune profiling technologies, their integration into real-world oncology practice, and the associated technical and analytical challenges, including sample variability, data harmonization, and multi-omics integration. Although challenges such as cost, throughput, and standardization persist, the merging of advanced technologies, bioinformatics, and clinical frameworks promises to reshape cancer diagnosis, therapy selection, and disease monitoring through personalized and data-driven strategies.
免疫谱分析已成为肿瘤学中的一种变革性工具，可提供有关肿瘤免疫相互作用的全面信息，并推动精准医学发展。诸如质谱流式细胞术（CyTOF）、单细胞RNA测序（scRNA-seq）、空间转录组学和液体活检等最新进展极大地增强了我们表征免疫异质性和预测治疗反应的能力。这些创新有助于识别新的生物标志物、治疗靶点和耐药机制，优化患者分层和临床结果。此外，现在正采用人工智能（AI）驱动的模型来整合多组学数据集并生成预测性见解，从而弥合研究与临床决策之间的差距。本综述研究了免疫谱分析技术的发展历程、它们在现实世界肿瘤学实践中的整合情况以及相关的技术和分析挑战，包括样本变异性、数据协调和多组学整合。尽管成本、通量和标准化等挑战依然存在，但先进技术、生物信息学和临床框架的融合有望通过个性化和数据驱动的策略重塑癌症诊断、治疗选择和疾病监测。
