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<p>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.</p>

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Immune profiling in oncology: bridging the gap between technology and treatment

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月26日

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  • Nanthini Ravi,
  • Gee Jun Tye,
  • Satvinder Singh Dhaliwal,
  • Muhamad Yusri Musa,
  • Matthew Tze Jian Wong,
  • Ngit Shin Lai

摘要

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.