Decoding immunotherapy response through computational modeling

Bingrui Li

doi:10.1038/s41467-026-71364-5

2026-04-15

DOI：10.1038/s41467-026-71364-5

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摘要

En 中文

免疫疗法在治疗癌症患者方面取得了成功，但反应的差异性凸显了有效患者分层和治疗方案规划的必要性。计算工具在整合多组学、成像和机器学习方面已取得进展，但可靠的个性化预测仍具挑战性。本文通过四个汇聚范式分析该领域：传统机器学习、深度学习、图和网络建模以及机制系统生物学。我们考察了从相关性特征到表示学习、关系推理和肿瘤-免疫动态因果模拟的演变，强调了向多模态融合和可解释、临床可部署模型的转变。通过提供这些计算工具的综合综述，我们希望将社区更接近实现精准免疫肿瘤学以用于个性化癌症治疗。多组学数据通过促进治疗规划的人口和个人化方面，已彻底改变了免疫肿瘤学研究。本文作者综述了当前用于分析多组学数据并促进癌症治疗优化的计算工具，将其分为四个“范式”。

Keyword:

Computational biology and bioinformatics

Immunotherapy

Systems analysis

Tumour immunology

Science

Humanities and Social Sciences

multidisciplinary

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期刊

Nature Communications

IF:

15.7

论文数:

9.6W

被引数:

91.2W

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University of Texas MD Anderson Cancer Center

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