免疫疗法的最新发展，包括免疫检查点阻断（ICB）和过继细胞疗法（ACT），遇到了免疫相关不良事件和耐药性等挑战，特别是在实体瘤中。为了推进该领域的发展，更深入地了解治疗反应和耐药性背后的分子机制至关重要。然而，缺乏功能特征的免疫相关基因集限制了数据驱动的免疫学研究。为了解决这一差距，我们对 83 个人类批量 RNA 测序 (RNA-seq) 数据集采用非负矩阵分解，并构建了 28 个免疫特异性基因集。经过免疫学家主导的严格手动注释以及跨免疫学背景和功能组学数据的正交验证，我们证明这些基因集可用于细化泛癌免疫亚型、改善 ICB 反应预测并对空间转录组数据进行功能注释。这些功能基因集告知不同的免疫状态，将增进我们对免疫学和癌症研究的理解。

Recent developments in immunotherapy, including immune checkpoint blockade (ICB) and adoptive cell therapy (ACT), have encountered challenges such as immune-related adverse events and resistance, especially in solid tumors. To advance the field, a deeper understanding of the molecular mechanisms behind treatment responses and resistance is essential. However, the lack of functionally characterized immune-related gene sets has limited data-driven immunological research. To address this gap, we adopted non-negative matrix factorization on 83 human bulk RNA sequencing (RNA-seq) datasets and constructed 28 immune-specific gene sets. After rigorous immunologist-led manual annotations and orthogonal validations across immunological contexts and functional omics data, we demonstrated that these gene sets can be applied to refine pan-cancer immune subtypes, improve ICB response prediction and functionally annotate spatial transcriptomic data. These functional gene sets, informing diverse immune states, will advance our understanding of immunology and cancer research.