用原型网络从空间蛋白组数据中发现肿瘤微环境的可解释模式
ProteinPNet: Prototypical Part Networks for Concept Learning in Spatial Proteomics
- 基于原型部件网络，直接学习可解释的空间特征原型
- 在真实肺癌数据中识别出与不同肿瘤亚型对应的生物有意义原型
- 适合从事空间组学、肿瘤微环境研究的科研人员
理解肿瘤微环境（TME）的空间架构对推动精准肿瘤学至关重要。我们提出ProteinPNet，一种基于原型部件网络的新框架，用于从空间蛋白组数据中发现TME基序。不同于传统的后验可解释性模型，ProteinPNet通过监督训练直接学习具有判别性、可解释且忠实的空间原型。我们在带有真实基序标签的合成数据集上验证该方法，并进一步在真实世界肺腺癌空间蛋白组数据集上测试。ProteinPNet始终能识别出与不同肿瘤亚型一致的生物学上有意义的原型。通过图结构和形态学分析，我们表明这些原型捕捉到了免疫浸润程度和组织模块性的可解释特征。结果表明，基于原型的学习有望揭示TME中的可解释空间生物标志物，对空间组学机制发现具有重要意义。
原文摘要 · Abstract (English)
Understanding the spatial architecture of the tumor microenvironment (TME) is critical to advance precision oncology. We present ProteinPNet, a novel framework based on prototypical part networks that discovers TME motifs from spatial proteomics data. Unlike traditional post-hoc explanability models, ProteinPNet directly learns discriminative, interpretable, faithful spatial prototypes through supervised training. We validate our approach on synthetic datasets with ground truth motifs, and further test it on a real-world lung cancer spatial proteomics dataset. ProteinPNet consistently identifies biologically meaningful prototypes aligned with different tumor subtypes. Through graphical and morphological analyses, we show that these prototypes capture interpretable features pointing to differences in immune infiltration and tissue modularity. Our results highlight the potential of prototype-based learning to reveal interpretable spatial biomarkers within the TME, with implications for mechanistic discovery in spatial omics.
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