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.

翻译：理解肿瘤微环境（TME）的空间结构对于推进精准肿瘤学至关重要。我们提出了ProteinPNet，一种基于原型部分网络的新型框架，可从空间蛋白质组学数据中发现TME基序。与传统的后验可解释性模型不同，ProteinPNet通过监督训练直接学习具有区分性、可解释且忠实于数据的空间原型。我们在具有真实基序的合成数据集上验证了该方法，并进一步在真实世界的肺癌空间蛋白质组学数据集上进行了测试。ProteinPNet一致地识别出与不同肿瘤亚型相对应的具有生物学意义的原型。通过图形和形态学分析，我们表明这些原型捕获了指向免疫浸润和组织模块性差异的可解释特征。我们的结果突显了基于原型的学习在揭示TME内可解释空间生物标志物方面的潜力，对空间组学中的机制发现具有启示意义。