ProteinPNet: Prototypical Part Networks for Concept Learning in Spatial Proteomics

2025-12-02

AI Summary

Resolving the spatial heterogeneity of the tumor microenvironment (TME) is critical for precision oncology, yet existing methods struggle to learn biologically grounded, discriminative, and interpretable prototypes directly from spatial proteomics data. To address this, we propose an end-to-end learnable Prototype-Part Network that jointly integrates supervised contrastive learning, graph-structured modeling, and morphological analysis to automatically discover and interpretably model spatial functional modules within the TME. Evaluated on both synthetic benchmarks and real-world spatial proteomics data from non-small cell lung cancer, our method robustly identifies immune infiltration patterns and tissue modularity features highly concordant with histopathological subtypes. Notably, it achieves the first supervised prototype learning of spatial motifs in the TME—recurring, biologically meaningful spatial configurations of protein expression. This establishes a novel, mechanism-driven paradigm for discovering spatially resolved biomarkers with direct biological interpretability.

Technology Category

• Machine Learning: Multimodal Learning
• Computer Vision: Multi-modal Vision
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics

Abstract

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.

Problem

• Discovers TME motifs from spatial proteomics data
• Learns interpretable spatial prototypes through supervised training
• Reveals interpretable spatial biomarkers within the tumor microenvironment

Innovation

• Prototype-based framework for spatial proteomics analysis
• Supervised learning of interpretable spatial prototypes
• Identifies biological motifs aligned with tumor subtypes