PathMoG: A Pathway-Centric Modular Graph Neural Network for Multi-Omics Survival Prediction

Di Wang; Chupei Tang; Junxiao Kong; Jixiu Zhai; Moyu Tang; Tianchi Lu

cs.LGApr 27, 2026

PathMoG: A Pathway-Centric Modular Graph Neural Network for Multi-Omics Survival Prediction

Authors: Di Wang, Chupei Tang, Junxiao Kong, Jixiu Zhai, Moyu Tang, Tianchi Lu

Organizations: 1Cuiying Honors College, Lanzhou University, China · School of Mathematics and Statistics, Lanzhou University, China · School of Mathematics and Statistics, Lanzhou University, China and Shanghai Innovation Institute · Department of Computer Science, City University of Hong Kong, China

Abstract

Cancer survival prediction from multi-omics data remains challenging because prognostic signals are high-dimensional, heterogeneous, and distributed across interacting genes and pathways. We propose PathMoG, a pathway-centric modular graph neural network for multi-omics survival prediction. PathMoG reorganizes genome-scale inputs into 354 KEGG-informed pathway modules, introduces a Hierarchical Omics Modulation module to condition gene-expression representations on mutation, copy number variation, pathway, and clinical context, and uses dual-level attention to capture both intra-pathway driver signals and inter-pathway clinical relevance. We evaluated PathMoG on 5,650 patients across 10 TCGA cancer types and observed consistent improvements over representative survival baselines. The framework further provides gene-level, pathway-level, and patient-level interpretability, supporting biologically grounded and clinically relevant risk stratification.

Explore similar work

ProtoPathway: Biologically Structured Prototype-Pathway Fusion for Multimodal Cancer Survival Prediction
May 20, 2026Amaya Gallagher-Syed, Costantino Pitzalis, Myles J. Lewis +2Cancer Genome Atlas Multimodal Clinical Data
Graph Transformer-Based Pathway Embedding for Cancer Prognosis
Apr 17, 2026Koushik Howlader, Md Tauhidul Islam, Wei LeMulti-Omic Framework
Biologically Informed Deep Neural Networks for Multi-Omic Integration, Pathway Activity Inference and Risk Stratification in Cancer
Jul 6, 2026Pedro Henrique da Costa Avelar, Le Ou-Yang, Min Wu +1Multi-Omic Framework Model Interpretability

May 20, 2026 · cs.CVJ/K move · Enter open · S save

ProtoPathway: Biologically Structured Prototype-Pathway Fusion for Multimodal Cancer Survival Prediction

Amaya Gallagher-Syed, Costantino Pitzalis, Myles J. Lewis, Michael R. Barnes+1

Queen Mary University of London

We introduce ProtoPathway, an interpretable-by-design multimodal framework for cancer survival prediction that unifies whole slide imaging and transcriptomics through encoders producing biologically grounded representations on both sides of the fusion. On the histopathology side,

K

learnable morphological prototypes, trained end-to-end with the survival objective, serve as the slide representation itself: patches flow into prototype tokens via soft assignment, compressing variable-length patch sets into fixed task-adaptive tokens. On the genomic side, a bipartite graph neural network encodes gene expression within the Reactome pathway hierarchy, producing pathway embeddings that reflect both constituent genes and their broader biological context through bidirectional message passing over a shared gene--pathway graph. Cross-modal attention then operates over a compact prototype

\times

pathway matrix in which prototypes query pathways, modeling the biological direction in which molecular programs give rise to tissue morphology. Because both axes carry stable task-learned identity, the attention matrix is itself an interpretability output, yielding native inference-time attribution across the full biological hierarchy, from genes through pathways and prototypes to spatial tissue maps. We evaluate on five TCGA cancer cohorts, demonstrating competitive or superior survival prediction with substantially improved biological interpretability and reduced computational cost, with interpretability claims validated through fold-stratified rank-based population-level analysis. Our source code, model weights, and Reactome pathways, together with a unified codebase reimplementing all multimodal survival baselines under identical preprocessing and evaluation, are available at: https://github.com/AmayaGS/ProtoPathway.

Cancer Genome Atlas Multimodal Clinical Data Morphology Fusion

Open paper Ask about it PDF

Abstract

Explore similar work

ProtoPathway: Biologically Structured Prototype-Pathway Fusion for Multimodal Cancer Survival Prediction

Graph Transformer-Based Pathway Embedding for Cancer Prognosis

Biologically Informed Deep Neural Networks for Multi-Omic Integration, Pathway Activity Inference and Risk Stratification in Cancer