Abstract 991: PARADIGM: an embeddings-based multimodal learning framework with foundation models and graph neural networks

Abstract Introduction: Cancer research faces significant challenges in integrating heterogeneous data across varying spatial and temporal scales, limiting the ability to gain a comprehensive understanding of the disease. PARADIGM(Pan-Cancer Embeddings Representation using Advanced Multimodal Learning with Graph-based Modeling) addresses this challenge by providing a framework leveraging foundation models (FMs) and Graph Neural Networks (GNN). PARADIGM framework generates embeddings from multi-resolution datasets using modality-specific FMs, aggregates sample embeddings, fuses them into a unified graph representation, and uses GNNs for survival prediction. Methods: We applied PARADIGM for survival analysis of five squamous cell carcinomas; head and neck (527), lung (496), bladder (408), cervical (294), and esophageal (184) cancers, and five adenocarcinomas; colon (393), lung (549), prostate (490), rectum (157), and stomach (406) cancers. Additionally, we validated using lung squamous cell data from Moffitt Cancer Center (103 patients). Multimodal data for training and evaluation included EHR data (e.g., age, gender, and smoking status), whole slide images (WSIs), pathology reports, and molecular data (e.g., gene expression, miRNA, and protein expression). Model performance was assessed using concordance index (C-index) for survival prediction, with 7-fold cross-validation ensuring robustness. Results & Conclusions: PARADIGM demonstrated significant improvements in survival prediction compared to unimodal and multimodal approaches such as multilayer perceptron, Transformers, and XGBoost (Table 1). Our findings indicate that integrating diverse data modalities into a cohesive representation yields more accurate and insightful predictions. This approach highlights the potential of embeddings-based multimodal learning to foster improved outcome predictions and provide actionable insights for personalized treatment strategies. Citation Format: Asim Waqas, Aakash Tripathi, Mia Naeini, Paul A. Stewart, Matthew B. Schabath, Ghulam Rasool. PARADIGM: an embeddings-based multimodal learning framework with foundation models and graph neural networks [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 991.