Primary Immune Regulatory Disorders (PIRDs), which includes Common Variable Immunodeficiency (CVID) and Combined Immunodeficiency (CID), affect about 77,500 patients across Europe. They are characterized by immune dysregulation. Although 25% of cases have a monogenic cause, the majority lack a molecular diagnosis, leaving clinicians to rely on empirical, trial-and-error treatment selection that prolongs disease activity and increases healthcare burden.

We propose a multi-omics integration framework that combines genomics, transcriptomics, proteomics, epigenomics, microbiome, and clinical data for patient stratification and therapy prioritization. This approach uses trained models, like HyenaDNA and RNABERT to encode each type of data. Then it combines them through a fusion module to produce a patient-level embedding and contextualizes this embedding with a biomedical knowledge graph to output ranked drug candidates.

Classical multi-omics integration methods such as MOFA or DIABLO identify patterns in the data but are not designed to use external biological knowledge, which is crucial when patient cohorts are small. Coupling modality-specific embeddings with a knowledge graph helps compensating for this scarcity by injecting curated priors on gene-gene, gene-drug, and pathway relationships while also capturing interactions across immune layers that no single omic can reveal on its own.

By coupling multi-omics patient embeddings with a biomedical knowledge graph, this design aims to leverage scarce rare-disease data using structured biological priors. The approach is intended to enable precision medicine in PIRDs by generating interpretable, patient-specific therapy rankings.

Co-authors: Ali Saadat, Mariam Ait Oumelloul, Jacques Fellay