Ontology-guided clustering enables proteomic analysis of rare pediatric disorders.

The study of rare pediatric disorders is fundamentally limited by small patient numbers, making it challenging to draw meaningful biological conclusions. To address this, we developed a framework integrating clinical ontologies with proteomic profiling, enabling the systematic analysis of rare conditions in aggregate. We applied this approach to urine and plasma samples from 1140 children and adolescents, encompassing 394 distinct disease conditions and healthy controls. Using advanced mass spectrometry workflows, we quantified over 5000 proteins in urine, 900 in undepleted (neat) plasma, and 1900 in perchloric acid-depleted plasma. Embedding SNOMED CT clinical terminology in a network structure allowed us to group rare conditions based on their clinical relationships, enabling statistical analysis even for diseases with as few as two patients. This approach revealed molecular signatures across developmental stages and disease clusters while accounting for age- and sex-specific variation. Our framework provides a generalizable solution for studying heterogeneous patient populations where traditional case-control studies are impractical, bridging the gap between clinical classification and molecular profiling of rare diseases.