Ontology-guided clustering enables proteomic analysis of rare pediatric disorders

ABSTRACT: The study of rare pediatric disorders is fundamentally limited by small patient numbers, making it challenging to draw meaningful conclusions about their molecular basis. To address this, we developed a framework that integrates clinical ontologies with proteomic profiling, enabling the systematic analysis of rare conditions in aggregate rather than isolation. We demonstrated this approach by analyzing urine and plasma samples from 1,140 children and adolescents, encompassing 394 distinct disease conditions and healthy controls. Using advanced mass spectrometry workflows, we achieved deep proteome coverage with over 5,000 proteins quantified in urine and 900 in plasma. By embedding SNOMED CT clinical terminology in a network structure and connecting it to proteomic data, we could group rare conditions based on their clinical relationships, allowing 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.

INSTRUMENT(S):

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Urine, Blood Plasma

SUBMITTER: Mario Oroshi  

LAB HEAD: Matthias Mann

PROVIDER: PXD058960 | Pride | 2025-06-10

REPOSITORIES: Pride