Identification of a novel proteomic Biomarker in Parkinson's Disease: Discovery and Replication in Blood, brain and CSF

Biomarkers to aid diagnosis and delineate progression of Parkinsons Disease (PD) are vital for targeting treatment in the early phases of disease. Here, we aim to discover a multi-protein panel representative of PD and make mechanistic inferences from protein expression profiles within the broader objective of finding novel biomarkers. We used aptamer-based technology (SomaLogic(R)) to measure proteins in 1,599 serum samples, 85 CSF samples and 37 brain tissue samples collected from two observational longitudinal cohorts (Oxford Parkinsons Disease Centre and Tracking Parkinsons) and the PD Brain Bank, respectively. Random forest machine learning was performed to discover new proteins related to disease status and generate multi-protein expression signatures with potential novel biomarkers. Differential regulation analysis and pathway analysis was performed to identify functional and mechanistic disease associations. The most consistent diagnostic classifier signature was tested across modalities (CSF AUC = 0.74, p-value = 0.0009; brain AUC = 0.75, p-value = 0.006; serum AUC = 0.66, p-value = 0.0002). In the validation dataset we showed that the same classifiers were significantly related to disease status (p-values < 0.001). Differential expression analysis and Weighted Gene Correlation Network Analysis (WGCNA) highlighted key proteins and pathways with known relationships to PD. Proteins from the complement and coagulation cascades suggest a disease relationship to immune response. The combined analytical approaches in a relatively large number of samples, across tissue types, with replication and validation, provides mechanistic insights into the disease as well as nominating a protein signature classifier that deserves further biomarker evaluation.