Parkinson's Disease Biomarkers: Resources for Discovery and Validation

Abstract

Parkinson's disease (PD) is currently diagnosed using clinical features. While experienced neurologists can typically diagnose PD with 70–90% accuracy, there are many situations early in the course of the disease when clinical diagnosis is less precise. There is a paucity of objective measures that could be employed to improve the diagnosis, stratify patients by subtypes, and track underlying disease progression. Development of innovative new therapies that slow or stop the progression of the disease would be accelerated by such objective biomarkers. Various imaging modalities including specific dopaminergic markers and morphometric measures are available, but have not been firmly established as markers for these purposes. Markers that can be detected in easily obtainable biofluids would be ideal for these purposes and would increase the power to detect the effect of therapeutic agents in a shorter time with reduced cost. Unbiased exploratory examination of panels of RNAs, both mRNA and noncoding RNAs, exosomes, proteins, antibodies, and metabolites in small individual cohorts have yielded promising candidate markers that yearn for replication in independent cohorts (Kroksveen et al., 2011). More targeted approaches to identify biomarkers based on PD pathophysiology have shown disease-related proteins as strong candidates. Alpha-synuclein is decreased in the cerebrospinal fluid of PD patients (Hong et al., 2010), although significant overlap with controls makes it less useful in assisting individual diagnosis. Other potential protein candidates include DJ-1 and inflammatory cytokines. Post-translationally modified forms of these proteins may improve the biomarker specificity. Most of these studies have utilized CSF (Parnetti et al., 2013), although increased oxidized DJ-1 in PD patients was detected in blood (Saito et al., 2009) and epidermal growth factor levels in plasma has been shown to predict cognitive decline in PD (Chen-Plotkin et al., 2011). Ultimately one could predict that a panel of multiple biochemical markers could be combined to increase the accuracy of diagnosis and disease progression. Heterogeneity of patient populations and lack of standardization of collection methods may contribute to the inconsistent and variable observations in the literature. A major need in the field is the availability of biospecimens from well-characterized cohorts for discovery of new biomarkers and validation. In response to these needs, three major programs have been launched. The Parkinson's Progression Marker Initiative (PPMI) is a prospective study of 400 newly diagnosed PD patients and 200 controls that will be followed over 5 years and collect extensive clinical (motor and non-motor information), imaging, and biosample (blood, cerebrospinal fluid, DNA/RNA from blood, urine) information from all 600 subjects at sites around the world (Marek, 2011). PPMI expanded to include a prodromal cohort of 100 individuals at risk for developing PD to develop biomarkers that are present prior to the onset of clinical motor symptoms. The Fox Investigation for New Discovery of Biomarkers (BioFIND) is a study supported in collaboration with National Institute of Neurological Diseases and Stroke (NINDS) focused on novel biomarker discovery by enrolling 120 rigorously defined clinically typical PD in mid-stage and 120 age- and gender-matched controls at one timepoint in US sites. BioFIND was launched to serve as a platform to test new biomarkers in somewhat narrow spectrum of clinically typical PD in moderate stages to maximize the chance of discovering differences in a less heterogeneous population. The Parkinson's Disease Biomarker Program (PDBP) is a program designed to support new and existing biomarker cohorts that collect biospecimens using standardized protocols. Biospecimens and data from all above studies are available to the research community. Through standardization and coordination of data and sample collection, we are optimistic that new markers will emerge which will assist in clinical trials and ultimately result in improved management of the disease.