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Funded Studies

Identification and Validation of Blood-borne Autoantibody Biomarkers for Parkinson's Disease

Objective/Rationale:
We recently discovered that self-reactive antibodies (autoantibodies) are abundant in blood and that detection of certain types of autoantibodies reveals the presence of disease. Using protein microarrays, we identified 10 autoantibodies from a single drop of blood that can identify Parkinson’s disease in patients with 97.1% accuracy. We propose to verify our findings using the DATATOP serum samples and also test whether this strategy can be used to identify different stages of Parkinson’s disease (PD) progression.

Project Description:
Diluted serum from one drop of blood will be spread out over the surface of glass specimen slides containing protein microarrays, each spotted with 9,486 different human proteins that serve as a target to attract serum autoantibodies. Binding of autoantibodies to their specific targets generates tiny green fluorescent spots on the slide that can be accurately measured. Analysis of arrays with special computer statistical programs has revealed 10 disease-specific autoantibodies that can serve as accurate diagnostic biomarkers for Parkinson’s disease. The high quality DATATOP serum samples from 50 PD patients will be used to verify our selection of these 10 biomarkers. Also, comparison of DATATOP serum samples taken from the same individuals at early- and late-stage PD will allow identification of autoantibody biomarkers that can distinguish different stages of Parkinson’s disease progression.

Relevance to Diagnosis/Treatment of Parkinson’s Disease:
There are currently no blood or laboratory tests that can accurately diagnose PD or delineate the different stages of its progression. A reliable blood test for PD will have a tremendous clinical impact, not only to patients and their physicians, but also to pharmaceutical companies trying to gauge the effectiveness of disease-modifying drugs in clinical-trials. The relative non-invasiveness, broad availability, low cost and versatility of protein microarrays make a technology of this kind well-suited for incorporation into routine health care.

Anticipated Outcome:
We expect to validate our recent determination that a small subset of blood-borne autoantibody biomarkers can be used to diagnose PD with exceptionally high accuracy. Use of the high quality DATATOP serum samples will also allow us to fine-tune our selected panel of diagnostic PD autoantibody biomarkers that will optimize PD detection and diagnosis. Lastly, we expect to be able to identify new panel(s) of diagnostic PD autoantibody biomarkers that can accurately identify successive clinical stages of PD progression.
 


PARTNERING PROGRAM

This grant was selected by The Michael J. Fox Foundation staff to be highlighted via the Foundation’s Partnering Program.

Partnering Program Two-Pager more

Progress Report

The DATATOP serum samples come from patients at an early stage of Parkinson’s disease.  In our study, we have used these sera, human protein microarrays and our diagnostic strategy to successfully identify a new panel of autoantibody biomarkers that can be used to diagnose early-stage Parkinson’s disease with greater than 90% overall accuracy (sensitivity = 91.5%; specificity = 90.0%).  Results of this study also suggest that additional planned technical refinements may further increase the overall diagnostic accuracy into the mid-90% range or above.  A diagnostic test for early Parkinson’s disease is highly desired because it would achieve the following; (1) allow early enrollment of patients into clinical trials, (2) allow physicians to monitor progression of the disease in their patients through the recognized stages and, (3) allow detection of delayed progression as a beneficial effect in patients being treated with new drugs in clinical trials. 

Presentations & Publications

A publication describing these findings is being compiled.  We expect for it to be ready for submission by early-mid December.

December 2012


Researchers

  • Robert G. Nagele, PhD

    New Brunswick, NJ United States


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