Autoantibodies as Diagnostic Biomarkers for the Detection and Subtyping of Multiple Sclerosis

The goal of this preliminary proof-of-concept study was to use human protein microarrays to identify blood-based autoantibody biomarkers capable of diagnosing multiple sclerosis (MS). Using sera from 112 subjects, including 51 MS subjects, autoantibody biomarkers effectively differentiated MS subjects from age- and gender-matched normal and breast cancer controls with 95.0% and 100% overall accuracy, but not from subjects with Parkinson\u27s disease. Autoantibody biomarkers were also useful in distinguishing subjects with the relapsing-remitting form of MS from those with the secondary progressive subtype. These results demonstrate that autoantibodies can be used as noninvasive blood-based biomarkers for the detection and subtyping of M

Detection of Alzheimer\u27s disease at mild cognitive impairment and disease progression using autoantibodies as blood-based biomarkers

Introduction There is an urgent need to identify biomarkers that can accurately detect and diagnose Alzheimer\u27s disease (AD). Autoantibodies are abundant and ubiquitous in human sera and have been previously demonstrated as disease-specific biomarkers capable of accurately diagnosing mild-moderate stages of AD and Parkinson\u27s disease. Methods Sera from 236 subjects, including 50 mild cognitive impairment (MCI) subjects with confirmed low CSF Aβ42 levels, were screened with human protein microarrays to identify potential biomarkers for MCI. Autoantibody biomarker performance was evaluated using Random Forest and Receiver Operating Characteristic curves. Results Autoantibody biomarkers can differentiate MCI patients from age-matched and gender-matched controls with an overall accuracy, sensitivity, and specificity of 100.0%. They were also capable of differentiating MCI patients from those with mild-moderate AD and other neurologic and non-neurologic controls with high accuracy. Discussion Autoantibodies can be used as noninvasive and effective blood-based biomarkers for early diagnosis and staging of AD

Potential utility of autoantibodies as blood-based biomarkers for early detection and diagnosis of Parkinson’s disease

Introduction There is a great need to identify readily accessible, blood-based biomarkers for Parkinson’s disease (PD) that are useful for accurate early detection and diagnosis. This advancement would allow early patient treatment and enrollment into clinical trials, both of which would greatly facilitate the development of new therapies for PD. Methods Sera from a total of 398 subjects, including 103 early-stage PD subjects derived from the Deprenyl and Tocopherol Antioxidative Therapy of Parkinsonism (DATATOP) study, were screened with human protein microarrays containing 9,486 potential antigen targets to identify autoantibodies potentially useful as biomarkers for PD. A panel of selected autoantibodies with a higher prevalence in early-stage PD was identified and tested using Random Forest for its ability to distinguish early-stage PD subjects from controls and from individuals with other neurodegenerative and non-neurodegenerative diseases. Results Results demonstrate that a panel of selected, blood-borne autoantibody biomarkers can distinguish early-stage PD subjects (90% confidence in diagnosis) from age- and sex-matched controls with an overall accuracy of 87.9%, a sensitivity of 94.1% and specificity of 85.5%. These biomarkers were also capable of differentiating patients with early-stage PD from those with more advanced (mild-moderate) PD with an overall accuracy of 97.5%, and could distinguish subjects with early-stage PD from those with other neurological (e.g., Alzheimer’s disease and multiple sclerosis) and non-neurological (e.g., breast cancer) diseases. Conclusion These results demonstrate, for the first time, that a panel of selected autoantibodies may prove to be useful as effective blood-based biomarkers for the diagnosis of early-stage PD

Diagnosis of Alzheimer's Disease Based on Disease-Specific Autoantibody Profiles in Human Sera

After decades of Alzheimer's disease (AD) research, the development of a definitive diagnostic test for this disease has remained elusive. The discovery of blood-borne biomarkers yielding an accurate and relatively non-invasive test has been a primary goal. Using human protein microarrays to characterize the differential expression of serum autoantibodies in AD and non-demented control (NDC) groups, we identified potential diagnostic biomarkers for AD. The differential significance of each biomarker was evaluated, resulting in the selection of only 10 autoantibody biomarkers that can effectively differentiate AD sera from NDC sera with a sensitivity of 96.0% and specificity of 92.5%. AD sera were also distinguishable from sera obtained from patients with Parkinson's disease and breast cancer with accuracies of 86% and 92%, respectively. Results demonstrate that serum autoantibodies can be used effectively as highly-specific and accurate biomarkers to diagnose AD throughout the course of the disease

Identity and significance of five AD vs. PD diagnostic biomarkers.

<p>Identity and significance of five AD vs. PD diagnostic biomarkers.</p

Differential Expression of PTCD2 and FRMD8 autoantibodies in AD and NDC sera.

<p>Microarray fluorescence values reflecting individual serum autoantibody titers demonstrate a difference in the expression of anti-PTCD2 and anti-FRMD8 in AD (n = 50) and NDC (n = 40) sera (a,c). This difference was confirmed in independent dot blots that assessed AD and NDC sera reactivity to purified PTCD2 and FRMD8 protein antigens (b,d).</p

Identity and significance of 10 ad vs. Ndc diagnostic biomarkers.

<p>Identity and significance of 10 ad vs. Ndc diagnostic biomarkers.</p

Estimate of autoantibodies per sample group.

<p>Estimate of autoantibodies per sample group.</p

Diagnostic accuracies of selected biomarkers.

<p>*<i>The biomarkers used for this classification are those of </i><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0023112#pone-0023112-t005" target="_blank"><i>Table 5</i></a><i>; all others are the biomarkers identified in </i><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0023112#pone-0023112-t003" target="_blank"><i>Table 3</i></a><i>.</i></p

Biomarker selection and Training / Testing Analysis.

<p>Before biomarker selection, our total sample pool was split into two randomized groups: the Training Set and Testing Set. <i>Prospector</i> and <i>PAM</i> statistical analyses were performed on the Training Set to identify the top 10 most significant autoantibody classifiers of AD and NDC. We then verified the diagnostic accuracy of these selected biomarkers by using <i>Random Forest</i> to predict sample classification in the Training Set, Testing Set, and then both sets combined.</p