High-Throughput Profiling of Circulating Antibody Signatures for Stroke Diagnosis Using Small Volumes of Whole Blood
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Accurate stroke recognition during triage can streamline care and afford patients earlier access to life-saving interventions. However, the tools currently available to clinicians for prehospital and early in-hospital identification of stroke are limited. The peripheral immune system is intricately involved in stroke pathology and thus may be targetable for the development of immunodiagnostics. In this preliminary study, we sought to determine whether the circulating antibody pool is altered early in stroke, and whether such alterations could be leveraged for diagnosis. One hundred microliters of peripheral whole blood was sampled from 19 ischemic stroke patients, 17 hemorrhagic stroke patients, and 20 stroke mimics in the acute phase of care. A custom-fabricated high-density peptide array comprising 125,000 unique probes was used to assess the binding characteristics of blood-borne antibodies, and a random forest-based approach was used to select a parsimonious set of probes with an optimal ability to discriminate between groups. The coordinate antibody binding intensities of the top 17 probes identified in our analysis displayed an ability to differentiate the total pool of stroke patients from stroke mimics with 92% sensitivity and 90% specificity, as well as detect hemorrhage with 88% sensitivity and 87% specificity, as determined using a same-set cross-validation. These preliminary findings suggest that stroke-associated alterations in the circulating antibody pool may have clinical utility for diagnosis during triage, and that such a possibility warrants further investigation.
Key WordsBiomarkers triage proteomics molecular diagnostics machine-learning immunology
We would foremost like to thank the patients and their families, as this work was made possible by their selfless contribution. We would also like to thank both the stroke and emergency medicine teams at University of Cincinnati Medical Center for their support.
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Disclosure forms provided by the authors are available with the online version of this article.
Work was conceptualized by GCO. GCO and PS collected molecular data. KBW, OA, and TLB managed subject enrolment and collected clinical data. Statistical analysis was performed by GCO. GCO wrote the manuscript with contributions from PS, KBW, OA, and TLB.
This work was funded by Valtari Bio Incorporated.
Compliance with Ethical Standards
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee (detailed in the Materials and Methods section) and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.
Conflict of Interest
TLB serves as Chief Scientific Officer of Valtari Bio Incorporated. GCO and TLB have a pending patent re: computer implemented discovery of antibody signatures, as well as a pending patent re: genomic patterns of expression for stroke diagnosis. GCO holds stock in Valtari Bio Incorporated. GCO has received consulting fees from Valtari Bio Incorporated. The remaining authors have no potential conflicts of interest to declare.
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