4.3 Article

Validation of a proteomic biomarker panel to diagnose minor-stroke and transient ischaemic attack: phase 2 of SpecTRA, a large scale translational study

Journal

BIOMARKERS
Volume 23, Issue 8, Pages 793-803

Publisher

TAYLOR & FRANCIS LTD
DOI: 10.1080/1354750X.2018.1499130

Keywords

TIA; transient ischaemic attack; TIA biomarkers; stroke biomarkers; stroke proteomic; plasma biomarkers

Funding

  1. Genome Alberta [4125-Penn]
  2. Genome Canada [4125-Penn]
  3. Genome British Columbia [4125-Penn, 204PRO, 214PRO]
  4. Leading Edge Endowment Fund (University of Victoria)
  5. Segal McGill Chair in Molecular Oncology at McGill University (Montreal, Quebec, Canada)
  6. Warren Y. Soper Charitable Trust
  7. Alvin Segal Family Foundation

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OBJECTIVE: To validate our previously developed 16 plasma-protein biomarker panel to differentiate between transient ischaemic attack (TIA) and non-cerebrovascular emergency department (ED) patients. METHOD: Two consecutive cohorts of ED patients prospectively enrolled at two urban medical centers into the second phase of SpecTRA study (training, cohort 2A, n = 575; test, cohort 2B, n = 528). Plasma samples were analyzed using liquid chromatography/multiple reaction monitoring-mass spectrometry. Logistic regression models which fit cohort 2A were validated on cohort 2B. RESULTS: Three of the panel proteins failed quality control and were removed from the panel. During validation, panel models did not outperform a simple motor/speech (M/S) deficit variable. Post-hoc analyses suggested the measured behaviour of L-selectin and coagulation factor V contributed to poor model performance. Removal of these proteins increased the external performance of a model containing the panel and the M/S variable. CONCLUSIONS: Univariate analyses suggest insulin-like growth factor-binding protein 3 and serum paraoxonase/lactonase 3 are reliable and reproducible biomarkers for TIA status. Logistic regression models indicated L-selectin, apolipoprotein B-100, coagulation factor IX, and thrombospondin-1 to be significant multivariate predictors of TIA. We discuss multivariate feature subset analyses as an exploratory technique to better understand a panel's full predictive potential.

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