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    Oral Communication

    Diagnostics and OMICs

    High Throughput Methods for Improved Understanding of Coagulation and Bleeding

    OC 57.2 - Integrated proteomic and computational approach reveals determinants of bleeding risk in factor XI deficiency

    Tuesday, June 27, 2023
    11:00 – 11:15 ET
    Room: 518
    Session Available As: On-Demand

      Presenting Author(s)

    • Stéphanie E. Reitsma, PhD

      Postdoc
      Department of Pathology and Laboratory Medicine, UNC Blood Research Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
      Chapel Hill, North Carolina, United States

      • Co-Author(s)

    • SS

      Suzanne Sindi

      Professor
      Department of Applied Mathematics, University of California Merced
      Merced, California, United States

    • DK

      Daniel Kissau

      Medical student
      Department of Biochemistry and Molecular Genetics, University of Colorado Anschutz Medical Campus
      Aurora, Colorado, United States

    • JB

      John Burthem

      Professor
      Department of Haematology, Manchester Royal Infirmary, Institute of Cancer Sciences, The University of Manchester
      Manchester, England, United Kingdom

    • Paula H.B Bolton-Maggs, DM

      Consulting Hematologist
      Division of Cardiovascular Sciences, The University of Manchester, Manchester
      Manchester, England, United Kingdom

    • GG

      Gillian Gidley

      Consultant Haematologist
      Leeds Teaching Hospitals Trust
      Leeds, England, United Kingdom

    • KH

      Kirk Hansen

      Professor
      Department of Biochemistry and Molecular Genetics, University of Colorado Anschutz Medical Campus
      Aurora, Colorado, United States

    • Karin Leiderman, PhD (she/her/hers)

      Associate Professor
      University of North Carolina at Chapel Hill
      Chapel Hill, North Carolina, United States

    • Alisa S. Wolberg, PhD

      Professor
      Univerisity of North Carolina at Chapel Hill
      Chapel Hill, North Carolina, United States

    Background: Individuals with factor (F)XI deficiency have variable bleeding risk that is not predicted by FXI activity, posing clinical challenges. Plasma-based clot formation and fibrinolysis assays can differentiate bleeding risk in FXI-deficient individuals, suggesting differences in the plasma proteome determines the phenotype.

    Aims: Determine if analysis of the plasma coagulome in a computational simulation of thrombin generation can predict bleeding phenotype.

    Methods: Citrated plasmas from healthy individuals (N=49) and individuals with FXI deficiency (N=57; non-bleeders [N=34] and bleeders [N=23] defined by bleeding after tonsillectomy and/or dental extraction before diagnosis of FXI deficiency) were analyzed by mass spectrometry (FXI, FX, FIX, FVII, FII, antithrombin) or ELISA (FVIII, FV, TFPI). Values were converted to % using the control group median. ”Coagulome avatars” of each individual were analyzed in an experimentally-validated computational model of thrombin generation initiated with 1-15 fmol/cm2 tissue factor (TF).

    Results: Median FVIII was significantly higher in non-bleeders than bleeders, but logistic regression of FXI and FVIII levels, alone or together, did not separate non-bleeders from bleeders (area under the receiver operator characteristic curve [AUROC] 0.582, 0.780, and ~0.781, respectively). Principal component analysis of the measured plasma coagulome separated controls from FXI-deficient individuals, but did not separate non-bleeders from bleeders. Thrombin simulations of the coagulome triggered with high TF (15 fmol/cm2) did not differentiate non-bleeders and bleeders; however, the lag time, time to inflection, and thrombin peak from simulations with low TF (3 fmol/cm2) distinguished non-bleeders and bleeders. Non-bleeders produced more FVIIIa and prothrombinase than bleeders. Thrombin simulations using only patient levels of FXI and FVIII and normal levels of all other factors produced parameters that when integrated with FXI and FVIII yielded additional power to classify bleeders and non-bleeders (AUROC 0.866; Figure 1).

    Conclusion(s): Integration of patient FXI and FVIII levels and a computational model of plasma coagulation separates FXI-deficient non-bleeder and bleeder phenotypes.

    Learning Objectives:

    • Upon completion, participant will be able to define FXI function and the clinical challenge in identifying bleeding risk
    • Upon completion, participant will be able to describe criteria used for adjudicating individuals without and with a bleeding risk
    • Upon completion, participant will be able to describe an approach for using proteomic information to identify individuals with a bleeding risk