An Interactive Biomarker Website for Duchenne Muscular Dystrophy

Topic:

Translational Research

Poster Number: P157

Author(s):

Utkarsh Dang, PhD, Carleton University, Wangshu Tu, PhD, Carleton University, Rebecca Tobin, Carleton University, Leenah Abdelrazeq, Carleton University, Cristina Al-Khalili Szigyarto, PhD, KTH Royal Institute of Technology, Roula Tsonaka, PhD, Leiden University Medical Center, Chiara Degan, Leiden University Medical Center, Sharon de Vries, Leiden University Medical Center, Yuri van der Burgt, PhD, Leiden University Medical Center, Jordi Diaz-Manera, MD, John Walton Muscular Dystrophy Research Centre, Michela Guglieri, MD, Newcastle University, Newcastle upon Tyne, England, UK, Pietro Spitali, PhD, Leiden University Medical Center, Yetrib Hathout, PhD, Binghamton University

Background: The promise of fit-for-purpose biomarkers is immense. However, many biomarker efforts have been hampered by lack of reproducibility and accuracy, discordant findings from different technologies, and unclear concordance of signals between studies due to confounders. For Duchenne muscular dystrophy (DMD), biomarkers are urgently needed that inform on prognosis, pharmacodynamic response, predictive response, monitoring, etc. Researchers interested in such biomarkers often start from pure discovery/screening without taking full advantage of previously published evidence. Thus far, there is no central resource available to get an overview of published evidence around biomarkers in DMD.
Objectives: Build a dynamic, searchable, openly available database/platform that compiles evidence on biomarkers for DMD.
Methods: Thousands of proteins from serum and muscle (biopsies) biomarker publications (15 datasets) were compiled with a focus on (minimally invasive) serum markers. Findings were obtained from supplemental material of published papers, or by applying standardized processing pipelines when raw data were available. Evidence was aggregated around each biomarker’s association with DMD, corticosteroid treatment, age, clinical outcomes, and other biomarkers. These findings are annotated with attributes (e.g., age range, treatment type) that can be easily filtered.
Results: The interactive application provides exportable summaries and outputs (tables, figures) of findings. The website and interactive Shiny application prioritize an intuitive user experience (tutorial and FAQ included) and have incorporated feedback from international researchers. This resource will be continually updated with new/additional findings to make it a living resource.
Conclusions: This knowledge database and repository/tool provides summary estimates around individual studies’ effect sizes and helps assess cumulative evidence not possible with a single study’s findings. This will facilitate a) quick comparison of new findings from a research lab to published findings, b) new knowledge in terms of nuanced meta-analyses for a specific target, and c) reduce preparatory time and aid with the design of future experiments.