Defining a circulating serum protein signature for monitoring Duchenne Muscular Dystrophy

Topic:

Translational Research

Poster Number: 288 T

Author(s):

Utkarsh Dang, PhD, Carleton University, Chiara Degan, Leiden University Medical Center, Rebecca Tobin, Carleton University, Sharon de Vries, Leiden University Medical Center, Albert Jiménez-Requena, KTH Royal Institute of Technology, Michela Guglieri, MD, Newcastle University, Newcastle upon Tyne, England, UK , Jordi Diaz-Manera, Newcastle University, Yuri EM van der Burgt, PhD, Leiden University Medical Center, Yetrib Hathout, PhD, Binghamton University, Cristina Al-Khalili Szigyarto, PhD, KTH Royal Institute of Technology, Roula Tsonaka, PhD, Leiden University Medical Center, Pietro Spitali, PhD, Leiden University Medical Center

Background: Duchenne muscular dystrophy (DMD) is a progressive neuromuscular disorder for which monitoring biomarkers are urgently needed to complement functional assessments, particularly in early disease stages and clinical trials.
Objectives: Build a circulating (blood serum) protein based biomarker signature for DMD in young boys.
Methods: We modeled longitudinal serum proteomic profiles from the FOR-DMD clinical trial, which compared daily and intermittent corticosteroid regimens in boys aged 4–8 years at baseline. Using aptamer-based SomaScan® platform, we quantified 1,500 proteins in a subset of the trial participants. Associations between protein levels and motor outcomes, such as rise from the floor velocity (RFV), 10-meter run/walk velocity (10MRWV), and North Star Ambulatory Assessment (NSAA), were modeled using linear mixed models. Single protein focused analyses were followed up by penalized mixed models, applied to evaluate the joint/simultaneous predictive capacity of proteins. The prediction accuracy of the models (evaluated by optimism-corrected root mean squared error) was assessed and compared to a simpler model.
Results: Across-patient and within-patient analyses revealed consistent associations with the three functional tests for a subset of proteins, notably RGMA, ART3, ANTXR2, and CFB. Multivariate models incorporating these proteins significantly associated with at least two motor function tests improved prediction accuracy for NSAA and RFV by 21% and 8%, respectively. These models also revealed a subset of proteins that were consistently selected. Some of the selected biomarkers were validated using ELISA and targeted mass spectrometry as orthogonal methods. Quantification of CFB, RGMA, ANTXR2, and SERPINF1 using SomaScan showed strong agreement with measurements obtained through ELISA and MRM-MS.
Conclusions: Our findings support the utility of serum protein signatures as objective, quantitative tools for monitoring disease progression and treatment response in DMD, with potential applications in clinical trial stratification, early therapeutic evaluation, and remote monitoring.