Tracking disease progression non‐invasively in Duchenne and Becker muscular dystrophies

Abstract

BackgroundAnalysis of muscle biopsies allowed to characterize the pathophysiological changes of Duchenne and Becker muscular dystrophies (D/BMD) leading to the clinical phenotype. Muscle tissue is often investigated during interventional dose finding studies to show in situ proof of concept and pharmacodynamics effect of the tested drug. Less invasive readouts are needed to objectively monitor patients' health status, muscle quality, and response to treatment. The identification of serum biomarkers correlating with clinical function and able to anticipate functional scales is particularly needed for personalized patient management and to support drug development programs.MethodsA large‐scale proteomic approach was used to identify serum biomarkers describing pathophysiological changes (e.g. loss of muscle mass), association with clinical function, prediction of disease milestones, association with in vivo 31P magnetic resonance spectroscopy data and dystrophin levels in muscles. Cross‐sectional comparisons were performed to compare DMD patients, BMD patients, and healthy controls. A group of DMD patients was followed up for a median of 4.4 years to allow monitoring of individual disease trajectories based on yearly visits.ResultsCross‐sectional comparison enabled to identify 10 proteins discriminating between healthy controls, DMD and BMD patients. Several proteins (285) were able to separate DMD from healthy, while 121 proteins differentiated between BMD and DMD; only 13 proteins separated BMD and healthy individuals. The concentration of specific proteins in serum was significantly associated with patients' performance (e.g. BMP6 serum levels and elbow flexion) or dystrophin levels (e.g. TIMP2) in BMD patients. Analysis of longitudinal trajectories allowed to identify 427 proteins affected over time indicating loss of muscle mass, replacement of muscle by adipose tissue, and cardiac involvement. Over‐representation analysis of longitudinal data allowed to highlight proteins that could be used as pharmacodynamic biomarkers for drugs currently in clinical development.ConclusionsSerum proteomic analysis allowed to not only discriminate among DMD, BMD, and healthy subjects, but it enabled to detect significant associations with clinical function, dystrophin levels, and disease progression.

Longitudinal serum biomarker screening identifies malate dehydrogenase 2 as candidate prognostic biomarker for Duchenne muscular dystrophy

“…A total of 30 proteins showed significant association with age. At least 21 of them were already known in the DMD field as proteins able to discriminate between DMD patients and heathy controls in other recent studies in patients' sera . Less evidence was available for the other nine proteins, namely, NES, BASP1, C4A, MAP 4, C4BPA, CFH, KRT10, RELB, and PDZK1.…”

Longitudinal Study of Three microRNAs in Duchenne Muscular Dystrophy and Becker Muscular Dystrophy

“…However, there is little information regarding changes in miRNAs abundances/expression over time. Previous studies used age as an indicator of disease progression and compare miRNA levels in patients of different ages (18). Also, the effects of corticoid treatment are most commonly accessed between the treated and untreated groups of patients.…”

Biomarkers for Duchenne muscular dystrophy: myonecrosis, inflammation and oxidative stress

“…More recently, extensive proteomic, RNA and metabolite analyses have been carried out in animal models and patients, as discussed in a number of excellent reviews on potential biomarkers for muscle, blood and urine (Aartsma-Rus et al, 2018; Aartsma-Rus and Spitali, 2015;Dowling et al, 2019;Hathout et al, 2014Hathout et al, , 2016Lourbakos et al, 2017;Parolo et al, 2018;Szigyarto and Spitali, 2018;Thangarajh et al, 2019). A large-scale proteomic approach to identify serum biomarkers associated with pathophysiological change over time (Spitali et al, 2018) concluded that ∼33 proteins were bona fide biomarkers as they were able to discriminate between DMD patients and healthy controls in all cohorts, with a concordant directional change towards either a consistent increase or decrease in patients.…”