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

Abstract

Background: Analysis 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.

Methods: A 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 ³¹ P 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.

Results: Cross-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.

Conclusions: Serum 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.

Keywords: Becker muscular dystrophy; Biomarker; Duchenne muscular dystrophy; Longitudinal analysis; Proteomics; Sarcopenia.

Figure 1

Cross‐sectional analysis of DMD patients. (A) Volcano plot showing the estimated change in DMD patients compared with healthy controls (x‐axis) and the −log10 of the adjusted P‐values. Black circles represent proteins that are not differentially represented in patients compared to healthy controls, while red circles represent the 285 proteins surviving multiple testing correction. (B) Overlap with known proteins able to discriminate between DMD and healthy controls identified using SOMAmers. (C) Violin plots showing four examples of proteins differentially represented between DMD patients and healthy controls. Carbonic anhydrase 3 (CA3) and fructose‐bisphosphate aldolase A (ALDOA) were elevated in patients over controls, while growth differentiation factor 2 (GDF2) and complement component 3 (C3) levels were reduced in DMD patients compared with healthy controls (adjusted P < 0.01 for all). (D–E) Circular plots showing correlation‐based hierarchical clustering of the 285 differentially expressed proteins in healthy individuals (D) and DMD patients (E). Connections shown in red represent correlations between protein levels in each group. Only correlations above 0.8 are shown. The thickness of the lines is proportional to the correlation strength.

Figure 2

Cross‐sectional analysis of BMD patients. (A) Volcano plot showing the estimated change in BMD patients compared with healthy controls (x‐axis) and the −log10 of the adjusted P‐values. Black circles represent proteins that are not differentially represented in BMD patients compared to healthy controls, while red circles represent the 13 proteins surviving multiple testing correction. (B) Volcano plot showing the estimated change in BMD patients compared with DMD patients. Red circles represent the 121 proteins that were significant after multiple testing correction. (C) Venn diagram showing the overlap between proteins differentially expressed in the DMD, BMD, and healthy. (D–F) Dot plots showing three examples of proteins able to discriminate among DMD, BMD, and healthy (D), or discriminating between DMD and healthy but not between BMD and healthy (E), or able to discriminate between dystrophic and non‐dystrophic but unable to separate DMD and BMD (F).

Figure 3

Association of serum proteomic signature with ³¹P MRS data, functional data, and dystrophin levels. (A) Correlation plot showing the high degree of (Pearson) correlation between the different functional scores and moderate correlation among ³¹P MRS data with the exception of the Pi/PCr and Pi/ATP ratios. This plot was used to identify potential correlating covariates to exclude during modelling. (B) Heat map showing the Pearson correlation of IGF2R, CDNF, RGMA, and BMP6 with upper and lower limb functional data. (C) Scatter plot showing the linear association between elbow flection and the concentration of these four proteins in serum. (D) Scatter plot showing the association between TIMP2 and SERPIND1 with dystrophin protein levels in muscle.

Figure 4

Longitudinal proteomic analysis of DMD patients. (A) Pie chart showing the number of proteins unaffected, increasing, and decreasing over time in DMD patients followed‐up longitudinally. (B) Volcano plot showing the estimated mean log change per year (x axis) for each protein and the −log10 of the P‐value (y axis). Black circles represent proteins stable during disease progression, green and red circles represent proteins with decreasing and increasing concentration over time, respectively. (C) Venn diagram showing the overlap of proteins identified in the cross‐sectional and longitudinal proteomic studies. (D–E) Scatter plots showing exemplar proteins decreasing (D) and increasing (F) over time in DMD patients. Age is plotted on the x axis, while protein concentration is plotted on the y axis. (F) Volcano plot showing the hazard ratios (x axis) and −log10 of the P‐value of the survival analysis modelling the time to loss of ambulation. No protein is significant after multiple testing correction.