Serum biomarkers associated with baseline clinical severity in young steroid-naïve Duchenne muscular dystrophy boys

Utkarsh J Dang, Michael Ziemba, Paula R Clemens, Yetrib Hathout, Laurie S Conklin, CINRG Vamorolone 002/003 Investigators, Eric P Hoffman, Utkarsh J Dang, Michael Ziemba, Paula R Clemens, Yetrib Hathout, Laurie S Conklin, CINRG Vamorolone 002/003 Investigators, Eric P Hoffman

Abstract

Duchenne muscular dystrophy (DMD) is caused by loss of dystrophin in muscle, and while all patients share the primary gene and biochemical defect, there is considerable patient-patient variability in clinical symptoms. We sought to develop multivariate models of serum protein biomarkers that explained observed variation, using functional outcome measures as proxies for severity. Serum samples from 39 steroid-naïve DMD boys 4 to <7 years enrolled into a clinical trial of vamorolone were studied (NCT02760264). Four assessments of gross motor function were carried out for each participant over a 6-week interval, and their mean was used as response for biomarker models. Weighted correlation network analysis was used for unsupervised clustering of 1305 proteins quantified using SOMAscan® aptamer profiling to define highly representative and connected proteins. Multivariate models of biomarkers were obtained for time to stand performance (strength phenotype; 17 proteins) and 6 min walk performance (endurance phenotype; 17 proteins) including some shared proteins. Identified proteins were tested with associations of mRNA expression with histological severity of muscle from dystrophinopathy patients (n = 28) and normal controls (n = 6). Strong associations predictive of both clinical and histological severity were found for ERBB4 (reductions in both blood and muscle with increasing severity), SOD1 (reductions in muscle and increases in blood with increasing severity) and CNTF (decreased levels in blood and muscle with increasing severity). We show that performance of DMD boys was effectively modeled with serum proteins, proximal strength associated with growth and remodeling pathways and muscle endurance centered on TGFβ and fibrosis pathways in muscle.

© The Author(s) 2020. Published by Oxford University Press.

Figures

Figure 1
Figure 1
Scatterplot matrix of timed function tests, 6MWT, NSAA and age. Shown is mean of four measures over ~6-week time frame for 48 DMD subjects (4 to x-axis) versus time to climb (y-axis).
Figure 2
Figure 2
Overview of experimental design to define disease severity biomarkers in DMD. Sequential data reduction approaches were used for TTSTAND and 6MWT motor outcomes (WGCNA). The resulting regression model included 17 serum proteins associated with TTSTAND severity (proximal strength) and 17 proteins associated with 6MWT severity (endurance). The selected serum proteins were tested for gene expression associated with disease status and histopathological severity and were also validated in additional outcomes (TTRW and TTCLIMB from TTSTAND model). Finally, the resulting data were used to build hypothetical biological models for disease severity in DMD.
Figure 3
Figure 3
Screening step showing module trait relationships between motor outcomes and modules of serum proteins. Correlations of 11 MEs for clusters of proteins with clinical outcomes. Pearson correlations and associated P-values in parenthesis are provided. P-values were not adjusted for multiple testing in this screening step. The yellow-highlighted cells were those carried forward for statistical model building for TTSTAND and 6MWT.
Figure 4
Figure 4
Clustering of serum proteins identified as associated with more affected phenotypes as determined by the outcome measures TTSTAND (A) and 6MWT (B) using unsupervised clustering. Patient-level clinical severity data is shown on the x-axis and serum protein levels on the y-axis. TTSTAND shows a clustering of clinically milder patients (red) in the center of dendrogram, corresponding to lower levels of most serum proteins from the model. 6MWT shows a clustering of clinically milder patients to the right of the dendrogram, corresponding to higher levels of most serum proteins in the model. Proteins marked with an asterisk are shared between the two clinical outcome models and demonstrate similar behavior in both models.
Figure 5
Figure 5
Unsupervised heatmap of muscle biopsies of defined histological severity clustered by mRNA expression levels of corresponding serum proteins from the TTSTAND (A) and 6MWT (B) models. mRNA levels were scaled, log-transformed data from a muscle biopsy data set on BMD, DMD and healthy control samples (2). The serum proteins selected by the TTSTAND and 6MWT models showed discriminatory power in differentiating muscle biopsies of variable histological severity, with distinct dendrogram branches for normal (left), mild (right) and severe (center) pathologies.

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