Clinical, histopathological, and biomarker characterization of XLMTM and ADCNM: Operational lessons, screening and baseline data of the Unite-CNM study

Background X-linked myotubular myopathy (XLMTM) and autosomal dominant centronuclear myopathy (ADCNM) are rare neuromuscular disorders characterized by severe weakness and respiratory impairment and have recently been the focus of therapeutic development. Robust baseline data are essential to understand disease trajectories and enable trial readiness. We present baseline clinical, functional, and biomarker findings from the terminated Unite-CNM trial (NCT04033159), a basket study of the antisense oligonucleotide DYN101, designed to modulate DNM2 expression in adolescents and adults with XLMTM or ADCNM. Methods Screening and baseline evaluations included medical history, patient-reported outcomes, quantitative muscle strength and motor function, respiratory testing, muscle ultrasound and histology, and biomarker analyses (DNM2 protein, creatinine, creatine kinase, cystatin C, myostatin, and microRNAs) in plasma and muscle. Results Twenty-six patients were screened (15 DNM2 , 11 MTM1 ); 14 entered the study. Patient-reported measures highlighted swallowing challenges and variable personal goals. Baseline respiratory impairment was variable in DNM2 and female MTM1 patients but consistently reduced in males with MTM1 (FVC% and FEV1% stable over follow-up to 78 weeks). MFM32 scores were similar across genotypes and sexes, while Myogrip strength was reduced versus age norms yet stable longitudinally. Muscle pathology was comparable across groups. Biomarker analyses showed altered DNM2 protein, reduced myostatin, and specific abnormalities in creatinine, creatine kinase, cystatin C, and microRNAs. Conclusion This dataset provides valuable phenotypic, functional, and biomarker characterization of adult XLMTM and ADCNM, emphasizes the importance of baseline data for ultrarare disease trials, and highlights the need for protocol flexibility in response to safety signals and disruptions (e.g. COVID-19).