Towards development of a statistical framework to evaluate myotonic dystrophy type 1 mRNA biomarkers in the context of a clinical trial

Adam Kurkiewicz, Anneli Cooper, Emily McIlwaine, Sarah A. Cumming, Berit Adam, Ralf Krahe, Jack Puymirat, Benedikt Schoser, Lubov Timchenko, Tetsuo Ashizawa, Charles A. Thornton, Simon Rogers, John D. McClure, Darren G. Monckton

Research output: Contribution to journalArticle

Abstract

Myotonic dystrophy type 1 (DM1) is a rare genetic disorder, characterised by muscular dystrophy, myotonia, and other symptoms. DM1 is caused by the expansion of a CTG repeat in the 3'-untranslated region of DMPK. Longer CTG expansions are associated with greater symptom severity and earlier age at onset. The primary mechanism of pathogenesis is thought to be mediated by a gain of function of the CUG-containing RNA, that leads to transdysregulation of RNA metabolism of many other genes. Specifically, the alternative splicing (AS) and alternative polyadenylation (APA) of many genes is known to be disrupted. In the context of clinical trials of emerging DM1 treatments, it is important to be able to objectively quantify treatment efficacy at the level of molecular biomarkers. We show how previously described candidate mRNA biomarkers can be used to model an effective reduction in CTG length, using modern high-dimensional statistics (machine learning), and a blood and muscle mRNA microarray dataset. We show how this model could be used to detect treatment effects in the context of a clinical trial.

Original languageEnglish (US)
Article numbere0231000
Pages (from-to)e0231000
JournalPLoS ONE
Volume15
Issue number4
DOIs
StatePublished - Apr 2020

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)
  • General

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