Towards personalized management of myasthenia gravis phenotypes: From the role of multi-omics to the emerging biomarkers and therapeutic targets

Abstract

Predicting the onset, progression, and outcome of rare and chronic neurological diseases, i.e. neuromuscular diseases, is an important goal for both clinicians and researchers and should guide clinical decision-making and personalized treatment plans. A prime example is myasthenia gravis (MG), an antibody-mediated disease that affects multiple components of the postsynaptic membrane, impairing neuromuscular transmission and producing fatigable muscle weakness. MG is characterized by several clinical phenotypes, defined by a broad spectrum of factors, which have contributed to the current lack of consensus on the optimal management and treatments of this disease and its related phenotypes (subtypes). This represents a crucial challenge in MG and encourages a revolutionary change in diagnostic, prognostic and therapeutic guidelines. Emerging factors, such as demographic, clinical and pathophysiological factors, must also be considered. Consequently, the different MG phenotypes are characterized by precise biological signatures, which could represent appropriate biomarkers and targets. Here we describe and discuss these new concepts, highlighting that, thanks to multi-omics technologies, the identification of emerging diagnostic/prognostic biomarkers, such as miRNAs, and the subsequent development of new diagnostic/therapeutic algorithms could be facilitated. The latter, in turn, could facilitate the management of different MG phenotypes also in a personalized manner. Limitations and advantages are also reported.