Validating ALS Molecular Subtypes For Diagnostics And Disease Stratification

Amyotrophic lateral sclerosis (ALS) is a complex disease which differs between individuals in terms of 1) symptoms and progression over time, and 2) biological causes. Although there are features common to all ALS patients in the latter stages of disease, it is thought that in the pre- symptomatic and early stages of ALS, these can vary greatly based on the individual's underlying biology.

Therefore, grouping patients into subgroups with similar biological characteristics, can increase our chances of finding genes and processes which could be used as personalised indicators of ALS progression.

Our previous work demonstrated that we could group patients into three molecular subtypes, based on brain specific gene expression signatures which were identified using machine learning. These subtypes are ALS-specific, can differentiate patients from non-affected individuals with high accuracy and are also present in independent ALS datasets from different populations.

This means that they show potential as indicators of ALS diagnosis and progression. In this project we will validate this method on a large-scale dataset as a diagnostic and stratification tool based on blood sample analysis that can be used by all people with ALS.