Understanding Disease Trajectory in Amyotrophic Lateral Sclerosis

Abstract

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease predominantly of motor neurons, characterized by progressive weakness of voluntary muscles and death from respiratory failure due to diaphragmatic paralysis, typically within three years of onset. Despite the very poor prognosis, there is considerable variation in the survival rate, and up to 10% of people with ALS live more than eight years from first symptoms. There is a strong genetic contribution to ALS risk. In 5% of cases or more, a family history of ALS or frontotemporal dementia is obtained, and the Mendelian genes responsible for ALS in such families have now been identified in about 70% of cases. Even in apparently sporadic cases, twin and population studies show the heritability is about 60%. Although risk genes reveal information about the mechanism of causation of ALS, it is also important to identify genomic variants that modify survival. Such variation could potentially be targeted directly, as could any RNA or protein product, to improve ALS survival. People with ALS have a progressive symptomatic course, and an understanding of the genetic burden requires an understanding of the phenotype. Survival modelling is important, as is an understanding of the way the disease progresses. Clinical staging is a method of achieving this.

In this thesis I explore the relationship between ALS disease progression, survival and genetics, developing and using various tools for measurement and assay of these parameters. I show that clinical staging using a simple system is feasible, usable by clinicians and other healthcare professionals, and matches with their expectations. I explore the use of such a system to analyse clinical trials, illustrating this with data from a clinical trial of Riluzole. I develop a pipeline for the analysis and interpretation of next generation sequence data and apply this to ALS whole genome sequences. I show that factors influencing survival in ALS include structural genomic variation, telomere length and rare genomic variants.

Date of Award	3 Sept 2019
Original language	English
Awarding Institution	King's College London
Supervisor	Ammar Al-Chalabi (Supervisor) & John Powell (Supervisor)