Disease Studies

ALS Patient Stratification Analysis

Amyotrophic Lateral Sclerosis (ALS – also known as Motor Neurone or Lou Gehrig’s Disease) is a complex, progressive neurodegenerative disease usually diagnosed between age 40-70. It is characterized by a peripheral neuropathy that migrates centrally, but disease causes, symptoms and progression vary greatly. There is no cure for the disease and there are only two approved therapies, which have limited efficacy.

In our initial ALS studies, we found 33 novel genes strongly associated with ALS risk. Having run the most drugable targets in patient derived cellular assays, three of the first five tool compounds we tested showed significant potential to improve motor neurone survival.

In a new analysis we have further stratified ALS patients using combinations of genetic features, and correlated these findings with the patients’ additional clinical and phenotypic data. We identified three distinct patient clusters in each cohort, each associated with different clinical presentation and disease severity.



Methodology

In this patient stratification study, we used genetic data from a small set of UK amyotrophic lateral sclerosis (ALS) patients. These patients were split into two distinct cohorts of 610 and 736 patient respectively, based on the single nucleotide polymorphism (SNP) array used for genotyping, and analyzed separately against healthy matched controls.

Additional clinical and phenotypic data was available for 1,386 of these genotyped ALS patients from the Motor Neuron Disease Association (MNDA). This included information about the ALS type, related diagnoses, age of onset, diagnosis, ALSFRS-R measurement, and death (if deceased) for patients. Data was also available for patients’ ethnicity, diagnosis of other neurodegenerative diseases in the patient or their family members, site of disease onset, and survival measures.


Findings/Results

Combinatorial analysis with the PrecisionLife® platform identified 24 risk-associated genes that were highly associated with these ALS cases. Biological analysis of the genes revealed that many have a plausible mechanistic connection to the regulation of neurodegenerative disease processes. When patients were clustered by their genetic variants, we identified three distinct patient clusters in each cohort.

Using combinatorial analysis of patient’s genotypes alongside additional phenotypic and clinical data, including disease severity, age of death, and ALS subtype diagnosis, we were able to infer clinical differences between the three patient clusters in each cohort. This included a severe disease patient cluster that had a significantly earlier age of death and a greater degree of functional impairment.

This analysis demonstrates that PrecisionLife’s combinatorial analysis approach is able to identify novel ALS risk-associated genes and stratify patients into potentially clinically relevant subgroups based on their genetic differences, even in such a complex and heterogeneous disease as ALS. These subgroups not only display different disease mechanisms, but also vary in disease progression rate and age of death.

Stratification of the patient subpopulations by PrecisionLife for an ALS patient cohort. Each circle represents a disease-associated SNP genotype; edges represent co-association in patients; and colors represent distinct patient subpopulations.

 

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