Amyotrophic lateral sclerosis (ALS) is a complex, progressive neurodegenerative disease. As the currently approved therapies for the treatment with ALS are scarce and have limited clinical benefits, there is an urgent need to find new targets for therapeutic interventions.
GWAS and other approaches have identified several disease-associated genes, but these findings have not translated into progress in clinical trials. This probably reflects the fact that GWAS is limited to identifying single variants with large effect sizes in a population, while the key to understanding complex diseases such as ALS that are influenced by multiple genetic loci, epidemiological and/or environmental factors is to find combinations of these disease associated factors that distinguish one patient subgroup from another.
The PrecisionLife platform utilises a hypothesis-free method for the detection of combinations of features that together are strongly associated with variations in disease risk, progression rates and other clinical phenotypes often observed in ALS patient subgroups.
Using patient datasets accessed as part of a collaboration with King’s College, London and the UK’s Motor Neuron Disease Association, PrecisionLife has performed a detailed genetic stratification of ALS patients, which has uncovered multiple genetic associations particularly linked to sporadic ALS.
We identified 33 novel targets, including ones associated with fast disease progression and progressive muscular atrophy phenotypes. Among this panel we also found targets potentially linked to existing drugs, e.g., riluzole and retigabine. For each of the targets, the PrecisionLife analysis has generated a set of companion genetic biomarkers to facilitate identifying patients predicted to respond to modulation of the protein. We have selected the highest scoring, most druggable targets to be validated in ALS-relevant, human iPSC-neuronal models including patient derived cells, followed by in vivo studies in animal models (zebrafish and mouse).
The results demonstrate that PrecisionLife combinatorial analysis is uniquely able to stratify heterogenous patient populations with complex disease pathologies. We can use these insights to identify more effective therapeutic strategies and accompanying biomarker sets to match them to the patient subgroups that are most likely to demonstrate benefit in downstream clinical trials.
Stratifying patient subgroups to increase the probability of future clinical trial success in Alzheimer's disease.