Alzheimer's disease (AD), like other complex diseases, is characterised by a high degree of heterogeneity in the patient population. GWAS have identified several disease-associated genes, but these findings have not translated into progress in clinical trials. This likely reflects the limitations of GWAS in only identifying single variants, while the key to understanding complex diseases that are influenced by multiple genetic loci is to find combinations of variants that distinguish one patient subgroup from another.
The PrecisionLife platform utilises a hypothesis-free method (combinatorial analysis) for the detection of combinations of features that together are strongly associated with variations in disease risk, symptoms, progression rates and therapy response.
To explore the substructures within AD we analysed a genomic dataset from the UK Biobank, including 882 patients, compared against healthy controls.
Our analysis identified combinations of genetic variants which mapped to 113 genes that are significantly associated with AD pathology, including known and novel genes. Clustering these combinations, based upon the degree of patient overlap, revealed six distinct subgroups of patients. Each patient subgroup reflected a specific biological function - lipid metabolism, neuroinflammation, autophagy, serotonin signaling, metal ion homeostasis, and metabolic dysfunction.
Several of these functions are also identified in patient subgroups in other neurological diseases, including FTD, schizophrenia and long COVID, indicating shared genetic aetiologies and underlying dysregulated processes common between them.
Among the genes identified within each AD subgroup, 32 are targeted by drugs in clinical development in other indications. We have developed a pipeline to systematically evaluate the potential of repurposing these to accelerate implementation of safe and effective therapies for AD patients.
The results demonstrate that combinatorial analysis can stratify heterogenous patient populations with complex pathologies to identify effective therapeutic strategies with accompanying biomarkers and enrich clinical trial design, improving the probability of success in AD drug development.
Stratifying patient subgroups to increase the probability of future clinical trial success in Alzheimer's disease.