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Alzheimer’s Disease Study

Understanding disease mechanisms, finding novel targets, and identifying patient stratification biomarkers for more accurately targeted clinical trial design

113

genes identified that are highly associated with the risk of developing Alzheimer's disease

6

major subgroups of patients identified, each with a different disease mechanism

>70%

of patients were associated with more than one subgroup

Overview

Neurodegenerative diseases such as Alzheimer’s disease (AD) are multifactorial, with multiple genetic and environmental components. Knowledge of the most obvious genetic correlations and pathophysiological changes observed in patients has not yet led to the development of any effective medications.

Over 100 clinical trials for drugs targeting the development of amyloid-β plaques and hyperphosphorylated tau proteins have failed. This is not because the drugs didn't impact their targets as intended, but because those targets weren't driving disease in sufficient patients in the trial to demonstrate the necessary level of clinical impact.

We analyzed genomic data from 882 patients diagnosed with AD against healthy controls with no family history of the disease, identifying 113 genes that are highly associated with the risk of developing AD. GWAS studies only identified the Apoε4 locus from this same dataset.

By clustering people who share the same disease-associated SNPs, we identified six major subgroups of patients with distinct AD risk genes. While highly plausible mechanistically, many of these represent novel targets for AD research. Each of these subgroups shares combinations of genes that are involved in different biological mechanisms and will benefit from a different type of medication, specific to their disease driver.

In understanding the biological mechanisms of these subgroups we can identify patients who will respond favorably to new drug candidates, to improve demonstration of efficacy and bring more effective medicines to market for people to receive personalized treatments.

Study summary

Existing Genome-Wide Association Studies (GWAS) have identified many loci and genes associated with AD and related diseases, such as APOE, APP, and PSEN1, among others.1 However, these have proved challenging to translate either into novel therapies or useful prediction tools that demonstrate clinical benefit. In part this is due to the inability of GWAS and other individual single nucleotide polymorphism (SNP)-based methods to identify the non-linear impact of epistatic and other interactions between multiple variants in heterogeneous, chronic disease conditions.

PrecisionLife analyzed a genomic dataset from the UK Biobank2, which included 882 patients diagnosed with AD compared against healthy controls with no reported neurodegenerative disorders. Combinations of risk-associated SNPs were identified and all the statistically validated SNP combinations that were significant in this AD population were mapped to the human reference genome (GRCh37)3 to identify disease-associated and clinically relevant genes.

Outcomes

Patient stratification biomarkers to identify responders to new treatments

We identified six major subgroups of patients with shared Alzheimer’s disease risk genes. Each of these subgroups shares combinations of genes that are involved in distinct biological mechanisms:

  • Subgroup 1: lipoprotein metabolism
  • Subgroup 2: changes in immune response following viral infection
  • Subgroup 3: autophagy and cell signaling
  • Subgroup 4: metal ion homeostasis,
  • Subgroup 5: brown fat differentiation and metabolic processes
  • Subgroup 6: serotonin signaling

These indicate that AD comprises at least six patient subgroups with clearly distinguishable disease development and progression drivers. Each of these groups will benefit from a different type of medication, specific to their disease driver.

The most prevalent of these groups (lipoprotein metabolism) represents a causative feature in only 32% of Alzheimer’s patients. This sets an upper limit on the maximum clinical efficacy that could be demonstrated for any drug targeting that mechanism if AD patients were chosen randomly for recruitment into a trial.

In contrast, we have patient stratification biomarkers (and novel druggable targets) for each of the 6 major patient subgroups to identify patients who will respond favorably to new drug candidates, to demonstrate efficacy and reduce the risk of future clinical trial failures.

Alzheimer's Disease Architecture

267 disesae risk loci (SNPs)

113 risk- associated genes mapped to disease-associated mechanisms

96 druggable targets

732 tool compounds

 

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Alzheimers-1

 

Collaborators

Logo - Biobank

UK Biobank

PrecisionLife analyzed a genomic dataset from the UK Biobank, which included 882 patients diagnosed with Alzheimer's disease as defined by ICD-10 code G30.x.

These patients were compared against healthy controls with no reported neurodegenerative disorders (ICD-10 codes Gxx.x), self-reported cognitive decline, or Alzheimer's disease, and no family history of Alzheimer's disease.

References

  1. Freudenberg-Hua, Y., Li, W., & Davies, P. (2018). The Role of Genetics in Advancing Precision Medicine for Alzheimer’s Disease-A Narrative Review. Frontiers in Medicine, 5, 108. https://doi.org/10.3389/fmed.2018.00108

  2. Bycroft, C., Freeman, C., Petkova, D., Band, G., Elliott, L. T., Sharp, K., Motyer, A., Vukcevic, D., Delaneau, O., O’Connell, J., Cortes, A., Welsh, S., Young, A., Effingham, M., McVean, G., Leslie, S., Allen, N., Donnelly, P., & Marchini, J. (2018). The UK Biobank resource with deep phenotyping and genomic data. Nature, 562(7726), 203–209. https://doi.org/10.1038/s41586-018-0579-z

  3. Church, D. M., Schneider, V. A., Graves, T., Auger, K., Cunningham, F., Bouk, N., Chen, H. C., Agarwala, R., McLaren, W. M., Ritchie, G. R., Albracht, D., Kremitzki, M., Rock, S., Kotkiewicz, H., Kremitzki, C., Wollam, A., Trani, L., Fulton, L., Fulton, R., Matthews, L., Hubbard, T. (2011). Modernizing reference genome assemblies. PLoS Biology, 9(7), e1001091. https://doi.org/10.1371/journal.pbio.1001091 

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