Lead Investigator: Paul Schulz, University of Texas Health Science Center
Title of Proposal Research: Identification of biomarkers associated with Alzheimer’s disease progression that correlate with responses to medications
Vivli Data Request: 5921
Funding Source: None
Potential Conflicts of Interest: None
Summary of the Proposed Research:
Alzheimer’s disease (AD) manifests with strikingly different ages and sites of onset, progresses at different rates and in different anatomic directions, and has varied responses to treatment 1. Disease-modifying AD clinical trials have been disappointing; however, detailed analyses often show a subset of patients with improved outcomes 2–5. We hypothesize that AD is heterogeneous and only subsets of patients may respond to each treatment. Using computational analysis, we propose to identify biomarkers that correlate with the four critical clinical variables (age and site of onset, rate and direction of progression) and to test whether those markers correlate with responses to AD medications. The results of our analysis could provide unparalleled insights into whether a subject would respond, or not, to an investigational medication and whether a subject is likely to have rapid disease progression. It could also inform the basis of inclusion criteria for future clinical trials, ultimately increasing the likelihood of a positive outcome for both the investigational medication and the research subjects.
Statistical Analysis Plan:
Bivariate and non-linear regression analyses will be used in Aim 1 to identify biomarkers associated with clinical disease variables throughout the AD spectrum. Bivariate analyses will utilize Kendall correlation, Kruskal-Wallis one way rank ANOVA, and the Mann-Whitney U test, as appropriate. Independent predictors of age and site of onset, and rate and anatomic direction of progression (p≤0.05) will be further assessed through non-linear regression analyses.
Our hypothesis mandates the use of either large treatment groups, or the combination of data from multiple datasets, in order to achieve statistical power to predict unique biomarker profiles. However, due to variations in acquisition methods and observations, biomarker predictions or treatment effects may be muted. Using a multi-modal, computational phenotyping method, nonnegative tensor factorization8,9, we can analyze multisite datasets across past clinical trials in order to maximize our sample population while mitigating the risks associated with noisy data.
Significant biomarkers identified in Aim 1, will provide the insights to facilitate Aim 2. The analyses for Aim 2 will include advanced machine learning, coupled nonnegative tensor factorization, to group patients into phenotype clusters. These patient biomarker profiles would be based on variables that are more similar within the cluster, than between clusters. Biological, imaging, demographic, and drug-effect variables will be incorporated in order to derive phenotype clusters. This multi-modal, computational method will enhance the robustness of the phenotypes. Phenotypes will be filtered to remove less discriminative ones based on statistical significance (p>0.05), and in order to establish phenotypes with distinct characteristics. This method will also provide the membership values of each variable, in order to determine the contribution of each variable to the derived phenotype. Phenotypes will be derived using a small test cohort of the population. Discriminative ability of each derived phenotype will be verified using the remaining population, validation cohort. The resulting phenotypes will be combined with additional data for use in Aim 3.
A similar study utilizing data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database was previously performed by our group to determine phenotypes7. This analysis, however, was limited to the variables and cases available in the ADNI database. While certain limitations still exist, the use of large, well-characterized, diverse datasets of patients from past trials will enhance our ability to detect and identify relevant biomarkers, create phenotype clusters, and develop a predictive algorithm for drug response.
Patient response rates to standard and investigational medications in models of AD and drug molecular features, along with the established phenotypes will be aggregated for use in Aim 3. We will then test the ability of individual phenotype clusters to predict the response to AD treatments using an end-to-end prediction algorithm. Individuals that are naïve to the treatment being assessed, non-responders to treatments, and initial responders to non-FDA approved medications will be used for prediction. Effect size will be calculated using area under the ROC curve in each placebo/treatment pair for each clinical trial dataset in each phenotype group. The same analysis will be completed through all derived phenotype clusters. This type of analysis will allow for prediction of improvement or worsening upon exposure to a drug of interest. We will mitigate the lack of drug response data in AD models using cold-start algorithms based on latent factor modeling.
Requested Studies:
Effect of LY450139 a y-Secretase Inhibitor, on the Progression of Alzheimer’s Disease as Compared With Placebo
Sponsor: Eli Lilly and Company
Study ID: NCT00762411
Sponsor ID: 11271
Effect of γ-Secretase Inhibition on the Progression of Alzheimer’s Disease: LY450139 Versus Placebo
Sponsor: Eli Lilly and Company
Study ID: NCT00594568
Sponsor ID: 7666
Open-Label Extension for Alzheimer’s Disease Patients Who Complete One of Two Semagacestat Phase 3 Double-Blind Studies (H6L-MC-LFAN or H6L-MC-LFBC)
Sponsor: Eli Lilly and Company
Study ID: NCT01035138
Sponsor ID: 5930
Public Disclosures:
Wang, D., Ma, X., Schulz, P.E., Jiang, X. and Kim, Y., 2024. Clinical outcome-guided deep temporal clustering for disease progression subtyping. Journal of Biomedical Informatics, 158, p.104732. Doi : 10.1016/j.jbi.2024.104732