Immune System Biomarkers in Alzheimer's Disease Progression: Study Update

1. Wellcome Trust Consortium for Neuroimmunology of Mood Disorders and Alzheimer’s Diseasework package 3: immune system biomarkers in ADprogress update – 30/10/15 Paul Morgan, Angharad Morgan, Caroline O’Hagan, Samuel Touchard

2. Complement • The complement system is a pivotal part of the immune system and inflammatory processes • The complement system is kept under tight control by soluble and membrane-bound regulators • Balance between complement activation and inhibition • Dysregulation of this balance may contribute to neuroinflammationand disease • Complement activation has been shown to occur in the AD brain, even at very early stages of the disease • Genetic studies have identified AD-associated variants in complement pathway genes

3. Study design • AD versus control • MCI versus control • MCI progression to AD versus non-progressors • MCI/AD correlation/prediction of rate of decline • Add value to structural imaging

4. Samples Total = 1147 ADC = 367, MCI =268, CTL =512 Dementia case register n=427 Addneuromed n=720 Still waiting for samples to arrive…. Available data: - Demographics - Cognitive measures - MRI data - Proteomics data

5. Assays MSD Assay set 1 C3, C4, C5, Fh, fB, fI Assay set 2 C5a, TCC, Bb, C3a, iC3b, fD ELISA Properdin, FHR, C1 inhibitor, CR1, CR2, C1q, CLU, C9

6. TREM2 • Rare variants in TREM2 increase susceptibility to AD, with an odds ratio similar to that of the apolipoprotein E4 • The encoded protein functions in immune response and may be involved in chronic inflammation by triggering the production of constitutive inflammatory cytokines • TREM2 expression is upregulated in the brain of patients with AD • Measurement of TREM2 protein levels in CSF reported at Alzheimer's Association International Conference 2015 • 2 studies, both reported increased TREM2 in AD

7. Pump priming award/ARUK network centre grant. • Better monoclonal antibodies against mouse and human TREM-2. • Wild-type mice will be immunised with recombinant soluble human TREM-2 and hybridomas developed using standard techniques. TREM-2 reactive clones will be identified by screening on recombinant protein and on cells expressing TREM-2. Positives will be subcloned to monoclonality. • TREM-2 knockout mice will be immunised with recombinant soluble mouse TREM-2 and hybridomas generated and screened as above and on wild-type and knockout leukocytes.

8. Additional samples 99 plasma samples Measure: CR1, CLU, C9, C1 inhibitor, TCC

9. Future work • Other markers • Variant-specific assays – DH variants, FB variants, C3 variants • Other inflammatory markers - Cytokines, Chemokines, MMPs • Other sample sets • Longitudinal samples • Link to structural imaging, genetics and other available datasets • Multivariate methods to arrive at highly predictive algorithms for early diagnosis, stratification, prediction of progression, response to therapy

10. SchizophreniaMaja Kopczynska

11. Inflammation and Schizophrenia • Increased levels of IL-6, IL-12, CRP in Schizophrenia patients • Maternal immune activation disrupts normal foetal brain development • Anti-inflammatory medication reduce the coresymptoms of schizophrenia

12. Complement and Schizophrenia • Complement system may play a role in neurogenesis, synapse remodelling and pruning during brain development • Glial atrophy or reduced cortical glial may contribute to synaptic abnormalities and impaired connectivity in schizophrenia

13. Methods • Patients and controls from “Stress + Psychosis Study 55516” and “Pump Study 55541” from King’s College Hospital in London • Sample volumes varied between 0.2 – 0.5 ml serum, all samples stored at -30°C since date of collection to arrival to Cardiff, currently stored at -80°C • 228 samples in total were tested in groups of 38 samples per plate for the presence of 16 complement markers • ELISA – 11 markers • C3, C4, C5, C1q, TCC, Factor B, Factor H, FHR, Properdin, C1 inh, CR1 • MSD – 5 markers • Bb, C4d, C5a, iC3b, TCC

14. Results 156 patients: 25 controls and 131 cases

16. Analysis • Logistic regression : • Regression model where the dependent variable is binary: 0 or 1 • It studies the relationship between this binary response and different predictors or independent variables, either continuous or categorical • It predicts the odds or probabilities that a sample or observation is a case, based on the values of the predictors • Extensions: multinomial logistic regression and ordinal logistic regression

17. Summary of the complete model with the 11 analytes Significant predictors: C5 and C1inh at 0.05 CR1 and FH at 0.1

18. Reduced model computed by stepwise selection Analytes selected: CR1, TCC, C3, C5, C1inh, FH

19. ROC Curve Area under the curve: 0.85 Area under the curve when only gender is studied: 0.73 Area under the curve when only the assays are studied: 0.77

20. Predicted probabilities for values of CR1, C1inh, C5 and age

21. Predicted probabilities for values of C1 inhibitor

22. Furtherwork • Polish the analysis by dealingwithsomemissing values • Recalibrate to correct absolute values • Explore furtherthissubset of assays, as interactions betweenanalytesalsoseem to have a greatereffect on schizophrenia

23. Further work on AD data • Classification(s): AD vs Control, MCI vs Control, convertors vs non-convertors... • Logistic regressions: standard or multinomial • Supervised learning: Random forest, Naive Bayes Simple, k- nearest neighbors... • Unsupervised learning: clustering • Cognitive decline: • Linear mixed models with longitudinal scores • Classification methods if the decline or deterioration can be graded in classes or groups

24. References • Sattlecker et al., Alzheimer’s disease biomarker discovery using SOMAscan multiplexed protein technology. Alzheimer and Dementia, 10(6):724-34, 2014 • Hye et al., Plasma proteins predict conversion to dementia from prodromal disease. Alzheimer and Dementia, 10(6):799-807.e2, 2014 • Khan et al., A Subset of Cerebrospinal Fluid Proteins from a Multi-Analyte Panel Associated with Brain Atrophy, Disease Classification and Prediction in Alzheimer's Disease. PLoS One, 10(8):e0134368, 2015