1. BIOMARKER STUDIES IN CLINICAL TRIALS Vicki Seyfert-Margolis, PhD

2. CLINICAL DATA (Ontologies) MECHANISM Flow Cytometry Autoantibody ELISPOT Cytokine Measures DISCOVERY Gene Expression SNP/Haplotype Proteomics

3. ITN Transplant Trial Model SERIES OF DAYS WEANING PERIOD ONE YEAR Drug Administration Drug Levels Drug Effects Serum Cytokines Cell Populations Gene Expressions Transplant Graft Assessment Time 0 Biopsy and Gene Expression Drug Levels Drug Effects IS Withdrawal Immune Response Cell Populations - Flow T Cell Function - IS Effects Rejection- Gene Expression Immediate Post Withdrawal Rejection - Gene Expression Cell Populations - Flow T Cell Function Follow Up: 2-5 years Tolerance Marker ID Gene Expression Regulatory Cells - Flow Cytometry Th1/Th2 Shift Serum Profiles Other Assays Baseline Screening End of Study DAY 0 ONE YEAR 2-5 YEARS Start of Study

5. Integration of domain-specific information Gene Expression Cytokine SecretionAntigen Expression Flow CytometryEliSPOT Microarray

6. High Level Analysis Plan

7. Original Biopsy Designation Counts by visit Classification On left column AR = Acute Rejection HEP = Mild HEP-MOD = Moderate To Severe

8. Gene Expression Statistical Framework Design  Comparisons of interest  Biological replicates Pre-processing  Normalization  Quality Assurance Inference  Statistic that incorporates variability  Fold Change (FC) and p-value cutoff  False Discovery Rate (FDR) estimation to handle multiple testing comparisons  Gene class testing, enrichment analysis to facilitate interpretation Classification  Supervised and supervised approaches  Support Vector Machines (SVM), K-means, Random Forests  Issues with with over fitting data  Using test set, training set approaches Validation  Follow-up study  Alternate assay Mechanism of Action Biomarker (SI) (TOL)(CAN)(HC)

9. Hierarchical Clustering (All Samples, V0, V6) Hierarchical Clustering (Pearson correlation) All visits Transcripts filtered for those differentially expressed between V6 and Baseline (V0) at FC >2 and FDR correction 4, 041 transcripts Blue = baseline Yellow = V6 Red = FCLB Baseline = 27 FCLB = 21 V6 = 12

10. Hierarchical Clustering (V6 vs. FCLB) Hierarchical Clustering (Pearson correlation) V6 vs. FCLB Transcripts filtered for those differentially expressed between FCLB and V6 at FC >1.5 and NO FDR correction 629 transcripts Blue = V6 Red = FCLB FCLB = 21 V6 = 12

11. Hierarchical Clustering – AR and Non AR FCLB Hierarchical Clustering (Pearson correlation) FCLB No AR vs. FCLB with AR Transcripts filtered for those differentially expressed between FCLB NO AR and FCLB with AR at FC >1.5 and NO FDR correction 580 transcripts Blue = FCLB No AR Red = FCLB with AR FCLB = 21 V6 = 12

12. ITN Standard Flow Panel Cell type/functionFITCPEPerCPPECy7APC DCsCD11c dump*HLA-DrCD123 DCs/costimulationCD11cCD80dump*HLA-DrCD123 CD11cCD86dump*HLA-DrCD123 CD11cIFN alphadump*HLA-DrCD123 Antigen presentation, activation and costimulation CD14CD4CD19CD3HLA-Dr monocytes, B cellsCD14CD80CD19CD3CD86 T cells/activation/naïve vs memoryCD45RACD45ROCD8CD4CD62L T cells/activationCD8CD69CD4CD3CD122 CD8IL-12RCD4CD3HLA-Dr T regulatory cellsCD8CD25CD4CD3CD62L Cytokines/chemokines Th1/Th2 profilesIFNgammaIL-4CD8CD3CD4 Cytotoxic T cellsperforingranzyme BCD8 CD3 Th1 cellsCD4CXCR3CD8CD3CCR5 Th2 cellsCD4CCR3CD8CD3CCR4 B cells Precursors,germinal center, plasmaCD38IgDCD138CD19CD10 B cells, immature/mature, naïveCD27 or CD1dIgDCD38 or CD20CD19IgM B cells, mature, naïveCD44IgDCD38CD19CD10 B cells, mature, naïveCD23IgDCD38CD19CD77 B1 cellsCD1dCD21CD5CD19CD23 ApoptosisAnnexin VCD95CD20CD19CD27 mature, costimulation, Ag. presn.CD27CD80HLA-DRCD19CD86 NK cellsCD57CD56, CD16CD14CD3CD8 NKT cells6B11v alpha 24CD4 CD8

13. Thistlethwaite – Activated CD3CD4 T Cells (CD62L)

14. Regulatory T cells

15. Associations across assays and trials CD19 IgG1 CD79A CD79B IgJ genes B cells- CD19 Naïve B cells- CD27 IgD+ IgMlo CD20 Urine RT - PCR Flow Cytometry Microarray Operationally Tolerant Individuals

16. Data Flow Raw Data Raw Data Analysis Pipeline Analysis Pipeline Biostatistical Repository Biostatistical Repository Curated ‘Results’ (Published) Curated ‘Results’ (Published) Data Center - Validated Raw Data TADA - Participant Annotation - Assay review, annotation - Quality Assurance - Normalization TADA - R or SAS scripting - Analysis Reports - Experimental design, Hypothesis, statistical modeling - Exploratory analyses Communications & TADA - Camera ready figures - Analysis revised or directed for manuscript, presentation, abstract etc.

17. National Institute of Allergy & Infectious Diseases Funded by: Juvenile Diabetes Research Foundation National Institute of Diabetes & Digestive & Kidney Diseases