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BIOMARKER STUDIES IN CLINICAL TRIALS

BIOMARKER STUDIES IN CLINICAL TRIALS. Vicki Seyfert-Margolis, PhD. CLINICAL DATA (Ontologies). MECHANISM Flow Cytometry • Autoantibody • ELISPOT • Cytokine Measures. DISCOVERY • Gene Expression • SNP/Haplotype • Proteomics. ITN Transplant Trial Model. ONE YEAR. DAY 0.

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BIOMARKER STUDIES IN CLINICAL TRIALS

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  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 ONE YEAR DAY 0 Start of Study SERIESOF DAYS Transplant • Graft Assessment • Time 0 Biopsy and Gene Expression • Drug Levels • Drug Effects BaselineScreening Drug Administration • Drug Levels • Drug Effects • Serum Cytokines • Cell Populations • Gene Expressions 2-5 YEARS ONE YEAR End of Study WEANING PERIOD 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

  4. Integration of domain-specific information Gene Expression Antigen Expression Cytokine Secretion Flow Cytometry EliSPOT Microarray

  5. High Level Analysis Plan

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

  7. (SI) (CAN) (TOL) (HC) Gene Expression Statistical Framework Design • Comparisons of interest • Biological replicates Pre-processing • Normalization • Quality Assurance Inference Classification Biomarker Mechanism of Action • 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 • 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

  8. 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

  9. 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

  10. 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

  11. ITN Standard Flow Panel

  12. Thistlethwaite – Activated CD3CD4 T Cells (CD62L)

  13. Regulatory T cells

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

  15. Data Flow Raw Data Analysis Pipeline Biostatistical Repository 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.

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

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