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

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BIOMARKER STUDIES IN CLINICAL TRIALS Vicki Seyfert-Margolis, PhD

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

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

BIOMARKER STUDIES IN CLINICAL TRIALS

Vicki Seyfert-Margolis, PhD

Page 2: 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

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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

BaselineScreening

End of Study

End of Study

DAY 0

ONE YEAR2-5 YEARS

Start of Study

Start of Study

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Page 5: BIOMARKER STUDIES IN CLINICAL TRIALS Vicki Seyfert-Margolis, PhD.

Integration of domain-specific information

Gene ExpressionCytokine SecretionAntigen Expression

Flow Cytometry

EliSPOT Microarray

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High Level Analysis Plan

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Original Biopsy Designation

Counts by visit

Classification On left column

AR = Acute RejectionHEP = MildHEP-MOD = ModerateTo Severe

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Gene Expression Statistical Framework

DesignComparisons of interestBiological replicates

Pre-processingNormalizationQuality Assurance

InferenceStatistic that incorporates variabilityFold Change (FC) and p-value cutoffFalse Discovery Rate (FDR) estimation to handle multiple testing comparisonsGene class testing, enrichment analysis to facilitate interpretation

Classification

Supervised and supervised approachesSupport Vector Machines (SVM), K-means, Random ForestsIssues with with over fitting dataUsing test set, training set approaches

Validation

Follow-up studyAlternate assay

Mechanism of Action Biomarker

(SI)

(TOL)(CAN) (HC)

Page 9: BIOMARKER STUDIES IN CLINICAL TRIALS Vicki Seyfert-Margolis, PhD.

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 = baselineYellow = V6Red = FCLB

Baseline = 27FCLB = 21V6 = 12

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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 = V6Red = FCLB

FCLB = 21V6 = 12

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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 ARRed = FCLB with AR

FCLB = 21V6 = 12

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ITN Standard Flow Panel

Cell type/function FITC PE PerCP PECy7 APC

DCs CD11c   dump* HLA-Dr CD123

DCs/costimulation CD11c CD80 dump* HLA-Dr CD123

        CD11c CD86 dump* HLA-Dr CD123

        CD11c IFN alpha dump* HLA-Dr CD123

Antigen presentation, activation andcostimulation

         

CD14 CD4 CD19 CD3 HLA-Dr

monocytes, B cells CD14 CD80 CD19 CD3 CD86

T cells/activation/naïve vs memory CD45RA CD45RO CD8 CD4 CD62L

T cells/activation CD8 CD69 CD4 CD3 CD122

        CD8 IL-12R CD4 CD3 HLA-Dr

T regulatory cells CD8 CD25 CD4 CD3 CD62L

Cytokines/chemokines          

Th1/Th2 profiles IFNgamma IL-4 CD8 CD3 CD4

Cytotoxic T cells perforin granzyme B CD8   CD3

Th1 cells CD4 CXCR3 CD8 CD3 CCR5

Th2 cells CD4 CCR3 CD8 CD3 CCR4

B cells          

Precursors,germinal center, plasma CD38 IgD CD138 CD19 CD10

B cells, immature/mature, naïve CD27 or CD1d IgD CD38 or CD20 CD19 IgM

B cells, mature, naïve CD44 IgD CD38 CD19 CD10

B cells, mature, naïve CD23 IgD CD38 CD19 CD77

B1 cells CD1d CD21 CD5 CD19 CD23

Apoptosis Annexin V CD95 CD20 CD19 CD27

mature, costimulation, Ag. presn. CD27 CD80 HLA-DR CD19 CD86

NK cells CD57 CD56, CD16 CD14 CD3 CD8

NKT cells 6B11 v alpha 24 CD4   CD8

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Thistlethwaite – Activated CD3CD4 T Cells (CD62L)

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Regulatory T cells

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Associations across assays and trials

CD19 IgG1 CD79A CD79B IgJ genes CD19 IgG1 CD79A CD79B IgJ genes

B cells- CD19Naïve B cells- CD27 IgD+ IgMlo

CD20Urine RT - PCR

FlowCytometry

Microarray

Operationally Tolerant Individuals

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Data Flow

RawDataRawData

AnalysisPipelineAnalysisPipeline

BiostatisticalRepository

BiostatisticalRepository

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.

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National Institute of Allergy & Infectious Diseases

Funded by:

Juvenile Diabetes Research Foundation National Institute of Diabetes & Digestive & Kidney Diseases