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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

Vicki Seyfert-Margolis, PhD


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CLINICAL DATA (Ontologies)

  • MECHANISM

  • Flow Cytometry

  • • Autoantibody

  • • ELISPOT

  • • Cytokine Measures

DISCOVERY

• Gene Expression

• SNP/Haplotype

• Proteomics


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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


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Integration of domain-specific information

Gene Expression

Antigen Expression

Cytokine Secretion

Flow Cytometry

EliSPOT

Microarray



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Original Biopsy Designation

Counts by visit

Classification

On left column

AR = Acute Rejection

HEP = Mild

HEP-MOD = Moderate

To Severe


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(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


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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


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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 = V6

  • Red = FCLB

    • FCLB = 21

    • V6 = 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 AR

  • Red = FCLB with AR

    • FCLB = 21

    • V6 = 12





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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


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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.


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Funded by:

National Institute of Allergy & Infectious Diseases

Juvenile Diabetes Research Foundation

National Institute of Diabetes & Digestive & Kidney Diseases


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