Considerations in Clinical Study Design with Gene Expression Endpoints

Music City Center 

Gene expression change has emerged as an important biomarker for evaluating treatment effects in immunology and inflammatory (I&I) indications. Historically, gene expression data have been primarily utilized as exploratory endpoints in many I&I studies. However, recent advancements have positioned gene expression data as key study endpoints (primary or secondary) with gene signatures in biomarker-driven mechanistic studies. Unlike traditional biomarkers with single-valued measurements, gene expression data are high-dimensional, often exceeding 20,000 variables. The implementation of gene expression data to select, validate, and evaluate gene signatures presents unique statistical challenges when used as key study endpoints. We address the following critical considerations for implementing gene expression endpoints in clinical study design:
1. Sample Size Calculation for Gene Signature Detection
2. Validation of Gene Signature in an Independent Sample
3. Evaluation of Gene Signature in a Different Cohort

Clinical trial design

sample size calculation

Gene expression

Gene signature 

Biopharmaceutical Section