Related Experiment Video

Updated: Aug 14, 2026

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization

Published on: October 3, 2025

Adaptive signature design: an adaptive clinical trial design for generating and prospectively testing a gene

Boris Freidlin¹, Richard Simon

¹Biometric Research Branch, Division of Cancer Treatment and Diagnosis, National Cancer Institute, Bethesda, MD 20892, USA. freidlinb@ctep.nci.nih.gov

Clinical Cancer Research : an Official Journal of the American Association for Cancer Research

|November 10, 2005

Abstract

Purpose:

A new generation of molecularly targeted agents is entering the definitive stage of clinical evaluation. Many of these drugs benefit only a subset of treated patients and may be overlooked by the traditional, broad-eligibility approach to randomized clinical trials. Thus, there is a need for development of novel statistical methodology for rapid evaluation of these agents.

Experimental Design:

We propose a new adaptive design for randomized clinical trials of targeted agents in settings where an assay or signature that identifies sensitive patients is not available at the outset of the study. The design combines prospective development of a gene expression-based classifier to select sensitive patients with a properly powered test for overall effect.

Results:

Performance of the adaptive design, relative to the more traditional design, is evaluated in a simulation study. It is shown that when the proportion of patients sensitive to the new drug is low, the adaptive design substantially reduces the chance of false rejection of effective new treatments. When the new treatment is broadly effective, the adaptive design has power to detect the overall effect similar to the traditional design. Formulas are provided to determine the situations in which the new design is advantageous.

Conclusion:

Development of a gene expression-based classifier to identify the subset of sensitive patients can be prospectively incorporated into a randomized phase III design without compromising the ability to detect an overall effect.

Insights

This study introduces an adaptive design for clinical trials evaluating targeted cancer agents. This method improves the evaluation of drugs benefiting only a subset of patients, reducing the risk of overlooking effective treatments.

Area of Science:

Oncology
Biostatistics
Genomics

Background:

Molecularly targeted agents show promise but often benefit only specific patient subsets.
Traditional randomized clinical trials (RCTs) may overlook effective targeted therapies due to broad eligibility criteria.
Novel statistical methodologies are needed for efficient evaluation of targeted agents.

Purpose of the Study:

To propose a new adaptive design for randomized clinical trials of targeted agents.
To address the challenge of evaluating drugs when a predictive biomarker is not initially available.
To ensure efficient and accurate assessment of targeted therapies in clinical settings.

Main Methods:

An adaptive design combining prospective development of a gene expression-based classifier with a powered test for overall effect.
Utilizing a simulation study to compare the adaptive design against traditional designs.
Developing formulas to identify advantageous scenarios for the new adaptive design.

Main Results:

The adaptive design significantly reduces the likelihood of falsely rejecting effective treatments when the sensitive patient proportion is low.
The adaptive design maintains comparable power to traditional designs for detecting overall treatment effects when the treatment is broadly effective.
Simulation results demonstrate the practical performance of the proposed adaptive design.

Conclusions:

A gene expression-based classifier can be prospectively integrated into randomized phase III trial designs.
This integration allows for the identification of sensitive patient subsets without compromising the ability to detect an overall treatment effect.
The proposed adaptive design offers a robust framework for evaluating molecularly targeted agents.

Read full analysis

Related Concept Videos

Pharmacogenomics: Identification of New Drug Targets

Pharmacogenomics: Identification of New Drug Targets

Advances in genomics have profoundly influenced drug discovery by increasing both the speed and accuracy of pharmaceutical development. Pharmacogenomics, which examines how genetic variation influences drug response, facilitates the identification of novel therapeutic targets and enables patient stratification for personalized treatment. These strategies contribute to improved drug efficacy, minimized adverse effects, and more efficient clinical trial design.Mapping genetic differences...

Pharmacogenetics of Drug Targets: β₂-Adrenergic Receptors, Apo E, Thymidylate Synthase

Pharmacogenetics of Drug Targets: β₂-Adrenergic Receptors, Apo E, Thymidylate Synthase

Genetic polymorphisms in drug targets have emerged as critical determinants of interindividual variability in drug response and toxicity. Pharmacogenomic investigations increasingly focus on identifying these variations to personalize and optimize therapeutic interventions. A drug target may be a receptor, enzyme, or signaling protein involved in pharmacologic responses or disease-related pathways. While early pharmacogenetic studies focused primarily on drug metabolism, current research...

Articles linked to this work by shared authors, journal, and citation graph.

Two-to-one randomization is associated with increased control-arm patient withdrawal.

Journal of the National Cancer Institute·2026

2D Ultrasound Elasticity Imaging of Abdominal Aortic Aneurysms Using Deep Neural Networks.

IEEE transactions on computational imaging·2026

Toward Patient-Specific Partial Point Cloud to Surface Completion for Pre to Intra-operative Registration in Image-Guided Liver Interventions.

Medical Image Understanding and Analysis. Medical Image Understanding and Analysis (Conference)·2026

Investigating the Domain Adaptability of General-Purpose Foundation Models for Left Atrium Segmentation from MR Images.

Functional imaging and modeling of the heart : ... International Workshop, FIMH ..., proceedings. FIMH (Conference)·2026

Evaluation of Intra-operative Patient-specific Methods for Point Cloud Completion for Minimally Invasive Liver Interventions.

Proceedings of SPIE--the International Society for Optical Engineering·2026

Assessing the Performance of the DINOv2 Self-supervised Learning Vision Transformer Model for the Segmentation of the Left Atrium from MRI Images.

Proceedings of SPIE--the International Society for Optical Engineering·2026

See all related articles