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Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow

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The cross-validated adaptive signature design.

Boris Freidlin¹, Wenyu Jiang, Richard Simon

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

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

|January 14, 2010

Summary

This summary is machine-generated.

A new cross-validation method enhances the adaptive signature design for clinical trials. This approach improves identifying patient subgroups likely to benefit from anticancer therapies, optimizing treatment effectiveness.

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Area of Science:

Biostatistics
Genomics
Clinical Trial Design

Background:

Many anticancer therapies benefit only a subset of patients, yet phase III trials often use broad eligibility criteria.
Genomic data from microarrays can identify patient subgroups likely to respond to specific therapies.
Developing reliable predictive classifiers for targeted therapies is challenging due to high-dimensional genomic data.

Purpose of the Study:

To propose a cross-validation extension of the adaptive signature design.
To optimize the efficiency of classifier development and validation within a single clinical trial.
To improve the identification of patient subpopulations who benefit most from anticancer treatments.

Main Methods:

Introduced the adaptive signature design combining classifier development and treatment effect testing.
Proposed a cross-validation extension to enhance the adaptive signature design.
Evaluated the new design using simulations and application to breast cancer trial data.

Main Results:

The cross-validation approach significantly improves the performance of the adaptive signature design.
Simulations demonstrated the effectiveness of the proposed method.
The design was successfully applied to real-world breast cancer trial data.

Conclusions:

The cross-validation extension offers a more efficient and reliable method for adaptive signature design.
This approach enhances the ability to identify sensitive subpopulations for targeted anticancer therapies.
Methods for estimating treatment effects in identified subpopulations were described.