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Combination Therapies and Personalized Medicine

Combination Therapies and Personalized Medicine

Combining two or more treatment methods increases the life span of cancer patients while reducing damage to vital organs or tissue from the overuse of a single treatment. Combination therapy also targets different cancer-inducing pathways, thus reducing the chances of developing resistance to treatment.
The combination of the drug acetazolamide and sulforaphane is a good example of combination therapy to treat cancer. The cells in the interior of a large tumor often die due to the hypoxic and...

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Related Experiment Video

Updated: Sep 20, 2025

Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies

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Published on: April 11, 2016

A Biomarker Signature-Guided Clinical Trial Design for Precision Medicine.

Yuan Li^1,2, Dejian Lai¹, Ruosha Li¹

¹Department of Biostatistics and Data Science, The University of Texas Health Science Center at Houston, Houston, TX, USA.

Statistics in Medicine

|May 23, 2025

Summary

This study introduces a novel clinical trial design to precisely identify cancer patients likely to benefit from targeted therapies. The approach enhances treatment efficacy by ensuring participants receive their optimal therapy, improving outcomes.

Keywords:

Bayesian adaptive randomization adaptive design clinical trial precision medicine signature design

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Last Updated: Sep 20, 2025

Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies

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Published on: April 11, 2016

Preparation of Peripheral Blood Mononuclear Cell Pellets and Plasma from a Single Blood Draw at Clinical Trial Sites for Biomarker Analysis

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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Area of Science:

Oncology
Biostatistics
Clinical Trial Design

Background:

Targeted cancer therapies require precise patient selection based on biomarkers for optimal efficacy.
Uncertainty exists regarding which patients will benefit from specific targeted therapies, necessitating improved clinical trial strategies.
Identifying sensitive patient subsets is crucial for maximizing treatment benefits and trial success.

Purpose of the Study:

To propose and evaluate a novel Phase IIB/III clinical trial design for precisely identifying biomarker signatures predictive of treatment response.
To enhance patient stratification for targeted cancer therapies, ensuring enrollment of sensitive individuals.
To maximize benefits for clinical trial participants by facilitating personalized treatment allocation.

Main Methods:

Utilizing a Bayesian methodology for response-adaptive randomization to optimize individual treatment assignment.
Implementing inverse-probability-of-treatment-weighted (IPTW) analysis to mitigate selection bias and control Type I error rates.
Evaluating the design based on statistical power, overall response rate, individual patient loss, and optimal treatment probabilities.

Main Results:

Simulations indicate the proposed design effectively maximizes participant benefits with minimal compromise on statistical power.
The design demonstrated potential for an improved overall response rate within the trial population.
A higher percentage of patients were identified as receiving their optimal treatment under the proposed design.

Conclusions:

The novel clinical trial design offers a precise method for identifying responsive patient subsets for targeted cancer therapies.
Response-adaptive randomization and IPTW analysis enhance treatment allocation and control statistical errors.
This approach promises improved clinical trial efficiency and better patient outcomes in precision oncology.