Assessing clinical response in early oncology development with a predictive biomarker

Area of Science:

• Oncology
• Clinical Trial Design
• Biomarker Research

Background:

• Limited biomarker data in early oncology trials complicates patient selection.
• Translating preclinical biomarker findings to clinical studies is challenging.
• Existing evidence relies on preclinical research and clinical observations.

Purpose of the Study:

• To propose a method for estimating clinical study response rates incorporating biomarker knowledge.
• To quantify uncertainty in biomarker predictability using a probabilistic model.
• To aid data-driven decisions on using predictive biomarkers for patient selection.

Main Methods:

• Incorporating biomarker prevalence, response association, and assay performance.
• Developing a probabilistic model to estimate response distributions.
• Comparing predicted response in biomarker-selected vs. unselected cohorts.

Main Results:

• Demonstrated utility with two real-world biomarker-guided therapies.
• Quantified uncertainty in biomarker predictability.
• Provided a framework for decision-making in early clinical development.

Conclusions:

• The proposed method supports informed decisions on predictive biomarker use in early oncology trials.
• This approach enhances the translation of preclinical biomarker data to clinical settings.
• Facilitates data-driven patient selection for improved trial outcomes.