Toward Practical Integration of Omic and Imaging Data in Co-Clinical Trials

Area of Science:

• Oncology
• Translational Medicine
• Bioinformatics

Background:

• Co-clinical trials evaluate therapeutics in patients and patient-derived xenografts (PDX) to bridge clinical and preclinical research.
• A significant challenge is managing and analyzing the vast, multi-scale data generated from these trials.
• Existing methods struggle to integrate imaging and molecular data across species and time points.

Purpose of the Study:

• To develop MIRACCL, a web-based analytical tool for molecular and imaging response analysis in co-clinical trials.
• To address the data integration and analysis challenges inherent in co-clinical trial designs.
• To evaluate the functionality of MIRACCL in correlating preclinical and clinical data.

Main Methods:

• Simulated co-clinical trial data for triple-negative breast cancer (TNBC).
• Paired pre- and on-treatment magnetic resonance imaging (MRI) from the I-SPY2 trial and PDX models.
• Simulated baseline and on-treatment RNA expression data for both TNBC and PDX models.
• Cross-referenced image features with omic data to assess MIRACCL's correlation capabilities.

Main Results:

• Demonstrated MIRACCL's ability to correlate MRI-derived tumor characteristics (size, vascularity, cellularity) with mRNA expression changes.
• Successfully integrated and visualized multi-modal data from both clinical and preclinical settings.
• Validated the tool's functionality in a simulated TNBC co-clinical trial setting.

Conclusions:

• MIRACCL provides a robust platform for analyzing complex co-clinical trial data.
• The tool facilitates the correlation of imaging and molecular responses, enabling better understanding of therapeutic effects.
• MIRACCL has the potential to enhance the predictive power of preclinical models for clinical outcomes.