IT Infrastructure of an Oncological Trial Where Xenografts Inform Individual Second Line Treatment Decision

Area of Science:

• Oncology
• Translational Research
• Bioinformatics

Background:

• Translational clinical research typically flows unidirectionally from clinical to molecular data.
• Phenotypic data is often used to understand molecular disease mechanisms.
• Existing models lack a robust mechanism for integrating patient-specific molecular data back into treatment decisions.

Purpose of the Study:

• To present the RESIST study, which establishes a bidirectional information flow in translational research.
• To utilize xenograft information for personalized treatment decisions in patients resistant to anticancer therapies.
• To address the IT infrastructure challenges and genomic privacy concerns in bidirectional translational projects.

Main Methods:

• Implementation of the RESIST study framework.
• Generation of xenograft models from patient tumors.
• Analysis of molecular data to guide treatment selection post-resistance.
• Development of IT solutions for secure data handling and bidirectional information flow.

Main Results:

• Demonstrated feasibility of using xenograft data for individual treatment decisions in resistant cancers.
• Established a bidirectional translational research model integrating clinical and molecular data.
• Identified and proposed solutions for IT infrastructure challenges, including safeguarding genomic privacy.

Conclusions:

• Bidirectional translational research, exemplified by the RESIST study, is crucial for advancing personalized oncology.
• Integrating patient-derived xenograft data can optimize treatment strategies for refractory cancers.
• Robust IT infrastructure and stringent genomic privacy measures are essential for successful bidirectional translational research.