PDX models for functional precision oncology and discovery science

Area of Science:

• Oncology
• Genomics
• Translational Medicine

Background:

• Precision oncology optimizes cancer treatment by analyzing tumor molecular profiles.
• Patient-derived xenograft (PDX) models are vital for preclinical validation of targeted therapies.
• Functional precision oncology (FPO) integrates functional assays with molecular data for improved patient biology recapitulation.

Purpose of the Study:

• To review advances and limitations of PDX models in precision oncology and FPO.
• To explore the future integration of AI with PDX models for enhanced cancer treatment prediction.

Main Methods:

• Utilizing diverse PDX and PDX-derived models for in vivo and ex vivo functional assays.
• Analyzing genomic landscapes and molecular alterations in tumors.
• Evaluating therapeutic responses based on specific mutations and signaling pathways.

Main Results:

• PDX models effectively recapitulate patient tumor biology for therapy testing.
• FPO assays provide functional insights complementing static molecular measurements.
• Integration of PDX models and FPO shows promise for personalized treatment strategies.

Conclusions:

• PDX and FPO models are essential tools for advancing precision oncology.
• The synergy of AI and PDX models holds potential for revolutionizing cancer treatment prediction.
• Future directions emphasize integrating functional and molecular data for faster, cost-effective, and personalized cancer therapies.