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Updated: Jun 5, 2025
Testing Targeted Therapies in Cancer using Structural DNA Alteration Analysis and Patient-Derived Xenografts
Published on: July 25, 2020
PDX models for functional precision oncology and discovery science
Zannel Blanchard1, Elisabeth A Brown1, Arevik Ghazaryan1
1Department of Oncological Sciences, University of Utah, Huntsman Cancer Institute, Salt Lake City, UT, USA.
Abstract:
Precision oncology relies on detailed molecular analysis of how diverse tumours respond to various therapies, with the aim to optimize treatment outcomes for individual patients. Patient-derived xenograft (PDX) models have been key to preclinical validation of precision oncology approaches, enabling the analysis of each tumour's unique genomic landscape and testing therapies that are predicted to be effective based on specific mutations, gene expression patterns or signalling abnormalities. To extend these standard precision oncology approaches, the field has strived to complement the otherwise static and often descriptive measurements with functional assays, termed functional precision oncology (FPO). By utilizing diverse PDX and PDX-derived models, FPO has gained traction as an effective preclinical and clinical tool to more precisely recapitulate patient biology using in vivo and ex vivo functional assays. Here, we explore advances and limitations of PDX and PDX-derived models for precision oncology and FPO. We also examine the future of PDX models for precision oncology in the age of artificial intelligence. Integrating these two disciplines could be the key to fast, accurate and cost-effective treatment prediction, revolutionizing oncology and providing patients with cancer with the most effective, personalized treatments.
Insights
Patient-derived xenograft (PDX) models are crucial for precision oncology, enabling personalized cancer therapy validation. Functional precision oncology (FPO) enhances these models for more accurate, in vivo and ex vivo treatment prediction.
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.
