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Updated: Aug 27, 2025
Testing Targeted Therapies in Cancer using Structural DNA Alteration Analysis and Patient-Derived Xenografts
Published on: July 25, 2020
Towards precision oncology with patient-derived xenografts
Eugenia R Zanella1, Elena Grassi1,2, Livio Trusolino3,4
1Candiolo Cancer Institute - FPO IRCCS, Candiolo, Italy.
Abstract:
Under the selective pressure of therapy, tumours dynamically evolve multiple adaptive mechanisms that make static interrogation of genomic alterations insufficient to guide treatment decisions. Clinical research does not enable the assessment of how various regulatory circuits in tumours are affected by therapeutic insults over time and space. Likewise, testing different precision oncology approaches informed by composite and ever-changing molecular information is hard to achieve in patients. Therefore, preclinical models that incorporate the biology and genetics of human cancers, facilitate analyses of complex variables and enable adequate population throughput are needed to pinpoint randomly distributed response predictors. Patient-derived xenograft (PDX) models are dynamic entities in which cancer evolution can be monitored through serial propagation in mice. PDX models can also recapitulate interpatient diversity, thus enabling the identification of response biomarkers and therapeutic targets for molecularly defined tumour subgroups. In this Review, we discuss examples from the past decade of the use of PDX models for precision oncology, from translational research to drug discovery. We elaborate on how and to what extent preclinical observations in PDX models have confirmed and/or anticipated findings in patients. Finally, we illustrate emerging methodological efforts that could broaden the application of PDX models by honing their predictive accuracy or improving their versatility.
Insights
Patient-derived xenograft models track tumor evolution under therapy, aiding precision oncology. These models help identify predictive biomarkers and therapeutic targets by recapitulating human cancer diversity and dynamics.
Area of Science:
- Oncology
- Translational Research
- Cancer Biology
Background:
- Tumor evolution under therapy necessitates dynamic monitoring beyond static genomic analysis.
- Current clinical research struggles to assess temporal and spatial effects of therapeutic insults on tumor regulatory circuits.
- Testing adaptive precision oncology strategies in patients is challenging due to evolving molecular landscapes.
Purpose of the Study:
- To review the application of patient-derived xenograft (PDX) models in precision oncology over the past decade.
- To evaluate the extent to which PDX model observations have confirmed or predicted clinical findings.
- To highlight emerging methods for enhancing PDX model predictive accuracy and versatility.
Main Methods:
- Review of studies utilizing PDX models for precision oncology, drug discovery, and biomarker identification.
- Analysis of PDX model capabilities in recapitulating interpatient diversity and monitoring cancer evolution.
- Discussion of translational research bridging preclinical PDX findings with clinical outcomes.
Main Results:
- PDX models serve as dynamic preclinical platforms for studying adaptive tumor evolution and therapeutic resistance.
- PDX models have demonstrated utility in identifying response biomarkers and therapeutic targets for specific tumor subgroups.
- Preclinical observations in PDX models have shown value in anticipating and confirming clinical findings in precision oncology.
Conclusions:
- PDX models are crucial for advancing precision oncology by enabling the study of complex tumor biology and treatment responses.
- Further methodological development can enhance the predictive power and applicability of PDX models in translational cancer research.
- PDX models facilitate the discovery of novel therapeutic strategies and biomarkers essential for personalized cancer care.
