Modeling Drug Responses and Evolutionary Dynamics Using Patient-Derived Xenografts Reveals Precision Medicine Strategies for Triple-Negative Breast Cancer
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Updated: Jun 8, 2025

Orthotopic Transplantation of Breast Tumors as Preclinical Models for Breast Cancer
Published on: May 18, 2020
Abigail Shea1,2, Yaniv Eyal-Lubling1, Daniel Guerrero-Romero1
1Department of Oncology, Cancer Research UK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge, United Kingdom.
Abstract:
The intertumor and intratumor heterogeneity of triple-negative breast cancers, which is reflected in diverse drug responses, interplays with tumor evolution. In this study, we developed a preclinical experimental and analytical framework using patient-derived tumor xenografts (PDTX) from patients with treatment-naïve triple-negative breast cancers to test their predictive value in personalized cancer treatment approaches. Patients and their matched PDTXs exhibited concordant drug responses to neoadjuvant therapy using two trial designs and dosing schedules. This platform enabled analysis of nongenetic mechanisms involved in relapse dynamics. Treatment resulted in permanent phenotypic changes, with functional and therapeutic consequences. High-throughput drug screening methods in ex vivo PDTX cells revealed patient-specific drug response changes dependent on first-line therapy. This was validated in vivo, as exemplified by a change in olaparib sensitivity in tumors previously treated with clinically relevant cycles of standard-of-care chemotherapy. In summary, PDTXs provide a robust tool to test patient drug responses and therapeutic regimens and to model evolutionary trajectories. However, high intermodel variability and permanent nongenomic transcriptional changes constrain their use for personalized cancer therapy. This work highlights important considerations associated with preclinical drug response modeling and potential uses of the platform to identify efficacious and preferential sequential therapeutic regimens. Significance: Patient-derived tumor xenografts from treatment-naïve breast cancer samples can predict patient drug responses and model treatment-induced phenotypic and functional evolution, making them valuable preclinical tools.
Insights
Patient-derived tumor xenografts (PDTX) accurately predict triple-negative breast cancer drug responses. This preclinical model reveals how therapies drive tumor evolution and phenotypic changes, aiding personalized treatment strategies.
Area of Science:
- Oncology
- Translational Research
- Cancer Heterogeneity
Background:
- Triple-negative breast cancer (TNBC) exhibits significant intertumor and intratumor heterogeneity, leading to variable drug responses and complex tumor evolution.
- Understanding these dynamics is crucial for developing effective personalized cancer treatment strategies.
Purpose of the Study:
- To develop and validate a preclinical experimental and analytical framework using patient-derived tumor xenografts (PDTX) from treatment-naïve TNBC.
- To assess the predictive value of PDTXs for personalized cancer therapy and investigate treatment-induced tumor evolution.
Main Methods:
- Established PDTX models from treatment-naïve TNBC patients.
- Conducted preclinical studies using two trial designs and dosing schedules to compare patient and PDTX drug responses.
- Utilized high-throughput drug screening in ex vivo PDTX cells and in vivo validation.
- Analyzed nongenetic mechanisms driving relapse dynamics and phenotypic changes.
Main Results:
- PDTX models demonstrated concordant drug responses with matched patients to neoadjuvant therapy.
- Treatment induced permanent phenotypic changes with functional and therapeutic consequences.
- Ex vivo drug screening identified patient-specific drug response alterations post-first-line therapy, validated in vivo (e.g., olaparib sensitivity changes after chemotherapy).
Conclusions:
- PDTXs are robust preclinical tools for predicting patient drug responses and modeling therapeutic regimens and evolutionary trajectories in TNBC.
- High intermodel variability and permanent nongenomic transcriptional changes are important considerations for personalized therapy.
- The PDTX platform can guide the identification of efficacious sequential therapeutic regimens.

