Modeling Drug Responses and Evolutionary Dynamics Using Patient-Derived Xenografts Reveals Precision Medicine Strategies for Triple-Negative Breast Cancer.
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ICR Authors
Authors
Shea, A
Eyal-Lubling, Y
Guerrero-Romero, D
Manzano Garcia, R
Greenwood, W
O'Reilly, M
Georgopoulou, D
Callari, M
Lerda, G
Wix, S
Giovannetti, A
Masina, R
Esmaeilishirazifard, E
Cope, W
Martin, AG
Nagano, A
Young, L
Kupczak, S
Cheng, Y
Bardwell, H
Provenzano, E
Kane, J
Lay, J
Grybowicz, L
McAdam, K
Caldas, C
Abraham, J
Rueda, OM
Bruna, A
Eyal-Lubling, Y
Guerrero-Romero, D
Manzano Garcia, R
Greenwood, W
O'Reilly, M
Georgopoulou, D
Callari, M
Lerda, G
Wix, S
Giovannetti, A
Masina, R
Esmaeilishirazifard, E
Cope, W
Martin, AG
Nagano, A
Young, L
Kupczak, S
Cheng, Y
Bardwell, H
Provenzano, E
Kane, J
Lay, J
Grybowicz, L
McAdam, K
Caldas, C
Abraham, J
Rueda, OM
Bruna, A
Document Type
Journal Article
Date
2025-02-01
Date Accepted
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.
Citation
Cancer Research, 2024,
Source Title
Cancer Research
Publisher
AMER ASSOC CANCER RESEARCH
ISSN
0008-5472
eISSN
1538-7445
Collections
Research Team
Preclin Paed Cancer Evo