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posted on 2025-02-01, 19:20 authored by Abigail Shea, Yaniv Eyal-Lubling, Daniel Guerrero-Romero, Raquel Manzano Garcia, Wendy Greenwood, Martin O’Reilly, Dimitra Georgopoulou, Maurizio Callari, Giulia Lerda, Sophia Wix, Agnese Giovannetti, Riccardo Masina, Elham Esmaeilishirazifard, Wei Cope, Alistair G. Martin, Ai Nagano, Lisa Young, Steven Kupczak, Yi Cheng, Helen Bardwell, Elena Provenzano, Justine Kane, Jonny Lay, Louise Grybowicz, Karen McAdam, Carlos Caldas, Jean Abraham, Oscar M. Rueda, Alejandra Bruna Olaparib treatment causes permanent phenotypic changes due to TF reprogramming. A, Heatmap displaying z-score (scaled by row) of the top 250 strong and variable genes. Clustering analysis performed using Euclidean distances. Columns indicate PDTX samples (labeled by mouse number). B, Top 10 significant gene sets by normalized enrichment score (NES) between untreated and post-treated samples, identified by GSEA (Hallmark gene sets). DN, down. C, IHC for phenotypic markers. D, Western blot for E-cadherin and vimentin. E, scRNA-seq data of all cells analyzed post-QC. Color indicates cell states (groups of MCs). F, Average gene expression (number of UMIs per 1,000 UMIs) of key markers across MCs. G, Percentage of cells from each condition that reside in each cell state. Immune-Act, immune activation. H, Top, epithelial and mesenchymal scores for individual MCs. Bottom, MC composition of each strata using mesenchymal minus epithelial scores to stratify MCs into 5 groups (EMT1–5). I, Top, mesenchymal and IER scores for individual MCs. Bottom, MC composition of each strata using the IER score to stratify MCs into 4 groups (IER1–4). J and K, Mean enrichment (log2 gene enrichment score) across strata for TFs of interest: EMT1–5 (J) and IER1–4 (K).
Funding
Cancer Research UK (CRUK)
Cancer Research UK Cambridge Institute, University of Cambridge (CRUK CI)
HORIZON EUROPE European Research Council (ERC)
NIHR Cambridge Biomedical Research Centre (NIHR Cambridge BRC)
Medical Research Council (MRC)
HORIZON EUROPE Marie Sklodowska-Curie Actions (MSCA)
Institute of Cancer Research (ICR)
History
ARTICLE 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.
