利用患者衍生异种移植物模拟药物反应和进化动态,揭示三阴性乳腺癌的精准医疗策略

IF 12.5 1区 医学 Q1 ONCOLOGY
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
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引用次数: 0

摘要

三阴性乳腺癌(TNBC)的瘤间和瘤内异质性反映在不同的药物反应中,并与肿瘤的进化相互影响。在这里,我们利用未经治疗的 TNBC 患者衍生肿瘤异种移植物(PDTX)开发了一个临床前实验和分析框架,以测试它们在个性化癌症治疗方法中的预测价值。使用两种试验设计和给药计划,患者及其匹配的 PDTX 对新辅助治疗表现出一致的药物反应。通过这一平台,可以分析复发动态中的非遗传机制。治疗导致了永久性的表型变化,并产生了功能和治疗效果。在体外患者衍生肿瘤异种移植细胞(PDTCs)中进行高通量药物筛选的方法揭示了患者特异性药物反应变化取决于一线治疗。这一点在体内得到了验证,例如,之前接受过临床相关周期标准化疗的肿瘤对奥拉帕利的敏感性发生了变化。总之,PDTXs 为测试患者的药物反应和治疗方案以及建立进化轨迹模型提供了一个强大的工具。然而,模型间的高变异性和永久性非基因组转录变化限制了它们在个性化癌症治疗中的应用。这项工作强调了与临床前药物反应建模相关的重要考虑因素,以及该平台在确定有效和优先顺序治疗方案方面的潜在用途。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Modeling Drug Responses and Evolutionary Dynamics using Patient-Derived Xenografts Reveals Precision Medicine Strategies for Triple Negative Breast Cancer
The inter- and intra-tumor heterogeneity of triple negative breast cancers (TNBC), which is reflected in diverse drug responses, interplays with tumor evolution. Here, we developed a preclinical experimental and analytical framework using treatment-naive TNBC patient-derived tumor xenografts (PDTX) to test their predictive value in personalized cancer treatment approaches. Patients and their matched PDTX exhibited concordant drug responses to neoadjuvant therapy using two trial designs and dosing schedules. This platform enabled analysis of non-genetic mechanisms involved in relapse dynamics. Treatment resulted in permanent phenotypic changes with functional and therapeutic consequences. High throughput drug screening methods in ex vivo patient derived tumor xenograft cells (PDTCs) 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 inter-model variability and permanent non-genomic 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.
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来源期刊
Cancer research
Cancer research 医学-肿瘤学
CiteScore
16.10
自引率
0.90%
发文量
7677
审稿时长
2.5 months
期刊介绍: Cancer Research, published by the American Association for Cancer Research (AACR), is a journal that focuses on impactful original studies, reviews, and opinion pieces relevant to the broad cancer research community. Manuscripts that present conceptual or technological advances leading to insights into cancer biology are particularly sought after. The journal also places emphasis on convergence science, which involves bridging multiple distinct areas of cancer research. With primary subsections including Cancer Biology, Cancer Immunology, Cancer Metabolism and Molecular Mechanisms, Translational Cancer Biology, Cancer Landscapes, and Convergence Science, Cancer Research has a comprehensive scope. It is published twice a month and has one volume per year, with a print ISSN of 0008-5472 and an online ISSN of 1538-7445. Cancer Research is abstracted and/or indexed in various databases and platforms, including BIOSIS Previews (R) Database, MEDLINE, Current Contents/Life Sciences, Current Contents/Clinical Medicine, Science Citation Index, Scopus, and Web of Science.
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