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患者来源的癌症模型:抗癌药物测试的宝贵平台。

Patient-derived cancer models: Valuable platforms for anticancer drug testing.

作者信息

Genta Sofia, Coburn Bryan, Cescon David W, Spreafico Anna

机构信息

Division of Medical Oncology and Hematology, Princess Margaret Cancer Centre, University Health Network, University of Toronto, Toronto, ON, Canada.

Division of Infectious Diseases, Toronto General Hospital, University Health Network, Toronto, ON, Canada.

出版信息

Front Oncol. 2022 Aug 12;12:976065. doi: 10.3389/fonc.2022.976065. eCollection 2022.

Abstract

Molecularly targeted treatments and immunotherapy are cornerstones in oncology, with demonstrated efficacy across different tumor types. Nevertheless, the overwhelming majority metastatic disease is incurable due to the onset of drug resistance. Preclinical models including genetically engineered mouse models, patient-derived xenografts and two- and three-dimensional cell cultures have emerged as a useful resource to study mechanisms of cancer progression and predict efficacy of anticancer drugs. However, variables including tumor heterogeneity and the complexities of the microenvironment can impair the faithfulness of these platforms. Here, we will discuss advantages and limitations of these preclinical models, their applicability for drug testing and in co-clinical trials and potential strategies to increase their reliability in predicting responsiveness to anticancer medications.

摘要

分子靶向治疗和免疫疗法是肿瘤学的基石,已在不同肿瘤类型中显示出疗效。然而,由于耐药性的出现,绝大多数转移性疾病无法治愈。包括基因工程小鼠模型、患者来源的异种移植以及二维和三维细胞培养在内的临床前模型已成为研究癌症进展机制和预测抗癌药物疗效的有用资源。然而,包括肿瘤异质性和微环境复杂性在内的变量会削弱这些平台的可信度。在此,我们将讨论这些临床前模型的优点和局限性、它们在药物测试和联合临床试验中的适用性,以及提高其预测抗癌药物反应性可靠性的潜在策略。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fcf3/9413077/19b5c6950e5b/fonc-12-976065-g001.jpg

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