文章：

功能精确肿瘤学和发现科学的PDX模型

PDX models for functional precision oncology and discovery science

原文发布日期：2024-12-16

DOI: 10.1038/s41568-024-00779-3

类型: Review Article

开放获取: 否

英文摘要：

Precision oncology relies on detailed molecular analysis of how diverse tumours respond to various therapies, with the aim to optimize treatment outcomes for individual patients. Patient-derived xenograft (PDX) models have been key to preclinical validation of precision oncology approaches, enabling the analysis of each tumour’s unique genomic landscape and testing therapies that are predicted to be effective based on specific mutations, gene expression patterns or signalling abnormalities. To extend these standard precision oncology approaches, the field has strived to complement the otherwise static and often descriptive measurements with functional assays, termed functional precision oncology (FPO). By utilizing diverse PDX and PDX-derived models, FPO has gained traction as an effective preclinical and clinical tool to more precisely recapitulate patient biology using in vivo and ex vivo functional assays. Here, we explore advances and limitations of PDX and PDX-derived models for precision oncology and FPO. We also examine the future of PDX models for precision oncology in the age of artificial intelligence. Integrating these two disciplines could be the key to fast, accurate and cost-effective treatment prediction, revolutionizing oncology and providing patients with cancer with the most effective, personalized treatments.

摘要翻译： 

精准肿瘤学依赖于对多种肿瘤如何响应不同疗法的详细分子分析，旨在优化个体患者的治疗结果。患者来源异种移植（PDX）模型已成为精准肿瘤学方法临床前验证的关键工具，能够分析每个肿瘤独特的基因组景观，并测试基于特定突变、基因表达模式或信号异常预测有效的疗法。为了扩展这些标准精准肿瘤学方法，该领域一直致力于通过功能性检测（称为功能性精准肿瘤学，FPO）来补充原本静态且通常为描述性的测量。通过利用多种PDX及PDX衍生模型，FPO凭借其采用体内和体外功能检测以更精准重现患者生物学特性的能力，已成为一种有效的临床前和临床工具。在此，我们探讨PDX及PDX衍生模型在精准肿瘤学和FPO中的应用进展与局限，并审视人工智能时代下PDX模型在精准肿瘤学中的未来。整合这两个学科可能成为实现快速、准确且经济高效治疗预测的关键，从而彻底改变肿瘤学领域，为癌症患者提供最有效的个性化治疗。

原文链接：

PDX models for functional precision oncology and discovery science