Blanchard Zannel, Brown Elisabeth A, Ghazaryan Arevik, Welm Alana L
Department of Oncological Sciences, University of Utah, Huntsman Cancer Institute, Salt Lake City, UT, USA.
Nat Rev Cancer. 2025 Mar;25(3):153-166. doi: 10.1038/s41568-024-00779-3. Epub 2024 Dec 16.
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.
精准肿瘤学依赖于对不同肿瘤如何对各种疗法作出反应的详细分子分析，目的是为个体患者优化治疗结果。患者来源的异种移植（PDX）模型一直是精准肿瘤学方法临床前验证的关键，能够分析每个肿瘤独特的基因组格局，并测试基于特定突变、基因表达模式或信号异常预计有效的疗法。为了扩展这些标准的精准肿瘤学方法，该领域一直致力于用功能测定法来补充原本静态且往往是描述性的测量，即所谓的功能精准肿瘤学（FPO）。通过利用多种PDX和源自PDX的模型，FPO作为一种有效的临床前和临床工具已获得认可，可通过体内和体外功能测定法更精确地重现患者生物学特征。在此，我们探讨用于精准肿瘤学和FPO的PDX及源自PDX的模型的进展与局限性。我们还审视在人工智能时代精准肿瘤学中PDX模型的未来。将这两个学科整合可能是实现快速、准确且具成本效益的治疗预测的关键，彻底改变肿瘤学并为癌症患者提供最有效、个性化的治疗。
