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对各种疗法的反应的详细分子分析，旨在优化个体患者的治疗结果。患者来源的异种移植物 （PDX） 模型一直是精准肿瘤学方法临床前验证的关键，能够分析每种肿瘤独特的基因组景观，并测试根据特定突变、基因表达模式或信号异常预测有效的疗法。为了扩展这些标准的精准肿瘤学方法，该领域努力用功能检测（称为功能精准肿瘤学 （FPO））来补充原本静态且通常具有描述性的测量。通过利用不同的 PDX 和 PDX 衍生模型，FPO 已成为一种有效的临床前和临床工具，可以使用体内和离体功能分析更精确地概括患者生物学。在这里，我们探讨了 PDX 和 PDX 衍生模型用于精准肿瘤学和 FPO 的进步和局限性。我们还研究了人工智能时代精准肿瘤学 PDX 模型的未来。整合这两个学科可能是快速、准确和具有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。