文章：

优化肺癌脑转移模型以支持时空动态研究与个体化治疗

Refining Lung Cancer Brain Metastasis Models for Spatiotemporal Dynamic Research and Personalized Therapy

原文发布日期：7 May 2025

DOI: 10.3390/cancers17091588

类型: Article

开放获取: 是

英文摘要：

Lung cancer brain metastasis (LCBM) is a major contributor to cancer-related mortality, with a median survival of 8–16 months following diagnosis, despite advances in therapeutic strategies. The development of clinically relevant animal models is crucial for understanding the metastatic cascade and assessing therapies that can penetrate the blood–brain barrier (BBB). This review critically evaluates five primary LCBM modeling approaches—orthotopic implantation, intracardiac injection, stereotactic intracranial injection, carotid artery injection, and tail vein injection—focusing on their clinical applicability. We systematically compare their ability to replicate human metastatic pathophysiology and highlight emerging technologies for personalized therapy screening. Additionally, we analyze breakthrough strategies in central nervous system (CNS)-targeted drug delivery, including microparticle targeted delivery systems designed to enhance brain accumulation. By incorporating advances in single-cell omics and AI-driven metastasis prediction, this work provides a roadmap for the next generation of LCBM models, aimed at bridging preclinical and clinical research.

摘要翻译： 

肺癌脑转移是癌症相关死亡的主要原因，诊断后中位生存期仅为8-16个月，尽管治疗策略已取得进展。建立临床相关动物模型对于理解转移级联反应及评估能穿透血脑屏障的治疗方案至关重要。本综述批判性评估了五种主要LCBM建模方法——原位植入、心内注射、立体定向颅内注射、颈动脉注射和尾静脉注射——重点关注其临床适用性。我们系统比较了这些模型模拟人类转移病理生理学的能力，并重点介绍了用于个体化治疗筛选的新兴技术。此外，我们分析了中枢神经系统靶向给药领域的突破性策略，包括旨在增强脑部蓄积的微粒靶向递送系统。通过整合单细胞组学与人工智能驱动的转移预测进展，本研究为连接临床前与临床研究的下一代LCBM模型提供了发展路线图。

原文链接：

Refining Lung Cancer Brain Metastasis Models for Spatiotemporal Dynamic Research and Personalized Therapy