Personalized in vitro cancer models to predict therapeutic response: Challenges and a framework for improvement.
Personalized in vitro cancer models to predict therapeutic response: Challenges and a framework for improvement.
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DOI：
10.1016/j.pharmthera.2016.05.007
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发表时间：
2016-09
影响因子：
13.5
通讯作者：
Beebe DJ
中科院分区：
文献类型：
--
作者：
Morgan MM;Johnson BP;Livingston MK;Schuler LA;Alarid ET;Sung KE;Beebe DJ
个性化癌症治疗的重点是表征患者的相关表型，以及患者的肿瘤，以预测最有效的癌症治疗。从历史上看，这些方法在预测治疗反应方面并没有被证明具有预测性。新兴的培养平台旨在更好地概括体内环境，因此，将患者样本整合到体外癌症模型中以评估治疗反应重新引起了人们的兴趣。由于患者样本采集、可变性和培养等问题，将体外反应转化为临床相关性的成功例子有限。我们将回顾传统的和新兴的个性化医疗体外模型，重点关注技术、微环境成分和所使用的读数。然后，我们将提供我们对如何应用毒理学和生态学的框架来设计改进的个性化癌症体外模型的观点。该框架可作为确定最佳读数和培养条件的工具，从而最大限度地从每个患者样本中获得信息。
Personalized cancer therapy focuses on characterizing the relevant phenotypes of the patient, as well as the patient’s tumor, to predict the most effective cancer therapy. Historically, these methods have not proven predictive in regards to predicting therapeutic response. Emerging culture platforms are designed to better recapitulate the in vivo environment, thus, there is renewed interest in integrating patient samples into in vitro cancer models to assess therapeutic response. Successful examples of translating in vitro response to clinical relevance are limited due to issues with patient sample acquisition, variability and culture. We will review traditional and emerging in vitro models for personalized medicine, focusing on the technologies, microenvironmental components, and readouts utilized. We will then offer our perspective on how to apply a framework derived from toxicology and ecology towards designing improved personalized in vitro models of cancer. The framework serves as a tool for identifying optimal readouts and culture conditions, thus maximizing the information gained from each patient sample.