Blom Kristin, Nygren Peter, Alvarsson Jonathan, Larsson Rolf, Andersson Claes R
Department of Medical Sciences, Uppsala University, Uppsala, Sweden
Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden.
J Lab Autom. 2016 Feb;21(1):178-87. doi: 10.1177/2211068215598117. Epub 2015 Aug 5.
Although medical cancer treatment has improved during the past decades, it is difficult to choose between several first-line treatments supposed to be equally active in the diagnostic group. It is even more difficult to select a treatment after the standard protocols have failed. Any guidance for selection of the most effective treatment is valuable at these critical stages. We describe the principles and procedures for ex vivo assessment of drug activity in tumor cells from patients as a basis for tailored cancer treatment. Patient tumor cells are assayed for cytotoxicity with a panel of drugs. Acoustic drug dispensing provides great flexibility in the selection of drugs for testing; currently, up to 80 compounds and/or combinations thereof may be tested for each patient. Drug response predictions are obtained by classification using an empirical model based on historical responses for the diagnosis. The laboratory workflow is supported by an integrated system that enables rapid analysis and automatic generation of the clinical referral response.
尽管在过去几十年间医学上的癌症治疗有了进步,但在几种被认为对诊断组同样有效的一线治疗方案之间做出选择却很困难。在标准方案失败后选择一种治疗方案就更难了。在这些关键阶段,任何关于选择最有效治疗方法的指导都是有价值的。我们描述了对患者肿瘤细胞进行药物活性离体评估的原则和程序,作为定制癌症治疗的基础。用一组药物检测患者肿瘤细胞的细胞毒性。声学药物分配在选择用于测试的药物方面提供了极大的灵活性;目前,可为每位患者测试多达80种化合物和/或其组合。通过使用基于该诊断的历史反应的经验模型进行分类来获得药物反应预测。实验室工作流程由一个集成系统支持,该系统能够进行快速分析并自动生成临床转诊反应。