Validating microfluidics-based personalized cancer therapy in mouse models

肝-胰-胆（HPB）道癌症的特点是预后非常差。这在很大程度上可以解释为HPB癌症的强大治疗耐药性。尽管近年来令人印象深刻的进展使人们对一般癌症，特别是hpb肿瘤的分子发病机制有了更好的了解，但这些进展尚未转化为这种恶性肿瘤患者预后的改善。原则上，以治疗个体化为目标的策略能够显著改善临床护理。然而，直到今天，只有很少的基因分析结果成功地转化为临床，例如用于治疗白血病、乳腺癌和结肠癌。表型分析允许对个体患者的肿瘤细胞对多种抗增殖物质（及其组合）进行敏感性测试，这可能是定义个性化治疗的替代方法，但它们需要通常无法从活检中获得的细胞数量。在初步研究中，我们的研究小组可以展示新开发的微流体平台能够预测已建立的人类细胞系和胰腺癌患者的小活检的治疗敏感性。我们用它来识别以前没有描述过的特别有效的协同作用的药物组合。例如，我们可以证明pht -427和Selumetinib联合应用通过阻断KRAS下游的两个独立途径是高效的。我们的平台允许对患者肿瘤材料的抗增殖药物的系统组合进行快速和自动化的测试，这通常只有非常少量的可用。由于每个样品的细胞数量较少（约100个），我们能够分析比现有方法多一到两个数量级的处理条件。与现有技术相比，我们的平台具有额外的独特优势，例如避免了可能混淆药物作用的细胞培养步骤。此外，我们的方法快速（48小时内可获得结果）且成本有效（每个患者样品的耗材成本约为150欧元）。我们现在的目标是对这种方法进行体内功能验证，这在以前利用不同的HPB肿瘤小鼠模型使用人类样本时是不可能的。在扩展数据的综合实验中，我们将a)预测体外最佳治疗方案，b)执行信号通路建模以了解耐药机制并识别潜在的新药物致敏剂，c)验证体内预测的最佳治疗方案。我们相信，这项工作将代表在没有微流体平台的情况下启动临床研究的最后一步。

Cancers of the hepato-pancreatico-biliary (HPB) tract arecharacterized by a very poor prognosis. which is largely explained byrobust therapy resistance of HPB cancers. Even though theimpressive advances of recent years have resulted in a much betterunderstanding of the molecular pathogenesis of cancer in general andHPB tumours in particular, these developments have not yettranslated in an improved prognosis of patients with this malignancy.In principle, strategies aiming at therapy individualization are able tosignificantly improve clinical care. However, until today only fewresults from genetic analyses have successfully been translated intothe clinics, e.g. for the treatment of leukemias, breast and coloncancer. Phenotypical analyses allowing for sensitivity testing oftumour cells of individual patients against a wide battery ofantiproliferative substances (and combinations thereof) could be analternative to define personalized therapies, but they require cellnumbers that are normally not available from biopsies. In preliminarystudies our research groups could show that a newly developedmicrofluidics platform is able to predict therapy sensitivity ofestablished human cell lines and small biopsies from patients withpancreatic cancer. We used it to identify particularly potent drugcombinations whose synergistic effects had not been describedbefore. For example, we could show that a combined application ofPHT-427 and Selumetinib is highly efficient by blocking twoindependent pathways downstream of KRAS. Our platform allows forrapid and automated testing of systematic combinations ofantiproliferative drugs on patient tumour material, which is usuallyavailable only in very small amounts. Due to the low number of cellsper sample (ca. 100) we are able to analyse one to two orders ofmagnitude more treatment conditions than with existing methods. Ourplatform has additional unique advantages compared to existingtechnologies, such as the avoidance of cell cultivating steps, whichcan potentially confound drug effects. Furthermore, our method israpid (results are available within 48h) and cost effective (~150 eurosconsumables costs per patient sample). We now aim for a functionalin vivo validation of this approach, which has not been possible previously using human samples, exploiting different HPB tumourmouse models. In comprehensive experiments with expandedreadouts we will a) predict the optimal therapy ex vivo, b) performsignalling pathway modelling to understand resistance mechanismsand identify potentially new drug sensitizers and c) validate thepredicted best treatment option in vivo. We are convinced that thiswork will represent the last missing step to initiate clinical studies withour microfluidics platform.