Validating microfluidics-based personalized cancer therapy in mouse models

肝胰管(HPB)癌的特点是预后非常差。这在很大程度上是由HPB癌症强大的治疗耐药性解释的。尽管近年来令人印象深刻的进展使人们对癌症特别是HPB肿瘤的分子发病机制有了更好的了解，但这些进展并没有转化为这种恶性肿瘤患者预后的改善。原则上，针对个体化治疗的策略能够显著改善临床护理。然而，到目前为止，只有少数基因分析结果成功地转化为临床，例如用于白血病、乳腺癌和结肠癌的治疗。允许对单个患者的肿瘤细胞进行对广泛的抗增殖物质(及其组合)的敏感性测试的表型分析可能是定义个性化治疗的另一种选择，但它们需要通常无法从活检中获得的细胞数。在初步研究中，我们的研究小组可能表明，一种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组织的治疗敏感性。我们用它来识别特别有效的药物组合，它们的协同作用以前没有被描述过。例如，我们可以证明，PHT-427和Selumetinib的联合应用通过阻断KRAS下游两条独立的通路是高效的。我们的平台允许对患者肿瘤材料上的抗增殖药物的系统组合进行快速和自动化的测试，这种药物通常只有很少的量。由于细胞样品的数量很少(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，我们的平台具有其他独特的优势，例如避免了细胞培养步骤，这可能会混淆药物效果。此外，我们的方法快速(48小时内就能得到结果)和成本效益(每个患者样本的耗材成本约为150欧元)。我们现在的目标是利用不同的HPB肿瘤小鼠模型，对这种方法进行体内功能验证，这在以前使用人类样本是不可能的。在扩展的综合实验中，我们将a)在体外预测最佳治疗方案，b)进行信号通路建模以了解耐药机制并确定潜在的新的药物敏感剂，以及c)在体内验证预测的最佳治疗方案。我们相信，这项工作将是利用我们的微流体平台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.