Tissue Engineering and Regenerative Medicine, University Hospital Wuerzburg, Germany.
Fraunhofer Institute for Silicate Research (ISC), Translational Center Regenerative Therapies, Wuerzburg, Germany.
Mol Oncol. 2018 Aug;12(8):1264-1285. doi: 10.1002/1878-0261.12323. Epub 2018 Jun 22.
Patient-tailored therapy based on tumor drivers is promising for lung cancer treatment. For this, we combined in vitro tissue models with in silico analyses. Using individual cell lines with specific mutations, we demonstrate a generic and rapid stratification pipeline for targeted tumor therapy. We improve in vitro models of tissue conditions by a biological matrix-based three-dimensional (3D) tissue culture that allows in vitro drug testing: It correctly shows a strong drug response upon gefitinib (Gef) treatment in a cell line harboring an EGFR-activating mutation (HCC827), but no clear drug response upon treatment with the HSP90 inhibitor 17AAG in two cell lines with KRAS mutations (H441, A549). In contrast, 2D testing implies wrongly KRAS as a biomarker for HSP90 inhibitor treatment, although this fails in clinical studies. Signaling analysis by phospho-arrays showed similar effects of EGFR inhibition by Gef in HCC827 cells, under both 2D and 3D conditions. Western blot analysis confirmed that for 3D conditions, HSP90 inhibitor treatment implies different p53 regulation and decreased MET inhibition in HCC827 and H441 cells. Using in vitro data (western, phospho-kinase array, proliferation, and apoptosis), we generated cell line-specific in silico topologies and condition-specific (2D, 3D) simulations of signaling correctly mirroring in vitro treatment responses. Networks predict drug targets considering key interactions and individual cell line mutations using the Human Protein Reference Database and the COSMIC database. A signature of potential biomarkers and matching drugs improve stratification and treatment in KRAS-mutated tumors. In silico screening and dynamic simulation of drug actions resulted in individual therapeutic suggestions, that is, targeting HIF1A in H441 and LKB1 in A549 cells. In conclusion, our in vitro tumor tissue model combined with an in silico tool improves drug effect prediction and patient stratification. Our tool is used in our comprehensive cancer center and is made now publicly available for targeted therapy decisions.
基于肿瘤驱动因素的个体化治疗有望成为肺癌治疗的一种方法。为此，我们结合了体外组织模型和计算机分析。我们使用具有特定突变的个体细胞系，展示了一种针对靶向肿瘤治疗的通用和快速分层方法。我们通过基于生物基质的三维（3D）组织培养来改进组织条件的体外模型，该模型允许进行体外药物测试：在携带有 EGFR 激活突变（HCC827）的细胞系中，它可以正确地显示出吉非替尼（Gef）治疗的强烈药物反应，但在具有 KRAS 突变的两个细胞系（H441、A549）中，用 HSP90 抑制剂 17AAG 治疗时则没有明显的药物反应。相比之下，二维测试错误地将 KRAS 作为 HSP90 抑制剂治疗的生物标志物，尽管这在临床研究中失败了。磷酸化芯片的信号分析显示，在 HCC827 细胞中，EGFR 抑制 Gef 的作用在 2D 和 3D 条件下相似。Western blot 分析证实，对于 3D 条件，HSP90 抑制剂治疗意味着 HCC827 和 H441 细胞中 p53 调节不同，MET 抑制减少。我们使用体外数据（western blot、磷酸激酶阵列、增殖和凋亡）生成了特定于细胞系的计算拓扑结构和特定于条件的（2D、3D）模拟，这些模拟正确地反映了体外治疗反应。网络使用人类蛋白质参考数据库和 COSMIC 数据库考虑关键相互作用和个体细胞系突变来预测药物靶点。基于潜在生物标志物的签名和匹配药物可改善 KRAS 突变肿瘤的分层和治疗。药物作用的计算机筛选和动态模拟导致了个体化的治疗建议，即针对 H441 中的 HIF1A 和 A549 中的 LKB1 进行靶向治疗。总之，我们的体外肿瘤组织模型与计算工具相结合，提高了药物效果预测和患者分层。我们的工具在我们的综合癌症中心使用，并现在可供公众用于靶向治疗决策。
