Helmholtz Zentrum München-German Research Center for Environmental Health, Institute of Computational Biology, Neuherberg, Germany.
Center for Mathematics, Technische Universität München, Garching, Germany.
PLoS Comput Biol. 2020 Mar 2;16(3):e1007147. doi: 10.1371/journal.pcbi.1007147. eCollection 2020 Mar.
Targeted cancer therapies are powerful alternatives to chemotherapies or can be used complementary to these. Yet, the response to targeted treatments depends on a variety of factors, including mutations and expression levels, and therefore their outcome is difficult to predict. Here, we develop a mechanistic model of gastric cancer to study response and resistance factors for cetuximab treatment. The model captures the EGFR, ERK and AKT signaling pathways in two gastric cancer cell lines with different mutation patterns. We train the model using a comprehensive selection of time and dose response measurements, and provide an assessment of parameter and prediction uncertainties. We demonstrate that the proposed model facilitates the identification of causal differences between the cell lines. Furthermore, our study shows that the model provides predictions for the responses to different perturbations, such as knockdown and knockout experiments. Among other results, the model predicted the effect of MET mutations on cetuximab sensitivity. These predictive capabilities render the model a basis for the assessment of gastric cancer signaling and possibly for the development and discovery of predictive biomarkers.
靶向癌症疗法是化疗的有力替代品，或者可以与之互补。然而，靶向治疗的反应取决于多种因素，包括突变和表达水平，因此其结果难以预测。在这里，我们开发了一种胃癌的机制模型，以研究西妥昔单抗治疗的反应和耐药因素。该模型捕捉了两种具有不同突变模式的胃癌细胞系中的 EGFR、ERK 和 AKT 信号通路。我们使用全面的时间和剂量反应测量结果来训练模型，并提供参数和预测不确定性的评估。我们证明，所提出的模型有助于识别细胞系之间的因果差异。此外，我们的研究表明，该模型可以预测对不同扰动的反应，例如敲低和敲除实验。在其他结果中，该模型预测了 MET 突变对西妥昔单抗敏感性的影响。这些预测能力使该模型成为评估胃癌信号的基础，并且可能用于开发和发现预测性生物标志物。
