Imoto Hiroaki, Yamashiro Sawa, Okada Mariko
Institute for Protein Research, Osaka University, Suita, Osaka 565-0871, Japan.
Center for Drug Design and Research, National Institutes of Biomedical Innovation, Health and Nutrition, Ibaraki, Osaka 567-0085, Japan.
iScience. 2022 Mar 10;25(3):103944. doi: 10.1016/j.isci.2022.103944. eCollection 2022 Mar 18.
Patient heterogeneity precludes cancer treatment and drug development; hence, development of methods for finding prognostic markers for individual treatment is urgently required. Here, we present Pasmopy (Patient-Specific Modeling in Python), a computational framework for stratification of patients using signaling dynamics. Pasmopy converts texts and sentences on biochemical systems into an executable mathematical model. Using this framework, we built a model of the ErbB receptor signaling network, trained in cultured cell lines, and performed simulation of 377 patients with breast cancer using The Cancer Genome Atlas (TCGA) transcriptome datasets. The temporal dynamics of Akt, extracellular signal-regulated kinase (ERK), and c-Myc in each patient were able to accurately predict the difference in prognosis and sensitivity to kinase inhibitors in triple-negative breast cancer (TNBC). Our model applies to any type of signaling network and facilitates the network-based use of prognostic markers and prediction of drug response.
患者异质性阻碍了癌症治疗和药物研发；因此，迫切需要开发用于寻找个体治疗预后标志物的方法。在此，我们展示了Pasmopy（Python中的患者特异性建模），这是一个利用信号动力学对患者进行分层的计算框架。Pasmopy将关于生化系统的文本和句子转化为可执行的数学模型。使用这个框架，我们构建了一个表皮生长因子受体（ErbB）信号网络模型，在培养的细胞系中进行训练，并使用癌症基因组图谱（TCGA）转录组数据集对377例乳腺癌患者进行模拟。每位患者中蛋白激酶B（Akt）、细胞外信号调节激酶（ERK）和原癌基因c-Myc的时间动态能够准确预测三阴性乳腺癌（TNBC）患者的预后差异以及对激酶抑制剂的敏感性。我们的模型适用于任何类型的信号网络，并有助于基于网络使用预后标志物和预测药物反应。
