Béal Jonas, Montagud Arnau, Traynard Pauline, Barillot Emmanuel, Calzone Laurence
Institut Curie, PSL Research University, Mines Paris Tech, Inserm, U900, Paris, France.
Front Physiol. 2019 Jan 24;9:1965. doi: 10.3389/fphys.2018.01965. eCollection 2018.
Logical models of cancer pathways are typically built by mining the literature for relevant experimental observations. They are usually generic as they apply for large cohorts of individuals. As a consequence, they generally do not capture the heterogeneity of patient tumors and their therapeutic responses. We present here a novel framework, referred to as PROFILE, to tailor logical models to a particular biological sample such as a patient tumor. This methodology permits to compare the model simulations to individual clinical data, i.e., survival time. Our approach focuses on integrating mutation data, copy number alterations (CNA), and expression data (transcriptomics or proteomics) to logical models. These data need first to be either binarized or set between 0 and 1, and can then be incorporated in the logical model by modifying the activity of the node, the initial conditions or the state transition rates. The use of MaBoSS, a tool based on Monte-Carlo kinetic algorithm to perform stochastic simulations on logical models results in model state probabilities, and allows for a semi-quantitative study of the model phenotypes and perturbations. As a proof of concept, we use a published generic model of cancer signaling pathways and molecular data from METABRIC breast cancer patients. For this example, we test several combinations of data incorporation and discuss that, with these data, the most comprehensive patient-specific cancer models are obtained by modifying the nodes' activity of the model with mutations, in combination or not with CNA data, and altering the transition rates with RNA expression. We conclude that these model simulations show good correlation with clinical data such as patients' Nottingham prognostic index (NPI) subgrouping and survival time. We observe that two highly relevant cancer phenotypes derived from personalized models, and , are biologically consistent prognostic factors: patients with both high proliferation and low apoptosis have the worst survival rate, and conversely. Our approach aims to combine the mechanistic insights of logical modeling with multi-omics data integration to provide patient-relevant models. This work leads to the use of logical modeling for precision medicine and will eventually facilitate the choice of patient-specific drug treatments by physicians.
癌症通路的逻辑模型通常是通过挖掘文献中的相关实验观察结果构建的。由于适用于大量个体队列，它们通常较为通用。因此，它们一般无法捕捉患者肿瘤的异质性及其治疗反应。我们在此提出一种新颖的框架，称为PROFILE，用于将逻辑模型定制到特定的生物样本，如患者肿瘤。这种方法允许将模型模拟与个体临床数据（即生存时间）进行比较。我们的方法侧重于将突变数据、拷贝数改变（CNA）和表达数据（转录组学或蛋白质组学）整合到逻辑模型中。这些数据首先需要进行二值化处理或设置在0到1之间，然后通过修改节点的活性、初始条件或状态转换率纳入逻辑模型。使用MaBoSS（一种基于蒙特卡罗动力学算法的工具，用于对逻辑模型进行随机模拟）可得出模型状态概率，并允许对模型表型和扰动进行半定量研究。作为概念验证，我们使用已发表的癌症信号通路通用模型以及来自METABRIC乳腺癌患者的分子数据。对于这个例子，我们测试了几种数据整合组合，并讨论了利用这些数据，通过用突变修改模型节点的活性（结合或不结合CNA数据）以及用RNA表达改变转换率，可获得最全面的患者特异性癌症模型。我们得出结论，这些模型模拟与临床数据（如患者的诺丁汉预后指数（NPI）分组和生存时间）显示出良好的相关性。我们观察到，从个性化模型得出的两个高度相关的癌症表型，即高增殖和低凋亡，是生物学上一致的预后因素：高增殖且低凋亡的患者生存率最差，反之亦然。我们的方法旨在将逻辑建模的机制见解与多组学数据整合相结合，以提供与患者相关的模型。这项工作推动了逻辑建模在精准医学中的应用，并最终将有助于医生选择患者特异性的药物治疗方案。
