Tsirvouli Eirini, Touré Vasundra, Niederdorfer Barbara, Vázquez Miguel, Flobak Åsmund, Kuiper Martin
Department of Biology, Norwegian University of Science and Technology, Trondheim, Norway.
Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway.
Front Mol Biosci. 2020 Oct 9;7:502573. doi: 10.3389/fmolb.2020.502573. eCollection 2020.
Cancer is a heterogeneous and complex disease and one of the leading causes of death worldwide. The high tumor heterogeneity between individuals affected by the same cancer type is accompanied by distinct molecular and phenotypic tumor profiles and variation in drug treatment response. modeling of cancer as an aberrantly regulated system of interacting signaling molecules provides a basis to enhance our biological understanding of disease progression, and it offers the means to use computer simulations to test and optimize drug therapy designs on particular cancer types and subtypes. This sets the stage for precision medicine: the design of treatments tailored to individuals or groups of patients based on their tumor-specific molecular cancer profiles. Here, we show how a relatively large manually curated logical model can be efficiently enhanced further by including components highlighted by a multi-omics data analysis of data from Consensus Molecular Subtypes covering colorectal cancer. The model expansion was performed in a pathway-centric manner, following a partitioning of the model into functional subsystems, named modules. The resulting approach constitutes a middle-out modeling strategy enabling a data-driven expansion of a model from a generic and intermediate level of molecular detail to a model better covering relevant processes that are affected in specific cancer subtypes, comprising 183 biological entities and 603 interactions between them, partitioned in 25 functional modules of varying size and structure. We tested this model for its ability to correctly predict drug combination synergies, against a dataset of experimentally determined cell growth responses with 18 drugs in all combinations, on eight cancer cell lines. The results indicate that the extended model had an improved accuracy for drug synergy prediction for the majority of the experimentally tested cancer cell lines, although significant improvements of the model's predictive performance are still needed. Our study demonstrates how a tumor-data driven middle-out approach toward refining a logical model of a biological system can further customize a computer model to represent specific cancer cell lines and provide a basis for identifying synergistic effects of drugs targeting specific regulatory proteins. This approach bridges between preclinical cancer model data and clinical patient data and may thereby ultimately be of help to develop patient-specific models that can steer treatment decisions in the clinic.
癌症是一种异质性和复杂性疾病，也是全球主要死因之一。同一癌症类型患者之间存在高度的肿瘤异质性，同时伴有明显不同的分子和表型肿瘤特征以及药物治疗反应的差异。将癌症建模为相互作用信号分子的异常调节系统，为增强我们对疾病进展的生物学理解提供了基础，也为利用计算机模拟来测试和优化针对特定癌症类型和亚型的药物治疗设计提供了手段。这为精准医学奠定了基础：即根据患者个体或群体的肿瘤特异性分子癌症特征来设计个性化治疗方案。在此，我们展示了如何通过纳入来自涵盖结直肠癌的共识分子亚型数据的多组学数据分析所突出的组件，进一步有效地增强一个相对较大的人工整理的逻辑模型。模型扩展是以通路为中心的方式进行的，首先将模型划分为功能子系统，即模块。由此产生的方法构成了一种中间向外的建模策略，能够使模型从一般和中等水平的分子细节进行数据驱动的扩展，从而更好地涵盖特定癌症亚型中受影响的相关过程，该模型包含183个生物实体以及它们之间的603种相互作用，被划分为25个大小和结构各异的功能模块。我们针对该模型预测药物联合协同效应的能力进行了测试，使用了一个包含18种药物所有组合的实验确定的细胞生长反应数据集，对8种癌细胞系进行测试。结果表明，尽管该模型的预测性能仍需显著提高，但扩展后的模型对大多数实验测试的癌细胞系在药物协同效应预测方面具有更高的准确性。我们的研究展示了一种肿瘤数据驱动的中间向外方法，用于完善生物系统的逻辑模型，如何能够进一步定制计算机模型以代表特定癌细胞系，并为识别针对特定调节蛋白的药物协同效应提供基础。这种方法架起了临床前癌症模型数据与临床患者数据之间的桥梁，从而最终可能有助于开发能够指导临床治疗决策的患者特异性模型。
