基于肿瘤系统生物学新方法的分子靶向联合治疗及协同增效作用机制研究

In order to overcome drug resistance in single-targeted molecular therapies, research focus has shifted to studying the complex molecular regulatory mechanisms and rationally developing combinatorial therapies by systems biology methods. Recently, abundant evidence has demonstrated the importance of tumor heterogeneity and personalized therapies, which will impose new demands for the rational design of combinatorial therapy and the development of systems biology theory and techniques. Thus, based on our previous studies, we will construct a new type of tumor molecular network for personalized medicine by integrating multiple big data in biology such as the known combinatorial therapy information. We will further develop and refine our "neighbor communities" network algorithm to analyze the difference in the molecular network properties between different tumor cells. By employing an interdisciplinary research approach, both computational and experimental methods are used to reveal neighbor communities and signaling pathways associated with the synergistic effect of drug combinations, and to elucidate the molecular mechanisms underlying the impact of the molecular network differences between tumor cells to the antitumor and synergistic effects of combinatorial therapies. This research is expected to provide scientific guidance for the rational design of new combinatorial therapy in personalized medicine, and to develop new theories and techniques for cancer systems biology study.

通过系统生物学方法研究靶点之间的复杂分子调控机制，有助于从逆转靶向治疗耐药的临床需求出发，合理设计疗效更佳的分子靶向联合治疗方案。近年来，肿瘤异质性和个体化治疗在肿瘤靶向治疗中显示出其重要性，必将对分子靶向联合治疗的合理设计以及系统生物学相关理论和技术提出新的要求。本项目将结合我们前期研究工作基础，通过整合已知分子靶向联合治疗方案等多种生物大数据，建立适用于个体化治疗研究的新型肿瘤分子网络模型。发展有自主知识产权的"邻接蛋白质群"网络算法，分析不同肿瘤细胞分子网络特征的差异。进一步综合运用多学科交叉的手段，紧密结合计算和实验方法，揭示与协同增效作用相关的邻接蛋白质群和信号通路，阐明不同肿瘤细胞分子网络的差异对分子靶向联合治疗疗效和协同增效作用的影响及分子机制。本项目的实施有望为肿瘤个体化分子靶向联合治疗新方案的合理设计提供科学依据，为肿瘤系统生物学相关研究提供新的理论和技术方法。

通过系统生物学方法研究靶点之间的复杂分子调控机制，有助于从逆转靶向治疗耐药的临床需求出发，合理设计疗效更佳的分子靶向联合治疗方案。肿瘤异质性和个体化治疗在肿瘤靶向治疗中显示出其重要性，必将对分子靶向联合治疗的合理设计以及系统生物学相关理论和技术提出新的要求。本项目通过整合已知分子靶向药物相互作用、基因表达、蛋白质相互作用等多种类型的生物组学大数据，建立了新型肿瘤分子网络模型。发展了全新的基于深度学习算法、利用肿瘤细胞分子网络特征的药物相互作用效果网络预测模型，证明了该模型优于现有药物组合预测模型。进一步综合运用多学科交叉的手段，紧密结合计算和实验方法，揭示与协同增效作用相关的邻接蛋白质群和信号通路，阐明肿瘤细胞分子网络对分子靶向联合治疗疗效和协同增效作用的影响及潜在分子机制。本项目的实施为肿瘤分子靶向联合治疗新方案的合理设计提供科学依据，为肿瘤系统生物学相关研究提供新的理论和技术方法。