基于ncRNA介导风险通路间crosstalk 的癌症联合用药优化及其机制研究

The occurrence and development of cancer is a complex process involving multi-factor crosstalk. The low efficiency of drugs is an important problem for clinical treatment. Combination therapy is currently the most effective way to improve cancer treatment，but currently approved drug combinations result mainly from empirical clinical experience. Therefore, it is meaningful to present an approach to discover new drug combinations for cancer treatment. In this work, we use complex disease (breast cancer/lung cancer) as disease models, integrate the disease and drug high-throughput data to construct the ncRNA-mediated crosstalk network between pathways of cancer and structure the therapeutic networks for drug. Base on the cross-talk inhibition, we develop a computational network biological approach to identify the effective drug which inhibition risk pathway cross-talk of cancer, and then filtrate and optimize the drug combination for cancer treatment. Mining the key cascade pathways of drug action and analyzing the mechanism of drug combination and verifying these results by biological experiments. Finally, we developed a comprehensive bioinformatics platform and database for screening and analysis of cancer combination drugs. The expected results of this study have important scientific significance and clinical application value in uncovering the regulatory mechanism of tumor pathogenesis and explore new treatment strategy for cancer.

癌症的发生发展是一个复杂的、涉及多因素交互的过程，药物有效率低是临床治疗面临的重要难题。联合用药是目前改善癌症治疗最有效的方式，而当前的药物联用主要来自临床经验。 因此，开发系统识别药物联用的方法对癌症防治有重大的意义。本项目以乳腺癌、肺癌为疾病模型，整合癌症多组学、药物组学、功能组学等生物医学大数据，构建全局的癌症ncRNA介导的风险通路crosstalk关联网络和药物作用关联网络；基于风险通路crosstalk抑制策略开发算法，优化筛选最佳的药物联用组合；挖掘关键的药物作用级联路径，解析药物联用机制，并进行生物学实验验证；最后，开发癌症联合用药筛选及分析的综合生物信息平台及数据库。本研究对于揭示癌症的发病机理、探索癌症治疗的全新用药策略，具有重要的实践指导意义和临床应用价值。

癌症的发生发展是一个复杂的、涉及多因素交互的过程，药物有效率低是临床治疗面临的重要难题。联合用药是目前改善癌症治疗最有效的方式，而当前的药物联用主要来自临床经验。因此，开发系统识别药物联用的方法对癌症防治有重大的意义。本项目我们首先获取了癌症多组学数据、miRNA-mRNA互作数据、生物学通路等数据资源，基于癌症风险基因和miRNA识别癌症miRNA介导的风险通路，并构建全局的crosstalk关联网络，最终，共鉴定出21种肿瘤中268条通路之间的15416个crosstalk及肿瘤crosstalk网络；进一步整合药物靶点数据，构建药物作用关联网络，基于风险通路crosstalk抑制策略开发算法，建立基于计算网络生物学的方法来识别抑制肿瘤风险通路crosstalk作用的有效药物，进而筛选和优化用于肿瘤治疗的药物组合，最终得到83种一线抗肿瘤药物的687个优化组合。分析联合用药的作用机制，通过生存分析并对算法的可靠性与准确性进行了全面的验证与评估。此外，我们开发了一个用户友好的web界面ODrugCP (http://bio-bigdata.hrbmu.edu.cn/oDrugCP/)，可用于探索与21种癌症相关的687种优化的药物组合。综上所述，本研究为肿瘤治疗提供了一种筛选精准药物组合的有效方法，对肿瘤治疗具有重要的科学意义和临床应用价值。本项目目前共完成SCI论文4篇，其中发表2篇，修稿2篇（均标注了本项目号），开发相关的生物信息学数据库资源及分析平台2个。