癌症研究中的重要问题之一，是如何鉴定大量基因改变在癌症发生中的功能以及它们之间的协同作用。微生物中研究表明，通过高通量实验建立基因间相互作用网络，寻找功能相似的基因簇，可以快速解析基因功能和通路信息。申请人前期对67个乳腺癌相关基因间相互作用的研究，验证了使用上述方法探索癌症基因功能的可行性。为研究在多种癌症中均有突变的两百个基因的功能，探索癌症发生中共有的分子机制，申请人首先将改进高通量组合RNAi的实验方法，然后在细胞中同时定量检测这些基因间约两万对相互作用，得到高密度的基因相互作用网络；再从网络中挖掘出功能相似的基因簇，以解析突变基因的未知功能；此外还将结合已有的蛋白相互作用、转录调控数据，癌症病人基因组和临床数据，阐述一部分癌症基因相互作用的分子机制，并寻找一些与临床转归相关的有相互作用的基因突变组合。这些研究将为系统研究癌症的发病机制以及发现新的诊断和治疗靶点提供理论和实验基础。

As cancer mutations are uncovered at an unprecedented rate recently, it remains a challenge to understand the functions of those genetic alterations and how they interact to produce cancer phenotypes. High-density genetic interaction mapping have enabled systematic exploration of gene functions and pathway information in microorganisms. Our preliminary genetic interaction mapping of 67 breast cancer related genes has revealed the feasibility of applying this method to understanding the function of cancer genes. To explore the function of two hundred genes that are frequently-mutated in multile cancer types,and to understand the common mechanisms underlying cancer development,we plan to improve a deep-sequencing based combinatorial RNAi methods for mapping interactions of about 20,000 gene pairs. We will perform network analysis and interaction profile clustering to reveal gene modules with similar functions in order to predict functions of uncharacterized genes and the pathways they belong to. We will also perform integrative analysis of our genetic interaction network and other genomics and clinical data to screen for genes and gene groups that are associated with cancer development and patient outcome. With the proposed aims, we hope to generate useful information to understand the mechanisms of cancer development,to improve risk assessment and to develop new therapies.