Mapping Gene Networks in Colon Cancer

描述(申请人提供)：结肠癌，以及一般的癌症，是一种基因组疾病，总是与基因的异常表达有关。目前还不清楚的是，这些基因是如何在支撑癌症表型的操作网络的背景下相互联系的。对癌症相关基因网络连接的了解为识别可用于治疗干预的关键分支点提供了可能性。该应用程序的目标是绘制来自NCI-60小组的一系列定义明确的结肠癌细胞系中的癌症基因网络。这将通过实施一个迭代过程来实现，该过程涉及表型分析、基因表达谱分析、转录因子结合分析和基因相互作用的计算建模。最终的网络模型将通过在称为RNA干扰(RNAi)的过程中用称为短发夹状RNAs(SiRNAs)的分子探针干扰癌细胞来进行测试。该网络中被RNAi击倒导致癌症表型丧失的基因被假设为调节下游效应基因的表达。癌症细胞系中的这种扰动将通过随后的一轮基因表达谱、转录因子结合分析和计算建模来检查，从而使我们能够逐步改进我们的癌症网络模型。在随后的一轮RNAi干扰和癌症丢失表型筛选中，将仔细检查修订后模型的有效性，并重复这个迭代过程。最后，我们的网络中基因的生物学意义将与分期的人类结肠癌(正常结肠癌、腺瘤、Dukes‘B、C和D期样本，以及肝转移)的表达谱数据交叉验证。我们的研究目标概括如下：(1)构建侵袭基因网络图谱，(2)构建(反)黏附基因网络图谱，(3)构建侵袭和(反)黏附基因网络概率模型。

DESCRIPTION (provided by applicant): Colon cancer, and cancer in general, is a disease of the genome which invariably is associated with aberrant expression of genes. What is not clearly understood is how these genes are interconnected in the context of an operational network that underlies the cancer phenotype. An understanding of the network connections of cancer-related genes offers the potential to identify critical branch points amendable to therapeutic intervention. The goal of this application is to map cancer gene networks in a well-defined series of colon cancer cell lines from the NCI-60 panel. This will be accomplished by implementing an iterative process involving phenotyping, gene expression profiling, transcription factor binding analysis, and computational modeling of gene interactions. The resulting network model will be tested by perturbing cancer cell lines with molecular probes called short hairpin RNAs (siRNAs) in a process known as RNA interference (RNAi). Genes within the network whose knockdown by RNAi leads to a loss-of-cancer phenotype are hypothesized to regulate the expression of downstream effector genes. This perturbation in cancer cell lines will be examined by a subsequent round of gene expression profiling, transcription factor binding analysis and computational modeling, thereby allowing us to progressively improve our cancer network model. The validity of the revised model will be scrutinized with an ensuing round of RNAi perturbation and loss-of- cancer phenotype screening, and the iterative process is repeated. Lastly, the biological significance of genes in our networks will be cross-validated with expression profiling data of staged human colon cancers (normal colon, adenomas, Dukes' B, C and D stage samples, and liver metastases). Our research objectives are summarized as follows: (1) Construct a map of the invasion gene network, (2) Construct a map of the (anti-)adhesion gene network, and (3) Build probabilistic models of the invasion and (anti-) adhesion gene networks.