NEW METHODS FOR CANCER DETECTION

描述(由申请人提供) 这一计划项目的应用代表了结肠癌分子图谱的综合和集中的多学科方法，以改进这种疾病的个体化治疗。在美国，每年有超过50,000名患者死于未加控制的转移性结肠癌。尽管有大量的形态和分子信息，但目前还不可能准确预测个别患者的预后，也不可能根据肿瘤的分子状态来校准治疗。因此，迫切需要寻找可靠的分子标志物或靶点用于癌症的诊断、预后和治疗。 在一个世纪的大部分时间里，内科医生一直依赖组织学和临床标准来判断预后。不幸的是，虽然这些标准可以预测平均结果，但肿瘤的可变性阻碍了有意义的个体预测和决定。使用SAGE、cDNA和寡核苷酸阵列对实体肿瘤进行初步的表达谱分析已经产生了大量的数据，但不同技术之间的差异导致不同技术之间几乎没有一致性，也没有准确的结果预测因素。目前的计划项目应用与以前的方法不同之处在于：(I)确定遗传和表观遗传学特征，(Ii)确定同一人类肿瘤样本的mRNA特征，以及(Iii)使用生物统计、聚类和神经网络分析来指导基因优先排序和结果分析。 我们的战略方法是：(I)开发和验证结肠癌特异的cDNA阵列、我们的连接酶检测反应/通用DNA阵列，以及我们基于EndoV/DNA连接酶的高通量检测结肠癌相关表达变化、突变、缺失、插入、基因扩增、LOH事件和甲基化变化的技术。(2)根据表达和突变特征的综合分析，在浸润性腺癌和转移癌中发现和表征新的癌症通路基因。(Iii)确定单个肿瘤中P53、细胞周期、细胞凋亡、Wnt信号、RPTK信号和转化生长因子β信号通路基因的分子变化模式。(Iv)描述在结肠癌从不典型隐窝病灶向转移的演变过程中，整体基因表达和特定癌症途径基因表达的渐进性和序贯变化。(V)根据遗传、表观遗传学和信使核糖核酸表达谱，制定结肠癌发生的分子分类，并利用这一分类来预测结果或对特定治疗模式的反应。

DESCRIPTION (provided by applicant) This program project application represents an integrated and focused multi-disciplinary approach to molecular profiling of colon cancer for improved individualized treatment of this disease. Over 50,000 patients die each year in the United Sates of uncontrolled metastatic colon cancer. Despite extensive morphological and molecular information, it is presently impossible to predict outcome accurately for individual patients, or to calibrate therapy to the molecular state of the tumor. Thus, there is an urgent need to discover reliable molecular markers or targets for the diagnosis, prognosis, and treatment of cancer. For the better part of a century physicians have relied on histological and clinical criteria for developing prognosis. Unfortunately, while these criteria predict average outcomes, tumor variability prevents meaningful individual prediction and decisions. Preliminary expression profiling of solid tumors using SAGE, cDNA, and oligonucleotide arrays has yielded volumes of data, yet variation between tumors has yielded little concordance among the different techniques, nor accurate predictors of outcome. The current program project application is distinguished from previous approaches by: (i) determining the genetic and epigenetic profiles, (ii) determining mRNA profiles on the same human tumor sample, and (iii) using biostatistic, clustering and neural net analysis to guide gene prioritization and outcome analysis. Our strategic approach is to: (i) Develop and validate colon-specific cDNA arrays, our ligase detection reaction/universal DNA array, and our EndoV/DNA ligase-based technologies for high-throughput detection of cancer-associated expression changes, mutations, deletions, insertions, gene amplification, LOH events, and methylation changes in colon cancers. (ii) Discover and characterize new cancer-pathways genes in invasive adenocarcinomas and metastases based on integrated analysis of expression and mutational profiles. (iii) Identify the patterns of molecular alterations that accumulate in the p53, cell cycle, apoptosis, Wnt signaling, RPTK signaling, and TGF Beta signaling pathway genes in individual tumors. (iv) Describe the progressive and sequential alterations in global gene expression and in the expression of specific cancer pathway genes during the evolution of colon neoplasia from atypical crypt focus to metastasis. (v) Develop a molecular classification of colon carcinogenesis, based upon genetic, epigenetic and mRNA expression profiling, and employ this taxonomy in predicting outcome or response to particular modes of therapy.