NEW METHODS FOR CANCER DETECTION

描述（由申请人提供） 该计划项目申请代表了一个综合和集中的多学科方法，以改善这种疾病的个性化治疗结肠癌的分子谱。在美国，每年有超过50，000名患者死于不受控制的转移性结肠癌。尽管有广泛的形态学和分子信息，但目前不可能准确预测个体患者的结果，或将治疗校准到肿瘤的分子状态。因此，迫切需要发现用于癌症的诊断、预后和治疗的可靠的分子标志物或靶标。 在世纪的大部分时间里，医生依靠组织学和临床标准来判断预后。不幸的是，虽然这些标准预测平均结果，但肿瘤变异性阻止了有意义的个体预测和决策。使用SAGE、cDNA和寡核苷酸阵列对实体瘤进行初步表达谱分析已经产生了大量数据，但是肿瘤之间的变化在不同技术之间几乎没有一致性，也没有准确的结果预测。目前的程序项目应用程序与以前的方法的区别在于：（i）确定遗传和表观遗传谱，（ii）确定同一人类肿瘤样本的mRNA谱，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级和结果分析。 我们的战略方针是：（i）开发和验证结肠特异性cDNA阵列、我们的连接酶检测反应/通用DNA阵列和我们的基于EndoV/DNA连接酶的技术，用于高通量检测结肠癌中与癌症相关的表达变化、突变、缺失、插入、基因扩增、洛事件和甲基化变化。(ii)基于表达和突变谱的综合分析，发现和表征浸润性腺癌和转移瘤中的新癌症通路基因。(iii)确定在单个肿瘤中p53、细胞周期、细胞凋亡、Wnt信号传导、RPTK信号传导和TGF β信号传导途径基因中积累的分子改变模式。(iv)描述在结肠肿瘤从非典型隐窝病灶到转移的演变过程中，整体基因表达和特定癌症通路基因表达的进行性和顺序性变化。(v)根据遗传学、表观遗传学和mRNA表达谱，开发结肠癌发生的分子分类，并采用这种分类法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.