Identification of molecular profiles and markers for response prediction in rectal cancer and their clinical application

直肠癌反应预测的分子谱和标志物鉴定及其临床应用

• 批准号: 50704539
• 负责人: Professor Dr. B. Michael Ghadimi
• 金额: --
• 依托单位: Institut für Humangenetik
• 依托单位国家: 德国
• 项目类别: Clinical Research Units
• 财政年份: 2007
• 资助国家: 德国
• 起止时间: 2006-12-31 至 2014-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/50704539?language=en
• 关键词: Identification; molecular; profiles; markers; response

Preoperative 5-FU (5-fluorouracil)-based chemoradiotherapy (CT/RT) is recommended as standard therapy for UICC stage ll/lll rectal cancer in Germany. However, tumor response to preoperative CT/RT is very heterogeneous, ranging from complete response to resistance, and it remains unclear how the individual prognosis of patients will be affected by this treatment. We recently demonstrated that gene expression profiling can be useful for predicting response to preoperative CT/RT: 54 genes showed significantly different expression levels between responders and nonresponders. In a follow-up study, we were able to show that these 54 genes are also relevant for predicting local recurrence and disease-free survival. Within this subproject, we first aim to generate a customized microarray, which will contain this set of 54 genes as well as relevant genes analyzed within other subprojects, for prospective testing in a much larger cohort of patients. Second, we will perform a whole-genome screen to evaluate whether other genes might complement this gene set for response prediction. Third, the experiments in gene expression profiling will be complemented by a genome-wide screening for single nucleotide polymorphisms (SNPs) and changes in genomic copy numbers associated with response to chemoradiotherapy.

在德国，术前基于5-FU（5-氟尿嘧啶）的放化疗（CT/RT）被推荐为UICC II/III期直肠癌的标准治疗。然而，肿瘤对术前CT/RT的反应是非常异质性的，从完全反应到耐药，目前还不清楚这种治疗对患者的个体预后有何影响。我们最近证明，基因表达谱可以用于预测术前CT/RT的反应：54个基因的表达水平显着不同的反应者和无反应者之间。在后续研究中，我们能够证明这54个基因也与预测局部复发和无病生存有关。在这个子项目中，我们的第一个目标是生成一个定制的微阵列，它将包含这组54个基因以及在其他子项目中分析的相关基因，用于在更大的患者队列中进行前瞻性测试。第二，我们将进行全基因组筛选，以评估其他基因是否可以补充该基因集进行反应预测。第三，基因表达谱分析实验将通过全基因组单核苷酸多态性（SNP）筛查和与放化疗反应相关的基因组拷贝数变化来补充。