Molecular Genetics of HNPCC

描述（由申请人提供）：本申请的总体目标是使用基于途径的方法阐明影响遗传性非息肉病性结直肠癌（HNPCC）发病年龄的遗传风险调节因子。该提议的总体假设是细胞周期途径中的SNP影响细胞周期进展的精细调节机制的效率。我们将继续建立和扩大我们独特的错配修复基因突变携带者的临床和标本资源，以实现这一目标。在目标1中，我们将确定细胞周期基因中的遗传变异对早发性HNPCC发展风险的作用。我们将使用Illumina Golden Gate平台对来自153个细胞周期基因的SNP进行基因分型。我们将使用两阶段研究设计评估这些基因中SNP的主要影响，以确定野生型基因型携带者与携带一个或两个SNP拷贝的携带者之间是否存在HNPCC发展的年龄相关风险差异。在目标2中，我们将使用新的机器学习分析工具来确定我们是否可以识别细胞周期SNP的组合，这些组合共同影响早发性HNPCC的发展风险。将采用两种方法来实现这一目标。一种是集中的候选基因方法，第二种是探索性的，基于途径的方法。在重点候选方法中，我们选择了12个候选基因，因为已知它们在细胞周期途径中相互作用，并且有证据表明这些基因中的SNP会影响HNPCC或其他癌症的风险。我们将确定这些SNP的组合是否共同影响HNPCC在早期的发展。在第二种方法中，我们将研究所有153个基因中的SNP，包括12个候选基因。我们推测，有许多可能的细胞周期基因的组合，一起工作，并在SNPS的影响下，有助于HNPCC在早期的发展。我们将使用随机森林（RF）建模来对来自这些基因的所有SNP进行排名。RF可以处理大量的预测变量，如SNP，并提供变量重要性的估计。然后，我们将使用CART来使用RF中排名最高的SNP构建年龄至发病树。这些研究将有助于我们的长期目标，即通过确定细胞周期基因中SNP在HNPCC风险中的作用，建立早发性HNPCC的风险模型。他们还将提供有关新的基因-基因相互作用的重要信息，有助于在较早的年龄HNPCC的发展风险。国家癌症研究所在其2006年预算提案中将风险预测列为一个具有非凡机会的领域。癌症风险预测模型可以提供有价值的工具，用于识别可能从预防性治疗或增加监测中受益的个体，或者是参与临床试验的良好候选人。公共卫生部门：拟议的研究将有助于我们建立HNPCC风险模型的长期目标，这也可能对散发性结直肠癌以及其他癌症部位具有重要意义。

DESCRIPTION (provided by applicant): The overall goal of this application is to elucidate genetic risk modifiers influencing age of onset of hereditary nonpolyposis colorectal cancer (HNPCC) using a pathway-based approach. The overall hypothesis of this proposal is that SNPs in cell cycle pathways influence the efficiency of the finely tuned mechanisms of cell cycle progression. We will continue to build and expand upon our unique clinical and specimen resource of mismatch repair gene mutation carriers in order to accomplish this goal. In aim 1, we will determine the role that genetic variants in cell cycle genes have on risk for development of early onset HNPCC. We will use the Illumina Golden Gate platform to genotype SNPs from 153 cell cycle genes. We will evaluate the main effects of SNPs in these genes using a two-phase study design in order to determine if there are differences in age-associated risk for development of HNPCC between carriers of the wild type genotype vs. those carrying one or two copies of the SNP. In aim 2, we will use novel machine learning analytic tools to determine if we can identify combinations of cell cycle SNPs that work together to influence risk for development of early onset HNPCC. Two approaches will be used to accomplish this goal. A focused candidate gene approach, and the second is an exploratory, pathways based approach. In the focused candidate approach, we have selected 12 candidate genes because they are known to interact with each other in the cell cycle pathways and there is evidence that SNPs in these genes influence risk for HNPCC or other cancers. We will determine if combinations of these SNPs work together to influence development of HNPCC at an early age. In the second approach we will investigate SNPs in all 153 of the genes, including the 12 candidate genes. We hypothesize that there are many possible combinations of cell cycle genes that work together and under the influence of SNPS, contribute to development of HNPCC at an early age. We will use Random Forests (RF) modeling to rank all of the SNPs from these genes. RF can handle large numbers of predictor variables such as SNPs and provides estimates of variable importance. We will then use CART to build age-to-onset trees using the SNPs ranked the highest from RF. The proposed studies will contribute to our long-term goal to develop a risk model for early onset HNPCC by defining the role that SNPs in cell cycle genes play in risk for HNPCC. They will also provide important information regarding novel gene-gene interactions contributing to risk for development of HNPCC at an earlier age. The National Cancer Institute in its 2006 budget proposal cited risk prediction as an area of extraordinary opportunity. Risk prediction models for cancer could provide valuable tools for identifying individuals who may benefit from preventive treatments or increased surveillance or who are good candidates to participate in clinical trials. PUBLIC HEALTH REVELANCE: The proposed studies will contribute to our long-term goal to develop a risk model for HNPCC, which may also have important implications for sporadic colorectal cancer as well as other cancer sites.