Genetic Predictors of Prostate Cancer Survival

前列腺癌(PC)的临床病程异质性很强。许多男人都有一台懒惰的电脑，可以 安全地观看了多年，没有进展。或者，其他男人有攻击性的个人电脑，并且可以 进展非常迅速。了解PC风险分层并将惰性疾病与侵袭性疾病分开是一项 关键的未得到满足的临床需求。越来越多的数据表明，遗传单核苷酸多态(SNPs)可能 为这一努力提供帮助。事实上，这是父R01(Klein Pi；Freedland Site PI)的基本基础。 根据这一总体观点，本补编支持阿西洛努博士的发展，将重点放在两个领域 风险分层：1)使用遗传SNP数据；2)使用流行病学数据(肥胖和糖尿病状况)。 在目标1中，我们的目标是建立在父母赠款中提出的基因分析的基础上。具体地说，我们将使用 孟德尔随机化(MR)确定2型糖尿病和肥胖症之间的因果关系 前列腺癌存活率。基于最近将2型糖尿病分为不同亚型的工作，我们将 ASK使用这些糖尿病亚型的遗传预测因子以及肥胖预测因子作为辅助变量 在孟德尔随机化分析中。通过将SNPs对这些变量的影响大小与 这些相同的SNP对前列腺癌生存率的影响大小(来自父母拨款的数据)，我们将 确定这些亚型糖尿病和肥胖直接导致前列腺癌恶化的程度 生死存亡。 在目标2中，我们将基于弗里德兰博士和他的团队的新发现，即糖尿病和肥胖症的出现 协同行动，打造更具进取心的个人电脑。这并不是针对总体PC风险，而是专门针对 咄咄逼人的PC。这种协同作用在高级别PC和PC死亡率的诊断中都被注意到 早期疾病的外科手术。基于这些发现，我们假设糖尿病和肥胖症 相互作用协同增加侵袭性PC(高级别PC和PC死亡率)的风险，但不是低级别PC PC机我们进一步假设，这些关联将独立于筛查模式和获得护理的机会 暗示了一个生物学基础。我们将使用退伍军人事务部(VA)的全国数据来验证这一假设。 卫生系统。我们收到了一笔单独的拨款，用于创建一个全国性的数据库，以研究 肥胖和种族在预测侵略性PC方面的作用。另一笔赠款的绝大多数工作现在已经完成。这 为Asilonu博士创造了一个极好的机会，以他目前的统计知识为基础，并完成 本补编中提出了复杂的分析。 从这项拟议的研究中，阿西罗努博士将在高级统计分析、遗传 分析，使用全国范围内的数据，以及手稿准备。我们还内置了指导性工作来帮助他 准备助学金。在近3年的补充资料结束时，我们不仅回答了 但他将准备好在他的职业生涯中迈出下一步--申请K奖或R系列奖。

Prostate cancer (PC) has a very heterogenous clinical course. Many men have an indolent PC that can be safely watched for years without progression. Alternatively, other men have an aggressive form of PC and can progress very rapidly. Understanding PC risk stratification and separating indolent from aggressive disease is a crucial unmet clinical need. Increasing data suggest that genetic single nucleotide polymorphisms (SNPs) may aid in this effort. Indeed, this is the fundamental basis of the parent R01 (Klein PI; Freedland site PI). In line with that overarching view, this supplement supports Dr. Asilonu's development to focus on two areas of risk stratification: 1) Using genetic SNP data; 2) using epidemiological data (obesity and diabetes status). In Aim 1, we aim to build upon our genetic analyses proposed in the parent grant. Specifically, we will use Mendelian Randomization (MR) to determine causal relationships between type 2 diabetes and obesity with prostate cancer survival. Building upon recent work classifying type 2 diabetes into different subtypes, we will ask use genetic predictors of these diabetes subtypes, as well as predictors of obesity, as instrumental variables in the Mendelian Randomization analysis. By comparing the effect size of SNPs on these variabes with the effect size of these same SNPs on prostate cancer survival (derived from data from the parent grant), we will determine the extent to which these subtypes of diabetes and obesity directly lead to worse prostate cancer survival. In Aim 2, we will build upon the novel finding by Dr. Freedland and his team that diabetes and obesity appear to act synergistically to create a more aggressive PC. This was not seen for overall PC risk, but specifically for aggressive PC. This synergistic interaction was noted for both diagnosis of high-grade PC and PC mortality after surgery for early-stage disease. Based upon these findings, we hypothesize that diabetes and obesity will interact to synergistically increase the risk of aggressive PC (high-grade PC and PC mortality) but not low-grade PC. We further hypothesize these associations will be independent of screening patterns and access to care suggesting a biological basis. We will test this hypothesis using nationwide data from the Veterans Affairs (VA) Health System. We received a separate grant to create a nationwide database to study the potential link between obesity and race in predicting aggressive PC. The vast majority of work for this other grant is now complete. This creates a great opportunity for Dr. Asilonu to build upon his current statistical knowledge and complete the complicated analyses proposed in this supplement. From the research proposed, Dr. Asilonu will develop new skills in advanced statistical analyses, genetic analyses, working with nationwide data, and manuscript preparation. We also built-in didactic work to help him prepare grants. At the end of the nearly 3-year supplement, not only will we have answered key questions in the field, but he will be ready to take the next step in his career – applying for a K-award or R-series grant.