Genetic Predictors of Prostate Cancer Survival

前列腺癌生存的遗传预测因素

• 批准号: 10533696
• 负责人: ROBERT J. KLEIN
• 金额: $ 6.82万
• 依托单位: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-01-15 至 2025-12-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10533696
• 关键词: African; Area; Biological; Clinical; Data; Databases; Development; Diabetes Mellitus; Diagnosis; Disease; Epidemiology; Funding; Genetic; Genomics; Grant; Health Services Accessibility; Health system; Indolent; K-Series Research Career Programs; Knowledge; Lead; Link; Malignant neoplasm of prostate; Manuscripts; Mendelian randomization; Native-Born; Non-Insulin-Dependent Diabetes Mellitus; Obesity; Operative Surgical Procedures; Parents; Pattern; Preparation; Race; Research; Risk; Risk Factors; Series; Single Nucleotide Polymorphism; Site; Statistical Data Interpretation; Testing; Veterans Health Administration; Work; base; cancer survival; career; career development; course development; epidemiologic data; genetic analysis; genetic predictors; men; mortality; novel; parent grant; prostate cancer risk; risk stratification; screening; skills

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.

前列腺癌（PC）具有非常异质性的临床过程。许多人有一个懒惰的PC，可以是 安全地观察了多年，没有进展。或者，其他男性有一种侵略性的PC形式，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个 未满足的临床需求。越来越多的数据表明，遗传单核苷酸多态性（SNP）可能 在这方面的援助。事实上，这是母体R 01（Klein PI; Freedland研究中心PI）的根本基础。 根据这一总体观点，本增刊支持Asilonu博士将重点放在两个领域 风险分层的方法：1）使用遗传SNP数据; 2）使用流行病学数据（肥胖和糖尿病状态）。 在目标1中，我们的目标是建立在我们在父母资助中提出的遗传分析的基础上。具体来说，我们将使用 孟德尔随机化（MR）确定2型糖尿病和肥胖之间的因果关系， 前列腺癌生存率基于最近将2型糖尿病分为不同亚型的工作，我们将 询问使用这些糖尿病亚型的遗传预测因子以及肥胖的预测因子作为工具变量 在孟德尔随机化分析中。通过比较SNP对这些变量的效应大小， 这些相同的SNP对前列腺癌生存率的影响大小（来自父母资助的数据），我们将 确定这些糖尿病和肥胖亚型直接导致前列腺癌恶化的程度 生存 在目标2中，我们将建立在Freedland博士和他的团队的新发现基础上，即糖尿病和肥胖出现在 协同行动，创造一个更具侵略性的PC。这并不适用于整体PC风险，但特别适用于 攻击性PC这种协同作用在高级别PC的诊断和PC死亡率方面都被注意到， 手术治疗早期疾病基于这些发现，我们假设糖尿病和肥胖将 相互作用，协同增加侵袭性PC（高级别PC和PC死亡率）的风险，但不影响低级别PC PC.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 这表明有生物学基础。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，用于建立一个全国性的数据库，以研究 肥胖和种族在预测攻击性PC中的作用。这另一笔赠款的绝大多数工作现已完成。这 为Asilonu博士创造了一个很好的机会，可以利用他目前的统计知识， 本补充中提出的复杂分析。 从拟议的研究中，Asilonu博士将开发先进的统计分析，遗传学和生物学方面的新技能。 分析、处理全国性数据和准备文稿。我们还内置了教学工作，以帮助他 准备赠款。在近3年的补充结束时，我们不仅将回答关键问题， 但他将准备采取下一步在他的职业生涯-申请K奖或R系列补助金。