Integrating epidemiologic, clinical, genomic and metabolomic profiles to predict pancreatic cancer risk in a multiethnic population

整合流行病学、临床、基因组和代谢组学特征来预测多种族人群的胰腺癌风险

基本信息

批准号：
10352444
负责人：
Brian Huang
金额：
$ 11.99万
依托单位：
UNIVERSITY OF SOUTHERN CALIFORNIA
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-02-15 至 2022-11-30
项目状态：
已结题

项目摘要

ABSTRACT Pancreatic cancer is a highly lethal malignancy that has a very poor prognosis in the United States. It has a 5- year survival rate of only 9% and is projected to become the second most common cancer death by 2030. Pancreatic cancer also has a disproportionate burden across race/ethnicity, with higher incidence rates observed among minority groups, such African Americans, Japanese Americans, and Native Hawaiians. Past prediction models have been developed to identify high-risk individuals and improve the earlier detection of this disease. However, these models were designed in individuals of primarily European or Asian ancestry and have not been validated in multiethnic populations. In addition, these models included mainly known epidemiologic risk factors and only a few incorporated data on genetic variants or health conditions. Thus, a model that employs more granular data, such as comorbidities/symptoms, genomics and metabolomics, for the prediction of pancreatic cancer across multiple races/ethnicities does not exist. In this study, we seek to apply an integrative systems biology approach to enhance the prediction of pancreatic cancer risk using data from the Multiethnic Cohort (MEC) Study. The MEC is a long-standing prospective cohort of over 215,000 racially diverse individuals that has comprehensive lifestyle, environmental, clinical, and genetic data. We will use data from existing resources of the MEC, including epidemiologic risk factors from questionnaires, clinical health conditions from Medicare claims, genetic data from a large biorepository of blood samples, and cancer incidence and mortality information from SEER Cancer registries and state and national mortality databases. We will also generate new metabolomic data for a subset of MEC participants. Our specific aims are: 1) to identify clusters or patterns of clinical conditions associated with pancreatic cancer risk; 2) to validate existing prediction models in a multiethnic population and develop an enhanced prediction model that incorporates epidemiologic, clinical and genomic data; 3) to identify metabolites associated with pancreatic cancer in a multiethnic population; and 4) to integrate epidemiologic, clinical, genomic and metabolomic data to identify individuals at high risk of pancreatic cancer. Results from this study are expected to elucidate etiologic mechanisms and improve the prediction of pancreatic cancer risk for heterogeneous populations. This will have significant implications for improving strategies for earlier detection and reducing the overwhelming burden of this fatal cancer.

抽象的胰腺癌是一种高度致命的恶性肿瘤，在美国的预后较差。它有5- 年份的生存率仅为9％，预计到2030年将成为第二常见的癌症死亡。胰腺癌在种族/种族之间也有不成比例的负担，观察到更高的发病率在少数群体中，这些非洲裔美国人，日裔美国人和夏威夷原住民。过去的预测已经开发出模型来识别高风险个体并改善对该疾病的早期发现。但是，这些模型是在主要是欧洲或亚洲血统的个体中设计的，但不是在多种族人群中得到验证。此外，这些模型主要包括已知的流行病学风险因素只有少数关于遗传变异或健康状况的数据。因此，采用更多的模型用于预测胰腺的颗粒数据，例如合并症/症状，基因组学和代谢组学跨多个种族/种族的癌症不存在。在这项研究中，我们试图应用一个集成系统使用来自多民族队列的数据来增强胰腺癌风险预测的生物学方法（MEC）研究。 MEC是一个长期的前瞻性队列，由超过215,000个种族多样化的人具有全面的生活方式，环境，临床和遗传数据。我们将使用现有资源中的数据 MEC的，包括问卷调查的流行病学风险因素，Medicare的临床健康状况主张，来自大量血液样本的遗传数据以及癌症的发病率和死亡率信息来自SEER癌症注册机构以及州和国家死亡率数据库。我们还将生成新的代谢组 MEC参与者子集的数据。我们的具体目的是：1）确定临床条件的簇或模式与胰腺癌风险相关； 2）验证多种族人口中的现有预测模型和开发一个增强的预测模型，该模型结合了流行病学，临床和基因组数据。 3）识别多种族人口中与胰腺癌相关的代谢产物； 4）要整合流行病学，临床，基因组和代谢组数据，以鉴定胰腺癌高风险的个体。结果预计研究将阐明病因机制，并改善胰腺癌风险的预测异质种群。这将对改善较早检测的策略产生重大影响并减轻了这种致命癌症的压倒性负担。