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：
10115540
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 2023-01-31
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，在美国预后非常差。它有一个 5- 年生存率仅为 9%，预计到 2030 年将成为第二大常见癌症死亡原因。胰腺癌在不同种族/民族中也有不成比例的负担，观察到发病率较高少数群体，如非裔美国人、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这种疾病的早期检测。然而，这些模型是为主要是欧洲或亚洲血统的个体设计的，并未被广泛应用。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危险因素并且只有少数关于遗传变异或健康状况的数据。因此，一个模型采用了更多用于预测胰腺疾病的精细数据，例如合并症/症状、基因组学和代谢组学跨种族/民族的癌症并不存在。在本研究中，我们寻求应用综合系统使用多种族队列的数据增强胰腺癌风险预测的生物学方法（MEC）研究。 MEC 是一个由超过 215,000 名不同种族人士组成的长期前瞻性群体，拥有全面的生活方式、环境、临床和遗传数据。我们将使用现有资源中的数据 MEC 的信息，包括调查问卷中的流行病学危险因素、医疗保险中的临床健康状况索赔、来自大型血液样本生物储存库的遗传数据以及癌症发病率和死亡率信息来自 SEER 癌症登记处以及州和国家死亡率数据库。我们还将产生新的代谢组学 MEC 参与者子集的数据。我们的具体目标是：1) 识别临床病症的集群或模式与胰腺癌风险相关； 2）验证多种族人群中现有的预测模型开发一个包含流行病学、临床和基因组数据的增强预测模型； 3）识别多种族人群中与胰腺癌相关的代谢物； 4）整合流行病学，临床、基因组和代谢组数据可识别胰腺癌高风险个体。结果由此研究有望阐明病因机制并改善胰腺癌风险的预测异质人群。这将对改进早期检测策略产生重大影响并减轻这种致命癌症的巨大负担。