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

• 批准号: 10745361
• 负责人: Brian Huang
• 金额: $ 24.9万
• 依托单位: UNIVERSITY OF SOUTHERN CALIFORNIA
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-09 至 2025-12-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10745361
• 关键词: African American population; Amino Acids; Asia; Asian ancestry; Asian population; Biological; Biological Assay; Biological Markers; Blood specimen; California; Cancer Etiology; Caucasians; Cessation of life; Clinical; Clinical Data; Cohort Studies; Data; Databases; Diagnosis; Diagnostic; Disease; Early Diagnosis; Epidemiology; Ethnic Origin; Ethnic Population; Etiology; Europe; European; European ancestry; Evaluation; Functional disorder; Gastrointestinal Diseases; Genetic; Genomics; Health; Hospitals; Incidence; Individual; Japanese American; Life Style; Malignant Neoplasms; Malignant neoplasm of pancreas; Medicare claim; Mentors; Metabolic; Metabolic syndrome; Minority; Minority Groups; Modeling; Native Hawaiian; Organ; Pancreas; Participant; Pathogenesis; Pathway interactions; Pattern; Performance; Phase; Population; Population Heterogeneity; Prevention; Prognosis; Prospective cohort; Questionnaires; Race; Research; Research Design; Resources; Risk; Risk Factors; Running; Sampling; Screening for cancer; Standardization; Subgroup; Survival Rate; Symptoms; Systems Biology; Techniques; United States; associated symptom; biobank; cancer risk; cohort; comorbidity; data integration; design; epidemiologic data; gastrointestinal symptom; genetic variant; genome-wide; genomic data; high risk; high risk population; improved; insight; metabolomics; mortality; multi-ethnic; neoplasm registry; novel; pancreatic cancer model; predictive modeling; racial diversity; risk prediction model; risk stratification; sex

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.

摘要 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。 胰腺癌在不同人种/种族间的负担也不成比例，观察到更高的发病率 在少数民族群体中，如非洲裔美国人、日裔美国人和夏威夷土著人。过去的预测 已经开发了一些模式，以确定高风险个人，并改进对这种疾病的早期检测。 然而，这些模型是在主要是欧洲或亚洲血统的个体中设计的， 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 只有少数纳入了遗传变异或健康状况的数据。因此，一个采用更多 颗粒数据，如合并症/症状，基因组学和代谢组学，用于预测胰腺癌 不存在跨多个种族/族裔的癌症。在这项研究中，我们试图应用一个综合系统， 使用多种族队列数据增强胰腺癌风险预测的生物学方法 (MEC)Study. MEC是一个长期存在的前瞻性队列，由超过215，000名种族多样化的个体组成， 拥有全面的生活方式、环境、临床和基因数据。我们将使用现有资源中的数据 包括来自问卷调查的流行病学风险因素，来自医疗保险的临床健康状况， 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 从SEER癌症登记处和州和国家死亡率数据库中。我们还将产生新的代谢组学 数据的一个子集的MEC参与者。我们的具体目标是：1）确定临床条件的集群或模式 与胰腺癌风险相关; 2）在多种族人群中验证现有的预测模型， 开发一个结合流行病学、临床和基因组数据的增强型预测模型; 3）识别 多种族人群中与胰腺癌相关的代谢物;和4）整合流行病学， 临床、基因组学和代谢组学数据，以确定胰腺癌高风险个体。结果从这个 这项研究有望阐明病因机制，提高胰腺癌风险的预测， 异质种群这将对改进早期检测策略具有重要意义 并减轻这种致命癌症的沉重负担。