An integrative omics approach to investigate gene-environment interaction in colorectal cancer risk

研究结直肠癌风险中基因与环境相互作用的综合组学方法

基本信息

批准号：
10668779
负责人：
William JAMES GAUDERMAN
金额：
$ 97.31万
依托单位：
FRED HUTCHINSON CANCER CENTER
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-06-01 至 2028-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10668779
关键词：
Acceleration Age Age of Onset Alcohols Atlases Biological Biological Markers Biological Process Biopsy C-reactive protein Cancer Etiology Cells Cessation of life Chromatin Chromosome Mapping Chronic Clinical Clinical Data Colorectal Colorectal Cancer Communities Complex Data Data Set Databases Development Diabetes Mellitus Diet Dietary Factors Disease Drug usage Environment Environmental Exposure Environmental Risk Factor Ethnic Population Gene Expression Gene Expression Regulation Genes Genetic Genetic Risk Genetic Variation Genetic study Genome Genomics Glucose Guide prevention Individual Inflammation Insulin Interleukin-6 Intervention Life Style Link Malignant Neoplasms Measures Metabolic Methods Mucous Membrane Multiomic Data Obesity Participant Process Resources Risk Risk Factors Sampling Scanning Smoking Statistical Methods Stress Technology Testing Tissues Training Transcriptional Regulation Translating Translations Tumor Subtype Untranslated RNA Variant biobank cell type cohort colorectal cancer prevention colorectal cancer risk computerized tools data integration deep learning deep learning model epidemiologic data ethnic diversity functional genomics gene discovery gene environment interaction genetic epidemiology genetic risk factor genetic variant genome-wide high dimensionality improved individualized prevention inflammatory marker innovation insight instrument interest lifestyle intervention modifiable risk multidisciplinary multimodality multiple omics novel personalized screening predictive marker preventive intervention racial diversity racial population risk prediction risk prediction model risk variant sex single cell technology study population translational potential

项目摘要

PROJECT SUMMARY/ABSTRACT Colorectal cancer (CRC) remains one of the leading causes of cancer-related deaths around the world. Many environmental risk factors and over 200 genetic risk variants have been identified for this complex, multifactorial disease. However, despite the strong biological rationale for the importance and abundance of gene-environment (GxE) interactions, the extent to which environmental risk factors (broadly defined here as lifestyle, diet, obesity, drug use and intermediate biomarkers) modulate genetic risk factors is poorly understood. To achieve the promise of precision prevention, we urgently need to gain a deeper understanding of GxE interactions in CRC risk. Understanding which modifiable risk factors modulate genetic risk, which is fixed, provides biological insights and actionable targets for new prevention intervention strategies. To accelerate the discovery of GxE interactions in CRC risk and to take an important next step towards translation, we propose a comprehensive innovative approach that combines single-cell multi-omics data, individual-level harmonized epidemiological and clinical data, and genome-wide data from large, well-characterized, diverse study populations, with novel computational and statistical approaches. Dramatic improvements in single-cell multimodal omics technologies, combined with new computational tools based on powerful deep-learning modeling approaches now allow us to predict the impact of genetic variants on gene regulation in a cell-type- specific holistic manner. Because simultaneously measured single-cell gene expression (scRNA-seq) and chromatin accessibility (scATAC-seq) data for normal colorectal mucosa tissue is lacking for racially and ethnically diverse samples with detailed assessment of environmental risk factors, we propose in Aim 1 to generate such data for 50 individuals. This resource, together with other single cell multi-omics compendia for colorectal tissue (like HTAN), will be leveraged to develop functional prediction scores for genetic variants across the genome. In Aim 2, we will use these functional prediction scores to boost statistical power for discovery of novel GxE interactions. We will perform genome-wide GxE scans in over 230,000 racially and ethnically diverse CRC cases and controls across key environmental risk factors, including obesity, diabetes, smoking, alcohol, drug use, dietary factors and intermediate biomarkers linked to metabolic dysregulation and chronic inflammation. To expand the number of key risk factors we can evaluate, we will utilize existing genetic instruments. In Aim 3, we will comprehensively characterize and translate GxE interactions. To do so, we will stratify GxE findings by clinical factors, including age of onset, racial and ethnic group, sex, and tumor subtypes. Additionally, we will incorporate GxE interactions and genetically predicted biomarkers in a comprehensive trans-ancestral risk prediction model to improve prediction and provide actionable information to reduce the burden of CRC. Our community advisors have stressed the importance of including the interplay between genetic and environmental risk factors in risk prediction modeling to enhance the acceptance of risk prediction models in the community.

项目概要/摘要结直肠癌（CRC）仍然是世界各地癌症相关死亡的主要原因之一。已确定该复合物的许多环境风险因素和 200 多种遗传风险变异，多因素疾病。然而，尽管有强有力的生物学原理证明了基因-环境（GxE）相互作用，环境风险因素（此处广泛定义为生活方式、饮食、肥胖、药物使用和中间生物标志物）调节遗传风险因素的了解还很少。为了实现精准预防的承诺，我们迫切需要对GxE有更深入的了解 CRC 风险中的相互作用。了解哪些可改变的风险因素调节遗传风险，这是固定的，为新的预防干预策略提供生物学见解和可行的目标。为加速发现 CRC 风险中的 GxE 相互作用，并在转化方面迈出重要的下一步，我们提出了综合创新方法，结合单细胞多组学数据，个体水平协调来自大型、特征明确、多样化研究的流行病学和临床数据以及全基因组数据人口，采用新颖的计算和统计方法。单细胞的显着改进多模态组学技术，与基于强大深度学习的新计算工具相结合建模方法现在使我们能够预测遗传变异对细胞类型中基因调控的影响具体整体方式。因为同时测量单细胞基因表达 (scRNA-seq) 和缺乏种族和民族的正常结直肠粘膜组织的染色质可及性 (scATAC-seq) 数据对环境风险因素进行详细评估的多样化样本，我们在目标 1 中建议生成这样的样本 50 个人的数据。该资源以及其他结直肠组织单细胞多组学概要（如 HTAN），将被用来开发整个基因组遗传变异的功能预测分数。在目标 2，我们将使用这些功能预测分数来提高发现新型 GxE 的统计能力互动。我们将对超过 230,000 个不同种族和民族的 CRC 病例进行全基因组 GxE 扫描并控制关键环境风险因素，包括肥胖、糖尿病、吸烟、酗酒、吸毒、饮食因素和中间生物标志物与代谢失调和慢性炎症有关。到为了扩大我们可以评估的关键风险因素的数量，我们将利用现有的遗传仪器。在目标 3 中，我们将全面描述和转化 GxE 交互。为此，我们将按以下方式对 GxE 调查结果进行分层：临床因素，包括发病年龄、种族和民族、性别和肿瘤亚型。此外，我们将将 GxE 相互作用和基因预测的生物标志物纳入综合跨祖先风险预测模型，以改进预测并提供可操作的信息以减轻 CRC 的负担。我们的社区顾问强调了遗传和环境之间相互作用的重要性风险预测模型中的风险因素，以提高社会对风险预测模型的接受度。