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

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.