Leveraging Omics and Electronic Health Records Data to study Colorectal Adenoma genetics and Drug Repurposing

Colorectal cancer (CRC) arises mostly from pre-existing colorectal adenoma (CRA). Removal of these precancerous lesions significantly reduces CRC incidence. However, ~30% of CRA patients will develop metachronous (recurrent) adenomas after their initial polypectomy. Yet, understanding of the genetic basis of CRA and recurrence, and identification of therapeutic drugs are currently very limited. Addressing the gap in genetic studies of CRA, we have recently established genome-wide association studies (GWAS) of ~8,000 CRA cases from European Americans (EA) and African Americans (AA) from the Vanderbilt DNA BioBank (BioVU). Furthermore, we have recently established Vanderbilt Colonoscopy Cohort of CRA cases after polypectomy (N=76,664), though a large-scale analysis of electronic health records (EHRs) and pathology reports. In this application, we propose to extend our efforts to establish the first large GWAS of EA and AA leveraging unique resources primarily from the BioVU, the Mass General Brigham (MGB) Biobank and All of Us, and conduct transcriptome-wide association studies (TWAS), methylome-wide association studies (MeWAS) and proteome-wide association studies (PWAS) to identify risk variants, DNA methylations, genes and proteins for CRA and recurrence. Specific aims are: Aim 1: Conduct GWAS, MeWAS, TWAS and PWAS among 25,000 CRA cases (~9,000 recurrence) and 140,000 controls in EA and 6,500 CRA cases (~2,000 recurrence) and 47,000 controls in AA participants. Existing data on colon or adenoma tissue DNA methylation, RNA-seq, and proteomics from EA (n=1,538) and from AA (n=465) from our parent studies, will be used to build racial-specific prediction models for DNA methylation, alternative splicing, and alternative polyadenylation, gene and protein expression. These prediction models will be applied to the GWAS data to identify risk DNA methylations, genes and proteins, both overall and by recurrence of CRA in AA and EA, respectively. We will integrate findings from EA and AA to identify risk genes across both populations and those contributing to racial disparity. We will also perform omics-based drug analysis to identify potential therapeutic drugs for CRA recurrence. Aim 2: Identify repurposing drug candidates to reduce CRA recurrence after polypectomy: Combing Vanderbilt (N=76,664) and MGB (N=~55,000) Colonoscopy Cohorts, we have identified ~39,400 CRA patients (~30% of total CRA cases after polypectomy) with a surveillance colonoscopy at least one year after previous polypectomy (with ~31,700 CRA recurrence and ~2,600 CRC). We will build a machine learning and statistical framework upon real-world EHRs to create and compare treated and control patient groups, and systematically screen repurposable drugs to reduce CRA recurrence and/or CRC risk. Aim 3: Perform functional assays to test the efficacy of six promising drugs in both in-vitro and mouse models. Given the unique resources and methodological strengths, we anticipate that the study will have significant potential for the optimization of colorectal polyp surveillance and therapeutic intervention of CRA/CRC.