Mathematical Optimization of Surveillance Ages to Intercept colitis-associated Colorectal cancer (MOSAIC)

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The practical goal of this project is to improve screening and surveillance strategies for Veterans living with inflammatory bowel disease (IBD) who are at increased risk of developing colorectal cancer. Although the risk in each individual patient is highly heterogenous, the VA surveillance program currently examines over 90,000 patients with IBD colitis under a fairly rigid “one-size-fits-all” paradigm: recommended colonoscopy every 1-3 years starting with an index screening 8 years after IBD diagnosis with hopes to detect early, treatable colitis- associated colorectal cancers (CA-CRC) before they are more dangerous. These intervals were based on minimal evidence because trials are difficult to perform, and they have not changed in over 30 years leading to thousands of unnecessary colonoscopies performed yearly in Veterans who have incredibly low risk of developing CA-CRC. Our study will address critical knowledge gaps in this field including 1) inherited genetic risk of CA-CRC in Veterans, and 2) timescales for cellular evolution that define ideal ‘windows of opportunity’ to intercept early cancers in IBD. Our team has extensive expertise in building computational tools for assessing screening and surveillance efficacy, deriving and validating clinical decision support tools for CA-CRC, and analyzing genomic evolution in IBD colitis. Our study will provide an unprecedented level of molecular detail that has not been achieved in any previous study of pre-cancer evolution in IBD colitis by leveraging thousands of genomes collected serially from UK patient cohorts. We will focus on risk prediction for patients with IBD colitis within the Million Veteran Program (MVP) and tailor risk prediction based on findings from genome-wide association studies that we will perform in the sub-cohort who developed CA-CRC. An impact of this work will be identification of genetic predictors of CA-CRC in Veterans using data from advanced multi-omic platforms. The long-term goal of this research is to shift the paradigm of cancer surveillance used both in the VA and more broadly to move beyond fixed intervals that mainly rely on presence/absence of dysplasia toward a conceptual model that incorporates cancer evolution mechanisms and genomics. Our main objective for this project is to apply these techniques for Veterans with IBD colitis, and herein we will design a novel framework, MOSAIC, for the Mathematical Optimization of Surveillance Ages to Intercept colitis-associated Colorectal cancer. To modernize outdated guidelines, we will first understand how CA-CRC arises in patients with IBD colitis using multiscale data, and then apply and validate this knowledge in a large VA cohort. The three specific aims for our project are: 1) Quantify IBD colitis incidence rates in MVP and derive polygenic risk scores; 2) Create mathematical models for IBD carcinogenesis that include biophysical details for evolution; and 3) Optimize patient surveillance strategies to effectively intercept CA-CRC. Through simulation of model-predicted optimal versus standard surveillance scenarios, we will use our model to quantify the number of colonoscopies avoided and the number of CA-CRCs successfully detected in early stages, and compare with MVP recorded outcomes within the VA Clinical Data Warehouse. The proposed project is innovative because we will infer critical evolutionary parameters in IBD colitis from genomic data for the first time, and we will build a novel risk prediction tool that will incorporate population-level incidence trends, polygenic risk in MVP, pathology findings at surveillance exams, and underlying evolutionary mechanisms within IBD tissue. This research is significant because it is expected to provide predictive models that incorporate dynamic biomarkers of CA-CRC progression in patients with IBD colitis to offer new strategies of risk-based surveillance. Importantly, study findings will inform future prospective studies to evaluate the impact of our enhanced, model-informed strategy versus usual care for identification of individuals who progress and develop high-risk lesions and CA-CRC on follow-up.