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

监测年龄的数学优化以拦截结肠炎​​相关结直肠癌 (MOSAIC)

• 批准号: 10581069
• 负责人: Kathleen M. Curtius
• 金额: --
• 依托单位: VA SAN DIEGO HEALTHCARE SYSTEM
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2027-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10581069
• 关键词: Age; Assessment tool; Barrett Esophagus; Biological Markers; Biophysics; Biopsy; Calibration; Cancer Model; Cirrhosis; Clinical; Clinical Data; Colitis; Colitis associated colorectal cancer; Colon; Colonic Diseases; Colonoscopy; Colorectal Adenoma; Colorectal Cancer; Communication; DNA Sequence Alteration; Dangerousness; Data; Detection; Development; Diagnosis; Dysplasia; Early Diagnosis; Epithelium; Esophageal Adenocarcinoma; Evolution; Foundations; Future; Genetic Risk; Genome; Genomics; Goals; Guidelines; Hereditary Nonpolyposis Colorectal Neoplasms; High-Risk Cancer; Incidence; Individual; Inflammatory Bowel Diseases; Inherited; Intercept; Intervention; Knowledge; Lesion; Life Style; Malignant Neoplasms; Maps; Mathematics; Measurement; Modeling; Modernization; Molecular; Onset of illness; Outcome; Pancreas; Pathology; Patients; Population; Population Programs; Positioning Attribute; Predictive Analytics; Prevalence; Process; Prospective Studies; Recommendation; Research; Risk; Schedule; Screening for cancer; Statistical Models; Surveillance Program; Techniques; Time; Tissues; Translating; Ulcerative Colitis; Update; Veterans; Work; cancer prevention; cancer risk; carcinogenesis; clinical data warehouse; clinical decision support; clinical predictors; cohort; colorectal cancer prevention; colorectal cancer progression; colorectal cancer screening; computerized tools; cost; data modeling; design; disease diagnosis; early screening; epidemiologic data; flexibility; follow-up; gastric intestinal metaplasia; genetic predictors; genome sequencing; genome wide association study; genomic data; health care service; high risk; improved; indexing; individual patient; innovation; mathematical methods; mathematical model; models and simulation; mortality; multiple omics; multiscale data; novel; personalized care; polygenic risk score; precision medicine; predictive modeling; predictive tools; premalignant; prevent; prognostic; programs; risk prediction; risk variant; screening; shared decision making; simulation; success; support tools; surveillance data; surveillance strategy; tool; treatment as usual; trend; web-based tool; whole genome

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.

该项目的实际目标是改善对退伍军人的筛查和监测战略， 炎症性肠病（IBD）患者患结直肠癌的风险增加。虽然风险 在每个病人的个体是高度异质性，退伍军人管理局监测计划目前检查超过90000 在相当严格的“一刀切”模式下，IBD结肠炎患者：建议每1-3次结肠镜检查 从IBD诊断后8年开始进行指数筛查，希望能发现早期可治疗的结肠炎- 相关的结直肠癌（CA-CRC）之前，他们更危险。这些间隔是基于 证据很少，因为审判很难进行，而且30多年来没有改变， 每年在退伍军人中进行数千次不必要的结肠镜检查，这些退伍军人的结肠镜检查风险非常低， 开发CA-CRC。我们的研究将解决这一领域的关键知识差距，包括1）遗传遗传 退伍军人中CA-CRC的风险，以及2）细胞进化的时间尺度，其定义了理想的“机会窗口”， 在IBD中阻断早期癌症。我们的团队在构建用于评估的计算工具方面拥有广泛的专业知识 筛选和监测疗效，推导和验证CA-CRC的临床决策支持工具，以及 分析IBD结肠炎的基因组进化。我们的研究将提供前所未有的分子细节， 在IBD结肠炎癌前演变的任何先前研究中， 从英国患者队列中连续收集的基因组。我们将重点关注IBD结肠炎患者的风险预测 在百万退伍军人计划（MVP）中，并根据全基因组研究结果定制风险预测 我们将在发生CA-CRC的子队列中进行相关研究。这项工作的影响将 使用来自先进的多组学平台的数据识别退伍军人中CA-CRC的遗传预测因子。 这项研究的长期目标是改变退伍军人管理局和卫生部使用的癌症监测模式， 并且更广泛地说，超越主要依赖于存在/不存在异型增生的固定间隔， 概念模型，结合癌症演变机制和基因组学。我们的主要目标是 项目是将这些技术应用于患有IBD结肠炎的退伍军人，在此我们将设计一个新的框架， MOSAIC，用于监测年龄的数学优化，以拦截结肠炎相关 大肠癌。为了使过时的指南现代化，我们将首先了解CA-CRC是如何在患者中产生的。 IBD结肠炎使用多尺度数据，然后在一个大型VA队列中应用和验证这一知识。三 我们项目的具体目标是：1）量化MVP中IBD结肠炎的发病率并得出多基因风险评分; 2)创建IBD致癌作用的数学模型，包括进化的生物物理细节;以及3） 优化患者监测策略，有效拦截CA-CRC。通过模拟模型预测 最佳与标准监测方案，我们将使用我们的模型来量化结肠镜检查的数量 避免和在早期阶段成功检测到的CA-CRC的数量，并与记录的MVP进行比较 VA临床数据仓库中的结果。该项目是创新的，因为我们将推断关键 这是首次从基因组数据中获得IBD结肠炎的进化参数，我们将建立一种新的风险预测方法， 该工具将纳入人群水平的发病率趋势、MVP中的多基因风险、 监测检查和IBD组织内的潜在进化机制。这项研究意义重大 因为它有望提供结合CA-CRC进展的动态生物标志物的预测模型 在IBD结肠炎患者中，提供基于风险的监测新策略。重要的是，研究结果将提供信息 未来的前瞻性研究，以评估我们的增强，模型知情的策略与常规护理的影响 用于识别进展和发展高风险病变和CA-CRC的个体。