Precision Medicine in Inflammatory Bowel Disease: Refining the Clinical and Genomic Predictors of Response to Anti-IL-12/23 Therapy

炎症性肠病的精准医学：完善抗 IL-12/23 治疗反应的临床和基因组预测因子

• 批准号: 10662031
• 负责人: Kelly Colleen Cushing-Damm
• 金额: $ 16.81万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-04-17 至 2028-02-29
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10662031
• 关键词: Abdominal Infection; Affect; American; Attention; Autoimmune; Behavior; Biological; Cells; Chronic; Clinical; Clinical Data; Clinical Trials; Crohn' s disease; Data; Decision Trees; Digestive System Disorders; Disease; Disease Progression; Drug Targeting; Exposure to; Funding; Gastrointestinal Diseases; Gene Expression; Gene Expression Profiling; Genes; Genetic; Genetic Markers; Genetic Risk; Genetic Transcription; Genotype; Goals; Health; Health Care Costs; Heterogeneity; Immune; Inflammatory; Inflammatory Bowel Diseases; Institution; Interleukin-12; Intestinal Diseases; Intestinal Obstruction; Intra-abdominal; Investigation; Lead; Learning; Location; Machine Learning; Malignant Neoplasms; Mediating; Metadata; Molecular; Morbidity - disease rate; Natural Immunity; Outcome; Pathogenesis; Pathway Analysis; Pathway interactions; Patient Selection; Patients; Pattern; Performance; Pharmaceutical Preparations; Positioning Attribute; Predisposition; Price; Psoriasis; Research; Risk; Selection for Treatments; Severities; Signal Transduction; TNF gene; Testing; Therapeutic; Tissues; Training; Trees; Variant; adaptive immunity; biomarker identification; career development; cell type; chronic inflammatory disease; clinical heterogeneity; clinical predictors; cohort; cost; disorder control; evidence base; genetic variant; genome wide association study; genome-wide; genomic data; genomic predictors; improved; insight; interest; novel; personalized care; precision medicine; predicting response; prognostic; programs; response; response biomarker; risk variant; skin disorder; statistics; therapy outcome; tool; transcriptome; transcriptomics; treatment response

ABSTRACT Crohn’s disease (CD) is a chronic immune-mediated gastrointestinal disease characterized by significant clinical and molecular heterogeneity. The clinical heterogeneity is evidenced by the wide variation in disease location, severity, and behavior. The molecular heterogeneity is evidenced by the numerous genetic risk loci which have been identified to date. Fortunately, there are an increasing number of therapeutic options for the treatment of CD including blockade of tumor necrosis factor  and blockade of IL-12/23 signaling. Unfortunately, patients rapidly cycle through medications, often due to lack of response, further increasing morbidity and healthcare costs. Therefore, there is an urgent need for data matching the mechanism of the disease to the treatment target to guide treatment selection. Our long-term goal is to improve first-line therapy selection in CD as more therapies become available at biosimilar prices. In this career development proposal, I will focus on acquiring data which provides a deeper understanding of the clinical and molecular attributes which associate with response to anti-IL-12/23 therapy to improve drug positioning and first-line therapy selection for this class of therapy in CD. We have shown that CD patients with autoimmune skin disease preferentially respond to anti-IL-12/23 therapy. We will utilize machine learning to identify additional clinical patterns which associate with preferential response to therapy, then develop a decision tool to aid clinicians in selection of patients for first-line anti-IL-12/23 therapy. Then, we will investigate genomic predictors of response using both a targeted and genome wide approach. Finally, we will identify transcriptional modules which associate with drug response which will yield insight into the tissue cell signatures which associate with differential response. The scientific and training objectives outlined will provide me with the expertise needed to pursue independent investigation in the field of precision medicine, with a specific emphasis on the utilization of large scale clinical and genomic datasets to predict prognostic and therapeutic outcomes in CD.

摘要 克罗恩病（CD）是一种慢性免疫介导的胃肠道疾病，其特征在于显著的 临床和分子异质性。临床异质性由疾病的广泛变化证明 位置、严重性和行为。许多遗传风险位点证明了分子异质性 迄今为止已被确认。幸运的是，有越来越多的治疗选择， 治疗CD包括阻断肿瘤坏死因子β和阻断IL-12/23信号传导。 不幸的是，患者通常由于缺乏反应而快速循环用药，进一步增加了 发病率和医疗费用。因此，迫切需要数据匹配的机制， 疾病的治疗目标，以指导治疗选择。我们的长期目标是改善一线治疗 随着越来越多的疗法以生物仿制药的价格上市，CD中的选择越来越多。在这份职业发展计划中，我 将专注于获取数据，提供对临床和分子属性的更深入了解 其与对抗IL-12/23治疗的应答相关，以改善药物定位和一线治疗 选择这类治疗的CD。我们已经证明，患有自身免疫性皮肤病的CD患者 优先响应抗IL-12/23治疗。我们将利用机器学习来识别其他临床 模式与治疗的优先反应，然后开发一个决策工具，以帮助临床医生， 选择患者进行一线抗IL-12/23治疗。然后，我们将研究基因组预测因子， 使用靶向和全基因组方法的反应。最后，我们将确定转录模块 与药物反应相关，这将深入了解组织细胞特征， 差分响应概述的科学和培训目标将为我提供所需的专业知识， 在精准医学领域进行独立调查，特别强调利用 大规模临床和基因组数据集，以预测CD的预后和治疗结果。