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

摘要 克罗恩病(CD)是一种慢性免疫介导的胃肠道疾病，其特征是 临床和分子的异质性。疾病的广泛变化证明了临床上的异质性。 地点、严重程度和行为。分子的异质性被大量的遗传风险基因所证明。 到目前为止已经被确认的。幸运的是，有越来越多的治疗选择 CD的治疗包括阻断肿瘤坏死因子和阻断IL-12/23信号传导。 不幸的是，患者经常由于缺乏反应而快速循环服药，进一步增加 发病率和医疗保健成本。因此，迫切需要与 以疾病为治疗靶点，指导治疗选择。我们的长期目标是改善一线治疗 随着更多的治疗方法以生物相似的价格出现，CD中的选择。在这份职业发展建议中，我 将专注于获取数据，以提供对临床和分子属性的更深入了解 它们与抗IL-12/23治疗的反应有关，以改善药物定位和一线治疗 CD中的这一类治疗的选择。我们已经证明CD患者患有自身免疫性皮肤病 优先接受抗IL-12/23治疗。我们将利用机器学习来识别其他临床 与对治疗的优先反应相关的模式，然后开发决策工具来帮助临床医生 选择一线抗IL-12/23治疗的患者。然后，我们将研究基因组预测因素 同时使用靶向和全基因组方法进行反应。最后，我们将确定转录模块 它们与药物反应有关，这将使我们深入了解与 不同的反应。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，重点是利用 大规模临床和基因组数据集用于预测CD的预后和治疗结果。

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.