Paneth cell phenotype as a predictive biomarker for ulcerative colitis

摘要 溃疡性结肠炎（UC;炎症性肠病的一种亚型）管理的挑战之一 缺乏一种预测性生物标志物，可以帮助对患者进行个性化治疗策略的分层。我们 先前已经表明，在克罗恩病（CD;另一种炎症性肠病亚型）中， 小肠潘氏细胞的形态表型很容易预测CD术后患者的预后。 鉴于CD和UC之间共有的遗传病因学和临床特征，我们假设潘氏细胞 表型也将预测UC的结果。我们的长期目标是确定 潘氏细胞表型可以预测结果炎性肠病。该补助金的目的是 确定潘氏细胞表型在多大程度上预测UC患者回肠并发症的发生。 中心假设是，在UC患者接受全结肠切除术和回肠袋肛门吻合术， （IPAA），在结肠切除术时获得的潘氏细胞表型将预测结肠袋炎和结肠癌的发展。 回肠袋中的新发CD。我们的基本原理是潘氏细胞表型将提供更好的结果预测， 目前的做法。我们的具体目标将测试以下假设：（目标1）潘氏细胞表型相关 IPAA UC患者的回肠并发症;（目的2）深度学习将减少观察者变异， 提高了手术病理学标本中潘氏细胞表型分析的精确性和可重复性， 鉴定与潘氏细胞功能相关新的临床因素;（目的3）与潘氏细胞功能相关的血清标志物 潘氏细胞表型可用于预测UC的预后。在结束时，我们将了解的作用 潘氏细胞在调节IBD病程中的作用。这一贡献意义重大，因为它将 建立潘氏细胞表型作为IBD关键预测生物标志物。建议的研究是创新的 因为我们使用最先进的深度学习方法来构建成像分析管道， 影响潘氏细胞功能的新的临床因素。识别具有先天免疫的上皮细胞如何 功能影响疾病过程将提供对其他炎症性疾病的了解。

ABSTRACT One of the challenges for the management of ulcerative colitis (UC; a subtype of inflammatory bowel disease) is the lacking of a predictive biomarker that can help stratify patients for personalized treatment strategies. We have previously shown that in Crohn’s disease (CD; another subtype of inflammatory bowel disease), the morphologic phenotype of small intestinal Paneth cells readily predicts outcome in post-surgical CD patients. Given the shared genetic etiology and clinical features between CD and UC, we hypothesize that Paneth cell phenotype will also predict outcome in UC. Our long-term goal is to define the clinical indications of inflammatory bowel disease of which Paneth cell phenotype can predict outcome. The objective of this grant is to determine to the extent of which Paneth cell phenotype predicts development of ileal complications in UC. The central hypothesis is that in UC patients undergoing total colectomy and ileal pouch anal anastomosis (IPAA), the Paneth cell phenotype obtained at time of colectomy will predict development of pouchitis and de novo CD in the ileal pouch. Our rationale is that Paneth cell phenotype will offer better outcome prediction over current practice. Our specific aims will test the following hypotheses: (Aim1) Paneth cell phenotype correlates with ileal complications in UC patients with IPAA; (Aim 2) Deep learning will reduce observer variation and enhance rigor and reproducibility of Paneth cell phenotype analysis in surgical pathology specimens, and also identify novel clinical factors that correlate with Paneth cell function; (Aim 3) Serum markers that correlate with Paneth cell phenotype could be used to predict outcome in UC. Upon conclusion, we will understand the role for Paneth cell function in modulating disease course in IBD. This contribution is significant since it will establish Paneth cell phenotype as a critical predictive biomarker for IBD. The proposed research is innovative because we use state-of-the-art deep learning approach to build an imaging analysis pipeline, and to identify novel clinical factors that affect Paneth cell functions. Identifying how epithelial cells with innate immune function affect disease course will provide insight into other inflammatory disorders.