ANALYSIS OF INTRAHEPATIC MACROPHAGE PROFILES FOR PREDICTING RISK OF FIBROSIS DEVELOPMENT IN PATIENTS WITH DIFFERENT TYPES OF CHRONIC LIVER DISEASE

项目概要/摘要 肝硬化和肝细胞癌正在增加健康和经济负担。非酒精性脂肪肝 疾病（NAFLD/NASH）、酒精相关性肝病（AALD）、慢性肝炎（CHC）和自身免疫性 肝炎（AIH）是常见病因。不幸的是，许多患者不坚持推荐的生活方式 因此，我们需要更好的技术来预测纤维化进展的风险， 在出现不良结果之前进行治疗。肝内巨噬细胞（Macs），肝窦内皮 细胞（LSEC）和星状细胞（HSC）可以极大地影响肝脏微环境的组成 和纤维化的发展。针对这些纤维化引发剂的治疗正在研究中， 然而，这些细胞中的潜在肝脏微环境和患者变异性， 在治疗前不考虑这些靶点的表达。我们使用最先进的光谱成像技术 显微镜结合NanoString技术来评估这些细胞和相关的促纤维化基因 在同一患者的肝活检中的表达谱在最初诊断时。从我们的肝脏组织生物库， 我们确定了225例不同慢性肝病（NASH、AALD、CHC和AIH）的活检标本， 在诊断时，来自通过重复活检或肝脏进行了充分随访的患者 替代（对于那些后来发展为肝硬化的人）。大多数显示肝纤维化无进展 随着时间的推移（n = 150），而一部分迅速发展为肝硬化（n = 75）。我们使用上述平台来评估 这些患者在其初始肝活检中的肝微环境的差异。我们建议测试 假设患者在病程早期肝脏微环境中存在可确定的促纤维化变化 疾病的发生率预测了他们发展纤维化的倾向。初步数据显示， 具有快速发展肝硬化倾向的患者具有增加的促纤维化巨噬细胞（例如， Mac 387+和CD 163+），增强Mac-LSEC-HSC的细胞相互作用，增加表达 治疗相关的目标（例如，CCR 2和半乳糖凝集素3）和促炎/促纤维化基因增加 表达谱（例如，CCL 2、TNF和TGF-β）。成像和分子生物信息学将用于数据 分析。对于目标1，我们将使用三组表型肝内MAC并检查它们的相互作用 与LSEC和HSC，并将评估促纤维化治疗相关靶点的表达差异。为宗旨 2，我们将分析另一半活检组织中超过200个Mac-LSEC-HSC相关和促纤维化基因， 目标1。所提出的方法将为我们的长期目标奠定基础： 治疗（例如，赛尼克韦罗或奥贝胆酸），类似于对免疫疗法的应答的方式。 通过癌症患者的组织染色来预测。在这项回顾性纵向研究中，我们将确定 哪种平台（光谱成像-Aim 1与NanoString-Aim 2）在确定潜在风险方面性能最佳 纤维化进展的目标，并且对于未来的临床实施是最具成本效益的。

PROJECT SUMMARY/ABSTRACT Cirrhosis and hepatocellular carcinoma are increasing health and economic burdens. Non-alcoholic fatty liver disease (NAFLD/NASH), alcohol-associated liver disease (AALD), chronic hepatitis (CHC), and autoimmune hepatitis (AIH) are common etiologies. Unfortunately, many patients do not adhere to recommended life style modifications, thus, we need better techniques for predicting risk of fibrosis progression and personalizing therapies prior to development of poor outcomes. Intrahepatic macrophages (Macs), liver sinusoidal endothelial cells (LSECs), and stellate cells (HSCs) can greatly influence the composition of the hepatic microenvironment and development of fibrosis. Therapies targeting these initiators of fibrosis are being investigated in phase II-III clinical trials; however, the underlying hepatic microenvironment and patient variability in these cells and expression of these targets is not being considered prior to treatment. We use cutting-edge spectral imaging microscopy combined with NanoString technology to evaluate these cells and associated pro-fibrotic gene expression profiles in the same patient's liver biopsy at the time of initial diagnosis. From our liver tissue biobank, we identified 225 biopsies with different chronic liver diseases (NASH, AALD, CHC, and AIH) that were collected at the time of diagnosis from patients that had adequate follow-up either with a repeat biopsy or by liver replacement (for those that later developed cirrhosis). The majority showed no progression of hepatic fibrosis over time (n = 150) while a portion rapidly developed cirrhosis (n = 75). We use the above platforms to assess differences in these patients' hepatic microenvironments in their initial liver biopsies. We propose to test the hypothesis that patients with definable pro-fibrotic variations in their hepatic microenvironment early in the course of disease predicts their propensity to develop fibrosis. Preliminary data showed that initial liver biopsies from patients with a predisposition to rapidly develop cirrhosis have increased profibrotic macrophages (e.g., Mac387+ and CD163+, respectively), enhanced cellular interactions of Mac-LSEC-HSCs, increased expression of therapy-related targets (e.g., CCR2 and galectin 3) and increased pro-inflammatory/pro-fibrotic gene expression profiles (e.g., CCL2, TNF, and TGF-beta). Imaging and molecular bioinformatics will be used for data analyses. For Aim 1, we will use three panels to phenotype intrahepatic Macs and examine their interactions with LSECs and HSCs, and will assess differences in expression of pro-fibrotic therapy-related targets. For Aim 2, we will analyze over 200 Mac-LSEC-HSC-related and pro-fibrotic genes in the other half of the biopsy from Aim 1. The proposed approach will lay the groundwork for our long-term objective: personalization of targeted therapies (e.g., cenicriviroc or obeticholic acid), similar to the manner in which the response to immunotherapy is predicted by staining of tissue in patients with cancer. In this retrospective longitudinal study, we will determine which platform (Spectral imaging-Aim1 vs. NanoString-Aim 2) is the most performant for determining potential targets of fibrosis progression and most cost efficient for clinical implementation in the future.