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

项目摘要/摘要 肝硬变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 肝炎(AIH)是常见的病因。不幸的是，许多患者并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和个性化 在发展之前的治疗效果不佳。肝内巨噬细胞、肝窦内皮细胞 细胞(LSECs)和星状细胞(HSCs)可以极大地影响肝脏微环境的组成 和纤维化的发展。针对这些纤维化启动者的治疗方法正在II-III阶段进行研究 临床试验；然而，潜在的肝脏微环境和患者在这些细胞和 在治疗前不考虑这些靶点的表达。我们使用尖端的光谱成像技术 显微镜结合纳米串技术评估这些细胞和相关的促纤维化基因 在最初诊断时，同一患者的肝活检组织中的表达谱。从我们的肝组织生物库， 我们确定了225例不同慢性肝病(NASH、AALD、CHC和AIH)的活检标本 在确诊时，患者进行了充分的随访，进行了重复活检或肝脏检查 替换(对于那些后来发展为肝硬变的人)。大多数患者未见肝纤维化进展。 随着时间的推移(n=150)，部分患者迅速发展为肝硬变(n=75)。我们使用上述平台来评估 这些患者在最初的肝活检中肝脏微环境的差异。我们建议测试一下 假设在病程早期肝微环境有明显的促纤维化变化的患者 疾病的发生率预示着他们发展成纤维化的倾向。初步数据显示，最初的肝活检来自 容易迅速发展为肝硬变的患者增加了促纤维化的巨噬细胞(例如， 分别为Mac387和CD163)，增强了Mac-LSEC-HSCs的细胞相互作用，增加了表达 治疗相关靶点(如CCR2和Galectin 3)和增加的促炎/促纤维化基因 表达谱(例如CCL2、肿瘤坏死因子和转化生长因子-β)。成像和分子生物信息学将用于数据 分析。对于目标1，我们将使用三个面板来表型肝内Mac并检查它们之间的相互作用 LSECs和HSCs，并将评估促纤维化治疗相关靶点表达的差异。为了达到目标 2，我们将分析另一半活检组织中200多个与Mac-LSEC-HSC相关和促纤维化的基因 目标1.建议的方法将为我们的长期目标奠定基础：目标个性化 治疗方法(如西尼昔洛韦或奥替胆酸)，类似于免疫治疗的应答方式 通过对癌症患者的组织进行染色来预测。在这项回溯性纵向研究中，我们将确定 哪种平台(光谱成像-Aim1与纳米线-Aim 2)在确定电势方面表现最好 纤维化进展的目标和未来临床实施的最具成本效益。

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.