Glycosylation and Immune Evasion in Urologic Tumors

项目摘要/摘要 肿瘤细胞逃避免疫系统的能力是众所周知的，但知之甚少。 癌症早期发展中的现象。尽管出现了前景看好的免疫治疗策略 针对这些相互作用，我们对受体-配体的完整谱系知之甚少。 导致免疫逃避的相互作用。我们试图理解糖基化是如何形成的 癌症中的异常修饰，帮助癌细胞逃避免疫系统。身份识别 调节免疫功能的糖蛋白可能会导致新型热病，也可以作为 辅助诊断生物标志物，以指导患者在早期时间点选择免疫疗法 前列腺癌和肾透明细胞癌。 首先，因为已知唾液酸在癌细胞表面过度表达，我们将使用完整的 内部开发的糖蛋白组学方法用于丰富和鉴定癌症和癌症患者的唾液糖蛋白 与病人的健康组织相匹配。定量的比较分析将揭示 唾液酸结合蛋白在肿瘤中的表达及候选配体的阐明 可能导致免疫失活的微环境。这些糖蛋白组学的相关性 具有集中于糖基因表达的RNA-seq数据的数据集将支持指定特定的 糖类作为癌症生物标记物。第二，使用免疫组织化学和CODEX方法，我们将 分析肿瘤表面唾液酸结合免疫球蛋白型凝集素(Siglec)受体蛋白的表达水平 并将这一发现与rna-seq数据以及免疫细胞标记物相互关联。我们 还将探测肿瘤细胞表面各种Siglec亚型的配体的存在，并获得空间 关于它们在完整的肿瘤组织中免疫细胞上分布的信息。对于标识为 突出地显示在肿瘤环境中的免疫细胞上，我们将开发基于细胞的分析来探测 它们对肿瘤细胞免疫反应的贡献。第三，我们将使用以下工具进行全基因组筛选 CRISPRi以确定促进Siglecs与癌细胞结合的基因。最后，我们将关联 来自AIMS 1、2和3的数据集，其中包含组织上包含的更大一组组织样本中的患者结果 微阵列，并评估其作为预后指标的实用性。

PROJECT SUMMARY/ABSTRACT The ability of tumor cells to evade the immune system is a well-known, yet poorly understood phenomenon in early cancer development. Despite promising immunotherapy strategies that have emerged from targeting these interactions, there is relatively little known about the complete repertoire of receptor-ligand interactions that contribute to immune evasion. We seek to understand how glycosylation, a well-established aberrant modification in cancer, aids cancer cells in evading the immune system. Identification of glycoproteins that modulate immune function could lead to new types of therpaies and could also serve as companion diagnostic biomarkers to guide patient selection of immunotherapies at an early time point in prostate cancer and clear cell renal cell carcinoma. First, because sialic acid is known to be overexpressed on the surface of cancer cells, we will use intact glycoproteomics methods developed in-house to enrich and identify sialoglycoproteins from cancerous and matched healthy tissues from patients. Quantitative comparative analyses will reveal changes in sialoglycoprotein expression and illuminate candidate ligands for sialic acid-binding proteins in the tumor microenvironment that potentially contribute to immune inactivation. Correlation of these glycoproteomic datasets with RNA-seq data focused on glycogene expression will bolster the assignment of specific glycoforms as cancer biomarkers. Second, using immunohistochemistry and CODEX methods, we will analyze expression levels of sialic acid-binding immunoglobulin-type lectin (Siglec) receptor proteins on tumor- resident immune cells and cross-correlate the findings with RNA-seq data as well as immune cell markers. We will also probe for the presence of ligands for various Siglec isoforms on tumor cell surfaces and obtain spatial information about their distribution on immune cells in intact tumor tissue. For any Siglecs identified as prominently displayed on immune cells in the tumor environment, we will develop cell-based assays to probe their contribution to tumor cell immunoreactivity. Third, we will perform a genome-wide screening using CRISPRi to identify genes that facilitate the binding of Siglecs to cancer cells. Finally, we will correlate the datasets from Aims 1, 2, and 3 with patient outcomes in a larger set of tissue samples contained on a tissue microarray, and evaluate their utility as prognostic indicators.