Glycosylation and Immune Evasion in Urologic Tumors

项目概要/摘要 肿瘤细胞逃避免疫系统的能力是众所周知的，但了解甚少。 早期癌症发展的现象。尽管已经出现了有希望的免疫治疗策略， 通过靶向这些相互作用，对受体-配体的完整库知之甚少。 导致免疫逃避的相互作用。我们试图了解糖基化，一个公认的 癌症中的异常修饰，帮助癌细胞逃避免疫系统。鉴定 调节免疫功能的糖蛋白可能导致新型疗法，也可能作为 伴随诊断生物标志物，以指导患者在早期时间点选择免疫疗法， 前列腺癌和透明细胞肾细胞癌。 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 内部开发的糖蛋白质组学方法，用于富集和鉴定癌性和 与病人的健康组织相匹配定量比较分析将揭示 唾液酸糖蛋白表达和阐明肿瘤中唾液酸结合蛋白的候选配体 这些微环境可能有助于免疫失活。这些糖蛋白质组的相关性 具有专注于糖基因表达的RNA-seq数据的数据集将支持特异性基因的分配。 糖型作为癌症生物标志物。其次，使用免疫组织化学和CODEX方法，我们将 分析肿瘤组织中唾液酸结合免疫球蛋白型凝集素（Siglec）受体蛋白的表达水平， 常驻免疫细胞，并将发现与RNA-seq数据以及免疫细胞标记物交叉关联。我们 还将探测肿瘤细胞表面上各种Siglec同种型的配体的存在，并获得空间分布。 关于它们在完整肿瘤组织中的免疫细胞上的分布的信息。对于任何标识为 突出显示在肿瘤环境中的免疫细胞上，我们将开发基于细胞的检测来探测 它们对肿瘤细胞免疫反应性的贡献。第三，我们将进行全基因组筛查， CRISPRi鉴定促进Siglecs与癌细胞结合的基因。最后，我们将把 来自目标1、2和3的数据集，包含组织上包含的较大组织样本组中的患者结局 微阵列，并评估其作为预后指标的效用。

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.