Single-cell immune landscape in the oral dysplasia's malignant transformation

口腔异型增生恶性转化中的单细胞免疫景观

• 批准号: 10714554
• 负责人: Xiaoyuan Han
• 金额: $ 17.79万
• 依托单位: UNIVERSITY OF THE PACIFIC-STOCKTON
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2027-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10714554
• 关键词: Archives; Atlases; Biopsy; Cell Count; Cells; Clinical; Collaborations; Cytometry; Data; Dental; Dental Students; Development; Diagnosis; Dysplasia; Elasticity; Formalin; Goals; High grade dysplasia; Histologic; Histopathologic Grade; Human; Image; Imaging technology; Immune; Immunoassay; Immunofluorescence Immunologic; Lesion; Malignant - descriptor; Malignant Neoplasms; Microscopy; Mild Dysplasia; Myelogenous; Myeloid-derived suppressor cells; Natural Immunity; Nature; Neighborhoods; Oral; Oral Pathology; Paraffin Embedding; Patients; Phagocytes; Phenotype; Play; Prognosis; Prognostic Marker; Proliferating; Proteins; Recurrence; Research; Risk; Risk Factors; Role; Sampling; School Dentistry; Severities; Signal Transduction; Stains; Surface; System; Tongue; Translational Research; Tumor-associated macrophages; Universities; bioinformatics pipeline; biomarker identification; cancer invasiveness; high dimensionality; immune cell infiltrate; improved; innovation; machine learning algorithm; machine learning prediction; molecular marker; mortality; mouse model; mouth squamous cell carcinoma; multidisciplinary; multiplexed imaging; neighborhood association; novel; novel therapeutics; oral dysplasia; pre-doctoral; predictive modeling; premalignant; student participation; tumor; tumor-immune system interactions

About 7.9-27.6% of oral dysplasia, a premalignant lesion, transit to the invasive oral cavity squamous cell carcinoma (OCSCC). Prognostic biomarkers are critically needed to determine patients with oral dysplastic lesions at risk for malignant transformation and to guide the targeted development of novel therapies. Our goal is to establish a single-cell atlas of premalignant immune microenvironment (PRIME) in oral dysplasia and to identify immune features that predict the malignant transformation to OCSCC. We propose an innovative approach that combines high-dimensional imaging mass cytometry (IMC) and machine learning predictive modeling (iEN) to analyze a total of ~200 Formalin-Fixed Paraffin-Embedded (FFPE) patient tongue biopsies from the Oral Pathology Archive at the University of the Pacific (UOP), Arthur A. Dugoni School of Dentistry. IMC is a new multiplex imaging technology which combines high- dimensional mass cytometry with microscopy. Immune Elastic Net (iEN) is a machine learning algorithm specifically developing for the analysis of high-dimensional mass cytometry data. We plan to identify immune features that differentiate oral dysplasia severity (Aim 1) and predict OCSCC malignant transformation (Aim 2). In addition, we will analyze the iEN-selected immune features at UOP Han’s lab by conducting multiplex immunofluorescence (mIF) staining on the whole sections (Aim 1&2) to validate and generalize the IMC findings. The proposed research will establish immune landscape in oral dysplasia and identify biomarkers to predict the malignant transformation. It also provides an opportunity for predental or dental students participating in translational research and collaborating with multidisciplinary team to identify biomarkers to improve oral pathology diagnosis.

口服异常增生的7.9-27.6％（一种前病变）转移到浸润性口腔鳞状细胞 癌（OCSCC）。确定口服患者的预后生物标志物是至关重要的 发育不良病变有恶性转化的风险，并指导新颖的靶向发展 疗法。我们的目标是建立一个初预免疫微环境（Prime）的单细胞图集 在口腔发育不良，并确定预测OCSCC恶性转化的免疫特征。 我们提出了一种创新的方法，该方法结合了高维成像质量细胞术（IMC）和 机器学习预测建模（IEN）总共分析了约200个福尔马林固定石蜡 （FFPE）来自太平洋大学（UOP）的口腔病理档案库的患者舌头活检， Arthur A. Dugoni牙科学院。 IMC是一种新的多重成像技术，结合了高度 尺寸质量细胞仪与显微镜。免疫弹性网（IEN）是一种机器学习算法 专门开发用于分析高维质量细胞仪数据。我们计划确定 免疫特征可区分口服发育不良严重程度（AIM 1）并预测OCSCC恶性肿瘤 转型（目标2）。此外，我们将在UOP Han的实验室分析IEN选择的免疫功能 通过进行多重免疫荧光（MIF）染色（AIM 1＆2）以验证 并概括IMC的发现。拟议的研究将在口腔中建立免疫景观 发育不良并识别生物标志物以预测恶性转化。它还提供了机会 对于参与翻译研究并与之合作的预期或牙科学生 多学科团队识别生物标志物以改善口腔病理学诊断。