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.

约 7.9-27.6% 的口腔发育不良（一种癌前病变）会转移至侵袭性口腔鳞状细胞 癌（OCSCC）。迫切需要预后生物标志物来确定患有口腔疾病的患者 具有恶变风险的发育不良病变，并指导新型药物的靶向开发 疗法。我们的目标是建立癌前免疫微环境的单细胞图谱（PRIME） 口腔异型增生并确定预测 OCSCC 恶变的免疫特征。 我们提出了一种创新方法，将高维成像质谱流式细胞术 (IMC) 与 机器学习预测模型 (iEN) 可分析总共约 200 个福尔马林固定石蜡包埋的样本 (FFPE) 来自太平洋大学 (UOP) 口腔病理学档案馆的患者舌活检， 亚瑟·A·杜戈尼牙科学院。 IMC 是一种新型多重成像技术，结合了高 显微镜下的三维质量细胞计数。免疫弹性网络（iEN）是一种机器学习算法 专门为分析高维质谱流式数据而开发。我们计划确定 区分口腔发育不良严重程度（目标 1）并预测 OCSCC 恶性的免疫特征 转型（目标 2）。此外，我们将在 UOP Han 实验室分析 iEN 选择的免疫特征 通过对整个切片进行多重免疫荧光 (mIF) 染色（目标 1&2）来验证 并概括 IMC 的发现。拟议的研究将建立口腔免疫景观 发育不良并识别生物标志物来预测恶性转化。也提供了一个机会 适合参加转化研究并与之合作的牙医预科或牙科学生 多学科团队识别生物标志物以改善口腔病理诊断。