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

大约7.9-27.6%的口腔异型增生是一种癌前病变，向侵袭性口腔鳞状细胞转移 癌症(OCSCC)。预后生物标记物是确定口腔癌患者的关键 异型增生性病变有恶变的风险并指导新的靶向开发 治疗。我们的目标是建立癌前免疫微环境(PRIME)的单细胞图谱 并确定预测口腔鳞状细胞癌恶变的免疫特征。 我们提出了一种创新的方法，将高维成像质量细胞术(IMC)和 机器学习预测建模(IEN)分析总共200个福尔马林固定的石蜡包埋 (FFPE)患者舌部活检，来自太平洋大学(UOP)口腔病理档案馆， 亚瑟·A·杜戈尼牙科学院。IMC是一种新的多路成像技术，它结合了高性能的 显微镜下的三维质量细胞术。免疫弹性网络是一种机器学习算法 专门为分析高维质量细胞仪数据而开发。我们计划找出 鉴别口腔异型增生严重程度和预测口腔鳞癌恶性程度的免疫特征(目标1) 转型(目标2)。此外，我们将在UOP han的实验室分析Ien选择的免疫特征 通过对整个切片(目标1和2)进行多重免疫荧光(MIF)染色来验证 并对IMC的调查结果进行概括。拟议的研究将在口腔中建立免疫环境。 并找出预测恶变的生物标志物。它还提供了一个机会 为牙科预科或牙科学生参与翻译研究并与 多学科团队确定生物标记物以提高口腔病理诊断水平。

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.