Elucidating mechanisms of cellular communication critical for head and neck cancer progression and metastasis.

阐明对头颈癌进展和转移至关重要的细胞通讯机制。

• 批准号: 10752228
• 负责人: Lina Kroehling
• 金额: $ 4.21万
• 依托单位: BOSTON UNIVERSITY (CHARLES RIVER CAMPUS)
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-11-01 至 2026-10-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10752228
• 关键词: Affect; Algorithms; Architecture; Atlases; Automobile Driving; B-Lymphocytes; Catalogs; Cell Communication; Cell Count; Cells; Clinical; Communication; Communication Methods; Communication Tools; Computing Methodologies; DNA Sequence Alteration; Data; Data Set; Death Rate; Development; Disease; Drug Targeting; Epithelium; Event; Fibroblasts; Gene Expression; Genes; Genetic Variation; Genotype; Head and Neck Cancer; Head and Neck Squamous Cell Carcinoma; Heterogeneity; Human; Human Papillomavirus; Immunohistochemistry; Incidence; Langerhans cell; Ligands; Link; Malignant Neoplasms; Molecular; Morbidity - disease rate; Neoplasm Metastasis; Outcome; Pathogenesis; Pathway interactions; Patients; Phenotype; Play; Plug-in; Population; Publishing; Resistance; Resolution; Role; Sampling; Signal Transduction; Specificity; Squamous cell carcinoma; Survival Rate; Tissues; Tumor Subtype; Tumor Tissue; Tumor stage; Validation; cancer initiation; carcinogenesis; cell population study; cell type; data resource; experimental study; human RNA sequencing; human tissue; immune cell infiltrate; in silico; innovation; insight; intercellular communication; knock-down; monocyte; mouse model; mouth squamous cell carcinoma; multiple datasets; neoplastic cell; neutrophil; overexpression; programs; receptor; repository; single cell analysis; single-cell RNA sequencing; stem; stem-like cell; targeted treatment; therapy design; therapy resistant; tool; transcriptomics; tumor; tumor growth; tumor heterogeneity; tumor microenvironment; tumor progression; Affect; Algorithms; Architecture; Atlases; Automobile Driving; B-Lymphocytes; Catalogs; Cell Communication; Cell Count; Cells; Clinical; Communication; Communication Methods; Communication Tools; Computing Methodologies; DNA Sequence Alteration; Data; Data Set; Death Rate; Development; Disease; Drug Targeting; Epithelium; Event; Fibroblasts; Gene Expression; Genes; Genetic Variation; Genotype; Head and Neck Cancer; Head and Neck Squamous Cell Carcinoma; Heterogeneity; Human; Human Papillomavirus; Immunohistochemistry; Incidence; Langerhans cell; Ligands; Link; Malignant Neoplasms; Molecular; Morbidity - disease rate; Neoplasm Metastasis; Outcome; Pathogenesis; Pathway interactions; Patients; Phenotype; Play; Plug-in; Population; Publishing; Resistance; Resolution; Role; Sampling; Signal Transduction; Specificity; Squamous cell carcinoma; Survival Rate; Tissues; Tumor Subtype; Tumor Tissue; Tumor stage; Validation; cancer initiation; carcinogenesis; cell population study; cell type; data resource; experimental study; human RNA sequencing; human tissue; immune cell infiltrate; in silico; innovation; insight; intercellular communication; knock-down; monocyte; mouse model; mouth squamous cell carcinoma; multiple datasets; neoplastic cell; neutrophil; overexpression; programs; receptor; repository; single cell analysis; single-cell RNA sequencing; stem; stem-like cell; targeted treatment; therapy design; therapy resistant; tool; transcriptomics; tumor; tumor growth; tumor heterogeneity; tumor microenvironment; tumor progression

PROJECT SUMMARY Head and neck squamous cell carcinoma (HNSCC) is a devastating disease associated with high morbidity, poor survival rates, and limited treatment options with the majority of cases presenting as oral squamous cell carcinoma (OSCC). Fatality due to this disease is most often caused by metastasis and resistance to treatment. To develop targeted therapies, a better mechanistic understanding of molecular signaling and their contribution to intra-tumor phenotypes is needed. Growing evidence has indicated that cell plasticity, including the loss of the epithelial state and the acquisition of a partial EMT (p-EMT) phenotype, as well as acquisition of stem-like features, contribute to cancer initiation and progression to aggressive disease. The degree of immune infiltration has been linked to EMT, supporting the idea that inter-cellular interaction events within the tumor microenvironment (TME) can affect tumor growth. While many studies focus on the interaction between cancer associated fibroblasts (CAFs) and CSCs, there are many other populations that have been shown to influence clinical outcome in these tumors, which we have also identified in our studies using mouse models of HNSCC, such as neutrophils, B cells, and Langerhans cells. However, the mechanisms through which these populations influence tumor progression is largely unknown. Studying how cell populations and cellular signaling interactions change across tumor phenotypes is essential for a deep mechanistic understanding of the disease and identification of targets for potential therapies, which our proposal seeks to do in 3 aims. In Aim 1 we will build a comprehensive human HNSCC single cell RNA-seq (scRNAseq) atlas which will provide unprecedented resolution to predict associations between phenotypes, genotypes, and cellular heterogeneity. We will leverage this atlas to catalogue all cell populations, identify rare cell types and tumor subtypes, quantify how these populations change across tumor stage, and produce a list of predicted interactions occurring in the TME. Through Aim 2 we will construct a pre-processing tool to be used prior to cell-cell communication algorithms to both increase accuracy and specificity of interaction predictions which we will apply to the HNSCC atlas created in Aim 1. Aim 3 will validate our in-silico interaction predictions using both targeted and nontargeted approaches. First, we will utilize mouse models to perform knockdown and overexpression experiments on our top three ligand-receptor pairs to demonstrate their role in tumor progression. Secondly, we will use RNAscope, immunohistochemistry and spatial transcriptomics with human HNSCC tumor tissues sections to elucidate the proximity of predicted interacting cell populations within the tissue architecture. Overall, our project aims to define cellular interaction events that drive tumor cell plasticity, progression and metastasis in tumors. We postulate critical interactions can provide potential targets for drugs to inhibit HNSCC progression.

项目摘要 头颈鳞状细胞癌（HNSCC）是一种毁灭性疾病，与高发病率有关 生存率差，大多数为口腔鳞状细胞的病例有限的治疗选择 癌（OSCC）。由于这种疾病而导致的死亡最常是由转移和抵抗引起的 治疗。为了开发有针对性的疗法，对分子信号及其它们的更好的机械理解 需要对肿瘤内表型的贡献。越来越多的证据表明，细胞可塑性，包括 上皮状态的丧失和部分EMT（P-EMT）表型的获取以及获取 类似茎状的特征，有助于癌症的启动和进展为攻击性疾病。程度 免疫浸润与EMT有关，支持以下观点。 肿瘤微环境（TME）会影响肿瘤的生长。许多研究重点是 癌症相关的成纤维细胞（CAF）和CSC，还有许多其他人群已被证明 影响这些肿瘤中的临床结果，我们在研究中也使用了小鼠模型在研究中鉴定 HNSCC，例如中性粒细胞，B细胞和Langerhans细胞。但是，这些机制 种群影响肿瘤进展很大程度上是未知的。研究细胞群体和细胞如何 跨肿瘤表型的信号传导相互作用发生变化对于对对的深入机械理解至关重要 我们的提议试图以3个目标进行疾病和鉴定潜在疗法的靶标。在 AIM 1我们将建立一个全面的人类HNSCC单细胞RNA-seq（Scrnaseq）地图 前所未有的分辨率可以预测表型，基因型和细胞异质性之间的关联。 我们将利用此图集来分类所有细胞群体，识别稀有细胞类型和肿瘤亚型，量化 这些种群如何在肿瘤阶段发生变化，并产生在 TME。通过AIM 2，我们将构建一个预处理工具，以便在细胞通信之前使用 算法提高了相互作用预测的精度和特异性，我们将应用于 在AIM 1中创建的HNSCC地图集。AIM3将使用靶向和 非目标方法。首先，我们将使用鼠标模型执行敲低和过表达 在我们前三名配体 - 受体对的实验以证明其在肿瘤进展中的作用。第二， 我们将使用人类HNSCC肿瘤组织使用RNASCOPE，免疫组织化学和空间转录组学 以阐明组织结构中预测相互作用的细胞群体的近端。 总体而言，我们的项目旨在定义驱动肿瘤细胞可塑性，进展和的细胞相互作用事件 肿瘤中的转移。我们假设关键相互作用可以为药物提供潜在的目标来抑制 HNSCC进展。