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

项目总结 头颈部鳞状细胞癌(HNSCC)是一种发病率高、致命性强的疾病。 存活率低，治疗选择有限，大多数病例表现为口腔鳞状细胞 癌(OSCC)。这种疾病的致死率最高的原因是转移和对 治疗。为了开发有针对性的治疗方法，更好地从机制上理解分子信号及其 对肿瘤内表型的贡献是必要的。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)表型的获得，以及 干细胞样特征，有助于癌症的发生和发展为侵袭性疾病。的程度 免疫渗透与EMT有关，支持这样一种观点，即细胞间相互作用事件在 肿瘤微环境(TME)可影响肿瘤生长。虽然许多研究都集中在相互作用上 癌症相关成纤维细胞(CAF)和CSCs，还有许多其他群体已被证明 影响这些肿瘤的临床结局，我们在使用小鼠模型的研究中也发现了这一点 HNSCC，如中性粒细胞、B细胞和朗格汉斯细胞。然而，通过这些机制 人口对肿瘤进展的影响在很大程度上是未知的。研究细胞群和细胞如何 跨肿瘤表型的信号相互作用的变化对于深入理解肿瘤的机制是必不可少的。 疾病和潜在治疗目标的确定，我们的提案试图在3个目标中做到这一点。在……里面 目的1我们将建立一个全面的人类HNSCC单细胞RNA-seq(ScRNAseq)图谱，它将提供 前所未有的分辨率来预测表型、基因类型和细胞异质性之间的关联。 我们将利用这份图谱对所有细胞群体进行分类，识别稀有细胞类型和肿瘤亚型，量化 这些群体如何在肿瘤分期中变化，并产生一个预测的相互作用发生在 我也是。通过AIM 2，我们将构建一个在细胞间通信之前使用的预处理工具 提高交互预测的准确性和特异性的算法，我们将应用于 HNSCC地图集在AIM 1中创建。AIM 3将使用目标和 无针对性的方法。首先，我们将利用鼠标模型来执行击倒和过度表达 对我们的前三个配体-受体对进行实验，以证明它们在肿瘤进展中的作用。第二， 我们将使用RNAScope、免疫组织化学和空间转录对人HNSCC肿瘤组织进行研究 切片以阐明预测的相互作用的细胞群体在组织架构内的接近程度。 总体而言，我们的项目旨在定义推动肿瘤细胞可塑性、进展和 肿瘤中的转移。我们假设关键的相互作用可以为药物提供潜在的靶点来抑制 HNSCC进展。

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.