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

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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.