Modeling tumor-stroma crosstalk in lung cancer to identify targets for therapy

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 DESCRIPTION (provided by applicant): There is an unmet need for molecularly targeted therapies for the treatment of non-small cell lung cancer (NSCLC). Taking into account the emerging paradigm that the reprogrammed intratumoral stromal cells contribute to carcinogenesis, we have employed integrated experimental and computational approaches to identify tumor-stroma crosstalk pathways that drive NSCLC progression. To explore paracrine/autocrine crosstalk, we performed RNA deep sequencing analysis of specific cellular myeloid and epithelial compartments isolated from freshly harvested lungs of NSCLC patients, and a genetically engineered mouse model of NSCLC. We compared transcriptomes of intratumoral myeloid cells (monocytic, neutrophils and macrophages) and tumor epithelial cells with their counterparts within matched adjacent non-neoplastic tissue. In this application, we will develop a multi-cellular crosstalk signaling network modeling and visualization software tool (Aim 1) and apply this model to multi-cellular RNA-seq data to identify tumor-stroma crosstalk pathways; genes involved in these signaling mechanisms will be considered potential candidates that mediate NSCLC tumor progression and will undergo rapid validation using in vitro assays (Aim 2). Finally, we will determine the function of selected crosstalk pathways in NSCLC progression and in mediating therapeutic resistance (Aim 3). In summary, this study explores the relatively understudied tumor-stroma crosstalk pathways as a largely untapped source of drug targets and has tremendous potential for the development of novel therapeutic strategies that target tumor-stroma interactions and may complement existing treatments that target cancer cells.