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.
In this project, we propose a collaborative project to study the tumor-stroma crosstalk signaling in Non-Small Cell Lung Cancer patients. Dr. Mittal has utilized clinical specimens and mouse models of lung cancer to generate RNA-Seq data of individual types of stroma and epithelial cells in NSCLC. Dr. Wong has developed a novel multi-cellular network model (P2GWAS) that employs evolutionary multiple-objective optimization to predict tumor-stroma crosstalk signaling pathways based on the multi-cellular RNA-Seq data generated by Dr. Mittal's lab. Our integrated computational and experimental biology approach offers valuable insights for developing and guiding therapeutic strategies that target the understudied tumor-stroma crosstalk. Pharmacological inhibitors of activated stroma will accelerate clinical trials either as monotherapies or as complements to existing conventional treatments that exclusively target cancer cells.
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