Lung cancer is the leading cause of cancer death in the United States, accounting for approximately 160,000 deaths in 2009. The molecular mechanisms implicated in lung cancer development and progressions are not well understood. Recent evidence points to a complex interaction between the malignant cells and their microenvironment. However, much of our knowledge of the role of the tumor microenvironment comes from studies isolating the interactions between the malignant cells and a single component of the microenvironment, along a single pathway. We will reconstruct the first Tumor Microenvironment Interactome (TMI) of lung adenocarcinoma, which will identify global intra- and inter-cellular regulatory interactions between human malignant cells and their associated infiltrating immune cells, endothelial cells and fibroblasts. The TMI will be derived from global gene expression analysis of specific tumor microenvironment cell populations directly obtained from human lung cancer specimens using fluorescence-activated cell sorting (Specific Aim 1). The TMI will be reconstructed using novel computational approaches for inferring regulation among modules of genes (Specific Aim 2). From the TMI, we will identify candidate mediating factors, such as secreted cytokines, that regulate processes across the multiple cell subpopulations. We will specifically focus on the factors most associated with survival outcomes, by leveraging public domain expression data with long term survival outcomes (Specific Aim 2). We will use a combination of cell lines and animal models in the validation studies to test the effect of the candidate mediating factors on tumor behavior (Specific Aim 3). Through the reconstructed TMI, we will create a more global understanding of the lung tumor microenvironment. Our ultimate goal is to identify biologically and clinically relevant molecular targets that could be used to develop more effective therapies for lung cancer. The lung adenocarcinoma TMI will also be made publically available to the scientific research community as a hypothesis generation tool for evaluating the role of genes of interest.