Cancer is recognized to be a result of changes in cellular genomes resulting in aberrant signaling proteins causing deregulated cell growth, survival, and metastasis. These changes rewire entire signaling 'circuits'resulting in aberrant growth and metastasis. Critical to protein function and signaling is the formation of signaling complexes and networks of signaling proteins that act in concert to produce a physiological signal. State of the art mass spectrometry is now able to accurately map protein-protein interaction (PPI) complexes and networks. This now allow a better understanding of how cancer proteins drive a signaling network to transform cells. The application of network theory to biology may enable a better understanding of cancer, improve ability to classify tumors, and suggest therapeutic approaches against cancer 'hub'proteins or suggest rational combination approaches (10-14). Despite the explosion of PPI datasets, most are limited in pre-clinical space and interrogations of PPI in human cancer specimens is lacking. Thus, how to make 'network medicine'a reality? Translation of these network approaches to tumor samples is hampered by a number of hurdles that preclude the ability to identify and quantify these networks in human cancer samples. One solution to mapping networks identified using mass spectrometry-based proteomics is proximal ligation assays (PLA). This technology is capable of detecting single protein events such as protein interactions. The assay provides exact spatial information on the location of the events and an objective means of quantifying the events. As little has been done to establish biomarker systems to measure protein-protein based biomarkers in cancer, the goal of this proposal is to develop PLA that can quantitatively measure defined protein-protein interactions driven by activation of the epidermal growth factor receptor (EGFR) in lung cancer specimens and relate expression of these interactions to clinical outcome variables. We will leverage experimentally derived mass spectrometry data defining interacting proteins within the EGFR network will guide selection of protein complexes for assay development.
In Aim 1, we will establish and validate proximal ligation assays that measure EGFR protein-protein interactions.
In Aim 2, we will characterize EGFR protein interactions in cell and tumor models with known EGFR mutation status.
In Aim 3, we will characterize changes in EGFR protein- protein interactions in response to EGFR tyrosine kinase inhibitors in primary lung cancer xenograft models and patient samples.
In Aim 4, we will determine if EGFR protein-protein interactions are associated with responses to EGFR tyrosine kinase inhibitors in human lung cancer samples.
Cancer is recognized to be a result of changes in genes resulting in rewiring of entire signaling 'circuits'that control cell growth and spreading. Here we will develop assays that can identify and measure protein-protein interactions in lung cancer cells. This approach could offer another method to classify tumors and identify patients best suited for particular treatments.