Abstract IA2: Network models in oncogene-addicted lung cancer

Lung cancer is a devastating worldwide disease yet enthusiasm exists for treatment of subsets of the disease with molecularly targeted agents. Mutations in the epidermal growth factor receptor (EGFR) or translocation of echinoderm microtubule associated protein like 4 – anaplastic lymphoma kinase (EML4-ALK) define two unique subsets of lung cancer characterized by sensitivity to tyrosine kinase inhibitors (TKI). Despite striking results with TKI, not all patients respond, the drugs are non-curative, and resistance is universal. Mutations in KRAS also define a group of patients awaiting therapeutic opportunities. We are characterizing signaling networks using tandem affinity purification (TAP) and liquid chromatography-mass spectrometry (LC-MS/MS) to map protein-protein interactions (PPI) and anti-phosphotyrosine immunoprecipitation coupled with LC-MS/MS to map tyrosine phosphorylation. In PC9 cells with mutated EGFR, we characterized a physical EGFR network consisting of 266 proteins by integrating both TAP and pTyr MS data. In H3122 cells harboring EML4-ALK, we identified a PPI network consisting of 113 proteins and using pTyr MS identified changes in tyrosine phosphorylation in 120 proteins (58 decreased, 62 increased) following exposure to ALK tyrosine kinase inhibitor. Functional proteins are being discovered from these networks using siRNA and inhibitor screens. In parallel studies, we have exploited the use of chemical proteomics to discern targets of promiscuous kinase inhibitors and enable optimal combination approaches. In this way, we view network mapping linked to chemical proteomics as one approach to discern novel drug combination studies for in vivo validation and ultimately translation to early phase clinical trials. To translate PPI based mass spectrometry studies to clinic, we are developing in situ assays that identify and quantify PPI using proximal ligation assays (PLA). Pilot studies identify EGFR:Grb2 interactions in formalin fixed human lung cancers. These approaches have the potential to enable ‘network medicine’ by identifying novel combination approaches as well as through identifying subtypes of cancers through network views of cancer.