Patients with non-small cell lung cancer (NSCLC) with epidermal growth factor receptor (EGFR) gene mutations have been successfully treated with EGFR tyrosine kinase inhibitors (EGFR-TKIs). Osimertinib has emerged as one of the preferred front-line agents given the significant improvement in progression-free survival and overall survival. However, the majority of patients will develop acquired resistance to this targeted therapy. Osimertinib is effective in combination with MEK/ERK signaling inhibitions in overcoming and even delaying acquired resistance to osimertinib in pre-clinical studies. Single-cell molecular profiling has identified that YAP inhibitors may overcome acquired resistance to osimertinib, even after exposure to MEK/ERK inhibitions. Bulk molecular assays and single-cell RNA-seq methods have been the mainstream approaches to evaluating the signaling alterations in cultures and tissues. However, there is little known about spatially resolved single-cell and subcellular details of signaling networks and interactions in NSCLCs. Recently, the rapid multiplexed immunofluorescence (RapMIF) assay (developed by the Pl) revealed the effect of osimertinib on the subcellular organization of signaling protein factors. To further evaluate the protein-protein interactions in NSCLC cells and tissues, this proposal will establish rapid multiplexed protein interaction (RapMPI) method to study drug-sensitive and resistant human cell lines in cultures and tissues from cell line-derived xenografts in mice and patient-derived xenografts (PDXs) with EGFR mutations (EGFRm). RapMPI will be optimized using automated labeling, multiplexing chemistry, and signaling interactome reconstructions. This project tests the hypotheses that single cell and spatial signaling networks and interaction maps will quantitatively model MEK/ERK upstream' s crosstalk with YAP's upstream to define 20 pre-selected protein pair interactions for rationally designed combination therapies to overcome acquired resistance to osimertinib. Aim 1 will map out spatial signaling interactors and their subcellular localization on major organelles using RapMPI in drug-sensitive and resistant EGFRm cells under control, osimertinib treatment only, MEKIERK, or YAP inhibition only, and osimertinib combined with MEKIERK or YAP inhibition conditions. Aim 2 will then measure spatial signaling interactome using RapMPI in vivo using MEK/ERK or YAP inhibitors in combination with osimertinib in mouse xenograft and PDX models (pretreatment, 1-week, 1-month, and until resistance occurs, n=5 each) and EGFRm human tissues with osimertinib-responsive or resistance clinical outcome (n=10 each). The long-term goal of this R33 is to uncover the spatially and temporally coordinated acquired resistance using the subcellular and cellular signaling network maps. This R33 is innovative because ii establishes an emerging subcellular image-based signaling analysis technology to guide rationally designed combinatorial intervention for overcoming resistance to osimertinib. The proposed work is significant because it generates new knowledge on compensatory signaling networks in NSCLCs at the highest molecular resolution on a single cell basis otherwise not attainable by bulk signaling protein assays.

Cell signaling therapies target lung cancer patients with a well-defined mutation in the common cellular signaling, but almost all patients ultimately develop acquired resistance to this targeted therapy due to the emergence of clones with genomic alterations that confer survival advantage and their tumors' molecular heterogeneity. The proposed research will build a molecular screening platform for imaging signaling protein interactions in human cell cultures and tissues from physiologically relevant cancer models using multiplexed measurement techniques to record spatial signaling interactome patterning of subcellular and cellular distributions in patients' biopsies with gene mutations. The biological lung cancer model system will demonstrate how subcellular wiring of signaling factors provides subset patients that can be used for personalized and precision signaling therapies in lung cancers.