Subcellular spatial signaling interactomics predict drug responses from multiplexed protein proximity maps

Introduction: Non-small cell lung cancer (NSCLC) comprises 80% of lung cancer. The subgroup of NSCLC harboring Epidermal Growth Factor Receptor mutations (EGFRm) has shown resistance to chemotherapeutic agents but benefited from EGFR tyrosine kinase inhibitors (TKIs), specifically Osimertinib.1 However, patients with EGFRm NSCLC can still develop acquired drug resistance due to the activation and crosstalk among signaling pathways. Protein-protein interactions (PPIs) regulating signaling pathways and cell phenotypes could shed light on the signaling crosstalk and solve the Osimertinib resistance. In our study, we developed a multiplexed protein detection assay, termed Rapid Multiplexed Protein Interactions (RapMPI), allowing us to detect multiple PPIs with up to 47 proteins involved in the AKT/mTOR, MEK/ERK, and YAP/TEAD pathways.2 We applied the method, followed by conventional immunofluorescence on both EGFRm NSCLC cell cultures and tissues.

Materials and

Methods: EGFRm NSCLC HCC827 cells were used to visualize the PPI distribution and expression among AKT/mTOR, MEK/ERK, and YAP/TEAD pathways. HCC827 cells were treated with and without 100 nM Osimertinib for 12 hours. Following fixation and permeabilization, the cells were ready for iterative staining, deactivation, and imaging process (Figure 1A). Following RapMPI, conventional immunofluorescence was utilized to visualize the cell phenotyping and organelles. The performance of RapMPI was measured by evaluating its sensitivity, specificity, and batch consistency as well as the effect of Osimertinib on protein baseline or PPI level. In addition to cell cultures, the PPIs in frozen mice and patient tissues with EGFRm NSCLC were visualized using RapMPI. We also demonstrated a spatial PPI graph neural networks encoding spatially resolved PPIs can predict the drug perturbations of single cells.

Results, Conclusions, and Discussions: Results and

Discussion: After performing RapMPI on HCC827 cells, we obtained 34-plex profiling for 13 PPIs and 8 Signaling and organelle proteins at a single-cell level under different drug perturbations. The contributions of subcellular PPIs were further visualized using spatial signaling networks (Figure 1B). P-ERK/c-Myc interactions were increased after Osimertinib treatment, indicating that Osimertinib may change the signaling dynamics by upregulating ERK pathways as a compensatory to EGFR inhibition (Figure 1C). By integrating the spatial PPI distribution with PPI quantification, the graph neural networks demonstrated a better performance in predicting cellular treatment states (Figure 1D), enhancing our understanding of the impact of the drug on signaling dynamics.

Conclusions: The expression profiles and subcellular localization dynamics of PPIs involved in the three pathways visualized the signaling dynamics potential crosstalk among them. RapMPI demonstrated the feasibility of detecting multiple PPI distributions at the subcellular level. It also demonstrated the value of modeling drug treatment by integrating both the quantification and spatial information of PPIs as inputs in the predictive model. It provides insights into the design of drugs for targeting the protein interactome.

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