The Epidermal Growth Factor Receptor (EGFR) signalling pathway activates multiple downstream sub-pathways which are frequently mutated in non-small cell lung cancer (NSCLC). While these cytoplasmic sub-pathways have been extensively characterized, our understanding of how each node along a given sub-pathway regulates transcription factors (TFs) and their target genes remains incomplete. Previous research has identified a limited subset of TFs (such as AP1, ELK1, and STAT3) downstream of EGFR, however activity-based evidence is lacking, with most conclusions being drawn from abundance-based assays. Moreover, the relative impact of how multiple nodes influence TF activity is largely unknown.

We have developed and validated a multiplexed reporter system which simultaneously measures 100 TF activities in a single experiment. Each reporter consists of a TF-specific response element driving expression of a barcoded mRNA, optimized through testing 36,000 different designs across multiple cell types and conditions. We will apply this system to NSCLC cell lines with diverse genetic backgrounds (including EGFR and KRAS constitutively activate mutants). We will systematically perturb nodes within a given sub-pathway using clinically relevant inhibitors, to identify the differential TF activity downstream of the EGFR signalling pathway.

Initial data reveal a distinct and overlapping sets of TFs controlled by each node within a single sub-pathway. For example, by treating EGFR constitutively active cells with an EGFR inhibitor (Osimertinib) or MEK inhibitor (Trametinib) revealed unique TF activity profiles, despite the often-linear representation. These observations are consistent across KRAS driven cells, despite the driver mutation differences.

This comprehensive mapping of EGFR-driven transcriptional networks will enhance our understanding of resistance mechanisms, identify novel drug targets beyond kinase inhibitors, and may inform rational drug repurposing strategies. Future applications include exploring tissue-specific variations in EGFR-TF networks across cancer types and crosstalk between receptors.