UCLA

Cancer hijacks how cells sense, integrate, and respond to cues present in their environment. For instance, cancer cells can dysregulate their receptor tyrosine kinase (RTK) to drive proliferation and migration to distant tissues. RTK inhibitors, such as those targeting the epidermal growth factor receptor (EGFR), are therefore often effective but are invariably limited by the development of resistance. A well-appreciated means of resistance to RTK inhibition is so-called “bypass” resistance, wherein an RTK not targeted by therapy, such as AXL, activates alternative oncogenic pathways. In this work, we developed a paired experimental and computational strategy to comprehensively characterize the signaling changes that the RTK AXL regulates in EGFR- dependent lung cancer cells when driving resistance. To do so, we generated a panel of lung cancer cell lines with different AXL phosphosite mutations and then measured both the proteomic and phenotypic changes during bypass resistance. To model such data, we developed and applied an algorithm, Dual Data-Motif Clustering, that identified the most prominent signaling pathways that AXL activates to mediate resistance to EGFR-targeted therapies, which we then experimentally validated. This work demonstrates a methodology for dissecting complex signaling networks and identifies several mechanisms by which AXL drives resistance-associated phenotypic changes.