Prime editing reveals the resistance landscape of EGFR tyrosine kinase domain variants

Resistance of Epidermal Growth Factor Receptor (EGFR) to tyrosine kinase inhibitors (TKIs) remains a major challenge in non-small-cell lung cancer (NSCLC). Here, we used prime editing to profile 1676 single-amino-acid variants and 10 insertion variants of exon 20 across the EGFR tyrosine kinase domain against afatinib, dacomitinib, and osimertinib. In an L858R-based NSCLC model, we evaluated ~90% of all possible single-amino-acid variants and integrated these data with a published exon 19 deletion-based dataset to define a context-independent resistance profile, capturing 10,255 drug-variant combinations across two EGFR-activating contexts and revealing shared and context-specific resistance patterns. Benchmarking against curated clinical annotations and survival data enabled high-confidence classification of resistance and sensitivity to afatinib for ~86% of all possible variants, to dacomitinib for ~74%, and to osimertinib for ~82%. This study provides a clinically relevant resource of EGFR TKI resistance and a framework for variant profiling.