Engineering isogenic models harboring resistance mechanisms to the latest-generation EGFR inhibitor in non-small cell lung cancer

Background: EGFR-mutant lung cancer was among the first epithelial cancer subsets where directly targeting an oncogene yielded significant clinical benefit. While improved inhibitors, such as third-generation EGFR inhibitor Osimertinib, have significantly improved clinical outcomes of non-small lung cancer (NSCLC) patients, acquired resistance to targeted therapies remains a major barrier to durable responses. To address this challenge, we developed three sets of isogenic NSCLC cell models harboring clinically resistant mutations to EGFR targeted therapy through genetic engineering approach. Method and Results: To systematically investigate resistance mechanisms and associated vulnerabilities, we engineered isogenic NSCLC cell lines to model clinically relevant mechanisms of acquired resistance. Using CRISPR gene editing, three sets of resistant cell lines were generated from three osimertinib-sensitive parental lines (HCC827, HCC4006, and NCI-H292). The engineered alterations included BRAF V600E, KRAS G12D, PIK3CA E545K, EGFR C797S, and additional fusion genes such as TPM3-NTRK1 . Sequence verification and osimertinib sensitivity assays were performed for all models. The engineered cell lines exhibited reduced osimertinib sensitivity consistent with the introduced resistance mechanisms. Initial genomic validation confirmed the intended edits and additional genetic screening will be performed to further characterize the isogenic cell lines. Selected models were further evaluated in 3D culture systems to assess phenotypic impact. Conclusion: These validated novel models provide a robust platform for the research community and industry to dissect mechanisms of drug resistance, identify therapeutic vulnerabilities and develop combination therapy strategies. This ATCC and Broad institution collaborative effort will also support the establishment of the Resistance Map (ResMap) within DepMap to systematically characterize vulnerabilities in EGFR-driven NSCLC.