Treatment Strategies for Non-Small Cell Lung Cancer with Common EGFR Mutations: A Review of the History of EGFR TKIs Approval and Emerging Data

Simple Summary The management of non-small cell lung cancer with a common EGFR mutation has evolved over the past decades. While frontline use of second- or third-generation EGFR tyrosine kinase inhibitors (TKIs) is preferred over first-generation EGFR-TKIs, choosing the ideal agent depends on multiple factors (drug availability, physician comfort, specific EGFR mutation, presence of brain metastasis, etc.). Furthermore, defining subsequent therapies at the time of progression will rely on numerous variables (extent of disease, frontline EGFR TKI generation used, mechanism of resistance, etc.). Consequently, defining an optimal sequencing strategy is both, crucial and challenging. In this review, we present a detailed summary of evidence supporting the use of EGFR TKIs with or without other therapeutic approaches, outline available options at the time of disease progression, summarize investigational strategies, and suggest an approach to therapeutic sequencing in patients with common EGFR mutations. The development of targeted therapies over the past two decades has led to a dramatic change in the management of EGFR-mutant non-small cell lung cancer (NSCLC). While there are currently five approved EGFR tyrosine kinase inhibitors (TKIs) for treating EGFR-mutant NSCLC in the first-line setting, therapy selection after progression on EGFR TKIs remains complex. Multiple groups are investigating novel therapies and drug combinations to determine the optimal therapy and treatment sequence for these patients. In this review, we summarize the landmark trials and history of the approval of EGFR TKIs, their efficacy and tolerability, and the role of these therapies in patients with central nervous system metastasis. We also briefly discuss the mechanisms of resistance to EGFR TKIs, ongoing attempts to overcome resistance and improve outcomes, and finalize by offering treatment sequencing recommendations.