Abstract

ObjectivesWhile Epidermal Growth Factor Receptor (EGFR) Tyrosine Kinase Inhibitors have been shown to be effective in phase III randomized trials, the value of targeted therapies has been challenging to evaluate at the population-level. We examined the impact of population-level EGFR testing and treatment on survival outcomes among non-squamous metastatic Non-Small Cell Lung Cancer (NSCLC) patients. Materials and methodsReal-world, population-level data were collected from all de novo non-squamous metastatic NSCLC patients in Alberta, Canada from 2004 to 2020. EGFR testing data were collected through Alberta Precision Laboratories. Differences in survival rates and overall survival (OS) pre (2004–2012) and post initiation (post) (2013–2019) testing periods were evaluated using interrupted time series analyses. The impact of testing and subsequent treatment was evaluated using multivariable Cox Proportional Hazards models. ResultsIn total, 4,578 non-squamous metastatic NSCLC patients were diagnosed pre-EGFR testing and 4,457 patients were diagnosed post-EGFR testing (2013–2019). Among patients diagnosed in the pre-EGFR testing period, the 6-month, 1-year, and 2-year survival probabilities were 0.39 (95 % CI: 0.38–0.41), 0.22 (95 % CI: 0.21–0.23), and 0.09 (95 % CI: 0.08–0.10), while the survival probabilities for patients diagnosed in the post-EGFR testing period were 0.45 (95 % CI: 0.43–0.46), 0.29 (95 % CI: 0.27–0.30), and 0.16 (95 % CI: 0.15–0.17), respectively. After adjusting for baseline patient and clinical characteristics, OS in the post-EGFR period was significantly improved compared to the pre-EGFR period (HR: 0.81; 95 % CI: 0.78–0.85). Among patients who were treated with systemic therapy, those tested for an EGFR mutation had significantly greater survival than patients who were not tested HR of 0.81 (95 % CI: 0.70–0.95). ConclusionThese results show the considerable impact of population-based molecular testing and subsequent targeted therapies on survival among metastatic NSCLC patients. The estimates here can be used in future studies to evaluate the population-level cost-effectiveness of testing and treatment.

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