Real-World Treatment Patterns and Effectiveness of Targeted and Immune Checkpoint Inhibitor-Based Systemic Therapy in Mutation-Positive NSCLC.

INTRODUCTION

mutations (present in 2%-3% of NSCLC) are a known oncogenic driver and emerging therapeutic target. There is a scarcity of real-world data describing the clinical characteristics, treatment patterns, and effectiveness of targeted -inhibiting and immune checkpoint inhibitor (ICI)-based systemic therapies, yet this is required for appropriate treatment decisions that optimize patient outcome.

METHODS

Demographic, clinical, treatment, and outcome data of patients with mutation-positive NSCLC diagnosed between 2018 and 2022 were identified from the Glans-Look Lung Cancer Research database and included in this analysis.

RESULTS

A total of 53 mutation-positive patients were identified (V600E, n = 35; non-V600E, n = 18). Furthermore, 46 patients (87%) were diagnosed with metastatic disease, of whom 61% were treated with systemic anticancer therapy, which significantly improved overall survival (34.1 versus 2.2 mo,  = 0.01). ICI-based regimens were found to have effectiveness in the first-line setting for both V600E and non-V600E cohorts (objective response rate: 38%-43%; real-world calculations of median progression-free survival: 10.5-10.8 mo, respectively). Dual-targeted / inhibition was also found to have effectiveness in the first-line setting for V600E patients (objective response rate: 33%, real-world calculations of median progression-free survival: 15.2 mo).

CONCLUSIONS

This study of real-world patients with mutations confirms the importance of effective systemic therapies. Both dual-targeted / inhibition and ICI-based regimens have evidence of benefit in this population revealing that real-world populations can experience similar clinical response and outcome to clinical trial cohorts on these treatment regimens. Future studies to clarify the role of co-mutations on response to both dual-targeted / inhibition and ICI-based regimens may be important to treatment selection and optimization of patient outcome.