Real-World Associations Between Smoking Status and Genetic Driver Mutations in Metastatic Lung Cancer in US Community Oncology
Author(s)
Lisa Herms, PhD, Matthew Whitesell, BS, Zhaohui Su, PhD, Jessica Paulus, ScD, Robert Reid, MD, FACP.
Ontada, Boston, MA, USA.
Ontada, Boston, MA, USA.
OBJECTIVES: As of 2023, lung cancer in never smokers (NS) has become the fifth leading cause of cancer mortality worldwide, with rising incidence. NS patients may harbor distinct genetic driver mutations, differentiating them clinically from ever-smokers (ES) and potentially influencing treatment strategies and outcomes. This real-world study examined the relationship between smoking status and genetic mutations in metastatic lung cancer patients treated within a large network of US community oncology clinics.
METHODS: Adults diagnosed with metastatic adenocarcinoma or large cell carcinoma between 2015 and 2024 were identified from an oncology-specific electronic health records system used by The US Oncology Network and non-Network practices. Biomarker testing within 90 days of metastatic diagnosis was descriptively analyzed for EGFR, ALK, KRAS, ROS1, PD-L1, NTRK, HER2, RET, MET, BRAF, and NRG1. Patients with self-reported tobacco history were classified as ES or NS.
RESULTS: The study population of 27,263 patients included 4,696 NS, 18,681 ES, and 3,886 patients with undocumented smoking status, with the proportion of NS patients remaining relatively steady over time (~17.3%). From 2015 to 2024, cumulative biomarker testing rates increased from 59.8% to 75.3% of the population, with variation by specific genes in alignment with approvals of corresponding targeted therapies. Higher alteration rates (p<0.01) were observed for NS than ES in EGFR (44.3% vs. 11.1%), ALK (11.0% vs. 2.7%), and, to a lesser extent, ROS1 (3.7% vs. 1.8%). Conversely, higher KRAS alteration rates were observed for ES than NS (40.7% vs. 13.8%; p<0.01).
CONCLUSIONS: This large-scale real-world analysis confirms the unique pattern and prevalence of metastatic lung cancer driver mutations in NS compared to ES in the community oncology setting. As new therapies increasingly rely on specific genetic alterations, the identification of the genetic driver mutations in lung cancer and discriminating the pattern by smoking history is a prerequisite for appropriate diagnosis and treatment assignment.
METHODS: Adults diagnosed with metastatic adenocarcinoma or large cell carcinoma between 2015 and 2024 were identified from an oncology-specific electronic health records system used by The US Oncology Network and non-Network practices. Biomarker testing within 90 days of metastatic diagnosis was descriptively analyzed for EGFR, ALK, KRAS, ROS1, PD-L1, NTRK, HER2, RET, MET, BRAF, and NRG1. Patients with self-reported tobacco history were classified as ES or NS.
RESULTS: The study population of 27,263 patients included 4,696 NS, 18,681 ES, and 3,886 patients with undocumented smoking status, with the proportion of NS patients remaining relatively steady over time (~17.3%). From 2015 to 2024, cumulative biomarker testing rates increased from 59.8% to 75.3% of the population, with variation by specific genes in alignment with approvals of corresponding targeted therapies. Higher alteration rates (p<0.01) were observed for NS than ES in EGFR (44.3% vs. 11.1%), ALK (11.0% vs. 2.7%), and, to a lesser extent, ROS1 (3.7% vs. 1.8%). Conversely, higher KRAS alteration rates were observed for ES than NS (40.7% vs. 13.8%; p<0.01).
CONCLUSIONS: This large-scale real-world analysis confirms the unique pattern and prevalence of metastatic lung cancer driver mutations in NS compared to ES in the community oncology setting. As new therapies increasingly rely on specific genetic alterations, the identification of the genetic driver mutations in lung cancer and discriminating the pattern by smoking history is a prerequisite for appropriate diagnosis and treatment assignment.
Conference/Value in Health Info
2025-11, ISPOR Europe 2025, Glasgow, Scotland
Value in Health, Volume 28, Issue S2
Code
RWD148
Topic
Epidemiology & Public Health, Real World Data & Information Systems, Study Approaches
Topic Subcategory
Health & Insurance Records Systems
Disease
Oncology