Urban infrastructure and spatiotemporal environmental features for EGFR -mutant lung cancer
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Background
Lung cancer in never-smokers is rising, with a substantial proportion harboring the EGFR mutation. While fine particulate matter (PM 2.5 ) is a recognized risk factor, other intervenable pollutants and built environmental factors remain unknown.
Objectives
To identify urban characteristics associated with EGFR -mutant (vs. wild-type) lung cancer using high-resolution spatiotemporal data.
Methods
We analyzed 2,699 lung cancer patients with documented EGFR status treated at a high-volume academic medical center in New York City. Patient residential addresses were linked to high-resolution (300m×300m) 5-year cumulative exposures to 3 air pollutants and 26 urban features. We developed Light Gradient Boosting Machine (LightGBM) models to classify EGFR status, comparing a basic clinical model with established predictors (Asian, female, never-smoking status, and adenocarcinoma histology) to an extended model with additional urban factors. Predictive performance was assessed based on discrimination (AUC).
Results
We included 2,699 patients, of whom 54.1% were female and 25.8% self-identified as Asian, 11.2% as Black, and 7.4% as Hispanic; and 29% had EGFR- mutated cancer. The extended model showed modest improvements in discrimination (AUC: 0.775 [95% CI, 0.739-0.809] vs. 0.768 [0.723-0.811]), compared to the clinical model. Newly identified factors for EGFR -mutant status included black carbon (BC), nitrogen dioxide (NO 2 ), proximity to airports, reduced access to public transportation, elevated noise levels, and lead exposure.
Conclusions
Traffic-related pollutants (BC, NO 2 ) from diesel engines and motor vehicles, and proximity to airports, were among the novel spatiotemporal features associated with EGFR -mutant lung cancer. These results may inform policy interventions.
