Recovery of human upper airway epithelium after smoking cessation is driven by a slow-cycling stem cell population and immune surveillance

The upper airway epithelium in humans is maintained in homeostasis by a resident population of basal stem cells. In the presence of tobacco smoke these gain mutations that significantly increase their risk of transformation to lung squamous cell carcinoma. A small proportion of stem cells avoid the mutational damage caused by carcinogens in tobacco and are more abundant in the lungs of former smokers than ongoing smokers, indicating unexplained tissue-level genomic recovery. This mirrors epidemiological risk, which falls rapidly after quitting smoking. Somatic evolutionary mechanistic hypotheses have been proposed to explain these observations. Here, we present a computational framework to model each of these hypotheses within the upper airway epithelial stem cell population over the entire patient lifetimes of a cohort with diverse smoking histories. Applying a mechanistic learning approach based on a set of biologically informed metrics to single cell-derived whole-genome sequencing data, we identified subtle differences between epithelia modelled under different combinations of hypotheses. A slow-cycling subpopulation of stem cells, combined with suppression of immune predation of highly mutated stem cells while smoking, best matched observed data, a result converged upon by multiple distinct machine learning methodologies. Our findings, drawing on an evolutionary model of mutagen exposure at a whole-lifetime scale that is not feasible to model in vivo, reveal the mechanisms behind reduction in lung squamous cell carcinoma risk on cessation of smoking and inform future therapeutic interventions to prevent lung cancer initiation.