Gene-environment interactions between clonal hematopoiesis of indeterminate potential and air pollution in non-small cell lung cancer among non-smokers.
Bibliographic record
Abstract
10514 Background: Small particulate matter air pollution (≤ 2.5µm diameter; PM2.5) is a recognized driver of non-small cell lung cancer (NSCLC) among non-smokers. PM2.5 recruits pro-inflammatory macrophages to the lungs, which facilitate the clonal expansion of mutated epithelial cells and eventual transformation to NSCLC. Clonal hematopoiesis of indeterminate potential (CHIP) is a common age-related condition caused by the acquisition of somatic mutations in hematopoietic stem cells. CHIP and its accompanying pro-inflammatory phenotype have been linked with various age-related diseases, including incident NSCLC; however, the mechanisms underlying this relationship are unknown. In this study, we aimed to explore the interaction between CHIP and exposure to PM2.5 in the development of incident NSCLC. Methods: This study was conducted using data from participants in the UK Biobank (n = 451,095). CHIP status was determined from peripheral blood whole exome sequencing data, and defined as the presence of a somatic driver mutation in the blood at variant allele frequency (VAF) ≥2% (PMID: 36652671). Incident NSCLC was determined from UK cancer registry data, and PM2.5 exposure was determined based on ambient regional measures from 2010. Cox proportional hazard models were used to evaluate associations between CHIP, PM2.5, and NSCLC. Proteomics data was measured using OLINK proteomics in a subset of UK Biobank participants (n = 44,625). Results: CHIP was prevalent in 3.4% of UK Biobank participants (n = 15,633; never smokers: 3.0%, n = 6,916/227,133). There were 1983 incident cases of NSCLC (never smokers: n = 307). CHIP status was associated with incident NSCLC when adjusting for smoking status (HR = 1.73, 95% CI: 1.48–2.02) and analyzing exclusively non-smokers (HR = 2.01, 95% CI: 1.34-3.00). PM2.5 levels were not associated with NSCLC in non-smokers (HR = 1.00, 95% CI: 0.89–1.12 per µg/m3 increase in PM2.5); however, there was a significant interaction between PM2.5 and CHIP (HR = 1.46, 95% CI: 1.02–2.11, pint = 0.04), suggesting that it is the interplay between the two that drives incident NSCLC risk. We found no association between PM2.5 levels and increased CHIP prevalence or CHIP VAF. PM2.5 and CHIP were also found to interact to increase systemic inflammatory markers C-reactive peptide (pint = 0.01) and IL-6 (pint = 0.002). Conclusions: PM2.5 and CHIP act as a novel gene x environment interaction pair that play a key role in NSCLC etiology among non-smokers. Rather than acting in isolation to increase risk of NSCLC, PM2.5 intensifies the relationship between CHIP and NSCLC, presumably by exacerbating systemic inflammation and the hyper-inflammatory lung microenvironment. With PM2.5 levels in the UK among the lowest in the world, we posit that the CHIP x PM2.5 interaction contributes substantially to the global burden of NSCLC in non-smokers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".