Genomic insights for personalised care in lung cancer and smoking cessation: motivating at-risk individuals toward evidence-based health practices
Bibliographic record
Abstract
BACKGROUND: Lung cancer and tobacco use pose significant global health challenges, necessitating a comprehensive translational roadmap for improved prevention strategies such as cancer screening and tobacco treatment, which are currently under-utilised. Polygenic risk scores (PRSs) may further motivate health behaviour change in primary care for lung cancer in diverse populations. In this work, we introduce the GREAT care paradigm, which integrates PRSs within comprehensive patient risk profiles to motivate positive health behaviour changes. METHODS: We developed PRSs using large-scale multi-ancestry genome-wide association studies and standardised PRS distributions across all ancestries. We validated our PRSs in 561,776 individuals of diverse ancestry from the GISC Trial, UK Biobank (UKBB), and All of Us Research Program (AoU). FINDINGS: Significant odds ratios (ORs) for lung cancer and difficulty quitting smoking were observed in both UKBB and AoU. For lung cancer, the ORs for individuals in the highest risk group (top 20% versus bottom 20%) were 1.85 (95% CI: 1.58-2.18) in UKBB and 2.39 (95% CI: 1.93-2.97) in AoU. For difficulty quitting smoking, the ORs (top 33% versus bottom 33%) were 1.36 (95% CI: 1.32-1.41) in UKBB and 1.32 (95% CI: 1.28-1.36) in AoU. INTERPRETATION: Our PRS-based intervention model leverages large-scale genetic data for robust risk assessment across populations, which will be evaluated in two cluster-randomised clinical trials. This approach integrates genomic insights into primary care, promising improved outcomes in cancer prevention and tobacco treatment. FUNDING: National Institutes of Health, NIH Intramural Research Program, National Science Foundation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".