Genomic Insights for Personalised Care in Lung Cancer and Smoking Cessation: Motivating At-Risk Individuals Toward Evidence-Based Health Practices

Authors

Tony Chen
Giang Pham
Louis Fox
Nina Adler
Xiaoyu Wang
Jingning Zhang
Jinyoung Byun
Younghun Han
Gretchen R B Saunders
Dajiang Liu
Michael J Bray
Alex T Ramsey
James McKay
Laura J Bierut
Christopher I Amos
Rayjean J Hung
Xihong Lin
Haoyu Zhang
Li-Shiun Chen

Publication Date

11-8-2024

Journal

EBioMedicine

DOI

10.1016/j.ebiom.2024.105441

PMID

39520911

PMCID

PMC11583727

PubMedCentral® Posted Date

11-8-2024

PubMedCentral® Full Text Version

Post-Print

Published Open-Access

yes

Keywords

Lung cancer, Polygenic risk scores, Cancer prevention, Personalised interventions, Translational roadmap, Health behaviour change

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.