Genomic insights for personalised care in lung cancer and smoking cessation: motivating at-risk individuals toward evidence-based health practices

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.

Keywords: Cancer prevention; Health behaviour change; Lung cancer; Personalised interventions; Polygenic risk scores; Translational roadmap.

Fig. 1

Care Paradigm: Genomic Informed Care for Motivating High Risk Individuals Eligible for Evidence-based Prevention (GREAT). The GREAT framework is a primary care paradigm that integrates genetic and clinical risk in precision health. Individuals and their providers in two upcoming trials (PRECISE and MOTIVATE) are enrolled and provided with multilevel interventions (e.g. RiskProfile and PrecisionTx) to promote clinical outcomes of lung cancer screening, tobacco treatment, and successful smoking cessation in primary care settings. Mechanisms of health behaviour changes (e.g. perceived benefit, self-efficacy, and outcome expectancy) will be evaluated. During the specific actionable recommendations phase, personalised shared decision-making will be facilitated by multilevel actions between patients and clinicians for better clinical outcomes.

Fig. 2

Roadmap for translating genetic data to a genetic risk profile as a multilevel intervention in primary care. In step 1, enrolled participants' genetic data are analysed by 23andMe's Clinical Laboratory Improvement Amendments (CLIA) certified genotyping process. Imputation and quality controls are conducted through the Trans-Omics for Precision Medicine (TOPMed) server to ensure the integrity and reliability of the genetic data, as well as to impute the GWAS variants. Step 2 involves identifying available GWAS variants and weights to create the raw Polygenic Risk Scores (PRS). The PRS is adjusted for genetic ancestry using reference data such as the 1000 Genomes Project Phase 3 and applied to validation data such as the UK Biobank to establish risk categories and compute ORs. In step 3, these scores are converted into 3 risk levels based on the established thresholds. In step 4, a report with precision treatment is created and communicated to both the participant and the provider to make informed and educated decisions. Behavioural interventionists (research staff who are trained, certified, and supervised by a team of genetic counsellor, psychologist, and psychiatrist) offer personalised guidance on behaviour change, leveraging the updated genetic insights. The outcome aims to increase lung cancer screening orders, improve participant adherence, promote smoking cessation, and highlight the benefits of tobacco treatment.

Fig. 3

Cross-dataset distribution of genetic ancestry via PCA Projections in 1000G, GISC, UKBB, and AoU. This figure illustrates the utility of principal components analysis (PCA) loadings obtained from the 1000 Genomes Project Phase 3 (1000G) in discriminating ancestries within external datasets, the Genetically Informed Smoking Cessation (GISC) trial. PCA was initially conducted on the globally diverse genotype data of 1000G. The resultant PCA-space was then used to project genotype data from GISC, UKBB, and AoU. The scatter plot displays the first and second PCs for each individual in these datasets, with points distinctly marked by genetically inferred ancestry.

Fig. 4

Ancestry adjustment of PRS for lung cancer and quit difficulty PRS across ancestral populations. We showcase the adjustment process for PRS for (a) lung cancer and (b) difficulty quitting smoking within the 1000 Genomes Project, GISC Trial, UK Biobank, and All of Us datasets. It displays both raw and ancestry-adjusted PRS, with data points color-coded according to genetically inferred ancestries. EAS, AMR, and SAS ancestries were removed for GISC due to their small sample sizes. Ancestry adjustment effectively centres the PRS for different ancestries, mitigating the risk of incorrect stratification due to ancestry-related biases. Dotted vertical lines correspond to the 20th and 80th percentiles for lung cancer PRS distribution and 33rd and 67th percentiles for difficulty quitting smoking PRS among all 3202 samples in the 1000 Genomes Project.

Fig. 5

Risk stratification for lung cancer and difficulty quitting smoking using raw and ancestry-adjusted PRS. This figure illustrates adjusted odds ratios with associated 95% confidence intervals of PRSs for (a) lung cancer and (b) difficulty quitting smoking among UK Biobank (N = 340,154 for lung cancer and N = 152,406 for difficulty quitting smoking), and All of Us (N = 210,826 for lung cancer and N = 152,916 for difficulty quitting smoking) participants. For difficulty quitting smoking, we adjusted for age, sex, and 20 ancestry PCs. For lung cancer, we additionally adjusted for smoking status (ever-smoker, never-smoker, or no-response). We compared risk stratification using a raw PRS with ancestry-matched percentiles, and our ancestry-adjusted PRS with the same percentiles for all individuals.

Fig. 6

Example 1 clinical report: RiskProfile. We present example 1 for genomically informed interventions using the GREAT framework. RiskProfile is designed to motivate lung cancer screening and tobacco treatment among screening-eligible patients. This intervention utilises ancestry-adjusted PRS to stratify patients into “at risk” (yellow), “high risk” (orange), and “very high risk” (red) genetic risk categories. RiskProfile focuses on prevention and expands beyond personalised risk to also provide personalised benefit of cancer screening and use a multilevel intervention design directed to both physicians and patients in clinical settings. In our PRECISE trial (NCT05627674), the effect of RiskProfile on clinician ordering and patient completion of lung cancer screening will be evaluated.

Fig. 7

Example 2 clinical report: PrecisionTx. We present example 2 for genomically informed interventions using the GREAT framework. PrecisionTx is designed to motivate tobacco treatment among patients who smoke. This intervention utilises ancestry-adjusted PRS to stratify patients into “at risk” (yellow), “high risk” (orange), and “very high risk” (red) genetic risk categories. PrecisionTx focuses on treatment and expands beyond personalised risk to also provide personalised benefit of tobacco treatment and use a multilevel intervention design directed to both physicians and patients in clinical settings. In our MOTIVATE trial (NCT05846841), the effect of PrecisionTx on clinician ordering, patient adherence, and smoking abstinence will be evaluated.