A genome-wide association study of non-HPV-related head and neck squamous cell carcinoma identifies prognostic genetic sequence variants in the MAP-kinase and hormone pathways

Genetic, clinical, lifestyle and sociodemographic risk factors for head and neck cancer: A UK Biobank study

INTRODUCTION

Despite a steady decline in tobacco smoking, head and neck cancer (HNC) incidence rates are on the rise. Therefore, novel risk factors for HNC are needed to identify at-risk patients at an early stage. Here, we used genetic, clinical, lifestyle, and sociodemographic data from UK Biobank (UKB) to evaluate the relative importance of known risk factors for HNC and identify novel predictors of HNC risk.

METHODS

All participants in the UKB between 2006 and 2021 were stratified into HNC cases and controls at baseline (cases: n =  534; controls: n =  501833) or during follow-up (cases: n =  1587; controls: n =  500246). A cross-sectional description of risk factors (clinical characteristics, lifestyle and sociodemographic) for HNC at baseline was performed, followed by multivariate Cox regression analysis (adjusted for age and sex) and gradient boosting machine learning to determine the relative importance of predictors (phenotypic predictors and SNPs) of HNC development after baseline.

RESULTS

In addition to known risk factors for HNC (age, male sex, smoking and alcohol consumption habits, occupation), we show that smoking cessation at ≤ 40 years of age is the strongest predictor of HNC risk. Although SNPs may play a role in HNC development, a predictive model containing phenotypic variables and SNPs (C-index 0.75) did not significantly outperform a model containing the phenotypic predictors alone (C-index 0.73).

CONCLUSION

Taken together, this study demonstrates that phenotypic variables such as past tobacco smoking habits, occupation, facial pain, education, pulmonary function, and anthropometric measures can be used to predict HNC risk.

A risk prediction model for head and neck cancers incorporating lifestyle factors, HPV serology and genetic markers

Head and neck cancer is often diagnosed late and prognosis for most head and neck cancer patients remains poor. To aid early detection, we developed a risk prediction model based on demographic and lifestyle risk factors, human papillomavirus (HPV) serological markers and genetic markers. A total of 10 126 head and neck cancer cases and 5254 controls from five North American and European studies were included. HPV serostatus was determined by antibodies for HPV16 early oncoproteins (E6, E7) and regulatory early proteins (E1, E2, E4). The data were split into a training set (70%) for model development and a hold-out testing set (30%) for model performance evaluation, including discriminative ability and calibration. The risk models including demographic, lifestyle risk factors and polygenic risk score showed a reasonable predictive accuracy for head and neck cancer overall. A risk model that also included HPV serology showed substantially improved predictive accuracy for oropharyngeal cancer (AUC = 0.94, 95% CI = 0.92-0.95 in men and AUC = 0.92, 95% CI = 0.88-0.95 in women). The 5-year absolute risk estimates showed distinct trajectories by risk factor profiles. Based on the UK Biobank cohort, the risks of developing oropharyngeal cancer among 60 years old and HPV16 seropositive in the next 5 years ranged from 5.8% to 14.9% with an average of 8.1% for men, 1.3% to 4.4% with an average of 2.2% for women. Absolute risk was generally higher among individuals with heavy smoking, heavy drinking, HPV seropositivity and those with higher polygenic risk score. These risk models may be helpful for identifying people at high risk of developing head and neck cancer.

Genome-wide association study of prevalent and persistent cervical high-risk human papillomavirus (HPV) infection

BACKGROUND

Genetic factors may influence the susceptibility to high-risk (hr) human papillomavirus (HPV) infection and persistence. We conducted the first genome-wide association study (GWAS) to identify variants associated with cervical hrHPV infection and persistence.

METHODS

Participants were 517 Nigerian women evaluated at baseline and 6 months follow-up visits for HPV. HPV was characterized using SPF10/LiPA25. hrHPV infection was positive if at least one carcinogenic HPV genotype was detected in a sample provided at the baseline visit and persistent if at least one carcinogenic HPV genotype was detected in each of the samples provided at the baseline and follow-up visits. Genotyping was done using the Illumina Multi-Ethnic Genotyping Array (MEGA) and imputation was done using the African Genome Resources Haplotype Reference Panel. Association analysis was done for hrHPV infection (125 cases/392 controls) and for persistent hrHPV infection (51 cases/355 controls) under additive genetic models adjusted for age, HIV status and the first principal component (PC) of the genotypes.

RESULTS

The mean (±SD) age of the study participants was 38 (±8) years, 48% were HIV negative, 24% were hrHPV positive and 10% had persistent hrHPV infections. No single variant reached genome-wide significance (p < 5 X 10- 8). The top three variants associated with hrHPV infections were intronic variants clustered in KLF12 (all OR: 7.06, p = 1.43 × 10- 6). The top variants associated with cervical hrHPV persistence were in DAP (OR: 6.86, p = 7.15 × 10- 8), NR5A2 (OR: 3.65, p = 2.03 × 10- 7) and MIR365-2 (OR: 7.71, p = 2.63 × 10- 7) gene regions.

CONCLUSIONS

This exploratory GWAS yielded suggestive candidate risk loci for cervical hrHPV infection and persistence. The identified loci have biological annotation and functional data supporting their role in hrHPV infection and persistence. Given our limited sample size, larger discovery and replication studies are warranted to further characterize the reported associations.