Head and neck squamous cell carcinoma (HNSCC) includes diverse cancers arising in the oral cavity, oropharynx, and larynx, with the main risk factors being environmental exposures such as tobacco, alcohol, and human papillomavirus (HPV) infection. The genetic factors contributing to susceptibility across different populations and tumour subsites remain incompletely understood. Here we show, through a genome-wide association and fine mapping study of over 19,000 HNSCC cases and 38,000 controls from multiple ancestries, 18 genetic risk variants and 11 signals from fine mapping of the human leukocyte antigen (HLA) region, all previously unreported. rs78378222, a regulatory variant for TP53 is associated with a 40% reduction in overall HNSCC risk. We also identify gene-environment interactions, with BRCA2 and ADH1B variants showing effects modified by smoking and alcohol use. Subsite-specific analysis of the HLA region reveals distinct immune-related associations across HPV-positive and HPV-negative tumours. These findings refine the genetic architecture of HNSCC and highlight mechanisms linking inherited variation, immunity, and environmental exposures.

• MeSH
• Alcohol Dehydrogenase genetics   MeSH
• Genome-Wide Association Study MeSH
• Squamous Cell Carcinoma of Head and Neck * genetics   MeSH
• Genetic Predisposition to Disease MeSH
• HLA Antigens genetics   MeSH
• Papillomavirus Infections genetics   complications   MeSH
• Gene-Environment Interaction MeSH
• Polymorphism, Single Nucleotide MeSH
• Smoking adverse effects   MeSH
• Middle Aged MeSH
• Humans MeSH
• Tumor Suppressor Protein p53 genetics   MeSH
• Head and Neck Neoplasms * genetics   MeSH
• Alcohol Drinking MeSH
• BRCA2 Protein genetics   MeSH
• Risk Factors MeSH
• Carcinoma, Squamous Cell * genetics   MeSH
• Case-Control Studies MeSH
• Check Tag
• Middle Aged MeSH
• Humans MeSH
• Male MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Names of Substances
• ADH1B protein, human MeSH Browser
• Alcohol Dehydrogenase MeSH
• BRCA2 protein, human MeSH Browser
• HLA Antigens MeSH
• Tumor Suppressor Protein p53 MeSH
• BRCA2 Protein MeSH
• TP53 protein, human MeSH Browser

Alternative polyadenylation (APA) modulates mRNA processing in the 3'-untranslated regions (3' UTR), affecting mRNA stability and translation efficiency. Research into genetically regulated APA has the potential to provide insights into cancer risk. In this study, we conducted large APA-wide association studies to investigate associations between APA levels and cancer risk. Genetic models were built to predict APA levels in multiple tissues using genotype and RNA sequencing data from 1,337 samples from the Genotype-Tissue Expression project. Associations of genetically predicted APA levels with cancer risk were assessed by applying the prediction models to data from large genome-wide association studies of six common cancers among European ancestry populations: breast, ovarian, prostate, colorectal, lung, and pancreatic cancers. A total of 58 risk genes (corresponding to 76 APA sites) were associated with at least one type of cancer, including 25 genes previously not linked to cancer susceptibility. Of the identified risk APAs, 97.4% and 26.3% were supported by 3'-UTR APA quantitative trait loci and colocalization analyses, respectively. Luciferase reporter assays for four selected putative regulatory 3'-UTR variants demonstrated that the risk alleles of 3'-UTR variants, rs324015 (STAT6), rs2280503 (DIP2B), rs1128450 (FBXO38), and rs145220637 (LDHA), significantly increased the posttranscriptional activities of their target genes compared with reference alleles. Furthermore, knockdown of the target genes confirmed their ability to promote proliferation and migration. Overall, this study provides insights into the role of APA in the genetic susceptibility to common cancers. Significance: Systematic evaluation of associations of alternative polyadenylation with cancer risk reveals 58 putative susceptibility genes, highlighting the contribution of genetically regulated alternative polyadenylation of 3'UTRs to genetic susceptibility to cancer.

• MeSH
• 3' Untranslated Regions * genetics   MeSH
• Genome-Wide Association Study * MeSH
• Genetic Predisposition to Disease * MeSH
• Polymorphism, Single Nucleotide MeSH
• Humans MeSH
• Quantitative Trait Loci MeSH
• RNA, Messenger genetics   metabolism   MeSH
• Cell Line, Tumor MeSH
• Neoplasms * genetics   MeSH
• Polyadenylation * MeSH
• Gene Expression Regulation, Neoplastic MeSH
• Check Tag
• Humans MeSH
• Male MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Names of Substances
• 3' Untranslated Regions * MeSH
• RNA, Messenger MeSH

BACKGROUND: Nineteen genomic regions have been associated with high-grade serous ovarian cancer (HGSOC). We used data from the Ovarian Cancer Association Consortium (OCAC), Consortium of Investigators of Modifiers of BRCA1/BRCA2 (CIMBA), UK Biobank (UKBB), and FinnGen to identify novel HGSOC susceptibility loci and develop polygenic scores (PGS). METHODS: We analyzed >22 million variants for 398,238 women. Associations were assessed separately by consortium and meta-analysed. OCAC and CIMBA data were used to develop PGS which were trained on FinnGen data and validated in UKBB and BioBank Japan. RESULTS: Eight novel variants were associated with HGSOC risk. An interesting discovery biologically was finding that TP53 3'-UTR SNP rs78378222 was associated with HGSOC (per T allele relative risk (RR)=1.44, 95%CI:1.28-1.62, P=1.76×10-9). The optimal PGS included 64,518 variants and was associated with an odds ratio of 1.46 (95%CI:1.37-1.54) per standard deviation in the UKBB validation (AUROC curve=0.61, 95%CI:0.59-0.62). CONCLUSIONS: This study represents the largest GWAS for HGSOC to date. The results highlight that improvements in imputation reference panels and increased sample sizes can identify HGSOC associated variants that previously went undetected, resulting in improved PGS. The use of updated PGS in cancer risk prediction algorithms will then improve personalized risk prediction for HGSOC.

• Publication type
• Journal Article MeSH
• Preprint MeSH