Abstract 7399: Polygenic risk score-based smoking and drinking behaviors and head and neck squamous cell cancer risk by HPV status
Bibliographic record
Abstract
Abstract Objective: HPV-positive and HPV-negative head and neck squamous cell carcinoma (HNSCC) are distinct entities from an epidemiological and biological standpoint. We aimed to evaluate the predictive performance of polygenic risk scores (PRS), which could be a surrogate when behavioral data are missing or deemed unreliable, for tobacco smoking (Smoking Initiation [SI], Age of Smoking Initiation, Cigarettes Per Day [CPD], Smoking Cessation) and alcohol consumption (Drinks Per Week [DPW]) and their impact on HNSCC risk stratified by HPV status. Methods: We analyzed data from 8013 participants in the VOYAGER consortium, including 4716 HNSCC cases and 3297 controls. PRS for the 5 phenotypes were generated based on the genome-wide significant variants (P < 5×10-8, r2 < 0.001) from the GSCAN consortium. Logistic regression models were used to evaluate the association between PRS and HNSCC risk, separately in HPV-positive and negative patients. A corresponding phenotype analysis based on actual smoking and alcohol behaviors is included for comparison. Results: All 5 PRS significantly predicted corresponding behaviors in the expected direction. For HPV-positive patients, higher PRS for SI and DPW were associated with a higher HNSCC risk. For HPV-negative patients, higher PRS for SI, CPD, and DPW had a higher risk of HNSCC. Phenotype analyses confirmed the predictive value of PRS and showed strong correlations between all 5 behaviors and HNSCC risk in both HPV-positive and negative patients. Conclusion: PRS-based models of smoking and drinking behaviors and are associated with HNSCC risk in both HPV-positive and negative patients. These findings highlight the role of a genetic predisposition to these behaviors as a surrogate marker influencing the risk of HNSCC. Citation Format: Tianzhichao Hou, Abhinav Thakral, Katrina Hueniken, Tom Dudding, Mark Gormley, Shama Virani, Andrew Olshan, Brenda Diergaarde, Tim Waterboer, Karl Smith-Byrne, Paul Brennan, David Neil Hayes, Eleanor C M Sanderson, Catherine Brown, Sophie Huang, David I. Conway, Cristina Canova, Scott V. Bratman, Anna Spreafico, Lorenzo Richiardi, John D. Almeida, Joel C. Davies, Laura Bierut, Mei Dong, Pagona Lagiou, Areti Lagiou, Jerry Polesel, Claire M. Healy, Mari Nygard, Antonio Agudo, Laia Alemany, Wolfgang Ahrens, Ivana Holcatova, Ariana Znaor, David P. Goldstein, Osvaldo Espin-Garcia, Rayjean J. Hung, Wei Xu, John JW. Lee, Geoffrey Liu. Polygenic risk score-based smoking and drinking behaviors and head and neck squamous cell cancer risk by HPV status [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 7399.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".