Abstract PR02: Mutational analysis of head and neck squamous cell carcinoma stratified by smoking status
Bibliographic record
Abstract
Abstract Smoking has historically been recognized as a negative prognostic factor in head and neck squamous cell carcinoma (HNSCC). This study aimed to assess the mutational differences between heavy smokers (>20 pack-years) and never smokers among the HNSCC patients within The Cancer Genome Atlas (TCGA). Single nucleotide variation (SNV) and copy number aberration (CNA) differences between heavy smokers and never smokers were compared within HPV-positive (n=67) and negative (n=431) TCGA HNSCC cohorts, and the impact of these mutations on survival was assessed. No genes were differentially mutated between smoking and never smoking patients with HPV-positive tumors. By contrast, in HPV-negative tumors, NSD1 and COL1A11 were found to be more frequently mutated in heavy smokers, while CASP8 was more frequently altered in never smokers. HPV-negative patients with NSD1 mutations experienced significantly improved overall survival compared with NSD1 wild-type patients. This improved prognosis was validated in an independent cohort of 77 oral cavity cancers and a meta-analysis that included two additional datasets (688 total patients, hazard ratio for death 0.44, 95% CI 0.30-0.65). NSD1 mutations are more common in HPV-negative heavy smokers and define a cohort with favorable prognosis, and may represent a clinically useful biomarker to guide treatment deintensification for HPV-negative patients. Citation Format: Farhad Ghasemi, Stephenie D. Prokopec, Danielle MacNeil, Neil Mundi, Christopher Howlett, William Stecho, Kevin Fung, John Yoo, Eric Winquist, Steven Gameiro, Axel Sahovaler, Paul Plantinga, Joe S. Mymryk, John W. Barrett, Paul C. Boutros, Anthony C. Nichols. Mutational analysis of head and neck squamous cell carcinoma stratified by smoking status [abstract]. In: Proceedings of the AACR-AHNS Head and Neck Cancer Conference: Optimizing Survival and Quality of Life through Basic, Clinical, and Translational Research; 2019 Apr 29-30; Austin, TX. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(12_Suppl_2):Abstract nr PR02.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".