Abstract PO-022: Characterizing genetic and molecular differences in head and neck cancer based on history of smoking
Bibliographic record
Abstract
Abstract Background: Even in the era of human papillomavirus (HPV)-associated disease, smoking remains an important cause of head and neck cancer (HNC). As genomic data emerges, it is important to examine whether there are molecular and genetic differences in HNC based on the history of smoking. Objective: This study aimed to characterize the genetic and molecular differences in a sample of patients with HNC based on smoking history. Methods: We used data from the Cancer Genome Atlas via cBioPortal for cancer genomics, and selected patients with a confirmed diagnosis of head and neck squamous cell carcinoma, independent of HPV status. Smoking history was defined as non-smokers (never smokers, n = 122) vs. smokers (current and previous history of smoking, n = 393). The genetic and molecular differences of interest were single nucleotide variation, copy number alteration, DNA methylation, mRNA expression and protein. Due to multiple testing, we report false discovery rate (FDR), with statistically significant FDR rate = 0.05. Results: The patients who were mainly White (85.8%), male (73.6%), diagnosed with stage IVA-C cancer (54.5%), and had a mean age of 60.8 years. There were significantly different copy number alterations on 10 genes, and the alterations were enriched in smokers (FDR < 0.05). Among these genes, two (FADD and CTTN) were significantly highly methylated in non-smokers (FDR<0.05), and one gene (ANO1) was marginally highly methylated in non-smokers (FDR=0.057). Four genes (PPFIA1, FGF19, CCND1 and LTO1) were highly expressed in mRNA in smokers (FDR< 0.05), while one gene (FADD) was marginally highly expressed in mRNA in smokers (FDR=0.079). FADD DNA methylation was negatively correlated with FADD mRNA expression in both non-smokers (Pearson r= -0.53, p<10−9) and smokers (Pearson r= -0.57, p<10−35). Among smokers, significant lower overall survival rate (p=0.033) among patients with FADD altered (median=35.3 months, CI 25.9-71.2) than those FADD unaltered (median=64.8 months, CI 48.2-89.3). Additionally, the CCND1 gene was highly mRNA expressed in smokers (FDR=0.007), and CCND1 mRNA expression was positively correlated with protein expressed in smokers (Pearson r=0.33, p<10−4) but not in non-smokers. For the CTTN gene, we found a significant high methylation in non-smokers (FDR=0.006) compared to smokers, but no significant difference in mRNA expression between smoking groups (FDR=0.17). Smokers with altered CTTN tended towards worse survival (p=0.069, median=35.8 months, CI 25.9-90.1) than unaltered CTTN ones (median=57.4 months, CI 48.2-88.8), and no difference in non-smokers. Conclusions: We found some significant genetic and molecular differences in HNC based on a history of smoking, especially for genes linked to mRNA overexpression. [which regulates tumor necrosis, apoptosis, and cell cycle] The findings suggest that there may be genetic and molecular differences in patients with HNC based on their history of smoking. However, further research is needed to confirm these findings in a larger study sample. Citation Format: Rong Jiang, Nosayaba Osazuwa-Peters, Tammara L. Watts. Characterizing genetic and molecular differences in head and neck cancer based on history of smoking [abstract]. In: Proceedings of the AACR-AHNS Head and Neck Cancer Conference: Innovating through Basic, Clinical, and Translational Research; 2023 Jul 7-8; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2023;29(18_Suppl):Abstract nr PO-022.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".