Abstract PO-081: Patterns of immune susceptibility in young, non-smoking, oral cancer patients
Bibliographic record
Abstract
Abstract Introduction: Oral squamous cell carcinoma (OSCC) is peculiarly increasing in non-smokers, many of whom are younger than traditional, smoking related OSCC. The cause of this trend is unknown, and it is unclear whether these cancers represent a distinct disease, as compared to smoking-related OSCC. We hypothesize that young non-smokers with OSCC have immunological differences that increase susceptibility to infectious and immunological disease (including cancer) as compared to smokers with OSCC. Therefore, we investigated the incidence of bacterial, viral, and autoimmune illness in young OSCC patients. Methods: We performed a retrospective cohort study at a single tertiary care institution of patients under age 55 diagnosed with OSCC from 2003-2021. Multiple encounters for the same ICD-10 code in the same year were counted as one. Bacterial infections included bacterial pneumonia (ICD-10 J13-J15), cellulitis and lymphangitis (L03), urinary tract infection (N39.0), and post-surgical infection (T81.4). Viral infections included viral pneumonia (J10-J12), skin herpesvirus (A60, A63, B00, B07), infectious mononucleosis (B27), herpes zoster-related infections (B02), HIV (B20), viral hepatitis (B15-19), CMV disease (B25), viral conjunctivitis (B30), and other unclassified viral diseases (B33-34). Autoimmune disease included rheumatoid arthritis (M05), systemic Lupus (M32, L93), psoriasis (L40), inflammatory bowel disease (K50-52), Sjogren disease (M35.0), type 1 diabetes (E10), multiple sclerosis (G35), myasthenia gravis (G70), and autoimmune thyroiditis (E06.3). When patients were known to be deceased, but date of death was unavailable, they were presumed deceased at date of last encounter. Kaplan Meier analysis was used to analyze survival. Poisson regression was used to model the incidence of illness. Results: In total, 749 patients were analyzed; 467 were never smokers (NS) and 282 were current or former smokers (S). In NS, median age (interquartile range) was 47.9 (40.4-51.9) and 267 (57.2%) were male. In S, median age was 49.3 (44.6-52.7) and 208 (73.8%) were male. Five-year OS was 89% (95% confidence interval 86-93%) in NS vs. 89% (85%-93%) in S (p=0.66). The incidence of bacterial infection was 25.9 per 1000 person-years (py) in NS vs. 37.0/1000py in S (p<0.01). The incidence of viral infection was 37.8/1000py in NS vs. 31.4/1000py in S (p=0.14). The incidence of autoimmune disease was 26.7/1000py in NS and 36.0/1000py in S (p=0.02). Conclusions: NS were more likely to be female vs. S. OS was similar between NS and S. The incidence of bacterial infections and autoimmune diseases were significantly higher in S vs. NS, in line with pre-existing evidence that smoking is associated with increased susceptibility to bacterial infection and increased incidence and severity of autoimmune disease. Interestingly, there was a trend towards increased viral infections in NS vs. S, although not statistically significant. In the context of pre-existing evidence showing that smoking increases risk of viral infection, this finding warrants further study. Citation Format: Maxwell Y. Lee, Fred M. Baik, John B. Sunwoo. Patterns of immune susceptibility in young, non-smoking, oral cancer patients [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-081.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".