Abstract PO-081: Patterns of immune susceptibility in young, non-smoking, oral cancer patients
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».