Abstract PO-090: Characterizing the intra-tumoral microbiome of laryngeal squamous cell carcinoma
Notice bibliographique
Résumé
Abstract Background: While the microbiome of oral squamous cell carcinoma has been intensely studied, little is known about the role of bacteria in laryngeal squamous cell carcinoma (LSCC). Our group recently demonstrated that pathogenic bacteria, such as Fusobacterium, are associated with oral cavity cancer and influence checkpoint ligand expression. To understand the potential role of bacteria in LSCC, we comprehensively profiled the intra-tumoral bacterial microbiome. Methods: DNA from tumor and adjacent normal tissue was isolated from formalin-fixed paraffin-embedded (FFPE) samples for 18 patients with early-stage (stage 1-2) and 27 patients with advanced-stage (stage 3-4) LSCC, diagnosed and treated between 2009 and 2020. 16S rRNA bacterial gene sequencing was performed. Established bioinformatics pipelines were used to characterize the intra-tumoral microbiome and correlate with clinical outcomes. Spatial profiling using Fluorescence In Situ Hybridization (FISH) with 16S bacterial probes was also performed. Machine learning algorithms were used to generate predictions relative to clinical outcomes. Results: Of the 27 patients with advanced-stage LSCC, the most commonly involved subsite was the supraglottis (N= 18, 66.7%), while the majority of the 18 patients with early-stage tumors had SCCA of the glottis (N=14, 78%). Bacterial alpha diversity in tumors was decreased when comparing the early and advanced-stage tumor samples with adjacent normal tissues. Advanced-stage tumors had significantly increased alpha diversity compared to early-stage tumors (p=0.021). With increased bacterial diversity of the advanced-stage tumors, there were also relative increases in pathogenic bacteria abundance, including Fusobacterium, Capnocytophaga, Prevotella, and Leptotrichia, when compared to early-stage tumor samples. There was a significant decrease in Rothia and Novosphingobium in the advanced-stage samples compared to normal adjacent and early-stage tumors (p<0.05). Using machine learning algorithms and receiver operating characteristic curves, the bacterial composition of tumor samples was able to effectively predict the group stage (AUC=0.83). In contrast, normal adjacent tissue microbiome composition was less likely to provide an accurate prediction (AUC=0.34). FISH revealed bacteria within the tumor cells and adjacent to cell membranes in all LSCC subsites examined. Conclusions: Little is known about the bacterial profile of LSCC. Here, we demonstrate that bacterial diversity increased in advanced-stage LSCC when compared to early-stage tumors. While advanced LSCC has a more diverse microbial community, this appears to be accounted for by increased pathogenic bacteria within the advanced-stage group, such as Fusobacterium (associated with oral and colon cancer), Capnocytophaga (associated with periodontitis), Prevotella (associated with GI disease and periodontitis), and Leptotrichia (associated with bacterial biofilms). Additional studies are needed to determine the mechanistic role of pathogenic bacteria in the development and progression of LSCC. Citation Format: Natalie Silver, David Hoying, Eric Lamarre, Brandon Prendes, Jamie Ku, Jin Dai, Daniel McGrail, Joseph Scharpf, August Culbert, Shauna Campbell, Emrullah Yilmaz, Jessica Geiger, Akeesha Shah, Jeffrey Myers, Kristiann Fredenburg, Neil Woody, Shlomo Koyfman. Characterizing the intra-tumoral microbiome of laryngeal squamous cell carcinoma [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-090.
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 | 0,000 |
| 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 | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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 ».