Abstract PO-090: Characterizing the intra-tumoral microbiome of laryngeal squamous cell carcinoma
Bibliographic record
Abstract
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.
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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 | 0.000 |
| 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) | 0.001 | 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".