Oral-microbiome-derived signatures enable non-invasive diagnosis of laryngeal cancers
Researched by:
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Karen Pendergrass
ID
Karen Pendergrass
Karen Pendergrass is a microbiome researcher specializing in microbiome-targeted interventions (MBTIs). She systematically analyzes scientific literature to identify microbial patterns, develop hypotheses, and validate interventions. As the founder of the Microbiome Signatures Database, she bridges microbiome research with clinical practice. In 2012, based on her own investigative research, she became the first documented case of FMT for Celiac Disease—four years before the first published case study.
Fact-checked by:
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Karen Pendergrass
ID
Karen Pendergrass
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Karen Pendergrass
Researched by:
-
Karen Pendergrass
ID
Karen Pendergrass
Fact-checked by:
-
Karen Pendergrass
ID
Karen Pendergrass
Microbiome Signatures identifies and validates condition-specific microbiome shifts and interventions to accelerate clinical translation. Our multidisciplinary team supports clinicians, researchers, and innovators in turning microbiome science into actionable medicine.
Karen Pendergrass
DOI: https://doi.org/10.1186/s12967-023-04285-2
What Was Studied?
The study investigated the use of oral microbiome-derived signatures for the non-invasive diagnosis of laryngeal squamous cell carcinoma (LSCC). By analyzing the oral rinse and tissue microbiome samples of patients, the study aimed to identify a microbiome signature associated with LSCC and develop a predictive classifier for early and non-invasive detection using oral microbiota.
Who Was Studied?
The study included 153 patients, divided into two groups: 77 patients with pathologically confirmed LSCC and 76 control patients with vocal cord polyps. Participants were matched by age and gender and excluded if they had recent antibiotic use, specific infections, or prior cancer treatments. The male-to-female ratio was 8.6:1, and most patients were between 56 and 70 years old.
What Were the Most Important Findings?
The study identified significant differences in microbial composition between LSCC and control samples. Tumor tissue samples showed an elevated abundance of genera such as Fusobacterium, Pseudomonas, and Acinetobacter, while genera like Ralstonia, Streptococcus, and Lactobacillus were reduced. In oral rinse samples, notable genera such as Saccharopolyspora and Actinobacillus were enriched in LSCC patients. Using these microbial signatures, a random forest classifier was developed, achieving an accuracy of 85.7% for LSCC detection from oral rinse samples. The study also revealed that oral rinse samples had lower within-group variation compared to tissue samples, indicating their potential as stable biomarkers.
What Are the Greatest Implications of This Study?
The study demonstrates that oral microbiota can serve as a reliable, non-invasive biomarker for the early detection of LSCC, which is critical given the challenges of early diagnosis in clinical settings. The findings suggest a potential for microbiome-based liquid biopsy technologies, paving the way for cost-effective, accessible diagnostic tools. These results also highlight the importance of microbiome dysbiosis in LSCC and support integrating microbiome signatures research into clinical oncology for early cancer detection.