Oral microbial signatures of head and neck cancer patients highlight diverse longitudinal patterns of oral mucositis severity
Kodikara, S.; Mao, J.; San Valentin, E. M. D.; Do, K.-A.; Reyes-Gibby, C.; Le Cao, K.-A.
Show abstract
BackgroundOral mucositis is a painful complication commonly observed in head and neck cancer patients receiving cancer treatment. Emerging evidence suggests that changes in the oral microbiome can contribute to oral mucositis development, making microbial signatures potential targets for therapeutic inter-ventions. This study aimed to: (1) characterize longitudinal microbial patterns of oral mucositis severity among head and neck cancer patients; (2) determine clinically relevant patient clusters based on oral mucositis severity trajectories; and (3) identify microbial signatures specific to these clusters. ResultsWe derived a calibrated oral mucositis score by applying non-negative sparse principal component analysis to seven oral mucositis related symptom ratings, using longitudinal microbiome data from 140 head and neck cancer patients. Functional data analysis and hierarchical clustering identified three distinct patient clusters with differing microbial trajectories of oral mucositis progression. One cluster exhibited patients with a rapid increase in oral mucositis severity following treatment initiation, while the other clusters displayed more gradual increase. Demographic comparisons revealed significant differences in age and weight distributions between clusters, with older, lighter patients more common in clusters experiencing more gradual oral mucositis progression. Partial least squares knockoff analysis identified cluster-specific microbial signatures: notably, Prevotella spp. positively associated with calibrated oral mucositis score across all clusters, while Alloprevotella (Alloprevotella0302) was significantly enriched only in patients experiencing rapid oral mucositis progression. Conversely, genera associated with oral health, including Haemophilus, Rothia, and Actinomyces, were negatively correlated with calibrated oral mucositis score. ConclusionsDistinct trajectories of oral mucositis scores in head and neck cancer patients are linked to specific oral microbial profiles and demographic factors. The identification of cluster-specific microbial profiles highlights the potential for microbiome-targeted interventions to manage oral mucositis severity. While most taxa were cluster-specific, Prevotella consistently ranked among the top taxa positively associated with the calibirated oral mucositis score across clusters, suggesting it may not differentiate between patient groups but rather reflects overall disease severity.
Matching journals
The top 8 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Microbial network inference for longitudinal microbiome studies with LUPINE 94%
- Defining the resilience of the human salivary microbiota by a 520 days longitudinal study in confined environment: the Mars500 mission 92%
- Seasonal shifts in the gut microbiome indicate plastic responses to diet in wild geladas 92%
Similar papers in this journal
- Multi-way modelling of oral microbial dynamics and host-microbiome interactions during induced gingivitis 93%
- The salivary microbiome shows a high prevalence of core bacterial members yet variability across human populations 92%
- Instance-based Transfer Learning Enables Cross-Cohort Early Detection of Colorectal Cancer 92%
Similar papers in this journal
Similar papers in this journal
- Gut Microbiome Dynamics and Predictive Value in Hospitalized COVID-19 Patients: A Comparative Analysis of Shallow and Deep Shotgun Sequencing 94%
- Comparative metatranscriptomics of periodontitis supports a common polymicrobial shift in metabolic function and identifies novel putative disease-associated ncRNAs 92%
- A resistome roadmap: from the human body to pristine environments 91%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.