Kodikara Saritha, Mao Jiadong, San Valentin Erin Marie D, Do Kim-Anh, Reyes-Gibby Cielito C, Lê Cao Kim-Anh
Melbourne Integrative Genomics, School of Mathematics and Statistics, The University of Melbourne, Royal Parade, 3052, Victoria, Australia.
Department of Dermatology, Case Western Reserve University, Cleveland, Ohio, USA.
bioRxiv. 2025 Jul 18:2025.07.15.665024. doi: 10.1101/2025.07.15.665024.
Oral 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 interventions. 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.
We 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, . positively associated with calibrated oral mucositis score across all clusters, while () was significantly enriched only in patients experiencing rapid oral mucositis progression. Conversely, genera associated with oral health, including , and , were negatively correlated with calibrated oral mucositis score.
Distinct 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, 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.
口腔黏膜炎是接受癌症治疗的头颈癌患者中常见的一种疼痛性并发症。新出现的证据表明,口腔微生物群的变化可能导致口腔黏膜炎的发生,使微生物特征成为治疗干预的潜在靶点。本研究旨在:(1)描述头颈癌患者口腔黏膜炎严重程度的纵向微生物模式;(2)根据口腔黏膜炎严重程度轨迹确定临床相关的患者聚类;(3)识别这些聚类特有的微生物特征。
我们对140名头颈癌患者的纵向微生物组数据应用非负稀疏主成分分析,得出了一个校准的口腔黏膜炎评分,该评分基于七个与口腔黏膜炎相关的症状评级。功能数据分析和层次聚类确定了三个不同的患者聚类,其口腔黏膜炎进展的微生物轨迹不同。一个聚类中的患者在治疗开始后口腔黏膜炎严重程度迅速增加,而其他聚类则显示出更渐进的增加。人口统计学比较显示,各聚类之间在年龄和体重分布上存在显著差异,年龄较大、体重较轻的患者在口腔黏膜炎进展较为渐进的聚类中更为常见。偏最小二乘仿冒分析确定了聚类特有的微生物特征:值得注意的是, 在所有聚类中与校准的口腔黏膜炎评分呈正相关,而 ( )仅在口腔黏膜炎进展迅速的患者中显著富集。相反,与口腔健康相关的属,包括 、 和 ,与校准的口腔黏膜炎评分呈负相关。
头颈癌患者口腔黏膜炎评分的不同轨迹与特定的口腔微生物谱和人口统计学因素有关。聚类特有的微生物谱的识别突出了以微生物组为靶点的干预措施在控制口腔黏膜炎严重程度方面的潜力。虽然大多数分类群是聚类特有的,但 在各聚类中始终位列与校准的口腔黏膜炎评分呈正相关的顶级分类群之中,这表明它可能无法区分患者群体,而是反映了整体疾病严重程度。