Zhang Liangliang, San Valentin Erin Marie D, John Teny M, Jenq Robert R, Do Kim-Anh, Hanna Ehab Y, Peterson Christine B, Reyes-Gibby Cielito C
Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, Ohio, USA.
Cancer. 2024 Jan 1;130(1):150-161. doi: 10.1002/cncr.35001. Epub 2023 Sep 8.
This study investigated the influence of oral microbial features on the trajectory of oral mucositis (OM) in patients with squamous cell carcinoma of the head and neck.
OM severity was assessed and buccal swabs were collected at baseline, at the initiation of cancer treatment, weekly during cancer treatment, at the termination of cancer treatment, and after cancer treatment termination. The oral microbiome was characterized via the 16S ribosomal RNA V4 region with the Illumina platform. Latent class mixed-model analysis was used to group individuals with similar trajectories of OM severity. Locally estimated scatterplot smoothing was used to fit an average trend within each group and to assess the association between the longitudinal OM scores and longitudinal microbial abundances.
Four latent groups (LGs) with differing patterns of OM severity were identified for 142 subjects. LG1 has an early onset of high OM scores. LGs 2 and 3 begin with relatively low OM scores until the eighth and 11th week, respectively. LG4 has generally flat OM scores. These LGs did not vary by treatment or clinical or demographic variables. Correlation analysis showed that the abundances of Bacteroidota, Proteobacteria, Bacteroidia, Gammaproteobacteria, Enterobacterales, Bacteroidales, Aerococcaceae, Prevotellaceae, Abiotrophia, and Prevotella_7 were positively correlated with OM severity across the four LGs. Negative correlation was observed with OM severity for a few microbial features: Abiotrophia and Aerococcaceae for LGs 2 and 3; Gammaproteobacteria and Proteobacteria for LGs 2, 3, and 4; and Enterobacterales for LGs 2 and 4.
These findings suggest the potential to personalize treatment for OM.
Oral mucositis (OM) is a common and debilitating after effect for patients treated for squamous cell carcinoma of the head and neck. Trends in the abundance of specific microbial features may be associated with patterns of OM severity over time. Our findings suggest the potential to personalize treatment plans for OM via tailored microbiome interventions.
本研究调查了口腔微生物特征对头颈部鳞状细胞癌患者口腔黏膜炎(OM)病程的影响。
在基线、癌症治疗开始时、癌症治疗期间每周、癌症治疗结束时以及癌症治疗结束后评估OM严重程度并收集颊拭子。通过Illumina平台对16S核糖体RNA V4区域进行测序来表征口腔微生物组。使用潜在类别混合模型分析对OM严重程度轨迹相似的个体进行分组。使用局部加权散点平滑法来拟合每组内的平均趋势,并评估纵向OM评分与纵向微生物丰度之间的关联。
为142名受试者确定了四个具有不同OM严重程度模式的潜在组(LG)。LG1的OM评分在早期较高。LG2和LG3分别在第8周和第11周之前开始时OM评分相对较低。LG4的OM评分总体较为平稳。这些LG在治疗、临床或人口统计学变量方面没有差异。相关性分析表明,在四个LG中,拟杆菌门、变形菌门、拟杆菌纲、γ-变形菌纲、肠杆菌目、拟杆菌目、气球菌科、普雷沃氏菌科、贫养菌属和普雷沃氏菌_7的丰度与OM严重程度呈正相关。对于一些微生物特征,观察到与OM严重程度呈负相关:LG2和LG3中的贫养菌属和气球菌科;LG2、LG3和LG4中的γ-变形菌纲和变形菌门;以及LG2和LG4中的肠杆菌目。
这些发现表明个性化治疗OM具有潜力。
口腔黏膜炎(OM)是头颈部鳞状细胞癌患者常见且使人衰弱的后遗症。特定微生物特征丰度的变化趋势可能与OM严重程度随时间的模式相关。我们的发现表明,通过定制的微生物组干预措施,有潜力为OM制定个性化治疗方案。