A generalizable cross-continent prediction of esophageal squamous cell carcinoma using the oral microbiome

Esophageal squamous cell carcinoma (ESCC) has a poor prognosis and limited tools for early detection. Saliva is easily accessible and its microbiome composition can serve as a marker for upper gastrointestinal tract disease. This study aims to evaluate the potential of an oral microbiome signature for classifying ESCC. In a cross-sectional study of 48 ESCC patients and 110 controls from South Africa, a region with high ESCC incidence, we studied the potential utility of an oral microbiome signature for the disease. We built models using nested cross-validation to evaluate whether this signature is generalizable to held-out samples and further evaluated generalizability in studies from China, a distinct geographic region. We find significant alterations in the oral microbiome in patients with ESCC including significantly reduced α diversity and increased abundance of Fusobacterium nucleatum. We also find that logistic regression models based on microbiome data can better classify ESCC in held-out samples (auROC=0.96) compared to clinical and demographic data (auROC = 0.69; DeLong p < 1 x 10-8). Lastly, we find that microbiome-based models trained across multiple studies can generalize well to geographically distinct studies. Our results show that the oral microbiome in individuals with ESCC is distinct from controls and that this signal can generalize across unseen samples, suggesting the potential of saliva to serve as a non-invasive screening tool for ESCC. Esophageal squamous cell carcinoma (ESCC) is a type of cancer that starts in the cells lining the esophagus, which connects the throat and stomach. It has a poor prognosis, and there is a need for non-invasive diagnostic methods. Saliva is easily accessible and the abundances of microbes in the oral cavity (the “oral microbiome”) can potentially be used to indicate the presence of cancer in the esophagus. This study explores the ability of computational models to use oral microbiome data to identify the presence of ESCC. Our results demonstrate that individuals with ESCC have a noticeably different oral microbiome compared to healthy individuals. We also show that classification models can distinguish ESCC across different geographical regions. These findings pave the way for the development of non-invasive tools for diagnosing ESCC. ElNaggar et al. evaluate the potential of the oral microbiome to classify esophageal squamous cell carcinoma (ESCC). They find that there is a distinct oral microbiome signature in patients with ESCC, and that oral microbiome-based models can classify ESCC across geographically distinct studies.