Abstract
The human microbiome has been strongly correlated with disease pathology and outcomes, yet remains relatively underexplored in patients with malignant endometrial disease. In this study, vaginal microbiome samples were prospectively collected at the time of hysterectomy from 61 racially and ethnically diverse patients from three disease conditions: (i) benign gynecologic disease (controls, n = 11), (ii) low-grade endometrial carcinoma (n = 30), and (iii) high-grade endometrial carcinoma (n = 20). Extracted DNA underwent shotgun metagenomics sequencing, and microbial alpha and beta diversities were calculated. Hierarchical clustering was used to describe community state types (CST), which were then compared by microbial diversity and grade. Differential abundance was calculated, and machine learning utilized to assess the predictive value of bacterial abundance to distinguish grade and histology. Both alpha- and beta-diversity were associated with patient tumor grade. Four vaginal CST were identified that associated with grade of disease. Different histologies also demonstrated variation in CST within tumor grades. Using supervised clustering algorithms, critical microbiome markers at the species level were used to build models that predicted benign versus carcinoma, high-grade carcinoma versus benign, and high-grade versus low-grade carcinoma with high accuracy. These results confirm that the vaginal microbiome segregates not just benign disease from endometrial cancer, but is predictive of histology and grade. Further characterization of these findings in large, prospective studies is needed to elucidate their potential clinical applications.
Significance: The vaginal microbiome reliably segregates not just benign gynecologic condition from endometrial cancer, but also predicts cancer grade and histology. Patterns of microbial abundance and gene expression should be increasingly considered as a factor in the evolution of precision medicine approaches, especially as they relate to cancer screening, disease pathogenesis, and patient-centered outcomes.