Characterization of the endometrial, cervicovaginal and anorectal microbiota in post-menopausal women with endometrioid and serous endometrial cancers.

Within the human body, there exists multitudes of microorganisms which colonize mucosal surfaces to form distinct microbial communities within body site niches. Together, these microorganisms outnumber human cells by a factor of 10 and are collectively termed the microbiome [ 1 ]. Bacterial communities existing in anatomic niches such as the gastrointestinal tract, oral cavity and genitourinary system can be classified as mutualists (symbiotically effective microbes), commensals (microbes that are neither harmful nor helpful to the host) and pathogens (colonizing microbes which may cause potential harm to the host) [ 2 ]. Mechanisms by which microbiota exert their influences on human health are not well-defined, but under certain circumstances the relative abundance of certain bacterial communities can become altered, thereby disrupting normal homeostasis and resulting in human disease [ 3 ]. Microbial disequilibrium can result in endothelial dysfunction, alteration of host immune systems, bacterial translocation, chronic inflammation, and global genomic instability, all of which are hallmarks of cancer [ 4 ]. Indeed multiple studies have linked alteration of the normal micriobiome with development of human cancers [ 5 – 9 ]. While disruption of the genitourinary microbiome may potentially promote gynecologic carcinogenesis, the exact role of the microbiome in gynecologic cancers remains unclear [ 10 ]. The vagina is a complicated microbial niche which allows for survival and proliferation of a number of both beneficial bacteria as well as opportunistic pathogens. The “ascending infection theory” postulates that disruption in the vaginal microbiome can allow pathogenic bacteria to ascend into the upper genital tract and cause polymicrobial infections, resulting in inflammation of the uterus, fallopian tubes and ovaries, a condition known as pelvic inflammatory disease (PID) [ 11 ]. As chronic inflammation is an etiologic factor in most cancers, this establishes a plausible hypothesis for how microbial dysregulation may promote tumor formation [ 12 ]. Furthermore, endometrial cancer is promoted by obesity, hormonal imbalances, diabetes and metabolic syndrome, all of which may promote changes in the microbiome [ 13 – 16 ]. The objective of the present study was to characterize the endometrial, cervicovaginal and anorectal microbiota of postmenopausal women undergoing hysterectomy for endometrioid and serous uterine cancers relative to controls undergoing hysterectomy for benign conditions.

A total of 35 patients had microbiome specimens collected and analyzed (14 with well-differentiated EAC, 11 with USC, and 10 controls) ( Table 1 ). The median age of the final cohort was 63.6 ± 9.2 years with no significant differences between groups (p = 0.47). Most patients were obese with a median BMI of 33.6 (IQR 26.3, 38.8) and there were no significant differences in BMI between groups (p = 0.10). There were no significant differences in race, ethnicity, medical comorbidities (diabetes, hypertension, hyperlipidemia) or smoking status across histologic groups. Of the 25 women with cancer, most (88.0%) had early stage (I or II) cancer with no significant differences between groups in terms of extent of disease (p = 0.57), tumor size (p = 0.68), or presence of lymphovascular space invasion (p = 0.13). Of the control women, 6 (60%) had uterine fibroids, 2 (20%) had ovarian cystadenomata, 1 (10%) had adenomyosis and 1 (10%) had pelvic organ prolapse. * Data with plus-minus values represent means ± standard deviation, otherwise reported as median (interquartile range). Categorical data are presented as N (%) associated with odds ratios and 95% confidence intervals. † Analysis based only on the 25 study subjects with cancer. The top 20 bacterial genera within the cervicovaginal, uterine and anorectal microbiota revealed significant community separation based on anatomical site ( Fig 1 ). The cervicovaginal microbiome was found to be dominated by Lactobacillus with additional elevated abundances of Prevotella and Gardnerella relative to other genera. There was a clear separation of the uterine microbiome from the other two niches and this body site was the most even in terms of genus diversity with a dominance of Flavobacterium . The anorectal microbiome was dominated by either Prevotella or Bacteroides (depending on the individual sample). PERMANOVA analysis using weighted unifrac distances revealed significant separation between the three anatomical niches ( Fig 2 , R2 = 0.25, p < 0.001). The 20 most abundant genera of the uterine, cervicovaginal and anorectal microbiota are shown and abundance is represented by color based on a log scale shown at the top left. The names of the bacterial genera are shown at the right of the heatmap. There is a clear separation of the uterine microbiome from the other two niches. The cervicovaginal and anorectal microbiota separate out with the exception of 6 samples. The PCoA performed using weighted unifrac distances after subsampling each sample library to 1,000 reads. Ellipses represent 95% confidence intervals for site based sample clustering. PERMANOVA analysis indicates significant community separation at the genus level based on anatomic location (R2 = 0.25, p < 0.001). Microbial alpha diversity analyses demonstrated that serous cancers were characterized by a significant reduction of diversity within taxa of the control uterine microbiome based on the Chao1 (p = 0.004) and Fisher (p = 0.007) measures with a reduction in Shannon diversity (p = 0.094) ( Fig 3 ). The anorectal and cervicovaginal microbiome alpha diversity was not correlated with case status with the exception of Chao1, which was reduced in endometrioid cancers (p = 0.026). The plot indicates that serous cancer is characterized by a significant reduction of the uterine microbiome diversity based on the Chao1 (p = 0.004) and Fisher (p = 0.007) measures with a marginally significant reduction in Shannon Diversity (p = 0.094). The Kruskal-Wallis (KW) statistic is shown at the top of each plot. ANCOM analysis was performed to identify biomarkers that can distinguish patients across the three groups following adjustment for patient age, race and BMI. Fig 4 shows composite hierarchical clustering performed using all biomarkers identified in the ANCOM analysis where W-stat>10 and FDR<0.05. Two distinct microbiome clusters were observed with cluster indicated as “Biomarker Cluster 1” having a significantly higher prevalence of serous cancer patients (7/10) as compared to “Biomarker Cluster 2” where the majority of the benign cases are found (n = 6/7) (p = 0.042). No significant differences were found when comparing the presence of benign cases vs. endometroid (p = 0.30) or endometroid vs. serous cases (p = 0.68). Biomarker cluster 1 had several bacterial genera elevated including uterine Pseudomonas while Biomarker cluster 2 was distinguished by a dominance of cervicovaginal Lactobacillus and Clostridium genera. Heatmap shows all ANCOM biomarkers with a W-stat>10 and FDR<0.05 clustered using the ward.D2 algorithm. Two microbial clades can be observed following k-means clustering based on the resulting dendrogram. Cluster designated as “Biomarker Cluster 1” has a significantly higher proportion of serous cancer cases (7/10) as compared to “Biomarker Cluster 2” where the majority of the benign cases are grouping (6/7) (p = 0.042). No significant separation is occurring with respect to the identified clusters and benign vs. endometroid samples (p = 0.30) or the endometroid vs. serous samples (p = 0.68). “Biomarker Cluster 1” has a significant depletion of cervicovaginal Lactobacillus and Clostridium and a higher overall abundance of uterine Pseudomonas . In order to identify potentially relevant functional pathways that may be associated with the serous cancer associated microbiome clusters, PICRUSt analysis was performed at KEGG level 3. Fig 5A shows PCA within the cervicovaginal region. A total of 49 pathways were identified to be significant ( S1 Fig ). In order to present the data in a concise manner, only pathways with a corrected FDR<0.05 and a fold change of 3 or greater are shown in Fig 5B . Similarly Fig 5C shows the PCA performed for the uterine samples with significantly differentially abundant pathways after correction for multiple testing (FDR<0.05) in Fig 5D . Panel A shows PCA performed using the KEGG level 3 for bacteria present within the cervicovaginal samples. Full sample separation can be observed across the principle component (PC1, 65.5%) between the two-microbiome clusters. Panel B shows significantly differentially abundant pathways within the cervicovaginal samples that have a minimum fold ratio of 3 or greater between the two clusters. Panel C shows the PCA performed using the uterine samples. Panel D shows significantly differentially abundant pathways within the uterine samples with the “Chaperone and folding catalyst” pathway highlighted in red.

The microbial diversity of anatomical ecological niches in postmenopausal women with EAC and USC is different compared to benign controls. This difference is both in terms of community structure as defined by a reduction of microbial alpha diversity within the uterus and by differences of bacterial taxa within the cervicovaginal and uterine regions. Microbial composition and presence of specific functional pathways may be necessary for development of endometrial cancer and further investigation using prospective datasets is warranted.

This prospective study was approved by the Albert Einstein College of Medicine Institutional Review Board and the Protocol Review and Monitoring Committee. Eligible subjects scheduled for hysterectomy were recruited from a gynecologic oncology clinic from September 2016 thru April 2019. Patients meeting inclusion criteria were approached initially by a gynecologic oncologist; those expressing interest in participation then met with a clinical study coordinator and were given the option of enrollment in the study and those willing provided informed written consent. Women were considered eligible for inclusion if they were English or Spanish-speaking, post-menopausal (defined as 50 years of age or older who have not had menses for 12 consecutive months or more) with biopsy proven well-differentiated endometrioid endometrial adenocarcinoma (EAC), uterine serous carcinoma (USC) or with non-cancerous conditions requiring hysterectomy. Exclusion criteria included history of prior cancer, prior chemotherapy or radiation therapy, prior bariatric surgery, history of human immunodeficiency virus or PID, use of douching, hormone therapy, systemic or local antibiotics, pro-biotics or anti-fungal medications within 2 weeks of initial consultation, or confirmed urinary tract or vaginal infection such as bacterial vaginosis, sexually transmitted disease or candidiasis within 1 month of initial consultation. A total of 55 potentially eligible patients were evaluated during the study period. Of these, 11 women had unexpected final pathology results rendering them ineligible, 8 lacked specimen collection and 1 was determined after enrollment to be pre-menopausal. These women were excluded from the study. After induction of anesthesia and prior to antibiotic administration and vaginal preparation, the patient was placed in dorsal lithotomy position. A sterile bi-valve speculum was inserted inside the vagina to expose the uterine cervix and vaginal canal. A sterile q-tip applicator was used to swab the vaginal fornices and ectocervix and was then placed into a 1 mL tube of Specimen Transport Medium (STM) (Digene Female Swab Specimen Collection Kit, Qiagen, CA). Anorectal swabs were obtained by inserting a nylon q-tip applicator approximately 1 cm beyond the anal verge and then placed in a tube containing STM. The patient was then surgically prepped and the hysterectomy proceeded as planned. After removal of the uterus, the specimen was placed on a sterile field and opened coronally using a sterile scalpel in order to expose the endometrial cavity. Swabs were obtained from the endometrial cavity and placed in STM as described above. The surgical specimen was then handed over to pathology for routine clinical diagnostic testing. All research samples were stored in a -20° freezer within one hour of collection. After pathologic confirmation of hysterectomy specimens as containing either non-cancerous tissue, well-differentiated endometrioid carcinoma or uterine serous cancer, clinical and pathologic information was abstracted from the medical record and stored in a secure database. Collected variables included patient age, race, ethnicity, body mass index (BMI in kg/m 2 ), parity, chart-indicated diagnosis of medical comorbidities (diabetes, hypertension, cardiovascular disease, smoking status), stage of cancer, tumor size, and presence of lymphovascular space invasion. Continuous variables were reported as means ± standard deviations. Categorical data were presented as number of patients with percentages. Bivariate analysis was performed to assess differences of covariates between groups (benign, endometrioid and serous). Continuous variables were assessed using one way analysis of variance (ANOVA) or Kruskal-Wallis tests as appropriate, whereas categorical and dichotomous variables were examined using x 2 or Fisher exact tests as appropriate. Data analysis was performed using Stata 14.2. DNA from samples stored in STM was extracted using the QIAamp DNA Kit (Qiagen, CA) following their standard protocol, modified by pre-incubation with Proteinase K and agitation with glass beads. Sterility was maintained by processing these samples in a sterile Biosafety Cabinet in an isolated extraction room. An aliquot of DNA from each STM sample was PCR amplified using barcoded primers annealing to conserved sequences within the V4 region (~250 base pairs) of the 16S ribosomal RNA (rRNA) gene. The 16SV4 rRNA region was chosen because of it’s robust ability to effectively distinguish between vaginal bacterial species when compared to alternate 16S regions [ 17 ]. Barcoded and purified DNA libraries were sequenced using an Illumina MiSeq (Illumina Inc., San Diego, CA) using paired-end reads. Paired end Illumina reads were left trimmed to remove bases that had a PHREAD quality score of 25 or lower using prinseq-lite [ 18 ]. Quality controlled reads were then demultiplexed using dual Golay barcodes [ 19 ] using denovobarcode [ 20 ]. Reads were then merged using PANDAseq [ 21 ] and processed using the QIIME2 platform [ 22 ]. Briefly, VSEARCH [ 23 ] was used to cluster sequences into operational taxonomic units (OTUs) and to assign taxonomy using the Greengenes [ 24 ] database. The phyloseq [ 25 ] package was used to import data into R [ 26 ] for final processing. Alpha and beta diversity was calculated using phyloseq [ 25 ] and ggplot2 [ 27 ] was used to make final figures. ANCOM [ 28 ] was used to identify bacterial markers for endometrial cancer with adjustment for multiple testing (FDR<0.05) as well as adjustment for age and race. Ward.D2 [ 29 ] algorithm was used to perform hierarchical clustering using identified biomarkers. ANCOM was utilized in order to overcome the compositional limitations inherent to the study of the microbiome. For more details on this issue and how ANCOM addresses it see the cited reference [ 30 ].

(TIF) Click here for additional data file.