Introduction

The American Cancer Society estimates that in 2023, about 19,710 new cases of ovarian cancer (OC) will be diagnosed and about 13,270 individuals will die from OC in the United States(1). Unlike most other cancers, the mortality rate for OC has declined only slightly in the last 40 years due to lack of early diagnostic tests and effective treatments. The 5-year survival rate for all types of OC averages 49.7%, much lower than many other cancers(2). The high mortality rate of OC is linked to a lack of understanding of ovarian carcinogenesis and precursor lesions, contributing to the failure of early detection and late-stage diagnosis. Even the cell origins of various OC subtypes have not been fully appreciated until recently, with ovarian carcinoma representing a heterogeneous collection of cancer subtypes, each with distinct origins and molecular drivers(3). The Fallopian tube epithelium is the likely origin of most high-grade serous carcinomas, the most common subtype of ovarian carcinoma. There is a critical need for innovative research to illuminate the origins and pathogenesis of this deadly cancer, thus enabling targeted approaches for early detection, treatment and prevention.

Based on the epidemiological data, we propose a novel hypothesis for ovarian carcinogenesis: ascending infection with some genital tract bacteria leads to inflammation in the fallopian tubes and DNA damage to cells contributing to neoplastic transformation. The evidence supporting this hypothesis includes: 1) The fallopian tubes form a conduit between the lower genital tract and the pelvic cavity(4, 5). 2) Blocking the communication between the fallopian tubes and the environment, such as through tubal ligation or hysterectomy, results in lower ovarian cancer incidences(6-8). 3) Increased cellular and bacterial transit between the lower genital tract and the peritoneal cavity, as in endometriosis or pelvic inflammatory disease, is associated with increased ovarian cancer risks(9, 10). 4) Inhibiting ovulation through oral contraceptives may decrease ovarian cancer risks(11-13) by reducing the opportunities of incorporating ascending pathogens into the ovarian surface epithelium during ovulation-induced microtrauma. 5) The fallopian tubal epithelium has been implicated in recent studies as the site of origin for at least a substantial proportion of high-grade serous carcinomas(14-16). Furthermore, recent studies have demonstrated the presence of genital tract bacteria in the upper reproductive tract of women without known infections(17-20). Therefore, bacteria ascending from the lower genital tract may reside in the fallopian tubes and could induce a pro-inflammatory environment, which could influence neoplasia of the tubal or ovarian epithelium, with outcome dependent on species of bacteria, concentration, host factors, and duration of exposure.

Even though the above epidemiological studies have hinted to the possible connection between an altered microbiota in the fallopian tube and OC, few studies have examined this plausible hypothesis. Even the existence of a microbiota in the fallopian tube has not been convincingly demonstrated, in part due to the technical challenges of accessing this site in a sterile fashion. Several studies have provided evidence that the FT may not be sterile(21-23), but rigorous controls and large sample sizes are needed to assess the microbiota in low biomass samples. Here, we examine the fallopian tube microbiota from a large study population with collection of swabs in a sterile fashion in the operating room from both patients with ovarian cancer and those without. The fallopian tube bacterial concentrations and compositions were compared between two groups: women who are cancer free, and women who have any type of OC. We built in numerous controls in our study design to help differentiate likely contaminants from signal of a fallopian tube microbiome.

Results

Patient overview

A total of 187 patients who met the inclusion and exclusion criteria (see Methods) were enrolled in this study. The patient characteristics were different between the cancer and non-cancer patients in their age and menopausal status as summarized in Table 1, with a higher percentage of the ovarian cancer patients in the older and postmenopausal groups. Most ovarian cancers were diagnosed in stage III and IV, with high grade serous carcinoma being the most common histology subtype (Table 1). Laparotomy was more commonly performed in ovarian cancer patients (Table 1).

Table 1.

Sample overview and low biomass study design

854 biological samples and 147 non-biological laboratory controls were analyzed for microbiota composition (Supplemental Table 1). 369 swabs were collected from the fallopian tube and ovarian surfaces. We also designed additional layers of controls and collected from multiple sites for comparisons. To assess the microbial environment in the operating room, 130 “air swabs” were collected from the patient’s room. To assess the cervical microbiota, 152 cervical swabs were collected before surgical area preparation and vaginal sterilization. To assess the skin microbiota that may be introduced into the pelvic cavity during laparoscopic port insertion, 81 “laparoscopic port swabs” were collected after the port insertion and before the start of operation. To assess the microbiota in the abdominal cavity away from the surgical or cancer sites, 122 “paracolic gutter swabs” were collected from the right paracolic gutter before the start of operation. To assess potential contamination of DNA extraction reagents, during each batch of DNA extractions we included “buffer controls” that contained only DNA extraction buffer without any swab or other DNA. These 36 “buffer controls” were processed and then sequenced together with the swab samples. To assess potential contamination during PCR and sequencing, we also included multiple no-template PCR controls that contained only PCR reagents and water without extracted DNA or buffer (n=111 total) during each sequencing run (Supplemental Table 1).

Bacterial concentration

The bacterial concentrations of FT and ovarian surfaces averaged 2.5 copies of 16S rRNA genes/μl of DNA (standard deviation, SD 4.6). Compared to the buffer controls and the OR air swabs, the FT and ovarian surface swabs contained higher concentrations of bacterial DNA than the controls (p-value < 0.001) (Figure 1) (Supplemental Table 2). As expected, the cervical swabs contained thousands of times higher bacterial concentrations than the fallopian tube and ovarian surface swabs (p-value < 0.001) (Figure 1). The bacterial concentrations from the paracolic gutter were similar to those from the FT and ovarian surface swabs (p-value = 0.11) (Figure 1) (Supplemental Table 2).

Figure 1.

Fallopian tube microbiota analysis

In the raw sequencing analysis, 892 bacterial species were present with at least 100 reads in at least one sample. After a series of filtering as described in the Methods, 84 bacterial species were present in at least one cervical and one FT sample with over 100 reads while not present in non-biological and air swabs (Supplemental Table 3). These 84 bacterial species may represent the FT microbiota that are present in some women. When analyzing the 340 samples that contained at least one read of these 84 species, the PCA plot showed a high level of similarity between the fallopian tube/ovarian surface and the paracolic gutter swabs, indicating these species represent the shared microbiota community in the abdominal and pelvic cavity within each individual (Figure 2A). An overview of these 84 bacterial species and their presence in each fallopian tube/ovarian surface sample shows that some of the bacterial species are prevalent in multiple individuals, such as Klebsiella or Anaerococcus, while others are only present in a few individuals, such as Casaltella or BV-associated bacterium 2 (BVAB2) (Figure 3). Clustering did not identify any obvious groupings by processing batch, cancer status, menopausal status or age for these putative FT bacterial species (Figure 3). The Shannon index was calculated for each sample to determine if there were differences in bacterial diversity. There were 726 bacterial species present in the cervix, FT/ovarian surface, and paracolic gutter after filtering out the bacteria present in no-template controls and DNA extraction controls (i.e. after step #5 in Supplemental Table 3). The cervical samples had lower Shannon diversity index than the FT/ovarian surface and paracolic gutter samples, while the non-cancer versus OC samples did not show any statistically significant difference in Shannon diversity within each sample type other than the cervical samples (Figure 2B).

Figure 2.

Figure 3.

Comparison between ovarian cancer and non-cancer microbiota

We compared the prevalence of each of these 84 bacterial species in patients with or without OC (Figure 4). Some bacteria, such as Streptococcus parasanguinis or Neisseriaceae, are more common in FT/ovarian surface samples from OC patients, while others (for example, Ruminiclostridium, Dialister invisus, or Bacteroides dorei) are only present in OC patients albeit overall representation was low. After ranking the FT bacteria based on the prevalence difference, we found a clear shift in the microbiota of the OC patients when compared to the non-cancer patients. Interestingly, 90% of the top 20 species that were most prevalent in the FT of OC patients were bacteria that predominantly exist outside the female reproductive tract. Among these, 12 (60%) predominantly reside in the gastrointestinal tract, such as Klebsiella, Faecalibacterium prausnitzii, Ruminiclostridium, and Roseburia. Six of the top 20 species (or 30%) normally reside in the mouth, such as Streptococcus mitis, Corynebacterium simulans/striatum, and Dialister invisus. On the contrary, vaginal bacterial species, such as Corynebacterium amycolatum, Gardnerella, and Lactobacillus iners, are more prevalent in the FT from non-cancer patients, representing 75% of the top 20 bacterial species that are most prevalent in non-cancer patients (Figure 4). This pattern was also consistently observed in the paracolic gutter samples which share the same low biomass environment of abdominal and pelvic cavity (Figure 4), suggesting that the above findings of FT microbiota were not random. When the histology subtypes were considered in the analysis, we found that the most common subtype, serous carcinoma, had higher prevalence of almost all 84 FT bacterial species compared to the other OC subtypes (Figure 5), indicating a higher level of perturbance in the FT microbiota in the serous carcinoma cases.

Figure 4.

Figure 5.

Since the majority of laparotomy cases were performed in OC patients and the non-OC cases were mostly performed with laparoscopy or robotic-assisted laparoscopy, we evaluated the influence of the surgical type on the FT microbiota profiles. When only laparotomy cases were considered in the comparison of OC versus non-cancer patients, the pattern of prevalence of majority of the 84 FT bacterial species was nearly identical as the overall comparison including all surgical types (Supplemental Figure 1). When laparotomy cases were compared to laparoscopic or robotic cases within the non-cancer patients, the prevalence of most of the 84 FT bacterial species was similar between the surgical types (Supplemental Figure 2). These analyses demonstrate that the differences we observed in the putative FT microbiota between OC and non-OC patients were not caused by the differences in surgical approaches.

Discussion

In this large prospective study, we analyzed the microbiota on swabs collected in a sterile fashion in the operating room during surgery, including from the FT and ovarian surface in 187 patients with or without OC, with the objective of determining a FT microbiome and identifying microbiome differences associated with OC. After detailed analyses and filtering out likely contaminants, we found 84 bacterial species that are present in FT or ovarian surface, which may represent a FT microbiota in some women. Comparison between the OC and non-cancer cases revealed a significantly higher prevalence of bacterial species that predominantly do not reside in the reproductive tract in patients with OC, while the FT from non-cancer patients favored vaginal bacterial species. It is unclear whether OC leads to this microbiota shift or if the microbiota shift precedes OC.

We proposed a novel hypothesis of ovarian carcinogenesis based on epidemiological evidence before initiating our study, namely that ascending infection with some genital tract bacteria leads to inflammation in the FT and causes DNA damage to cells resulting in OC. Our study showed the existence of bacterial species at a low concentration in the FT and ovarian surface in most women without overt signs of infection. Some of these bacterial species overlapped between the fallopian tube and the cervical swabs, indicating potential ascension from the lower to the upper genital tract. Some other bacterial species are not typical vaginal microbiota, and the origins of these bacteria in the fallopian tube samples are less clear. Patients with OC, especially the serous carcinoma subtype, showed significantly higher prevalence of many of the bacterial species we identified as the putative FT microbiota, indicating a potential link between a perturbed FT microbiota and OC. The FT microbiota may be different in different OC histology subtype, which was consistent with a recent study with limited sample size(23).

One of the most interesting findings from our study is that most of the top bacterial species that were more prevalent in OC patients had predominant niche outside of female reproductive tract, normally residing in the gastrointestinal or oral tract. Some of these bacterial species were also identified in previous small studies examining the bacteria incorporated into the FT and ovarian cancer tissues, such as Anaerococcus, Klebsiella, Bacteroides and Streptococcus(23, 24). The origin and route of ascension of these bacteria to the FT and ovarian surfaces remain unknown. In late-stage cancers, even when there are no overt signs of gastrointestinal invasion, the permeability of the intestinal tract may be increased which can increase the prevalence of these bacterial species in the FT, ovarian surfaces, and paracolic gutters. The causal effect of bacterial or other pathogens on the FT inflammation or carcinogenesis will need to be investigated in future studies using preclinical models. However, our study provides a list of top bacterial candidates that can be further investigated.

Our approach in collecting swabs in a sterile fashion in the operating room before the surgical case starts is unique and has not been used in previous studies(21-23). This approach enables us to evaluate the microbiota in each individual patient at multiple sites to infer transfer among sites. Previous studies mostly used the tissues collected from the pathology department after gross examination, which can introduce contamination. These studies infrequently included negative controls to assess for environmental or laboratory contamination, which is critically important when assessing a low biomass microbiome(25). We collected swabs not only from the site of interest, i.e., fallopian tube and ovarian surface, but also from key control sites linked to cases, such as the air in the OR, the laparoscopic port, the paracolic gutter, and the cervix. These biological controls, along with the DNA extraction and PCR controls during the sample processing and sequencing processes, provide a fuller picture of the patient-specific microbiota and differentiate the biologically meaningful bacterial species from likely contaminants. The approach taken here may serve as a guide for others undertaking microbiome studies of low biomass environments, particularly when considering study design, types of controls, and analytical approaches(26). The sample size is among the largest in microbiome studies of low biomass sites, which is helpful in differentiating signal from the noise in this setting. All previous microbiome studies that included FT as a study site had less than 40 patients in each group(21-23).

There are some limitations to our study. Due to the cross-sectional nature of the study, we have limited ability to link specific bacteria to ovarian carcinogenesis, as we would need to demonstrate that exposure to bacteria precedes the cancer. However, identifying associations between FT microbiota and OC is a critical first step. Further investigations, especially backed by in vitro studies, are needed to test our initial hypotheses. Due to the large sample size, we processed the samples in five batches and sequenced the libraries on four plates. Even though we separated the cervical samples with the low-biomass samples on different sequencing plates to prevent cross-contamination, we sequenced the low-biomass samples on three different sequencing plates, which could introduce bias due to batch effects. However, all samples were processed by a single experienced technician, and each sequencing plate contained samples from different processing batches, which helped to diminish potential batch effects. By requiring the bacterial species be present in both fallopian tube/ovarian surface swabs and the cervical swabs, we may have missed part of the fallopian tube microbiota that may have originated from sources other than the lower genital tract.

In conclusion, in this large prospective study with extensive controls, we analyzed the FT swabs from 187 patients together with controls that were collected intraoperatively in a sterile fashion, and identified a putative FT microbiota that exists in many women without any overt signs of infection. Patients with OC had a high prevalence of some key bacterial species, indicating a potentially perturbed microbiota co-existing with OC. Further studies are needed to investigate whether there is a causal relationship between these bacterial species and ovarian carcinogenesis.

Materials and Methods

Study population

Specimens and clinical information were obtained from patients who provided informed consent under a protocol approved by the institutional review board at University of Washington (Protocol # 2872). Surgical indications included ovarian cancers of various histological types, risk-reducing salpingo-oophorectomies due to germline BRCA or other mutations, and benign gynecological disorders such as ovarian cysts or endometriosis. Exclusion criteria include pelvic inflammatory disease, presence of an intrauterine device, use of antibiotics, endometrial biopsy, intrauterine device removal or hysteroscopy in the 30 days prior to the intended enrollment.

Sample collection

During the surgeries that involve salpingectomies, we collected fallopian tube swabs in the operating room in a sterile fashion. From most enrolled patients, we collected from the following sites: 1) a swab from the air in the operating room, 2) a cervical swab before vaginal sterile prep, 3) a swab from the laparoscopy port after the port insertion and before introduction of any instrument in laparoscopic cases, 4) a swab from the unaffected peritoneum in the paracolic gutter above the liver, 5) a swab from the fallopian tube and ovarian surface on each side. In some patients, the swabs may not have been collected from all sites due to surgical accessibility, time constraint, or unintentional omission. All biological samples were assigned anonymous participant identification numbers, which were matched with participants’ names in a securely stored link file.

DNA extraction and quantitative PCR

All swabs were vortexed and washed in 200 µl 0.9% saline, in which saline was filtered by MilliporeSigma™ Amicon™ Ultra Centrifugal 100k Da Filter Units (Thermo Fisher Scientific, Waltham, MA). DNA from swab prep mixture was extracted using the QIAamp BiOstic Bacteremia DNA KIT (QIAGEN, Germantown, MD) and eluted in a mixture of 25 μl EB buffer provided in the kit plus 25 µl 0.2X Tris-EDTA. In each quality control qPCR assay, 5 µl DNA was loaded per reaction. Digestion buffer control was DNA extracted from sham swabs to assess presence of contaminant in kit or during extraction procedure. Absence of PCR inhibitor was confirmed by an internal amplification control (IAC) quantitative polymerase chain reaction (qPCR)(27). IAC qPCR was designed to compare amplification of spiked-in jellyfish gDNA between DNA samples and water. Total bacterial concentration was measured by a broad range qPCR targeting V3-V4 region of the bacterial 16S rRNA gene(28).

Broad-Range PCR and Deep Sequencing of 16S rRNA Gene Amplicons

Broad-range amplicon PCR targeting V3–V4 hypervariable region of the 16S rRNA gene was performed using an adapter attached 338F and 806R primer formulation(28, 29). The optimal quantity of DNA per reaction is 2.4e+6 copies of the 16S rRNA gene. Maximum allowed volume was added in amplicon PCR (20 µl DNA/ 30 µl reaction) for samples from collection sites other than the cervix, which were defined as lower biomass samples. Filtered water was added to PCRs as a no-template (negative) control. A sham DNA extraction was performed to assess contamination of extraction regents and the resulting DNA added to PCRs as another form of negative control with each batch of samples processed. Amplicon PCR products were cleaned using Agencourt AMPure XP beads (Beckman Coulter, Indianapolis, IN) to remove primer dimer and reaction buffers. Cleaned amplicons were applied to barcoded Index PCR using NexteraXT index kits v2 (Illumina, San Diego, CA). Per index PCR, 15 µl of lower biomass amplicon or 5 µl of higher biomass amplicon was loaded. Index PCR product went through a second-round bead clean and was eluted in 30 µl 1× TE buffer. DNA concentration of each sample was measured by the Quant-iT dsDNA assay kit-HS (Thermo Fisher Scientific, Waltham, MA). For the low biomass samples, the entire volume of cleaned index PCR product was pooled into sub pools, which then were concentrated roughly fifty times by MilliporeSigma™ Amicon™ Ultra Centrifugal 10kDa Filter Units (Thermo Fisher Scientific, Waltham, MA). Equal quantities (8 nM or lower) of sub pools were pooled into a master pool, which was then subjected to deep sequencing.

Next Generation Sequencing (NGS) of the bacterial 16S rRNA gene PCR product was performed on the Illumina MiSeq instrument (Illumina). PhiX Control Library v3 (Illumina) was combined with the amplicon library at 15% for high biomass samples and at 20% for low biomass samples.

NGS Data Analysis

Sequence reads were processed using the DADA2 package(30) for error correction, dereplication, paired-end assembly, and chimera removal and a list of unique sequence variants (SVs) were generated. A custom vaginal reference set was used to assign taxonomy to individual SVs(28). Taxonomic assignments were made as described previously(31). Briefly, a multiple sequence alignment of both query and reference sequences was created using cmalign(32) and query sequences were placed on the phylogenetic tree using pplacer(33). Taxonomy was assigned to each unique SV based on location on the tree. Bacterial taxa represented by fewer than 100 reads in a sample were excluded from that sample to minimize environmental contaminant sequences from being included in the final dataset. The samples from the same site and the same patient were merged as one sample for downstream analysis. The NGS data was filtered using a processing pipeline that we developed in this study to track samples and sequence types (Supplemental Table 3). R packages were used for the statistical analyses and graphics. Sequences have been submitted to the NCBI Short Read Archive (Accession number pending).

Data Availability

All data produced in the present study are available upon reasonable request to the authors.

Acknowledgements

The authors acknowledge the surgeons and staff in the Department of Obstetrics and Gynecology at University of Washington for collecting samples for this study, and Noah Hoffman, MD, PhD from the Department of Laboratory Medicine at the University of Washington for his assistance with taxonomic analysis of the data.

Funding information

The study was funded by Akiko Yamazaki and Jerry Yang Faculty Scholar Fund in Pediatric Translational Medicine, the Stanford Maternal and Child Health Research Institute (to BY) and NIH K08CA222835 (to BY, mentored by EMS, DNF). Specimen collection was supported in part through funding from Seattle Translational Tumor Research (STTR).

Conflict of interest disclosures

The authors declare that there are no conflicts of interests.

Authors contribution statement

BY, EMS, DNF conceived and designed the study, obtained funding. CL processed the samples and prepared sequencing libraries. SP, SS processed and analyzed the data. EM, SL, EMS collected the samples. BY drafted the manuscript. CL, SP, SS, EMS, DNF edited the manuscript. All authors have read and approved the final manuscript.