Changes in the cervical microbiota of cervical cancer patients after primary radio-chemotherapy
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* Anastasia Tsakmaklis
* Maria Vehreschild
* Fedja Farowski
* Maike Trommer
* Christhardt Kohler
* Jan Herter
* Simone Marnitz

## Abstract

**Objective** Several recent studies have identified a potential interaction between the vaginal microbiota and gynecological cancers, but little is known about the cervical microbiota and its changes during cancer treatment. Therefore, the aim of the study was to evaluate the quantitative and qualitative changes of cervical microbiota in patients undergoing concurrent chemotherapy and radiation treatment for locally advanced cervical cancer.

**Methods** Cervical cytobrush samples of 15 cervical patients undergoing chemoradiation treatment were collected 1 day before starting external beam radiation therapy and on the day of the last fraction of brachytherapy. After DNA extraction, 16S rRNA amplicon sequencing of the V3–V4 region was performed on the MiSeq platform, followed by data processing and statistical analyses concerning the alpha and beta diversity of 16 samples (7 samples were excluded because of incomplete sample sets).

**Results** The amount of amplicon yield after polymerase chain reaction analysis in post-radiation samples was significantly lower compared with the baseline samples (pre 31.49±24.07 ng/µl; post 1.33±1.94 ng/µl; p=0.007). A comparison of pre-treatment and post-treatment samples did not show significant differences regarding beta diversity (weighted UniFrac). There was no significant difference in alpha diversity, which is used to characterize species diversity within a particular community and takes into account both number and abundance (Shannon Diversity Index pre-treatment samples: 2.167±0.7504 (95% CI 1.54 to 2.79); post-treatment samples: 1.97±0.43 (95% CI 1.61 to 2.33); p=0.38). Interindividual differences in patients could partly explain some variation of the samples (permutational multivariate analysis of variance).

**Conclusion** There was a strong reduction in cervical bacterial loads after chemoradiation. Neither alpha nor beta diversity varied significantly when baseline samples were compared with post-treatment samples.

*   uterine cervical neoplasms
*   radiotherapy dosage

### HIGHLIGHTS

*   Cervical microbiota composition differs between patients with histologically proven cervical cancer.

*   There is a strong reduction in the cervical bacterial loads after chemoradiation.

*   There is no change in terms of the composition of the cervical microbiota under chemoradiation.

## Introduction

The gastrointestinal tract harbors several hundred bacterial phylotypes, which not only play a significant role in aiding digestive processes, but also help maintain a healthy immune system. Chronic inflammation as a reaction to a disrupted microbiota appears to be a key mechanism by which specific bacterial species contribute to the development of cancer.1 Several studies have emphasized the role of the gut microbiota in carcinogenesis, but data regarding the role of specific bacteria in certain cancers are sparse.

More recent analyses have identified potential interactions between the microbiota and gynecological cancers.2–4 Although smaller in population, the cervicovaginal microbiota may affect local immune regulation and oncogenesis. It comprises 20–140 bacterial species with *Lactobacillus* species commonly being most abundant. *Lactobacillus* acidifies the vaginal pH by producing lactic acid. While this is well tolerated by *Lactobacillus*, a pH <4.5 inhibits the growth of several other bacterial species, including many pathogenic bacteria. Vaginal microbiota signatures identified in environments with a significantly higher pH are not dominated by *Lactobacillus,* but instead are characterized by high bacterial diversity and a higher prevalence of anaerobic species.5 6 

In addition to their carcinogenic potential, recent findings in the area of onco-immunology suggest a significant role of the gut microbiota in priming cells of the immune system that improve the endogenous antitumor response, mainly regulatory and CD8+ T cells. This type of immune activation has been shown to enhance the efficacy of checkpoint inhibitors, a class of immunomodulatory drugs that also relies on activation of the T cell response.7–10 The abscopal effect describes a systemic immune response to tumor metastases as a consequence of localized radiation therapy. It has equally been identified as a potential booster of checkpoint inhibition effects.11 It could be hypothesized that there should be a synergistic effect between microbiota based and radiation based immune activation. Such synergism might in turn offer the chance of therapeutic options in the field of gynecological oncology.

Few data, however, are available on the cervical microbiota of affected patients over the course of their treatment. Hence the aim of this study was to evaluate the quantitative and qualitative changes in the cervical microbiota in patients undergoing concurrent chemotherapy and radiation treatment for cervical cancer.

## Methods

Fifteen patients with histologically proven cervical cancer and an indication for primary chemoradiation treatment were included. Because of incomplete sample sets, seven patients were excluded from the analysis. Patient characteristics are shown in Table 1. The analysis was approved by the local ethics committee (ISI Study, ethics committee No 08–160). After CT planning in the supine position with an emptied rectum and filled bladder with kneefix and footfix on a big bore TOSHIBA CT, radiation was performed. It included external beam radiation with 6/10 MV photons using volumetric arc techniques on a linear accelerator (TrueBeam, Varian) and daily cone beam CT with 5 weekly single doses of 1.8 Gy to the primary tumor, including the uterus, pelvic and, in case of histologically confirmed para-aortic lymph nodes including the para-aortic node, up to the renal vessels, to a total dose of 50.4 Gy in 28 fractions. A simultaneous boost was given with 5 weekly single doses of 2.12 Gy to both parametric regions, to a total dose of 59.36 Gy in 28 fractions. Brachytherapy started in the third–fourth week of external beam with MRI planned three dimensional generated plans and five fractions of 5 Gy single doses to the cervix and residual tumor volume. Concomitant chemotherapy was given to every patient once a week (cisplatin 40 mg/m2 body surface area) for 5–6 applications. At the time of chemotherapy, comedication with dexamethasone 8 mg on day 1 of cisplatin and 4 mg on days 2 and 3 were administered. Proton pump inhibitor treatment was prescribed with oral pantoprazole 40 mg/day concomitantly with chemoradiation.

View this table:
[Table 1](http://ijgc.bmj.com/content/30/9/1326/T1)

Table 1 
Patients characteristics

Cervical cytobrush samples of each patient were collected 1 day before starting external beam radiation (baseline) and on the day of the last fraction of brachytherapy. Each sample was transferred into a clean collection tube and stored at −80°C within 1 hour. For microbiome analysis, samples were thawed and directly subjected to DNA extraction using the ZymoBIOMICS DNA miniprep Kit (Zymo Research Corp, Irvine, USA) following the manufacturer’s instructions. Afterwards, 16S rRNA amplicon sequencing of the V3–V4 region was performed as described in the Illumina 16S Sample Preparation Guide on the MiSeq platform (Illumina, San Diego, USA) in a 300 bp paired end run.12 The sequencing data were processed using the DADA2 pipeline and analyzed using QIIME 2.13 14 Quality profiles of the reads were analyzed. Reads were trimmed (trunc\_len\_f=290, trunc\_len_r=220) and processed by the QIIME DADA2 plugin with the denoise paired option and standard parameters (trunc_q=2, max_ee=2, chimera_method=consensus). Taxonomic classification was performed by a Naïve Bayes classifier (sklearn), who was trained on the SILVA database release 128.15 16 Rarefaction curves were determined based on the feature table, and analysis of the relative proportion of each bacterial taxon was made after the data were rarefied at a depth of 3500 sequences per sample. Statistical analyses were carried out using R for Statistical Computing (V.3.2.5, R Foundation for Statistical Computing, Vienna, Austria).17 Alpha and beta diversity scores were calculated using the R package phyloseq.18 Beta diversity, in this case the weighted UniFrac distances between the samples, was visualized using principal coordinate analysis. The effect of radiation status on beta diversity was tested by a permutational multivariate analysis of variance.

## Results

During the entire sample collection period, 23 cervical cytobrush samples were collected, 16 of which were included in further microbiome analyses. Seven samples were excluded from analysis because of incomplete sample sets, (ie, either the pre-treatment or post-treatment sample was missing because sampling had to be stopped due to pain). The mean concentration of the (total) extracted genomic DNA was 43.6±12.1 ng/µl (Qubit fluorometer). The product of the amplicon polymerase chain reaction—that is, of the 16S V3–V4 region—was checked by capillary electrophoresis. Although equal amounts of DNA template were used, the yield of the amplicon was much less in post-radiation samples (Figure 1). The QIAxcel Software was used to quantify the V3–V4 amplicons. DNA concentration of the pre-radiation amplicons was significantly higher compared with post-radiation amplicons (pre 31.49±24.07 ng/µl; post 1.33±1.94 ng/µl; p=0.007).

![Figure 1](http://ijgc.bmj.com/https://ijgc.bmj.com/content/ijgc/30/9/1326/F1.medium.gif)

[Figure 1](http://ijgc.bmj.com/content/30/9/1326/F1)

Figure 1 
Polymerase chain reaction (PCR) product of the amplicon PCR. Capillary electrophoresis 'gel' showing the PCR product obtained after 25 cycles, targeting the 16S V3 and V4 region (Klindworth 2013) followed by clean-up using AMPure XP beads. The expected size of the PCR product is approximately 550 bp.

There was no significant difference in alpha diversity, which is used to characterize species diversity within a particular community and takes into account both number and abundance (Shannon Diversity Index pre-treatment samples 2.167±0.7504 (95% CI 1.54 to 2.79); post-treatment samples: 1.971±0.4296 (95% CI 1.61 to 2.33); p=0.38) when we compared baseline samples with samples after chemoradiation samples. The same was true for beta diversity, which refers to diversity between two communities by measuring variation between multiple samples. In terms of beta diversity, principal coordinate analysis of the weighted UniFrac distances did not show any shift in the cervical microbiota composition after radiation therapy (permutational multivariate analysis of variance F=1.42, p=0.197)—that is, the 95% confidence ellipses around the centeroids of both groups overlapped (Figure 2).

![Figure 2](http://ijgc.bmj.com/https://ijgc.bmj.com/content/ijgc/30/9/1326/F2.medium.gif)

[Figure 2](http://ijgc.bmj.com/content/30/9/1326/F2)

Figure 2 
Principal coordinate analysis of bacterial community structures on the basis of weighted UniFrac distances of all samples; 95% confidence levels assuming normal (\- - -|) distribution and 95% confidence ellipses (–--). Red dots represent the samples collected before chemoradiation and blue dots represent the samples collected after chemoradiation.

Another permutational multivariate analysis of variance revealed that there was some spread of the samples that (in part) could be explained by interindividual differences in patients (unweighted UniFrac F=2.15, p<0.001; weighted UniFrac F=1.42, p=0.106). The effect of the patient identification number (PID) on several dissimilarity indices is listed in Table 2.

View this table:
[Table 2](http://ijgc.bmj.com/content/30/9/1326/T2)

Table 2 
Effect of the patient identification number (PID) on the dissimilarity indices

The relative abundances of the bacterial families detected within the patients’ cervical microbiota is shown in Figure 3.

![Figure 3](http://ijgc.bmj.com/https://ijgc.bmj.com/content/ijgc/30/9/1326/F3.medium.gif)

[Figure 3](http://ijgc.bmj.com/content/30/9/1326/F3)

Figure 3 
Relative abundance of different bacterial families in each sample. (A–H) Different patients, family XI=Clostridiales.

## Discussion

There is a growing body of data which suggests that the microbiota plays an underestimated role in the development of chronic inflammation, human papillomavirus infection, and human papillomavirus clearance, progressing to invasive cancer, and may also play a role in prevention of cancer.2–6 19–25 This pilot study is the first to analyze the effects of chemoradiation for cervical cancer on the cervical microbiota. Previous studies performed in this patient population have focused on analysis of the gut microbiota and its correlation with radiation related gastrointestinal toxicity. A systematic review26 analyzed data from three cohort studies27–29 on changes in the gut microbiome of 23 women with gynecological cancer. It has been shown that there is a change in microbiota quality in patients who developed gastrointestinal toxicity. An increase in unspecified bacterial species was seen in those with diarrhea, but patients without diarrhea maintained their initial bacterial profiles.26 Supplementation with *Lactobacillus acidophilus* and *Bifidobacterium bifidum* before and during radiation decreased the rate of grade 2 and 3 diarrhea significantly (treatment group 9% vs no treatment group 45%).30 

Our results suggest that there is an effect of chemoradiation therapy on the cervical microbiota. Although the DNA concentration measurement after DNA extraction from the swab samples showed high values, we have to consider that this could in part be due to high levels of simultaneously extracted host DNA. The results of electrophoresis nevertheless indicated a strong reduction in cervical bacterial loads after radiation therapy, as expected, as chemotherapy and radiation both lead to damage or destruction of cells.

Although the cervical microbiota of all patients considerably changed in terms of quantity, as revealed by electrophoresis, this conclusion cannot be drawn concerning quality—that is, the composition of the microbiota. Neither alpha nor beta diversity differed significantly when we compared pre-radiation with post-radiation samples. Clearly, this conclusion is limited by the small sample size of our study. What we could see, however, was interindividual differences in the composition of the cervical microbiota. Some women showed a stronger dominance of certain families, such as *Clostridiales*, *Lactobacillaceae*, and *Prevotellacecae*, while others showed a more diverse composition.

To our knowledge, no data on changes to the cervical microbiota during chemoradiation treatment have been published previously. As a first step, we analyzed eight patients before and after treatment. We demonstrated that chemoradiation results in quantitative, but not qualitative, changes in the cervical microbiota. Quantification is an aspect that has been under appreciated in microbiome research to date, but may have relevant clinical implications. It remains to be determined in future studies whether there is any impact of local bacteria on the response to treatment of cervical tumor or whether such responses are mediated by the much more abundant microbiota colonizing the gut.

## Footnotes

*   Contributors SM, MV, AT: concept and manuscript writing. JH, CK, and MT: manuscript writing. AT, and FF: laboratory work.

*   Funding This study was funded by Deutsche Forschungsgemeinschaft (DFG grant HE-6810/3-1) to JH.

*   Competing interests None declared.

*   Patient consent for publication Not required.

*   Ethics approval The analysis was approved by the local ethics committee (ISI Study, ethics committee No 08–160).

*   Provenance and peer review Not commissioned; externally peer reviewed.

*   Data availability statement All data relevant to the study are included in the article or available upon reasonable request.

*   Received August 7, 2019.
*   Revision received September 6, 2019.
*   Accepted September 10, 2019.


*   © IGCS and ESGO 2020. Re-use permitted under CC BY-NC. No commercial re-use. Published by BMJ.

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This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, an indication of whether changes were made, and the use is non-commercial. See: [http://creativecommons.org/licenses/by-nc/4.0/](http://creativecommons.org/licenses/by-nc/4.0/).

## References

1.   Samaras V , Rafailidis PI , Mourtzoukou EG , et al . Chronic bacterial and parasitic infections and cancer: a review. J Infect Dev Ctries 2010;4:267–81.[doi:10.3855/jidc.819](http://dx.doi.org/10.3855/jidc.819) pmid:http://www.ncbi.nlm.nih.gov/pubmed/20539059 
    
    [PubMed](http://ijgc.bmj.com/lookup/external-ref?access_num=20539059&link_type=MED&atom=%2Fijgc%2F30%2F9%2F1326.atom) 

2.   Champer M , Wong AM , Champer J , et al . The role of the vaginal microbiome in gynaecological cancer. BJOG 2018;125:309–15.[doi:10.1111/1471-0528.14631](http://dx.doi.org/10.1111/1471-0528.14631) pmid:http://www.ncbi.nlm.nih.gov/pubmed/28278350 
    
    [PubMed](http://ijgc.bmj.com/lookup/external-ref?access_num=http://www.n&link_type=MED&atom=%2Fijgc%2F30%2F9%2F1326.atom) 

3.   Champer M , Wong AM , Champer J , et al . The role of the vaginal microbiome in gynaecological cancer. BJOG 2018;125.[doi:10.1111/1471-0528.14631](http://dx.doi.org/10.1111/1471-0528.14631) pmid:http://www.ncbi.nlm.nih.gov/pubmed/28278350 
    
    

4.   Mitra A , MacIntyre DA , Marchesi JR , et al . The vaginal microbiota, human papillomavirus infection and cervical intraepithelial neoplasia: what do we know and where are we going next? Microbiome 2016;4.[doi:10.1186/s40168-016-0203-0](http://dx.doi.org/10.1186/s40168-016-0203-0) pmid:27802830 
    
    [PubMed](http://ijgc.bmj.com/lookup/external-ref?access_num=27802830&link_type=MED&atom=%2Fijgc%2F30%2F9%2F1326.atom) 

5.   Brusselaers N , Shrestha S , van de Wijgert J , et al . Vaginal dysbiosis and the risk of human papillomavirus and cervical cancer: systematic review and meta-analysis. Am J Obstet Gynecol 2019;221:9-18.e8. [doi:10.1016/j.ajog.2018.12.011](http://dx.doi.org/10.1016/j.ajog.2018.12.011) pmid:http://www.ncbi.nlm.nih.gov/pubmed/30550767 
    
    [PubMed](http://ijgc.bmj.com/lookup/external-ref?access_num=http://www.n&link_type=MED&atom=%2Fijgc%2F30%2F9%2F1326.atom) 

6.   Kyrgiou M , Mitra A , Moscicki A-B . Does the vaginal microbiota play a role in the development of cervical cancer? Transl Res 2017;179:168–82.[doi:10.1016/j.trsl.2016.07.004](http://dx.doi.org/10.1016/j.trsl.2016.07.004) pmid:http://www.ncbi.nlm.nih.gov/pubmed/27477083 
    
    [CrossRef](http://ijgc.bmj.com/lookup/external-ref?access_num=10.1016/j.trsl.2016.07.004&link_type=DOI) 
    
    [PubMed](http://ijgc.bmj.com/lookup/external-ref?access_num=http://www.n&link_type=MED&atom=%2Fijgc%2F30%2F9%2F1326.atom) 

7.   Tanoue T , Morita S , Plichta DR , et al . A defined commensal consortium elicits CD8 T cells and anti-cancer immunity. Nature 2019;565:600–5.[doi:10.1038/s41586-019-0878-z](http://dx.doi.org/10.1038/s41586-019-0878-z) pmid:30675064 
    
    [CrossRef](http://ijgc.bmj.com/lookup/external-ref?access_num=10.1038/s41586-019-0878-z&link_type=DOI) 
    
    [PubMed](http://ijgc.bmj.com/lookup/external-ref?access_num=30675064&link_type=MED&atom=%2Fijgc%2F30%2F9%2F1326.atom) 

8.   Gopalakrishnan V , Spencer CN , Nezi L , et al . Gut microbiome modulates response to anti-PD-1 immunotherapy in melanoma patients. Science 2018;359:97–103.[doi:10.1126/science.aan4236](http://dx.doi.org/10.1126/science.aan4236) pmid:http://www.ncbi.nlm.nih.gov/pubmed/29097493 
    
    [Abstract/FREE Full Text](http://ijgc.bmj.com/lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6Mzoic2NpIjtzOjU6InJlc2lkIjtzOjExOiIzNTkvNjM3MS85NyI7czo0OiJhdG9tIjtzOjIwOiIvaWpnYy8zMC85LzEzMjYuYXRvbSI7fXM6ODoiZnJhZ21lbnQiO3M6MDoiIjt9) 

9.   Matson V , Fessler J , Bao R , et al . The commensal microbiome is associated with anti-PD-1 efficacy in metastatic melanoma patients. Science 2018;359:104–8.[doi:10.1126/science.aao3290](http://dx.doi.org/10.1126/science.aao3290) pmid:http://www.ncbi.nlm.nih.gov/pubmed/29302014 
    
    [Abstract/FREE Full Text](http://ijgc.bmj.com/lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6Mzoic2NpIjtzOjU6InJlc2lkIjtzOjEyOiIzNTkvNjM3MS8xMDQiO3M6NDoiYXRvbSI7czoyMDoiL2lqZ2MvMzAvOS8xMzI2LmF0b20iO31zOjg6ImZyYWdtZW50IjtzOjA6IiI7fQ==) 

10.  Routy B , Le Chatelier E , Derosa L , et al . Gut microbiome influences efficacy of PD-1-based immunotherapy against epithelial tumors. Science 2018;359:91–7.[doi:10.1126/science.aan3706](http://dx.doi.org/10.1126/science.aan3706) pmid:http://www.ncbi.nlm.nih.gov/pubmed/29097494 
    
    [Abstract/FREE Full Text](http://ijgc.bmj.com/lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6Mzoic2NpIjtzOjU6InJlc2lkIjtzOjExOiIzNTkvNjM3MS85MSI7czo0OiJhdG9tIjtzOjIwOiIvaWpnYy8zMC85LzEzMjYuYXRvbSI7fXM6ODoiZnJhZ21lbnQiO3M6MDoiIjt9) 

11.  Hiniker SM , Reddy SA , Maecker HT , et al . A prospective clinical trial combining radiation therapy with systemic immunotherapy in metastatic melanoma. Int J Radiat Oncol Biol Phys 2016;96:578–88.[doi:10.1016/j.ijrobp.2016.07.005](http://dx.doi.org/10.1016/j.ijrobp.2016.07.005) pmid:27681753 
    
    [PubMed](http://ijgc.bmj.com/lookup/external-ref?access_num=http://www.n&link_type=MED&atom=%2Fijgc%2F30%2F9%2F1326.atom) 

12. Illumina. 16S sample preparation guide, 2016. Available: [http://www.illumina.com/content/dam/illumina-support/documents/documentation/chemistry\_documentation/16s/16s-metagenomic-library-prep-guide-15044223-b.pdf](http://www.illumina.com/content/dam/illumina-support/documents/documentation/chemistry_documentation/16s/16s-metagenomic-library-prep-guide-15044223-b.pdf) 
    
    

13.  Callahan BJ , McMurdie PJ , Rosen MJ , et al . DADA2: high-resolution sample inference from Illumina amplicon data. Nat Methods 2016;13:581–3.[doi:10.1038/nmeth.3869](http://dx.doi.org/10.1038/nmeth.3869) pmid:http://www.ncbi.nlm.nih.gov/pubmed/27214047 
    
    [CrossRef](http://ijgc.bmj.com/lookup/external-ref?access_num=10.1038/nmeth.3869&link_type=DOI) 
    
    [PubMed](http://ijgc.bmj.com/lookup/external-ref?access_num=27214047&link_type=MED&atom=%2Fijgc%2F30%2F9%2F1326.atom) 

14.  Caporaso JG , Kuczynski J , Stombaugh J , et al . QIIME allows analysis of high-throughput community sequencing data. Nat Methods 2010;7:335–6.[doi:10.1038/nmeth.f.303](http://dx.doi.org/10.1038/nmeth.f.303) pmid:http://www.ncbi.nlm.nih.gov/pubmed/20383131 
    
    [CrossRef](http://ijgc.bmj.com/lookup/external-ref?access_num=10.1038/nmeth.f.303&link_type=DOI) 
    
    [PubMed](http://ijgc.bmj.com/lookup/external-ref?access_num=20383131&link_type=MED&atom=%2Fijgc%2F30%2F9%2F1326.atom) 
    
    [Web of Science](http://ijgc.bmj.com/lookup/external-ref?access_num=000277175100003&link_type=ISI) 

15.  Pedregosa F , Varoquaux G , Gramfort A , et al . Scikit-learn tutorial: machine learning in python. J Mach Learn Res 2011;12:2825–30.
    
    [CrossRef](http://ijgc.bmj.com/lookup/external-ref?access_num=10.1016/j.cpc.2010.04.018&link_type=DOI) 
    
    [PubMed](http://ijgc.bmj.com/lookup/external-ref?access_num=23755062&link_type=MED&atom=%2Fijgc%2F30%2F9%2F1326.atom) 

16.  Quast C , Pruesse E , Yilmaz P , et al . The Silva ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res 2013;41:D590–6.[doi:10.1093/nar/gks1219](http://dx.doi.org/10.1093/nar/gks1219) pmid:http://www.ncbi.nlm.nih.gov/pubmed/23193283 
    
    [CrossRef](http://ijgc.bmj.com/lookup/external-ref?access_num=10.1093/nar/gks1219&link_type=DOI) 
    
    [PubMed](http://ijgc.bmj.com/lookup/external-ref?access_num=23193283&link_type=MED&atom=%2Fijgc%2F30%2F9%2F1326.atom) 
    
    [Web of Science](http://ijgc.bmj.com/lookup/external-ref?access_num=000312893300084&link_type=ISI) 

17. R Core Team. R: a language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing, 2016.
    
    

18.  McMurdie PJ , Holmes S . phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One 2013;8:e61217. [doi:10.1371/journal.pone.0061217](http://dx.doi.org/10.1371/journal.pone.0061217) pmid:http://www.ncbi.nlm.nih.gov/pubmed/23630581 
    
    [CrossRef](http://ijgc.bmj.com/lookup/external-ref?access_num=10.1371/journal.pone.0061217&link_type=DOI) 
    
    [PubMed](http://ijgc.bmj.com/lookup/external-ref?access_num=23630581&link_type=MED&atom=%2Fijgc%2F30%2F9%2F1326.atom) 

19.  Adebamowo SN , Ma B , Zella D , et al . Mycoplasma hominisand and Mycoplasma genitalium in the vaginal microbiota and persistent high-risk human papillomavirus infection. Front Public Health 2017;5.[doi:10.3389/fpubh.2017.00140](http://dx.doi.org/10.3389/fpubh.2017.00140) pmid:http://www.ncbi.nlm.nih.gov/pubmed/28695118 
    
    

20.  Brotman RM , Shardell MD , Gajer P , et al . Interplay between the temporal dynamics of the vaginal microbiota and human papillomavirus detection. J Infect Dis 2014;210:1723–33.[doi:10.1093/infdis/jiu330](http://dx.doi.org/10.1093/infdis/jiu330) pmid:http://www.ncbi.nlm.nih.gov/pubmed/24943724 
    
    [CrossRef](http://ijgc.bmj.com/lookup/external-ref?access_num=10.1093/infdis/jiu330&link_type=DOI) 
    
    [PubMed](http://ijgc.bmj.com/lookup/external-ref?access_num=24943724&link_type=MED&atom=%2Fijgc%2F30%2F9%2F1326.atom) 

21.  Fleming C , Cai Y , Sun X , et al . Microbiota-activated CD103+ DCs stemming from microbiota adaptation specifically drive γδT17 proliferation and activation. Microbiome 2017;5.[doi:10.1186/s40168-017-0263-9](http://dx.doi.org/10.1186/s40168-017-0263-9) pmid:http://www.ncbi.nlm.nih.gov/pubmed/28438184 
    
    

22.  Guerrero-Preston R , White JR , Godoy-Vitorino F , et al . High-resolution microbiome profiling uncovers Fusobacterium nucleatum, Lactobacillus gasseri/johnsonii, and Lactobacillus vaginalis associated to oral and oropharyngeal cancer in saliva from HPV positive and HPV negative patients treated with surgery and chemo-radiation. Oncotarget 2017;8:110931–48.[doi:10.18632/oncotarget.20677](http://dx.doi.org/10.18632/oncotarget.20677) pmid:http://www.ncbi.nlm.nih.gov/pubmed/29340028 
    
    [PubMed](http://ijgc.bmj.com/lookup/external-ref?access_num=http://www.n&link_type=MED&atom=%2Fijgc%2F30%2F9%2F1326.atom) 

23.  King CC , Jamieson DJ , Wiener J , et al . Bacterial vaginosis and the natural history of human papillomavirus. Infect Dis Obstet Gynecol 2011;2011:1–8.[doi:10.1155/2011/319460](http://dx.doi.org/10.1155/2011/319460) pmid:http://www.ncbi.nlm.nih.gov/pubmed/21869857 
    
    [CrossRef](http://ijgc.bmj.com/lookup/external-ref?access_num=10.1155/2011/942937&link_type=DOI) 
    
    [PubMed](http://ijgc.bmj.com/lookup/external-ref?access_num=21836811&link_type=MED&atom=%2Fijgc%2F30%2F9%2F1326.atom) 

24.  Kwon M , Seo S-S , Kim MK , et al . Compositional and functional differences between microbiota and cervical carcinogenesis as identified by shotgun metagenomic sequencing. Cancers 2019;11:309. [doi:10.3390/cancers11030309](http://dx.doi.org/10.3390/cancers11030309) pmid:http://www.ncbi.nlm.nih.gov/pubmed/30841606 
    
    [PubMed](http://ijgc.bmj.com/lookup/external-ref?access_num=http://www.n&link_type=MED&atom=%2Fijgc%2F30%2F9%2F1326.atom) 

25.  Mitra A , MacIntyre DA , Mahajan V , et al . Comparison of vaginal microbiota sampling techniques: cytobrush versus swab. Sci Rep 2017;7:9802. [doi:10.1038/s41598-017-09844-4](http://dx.doi.org/10.1038/s41598-017-09844-4) pmid:http://www.ncbi.nlm.nih.gov/pubmed/28852043 
    
    [CrossRef](http://ijgc.bmj.com/lookup/external-ref?access_num=10.1038/s41598-017-09844-4&link_type=DOI) 
    
    [PubMed](http://ijgc.bmj.com/lookup/external-ref?access_num=http://www.n&link_type=MED&atom=%2Fijgc%2F30%2F9%2F1326.atom) 

26.  Muls A , Andreyev J , Lalondrelle S , et al . Systematic review: the impact of cancer treatment on the gut and vaginal microbiome in women with a gynecological malignancy. Int J Gynecol Cancer 2017;27:1550–9.[doi:10.1097/IGC.0000000000000999](http://dx.doi.org/10.1097/IGC.0000000000000999) pmid:http://www.ncbi.nlm.nih.gov/pubmed/28590950 
    
    [Abstract/FREE Full Text](http://ijgc.bmj.com/lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6NDoiaWpnYyI7czo1OiJyZXNpZCI7czo5OiIyNy83LzE1NTAiO3M6NDoiYXRvbSI7czoyMDoiL2lqZ2MvMzAvOS8xMzI2LmF0b20iO31zOjg6ImZyYWdtZW50IjtzOjA6IiI7fQ==) 

27.  Nam Y-D , Kim HJ , Seo J-G , et al . Impact of pelvic radiotherapy on gut microbiota of gynecological cancer patients revealed by massive pyrosequencing. PLoS One 2013;8:e82659. [doi:10.1371/journal.pone.0082659](http://dx.doi.org/10.1371/journal.pone.0082659) pmid:http://www.ncbi.nlm.nih.gov/pubmed/24367534 
    
    [CrossRef](http://ijgc.bmj.com/lookup/external-ref?access_num=10.1371/journal.pone.0082659&link_type=DOI) 
    
    [PubMed](http://ijgc.bmj.com/lookup/external-ref?access_num=24367534&link_type=MED&atom=%2Fijgc%2F30%2F9%2F1326.atom) 

28.  Wang A , Ling Z , Yang Z , et al . Gut microbial dysbiosis may predict diarrhea and fatigue in patients undergoing pelvic cancer radiotherapy: a pilot study. PLoS One 2015;10:e0126312. [doi:10.1371/journal.pone.0126312](http://dx.doi.org/10.1371/journal.pone.0126312) pmid:http://www.ncbi.nlm.nih.gov/pubmed/25955845 
    
    [CrossRef](http://ijgc.bmj.com/lookup/external-ref?access_num=10.1371/journal.pone.0126312&link_type=DOI) 
    
    [PubMed](http://ijgc.bmj.com/lookup/external-ref?access_num=25955845&link_type=MED&atom=%2Fijgc%2F30%2F9%2F1326.atom) 

29.  Manichanh C , Varela E , Martinez C , et al . The gut microbiota predispose to the pathophysiology of acute postradiotherapy diarrhea. Am J Gastroenterol 2008;103:1754–61.[doi:10.1111/j.1572-0241.2008.01868.x](http://dx.doi.org/10.1111/j.1572-0241.2008.01868.x) pmid:http://www.ncbi.nlm.nih.gov/pubmed/18564125 
    
    [CrossRef](http://ijgc.bmj.com/lookup/external-ref?access_num=10.1111/j.1572-0241.2008.01868.x&link_type=DOI) 
    
    [PubMed](http://ijgc.bmj.com/lookup/external-ref?access_num=18564125&link_type=MED&atom=%2Fijgc%2F30%2F9%2F1326.atom) 

30.  Chitapanarux I , Chitapanarux T , Traisathit P , et al . Randomized controlled trial of live Lactobacillus acidophilus plus Bifidobacterium bifidum in prophylaxis of diarrhea during radiotherapy in cervical cancer patients. Radiat Oncol 2010;5.[doi:10.1186/1748-717X-5-31](http://dx.doi.org/10.1186/1748-717X-5-31) pmid:http://www.ncbi.nlm.nih.gov/pubmed/20444243