Epithelial ovarian cancer (OC) is the most deadly cancer of the female reproductive system. To date, there is no effective screening method for early detection of OC and current diagnostic armamentarium may include sonographic grading of the tumor and analyzing serum levels of tumor markers, Cancer Antigen 125 (CA-125) and Human epididymis protein 4 (HE4). Microorganisms (bacterial, archaeal, and fungal cells) residing in mucosal tissues including the gastrointestinal and urogenital tracts can be altered by different disease states, and these shifts in microbial dynamics may help to diagnose disease states. We hypothesized that the peritoneal microbial environment was altered in patients with OC and that inclusion of selected peritoneal microbial features with current clinical features into prediction analyses will improve detection accuracy of patients with OC. Blood and peritoneal fluid were collected from consented patients that had sonography confirmed adnexal masses and were being seen at SIU School of Medicine Simmons Cancer Institute. Blood was processed and serum HE4 and CA-125 were measured. Peritoneal fluid was collected at the time of surgery and processed for Next Generation Sequencing (NGS) using 16S V4 exon bacterial primers and bioinformatics analyses. We found that patients with OC had a unique peritoneal microbial profile compared to patients with a benign mass. Using ensemble modeling and machine learning pathways, we identified 18 microbial features that were highly specific to OC pathology. Prediction analyses confirmed that inclusion of microbial features with serum tumor marker levels and control features (patient age and BMI) improved diagnostic accuracy compared to currently used models. We conclude that OC pathogenesis alters the peritoneal microbial environment and that these unique microbial

Endometriosis is a common gynecological disease, which causes chronic pelvic pain and infertility in women of reproductive age. Due to limited efficacy of current treatment options, a critical need exists to develop new and effective treatments for endometriosis. Niclosamide is an efficacious and FDA-approved drug for the treatment of helminthosis in humans that has been used for decades. We have reported that niclosamide reduces growth and progression of endometriosis-like lesions via targeting STAT3 and NFĸB signaling in a mouse model of endometriosis. To examine the effects of niclosamide on macrophage-induced inflammation in endometriosis, a total of 29 stage III–IV endometrioma samples were used to isolate human endometriotic stromal cells (hESCs). M1 or M2 macrophages were isolated and differentiated from fresh human peripheral blood samples. Then, hESCs were cultured in conditioned media (CM) from macrophages with/without niclosamide. Niclosamide dose dependently reduced cell viability and the activity of STAT3 and NFκB signaling in hESCs. While macrophage CM stimulated cell viability in hESCs, niclosamide inhibited this stimulation. Macrophage CM stimulated the secretion of proinflammatory cytokines and chemokines from hESCs. Most of these secreted factors were inhibited by niclosamide. These results indicate that niclosamide is able to reduce macrophage-induced cell viability and cytokine/chemokine secretion in hESCs by inhibiting inflammatory mechanisms via STAT3 and/or NFκB signaling.

Endometriosis is an estrogen dependent gynecological disease associated with altered microbial phenotypes. The association among endogenous estrogen, estrogen metabolites, and microbial dynamics on disease pathogenesis has not been fully investigated. Here, we identified estrogen metabolites as well as microbial phenotypes in non-diseased patients (n = 9) and those with pathologically confirmed endometriosis (P-EOSIS, n = 20), on day of surgery (DOS) and ~1–3 weeks post-surgical intervention (PSI). Then, we examined the effects of surgical intervention with or without hormonal therapy (OCPs) on estrogen and microbial profiles of both study groups. For estrogen metabolism analysis, liquid chromatography/tandem mass spectrometry was used to quantify urinary estrogens. The microbiome data assessment was performed with Next generation sequencing to V4 region of 16S rRNA. Surgical intervention and hormonal therapy altered gastrointestinal (GI), urogenital (UG) microbiomes, urinary estrogen and estrogen metabolite levels in P-EOSIS. At DOS, 17β-estradiol was enhanced in P-EOSIS treated with OCPs. At PSI, 16-keto-17β-estradiol was increased in P-EOSIS not receiving OCPs while 2-hydroxyestradiol and 2-hydroxyestrone were decreased in P-EOSIS receiving OCPs. GI bacterial α-diversity was greater for controls and P-EOSIS that did not receive OCPs. P-EOSIS not utilizing OCPs exhibited a decrease in UG bacterial α-diversity and differences in dominant taxa, while P-EOSIS utilizing OCPs had an increase in UG bacterial α-diversity. P-EOSIS had a strong positive correlation between the GI/UG bacteria species and the concentrations of urinary estrogen and its metabolites. These results indicate an association between microbial dysbiosis and altered urinary estrogens in P-EOSIS, which may impact disease progression.