Potential tools for predicting response to chemotherapy in OC: Assessment of immune dysbiosis, participant's self-rated health and microbial dynamics.

Epithelial ovarian cancer (OC) is the deadliest female reproductive cancer; an estimated 13,270 women will die from OC in 2023. Platinum-based chemotherapy resistance mechanisms contribute to poor OC 5-year survival rates. Peripheral inflammation is linked to various disease states and we previously identified unique peritoneal microbial features predictive of OC. We hypothesized that unique peripheral immune profiles and peritoneal microbial features may be predictive of disease-free interval (time to recurrence) and response to chemotherapy in participants with OC. We also investigated self-rated health (SRH) scores in the context of peripheral inflammation as a potential screening tool for OC. Blood and peritoneal fluid were collected from participants with OC or a benign adnexal mass (BPM). Lymphocyte populations were analyzed using Fluorescence Activated Cell Sorting, serum cytokine levels were analyzed using the Human Th17 Magnetic Bead Panel assay and peritoneal fluid microbial features were analyzed using Next Generation Sequencing (NGS). Participants completed a standardized questionnaire on self-rated physical and emotional health. Participants were classified into three chemotherapy response categories: platinum-refractory, platinum-resistant or platinum-sensitive. A significant positive correlation was found between elevated inflammatory status on the day of surgery and longer disease-free interval. SRH measures did not correlate with immune status in participants with OC or a BPM. We identified a correlation between peritoneal microbial features and chemotherapy response. We conclude that immune dysbiosis may be useful in predicting OC recurrence. The immune findings reported here set the framework for additional studies utilizing immune profiles to predict platinum-based chemotherapy responsiveness in OC.

Assessment of peritoneal microbial features and tumor marker levels as potential diagnostic tools for ovarian cancer.

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 features are important for accurate diagnosis of OC. Our study warrants further analyses of the importance of microbial features in regards to oncological diagnostics and possible prognostic and interventional medicine.