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

• Published in: PloS one Vol. 15; no. 1; p. e0227707
• Main Authors: Miao, Ruizhong, Badger, Taylor C, Groesch, Kathleen, Diaz-Sylvester, Paula L, Wilson, Teresa, Ghareeb, Allen, Martin, Jongjin Anne, Cregger, Melissa, Welge, Michael, Bushell, Colleen, Auvil, Loretta, Zhu, Ruoqing, Brard, Laurent, Braundmeier-Fleming, Andrea
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 09-01-2020; Public Library of Science (PLoS)
• Subjects: 60 APPLIED LIFE SCIENCES; Aged; Antigens; Ascites; Ascitic Fluid - microbiology; Bioinformatics; Biology; Biology and Life Sciences; Biomarkers; Blood; CA-125 Antigen - blood; Cancer; Cancer detection and diagnosis; Cancer research; Carcinoma, Ovarian Epithelial - blood; Carcinoma, Ovarian Epithelial - diagnosis; Carcinoma, Ovarian Epithelial - microbiology; Carcinoma, Ovarian Epithelial - surgery; Clostridium; Computational biology; Computer and Information Sciences; Cross-Sectional Studies; Development and progression; Diagnostic imaging; Diagnostic medicine; Diagnostic software; Diagnostic systems; Disease; DNA, Bacterial - genetics; DNA, Bacterial - isolation & purification; Epididymis; Female; Fungi; Gynecology; Health screening; Humans; Hysterectomy; Immunology; Laparoscopy; Learning algorithms; Machine Learning; Markers; Medical diagnosis; Medical schools; Medicine; Medicine and Health Sciences; Membrane Proteins - blood; Microbiome; Microbiota - genetics; Microorganisms; Middle Aged; Model accuracy; Models, Biological; Mucosa; Next-generation sequencing; Obstetrics; Ovarian cancer; Ovarian carcinoma; Ovarian Neoplasms - blood; Ovarian Neoplasms - diagnosis; Ovarian Neoplasms - microbiology; Ovarian Neoplasms - surgery; Ovariectomy; Pathogenesis; Patients; Peritoneal fluid; Peritoneum; Pilot Projects; Preoperative Period; Prognosis; Reproductive system; RNA, Ribosomal, 16S - genetics; Serum levels; Surgery; Surgical oncology; Tumor markers; Tumors; Ultrasonic imaging; Ultrasound imaging; WAP Four-Disulfide Core Domain Protein 2 - analysis; Womens health; United States > US; Illinois; Tennessee
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0227707

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.