Assessment of peritoneal microbial features and tumor marker levels as potential diagnostic tools for ovarian cancer
Author(s)
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
Issue Date
2020-01-09
Keyword(s)
Cancer detection and diagnosis
Ascites
Machine learning
Diagnostic medicine
Clostridium
Microbiome
Surgical oncology
Ovarian cancer
Abstract
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.
Publisher
PLoS
Series/Report Name or Number
PLoS ONE; vol 15, no. 1, 2020
Type of Resource
text
Language
en
Permalink
http://hdl.handle.net/2142/106055
DOI
https://doi.org/10.1371/journal.pone.0227707
Copyright and License Information
Copyright 2020 Ruizhong Miao, Taylor C. Badger, Kathleen Groesch, Paula L. Diaz-Sylvester, Teresa Wilson, Allen Ghareeb, Jongjin Anne Martin, Melissa Cregger, Michael Welge, Colleen Bushell, Loretta Auvil, Ruoqing Zhu, Laurent Brard, and Andrea Braundmeier-Fleming
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