# Integrative Analysis of the Blood Plasma and Tumor Microbiome: A Novel Approach to Liquid Biopsy Screening for Early Epithelial Ovarian Cancer Detection > **NIH NIH P20** · UNIVERSITY OF KANSAS MEDICAL CENTER · 2024 · $224,840 ## Abstract ABSTRACT Women at risk for epithelial ovarian cancer (EOC) are in dire need of new approaches to detect early disease and novel studies are essential to advance our knowledge of how the EOC tumor microenvironment contributes to cancer initiation. Innovative approaches in microbial profiling can facilitate breakthroughs in the discovery of key molecular pathways that contribute to EOC and drive the development of novel liquid biopsy microbial-based quantitative assays for screening women in clinical practice settings. In preliminary studies, we have identified patterns and changes in known gut bacteria from plasma and fecal samples that may be related to ovarian malignancy. The detection of EOC-associated plasma bacteria would be highly favorable for screening particularly in the deadliest EOC histologic subtype, high-grade serous ovarian carcinoma. The overall objective of this proposal is to validate plasma bacteria in women with EOC and investigate the source. Our central hypothesis is bacteria found in the plasma of women with EOC originates from the ovarian tumor site. The rationale for this proposal is to determine if bacteria in the plasma can provide a means of early disease detection. To attain the overall objective, we will pursue the following two Specific Aims. Our first aim is to identify and validate candidate bacterial species as robust biomarkers to differentiate ovarian cancer cases from benign disease and other solid tumors. Our second aim is to evaluate plasma and intratumor bacteria for concordance in women with EOC. To achieve Aim 1, we will isolate nucleic acids from the plasma samples of women with EOC, non-EOC solid tumors, benign gynecologic conditions, and healthy controls and apply 16S rRNA gene sequencing to assess for differences in bacterial diversity and abundance; verify absolute abundance of the EOC-associated bacteria with quantitative PCR; and assess the informative bacteria biomarkers for sensitivity in plasma using samples from asymptomatic women before and after a clinical diagnosis of EOC. To achieve Aim 2, based on our prior preliminary data and newly discovered pathobionts that have high association with EOC, we will enrich plasma bacterial extracellular vesicles (BEVs), as a paracrine conduit to intra-tumoral bacteria and measure their abundance through the copy number of their respective 16S rRNA signature using droplet digital PCR and correlate with bacterial 16S rRNA gene levels in tumor samples. Subtle pathological changes are shown to facilitate paracellular transport of BEVs released from bacterial cells, and therefore, BEVs can be used to correlate EOC grade, stage, and/or histology of the tumor. The application of reproducible BEV microbial markers unique to early ovarian cancer would be vastly innovative to this field. This study proposal contributes to a dynamic change in thinking from the traditional notion of “infectious diseases” as the potential drivers of ovarian carcinogenesis to the mindse... ## Key facts - **NIH application ID:** 10849367 - **Project number:** 2P20GM130423-06 - **Recipient organization:** UNIVERSITY OF KANSAS MEDICAL CENTER - **Principal Investigator:** Diane E Mahoney - **Activity code:** P20 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2024 - **Award amount:** $224,840 - **Award type:** 2 - **Project period:** 2019-02-15 → 2029-03-31 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10849367 ## Citation > US National Institutes of Health, RePORTER application 10849367, Integrative Analysis of the Blood Plasma and Tumor Microbiome: A Novel Approach to Liquid Biopsy Screening for Early Epithelial Ovarian Cancer Detection (2P20GM130423-06). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10849367. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*