A quantitative, systems biology, multi-omic approach to diagnose and predict response to treatment for gynecologic cancers

A quantitative, systems biology, multi-omic approach to diagnose and predict response to treatment for gynecologic cancers

In this new project, we will study the microbiota of patients with endometrial cancer and ovarian cancer. Urogenital microbiota, fecal microbiota and peritoneal microbiota will be characterized using genotyping, metagenomics and metatranscriptomics. We will identify microbial signatures that can assist early detection and diagnosis of gynecologic cancer pathology. I will focus on the computational processing and statistical analysis  of meta-omics data on the Titan supercomputer using our Omega, Sigma and UniFam algorithms.

Team: Melissa Cregger (ORNL), Dan Close(ORNL), Chongle Pan (ORNL), Andrea Braundmeier (Southern Illinois University), Laurent Brard (Southern Illinois University), and Assad Semaan (Southern Illinois University)