University of Minnesota
University Relations

Minnesota Supercomputing Institute

Log out of MyMSI

Research Abstracts Online
January 2010 - March 2011

Main TOC

Mayo Clinic College of Medicine
Department of Laboratory Medicine and Pathology

PI: Jeremy Chien

Genomics of Ovarian Cancer

Epithelial ovarian cancer (EOC) is the most lethal form of gynecologic cancers in the United States. The main cause of death associated with this cancer is the treatment failure for advanced disease. The five-year survival for advanced ovarian cancer is less than 30% compared to over 75% for early-stage disease. Therefore, early detection of ovarian cancer is critical to improve the patient’s outcome. Unfortunately, there are no reliable early detection biomarkers for ovarian cancer. Recent progress in algorithms for protein-based biomarkers is promising, but there is also a critical need to innovate and develop novel strategies for early detection of ovarian cancer. With recent advances, it is now possible to obtain comprehensive genomics of early-stage ovarian cancer. This genomic knowledge is expected to lay the groundwork for further development of genome-based biomarkers for detection and diagnosis of ovarian cancer. With this objective in mind, these researchers are collaborating with an industry partner to generate comprehensive genomic portraits of early-stage, high-grade serous cancer. In this collaboration, they selected 25 patients with early-stage ovarian cancer (19 high-grade serous, 2 clear cell, 2 endometrioid, and 2 mucinous) for gene expression analysis, methylation array analysis, reduced representation bisulfite sequencing, small RNA sequencing mRNA sequencing, and whole genome sequencing analyses. Whole genome sequencing was performed for paired-end 100-bp reads for tumor and matched normal genomes, and it produced over 135 Gbs of mapped sequence data for each genome, representing ~45X coverage of the haploid genome. Due to the large dataset and computational power needed to perform secondary analysis, the researchers are using Itasca for this project.

Group Member

Sabine Dietmann, Research Associate