Advanced Metabolite Detection May Allow Earlier Ovarian Cancer Detection

Professor John McDonald PhD Director of its Integrated Cancer Research Center School of Biology at the Georgia Institute of Technology

Dr. McDonald

MedicalResearch.com Interview with:
Professor John McDonald PhD
Director of its Integrated Cancer Research Center
School of Biology at the Georgia Institute of Technology

Medical Research: What is the background for this study? What are the main findings?

Response: Ovarian cancer is a deadly disease because it cannot be diagnosed at early stages when it can be most effectively effectively treated.

It has long been recognized that there is a great need for an accurate diagnostic test for early stage ovarian cancer.

Until now, efforts to develop a highly accurate way to detect early stage ovarian cancer have been unsuccessful.

We have used a novel approach that integrates advanced methods in analytical chemistry with advanced machine learning algorithms to identify 16 metabolites that collectively can detect ovarian cancer with extremely high accuracy (100% in the samples tested in our study)

Medical Research: What should clinicians and patients take away from your report?

Response: That early stage ovarian cancer can be detected with extremely high accuracy.

Medical Research: What recommendations do you have for future research as a result of this study?

Response: The methods needs to be validated across a larger cohort of women to insure that it is accurate across all ethnic/racial groups.

Further tests will ideally be carried out among groups of women with a high-risk of developing ovarian cancer (e.g., BRCA positive individuals, etc) .

Citation:

David A. Gaul, Roman Mezencev, Tran Q. Long, Christina M. Jones, Benedict B. Benigno, Alexander Gray, Facundo M. Fernández, John F. McDonald. Highly-accurate metabolomic detection of early-stage ovarian cancer.
Scientific Reports, 2015; 5: 16351
DOI:10.1038/srep16351

Professor John McDonald PhD (2015). Advanced Metabolite Detection May Allow Earlier Ovarian Cancer Detection