Scientists Edge Closer to Early Detection of Ovarian Cancer

Scientists Edge Closer to Early Detection of Ovarian Cancer

November 20, 2015

Research scientists have developed a test that may be able to aid in the diagnosis of ovarian cancer at an early stage.

Research scientists have developed a test that may be able to aid in the diagnosis of ovarian cancer at an early stage. Researchers at the Georgia Institute of Technology have been working on a screening test that could diagnose the disease in stage I or stage II – when the cancer is still confined to the ovaries.

This was first reported November 17, 2015, in the journal Scientific Reports, an open access journal from the publishers of Nature.

This disease is generally asymptomatic until late stages, so ovarian cancer is oftentimes difficult to diagnosis before it has metastasized. Depending on the cancer stage, the overall 5-year relative survival rate is approximately 45%. So needless to say, this important finding may not only be practice-changing, but life-saving.

Current diagnostic tools to detect this deadly cancer include transvaginal ultrasound and measurement of CA-125 levels. This tumor marker is seen at higher levels in women with ovarian cancer. Woman younger than 50 with conditions such as endometriosis, pelvic inflammatory disease, and uterine fibroids, may also have an increased CA-125 level. So, it is more accurate to test women older than 50 years of age who have gone through menopause, for CA-125 as it relates to ovarian cancer.

John McDonald, PhD, a professor in the School of Biology at Georgia Tech and director of its Integrated Cancer Research Center, has been taking an integrated systems approach to the study of cancer. In his lab, he and his team view cancer not as a defect in any particular gene or protein, but as a deregulated cellular/intercellular process. This viewpoint led them to study how ovarian cancer can be diagnosed using algorithms and advanced tools.

Using techniques such as advanced liquid chromatography and mass spectrometry along with machine learning computer algorithms, his research team identified 16 metabolite compounds that accurately (>90%) distinguished 46 women with early-stage ovarian cancer from a control group of 49 women who did not have the disease.

“People have been looking at proteins for diagnosis of ovarian cancer for a couple of decades, and the results have not been very impressive,” said Facundo Fernández, PhD, professor in Georgia Tech’s School of Chemistry and Biochemistry who led the analytical chemistry part of the research, in news release. “We decided to look in a different place for molecules that could potentially provide diagnostic capabilities. It’s one of the places that people had really not studied before.”

The predictive accuracy of support vector machine (SVM)-derived biomarkers heavily depends on the representative nature of the biological samples used in building the model. Because of this, blood samples for the study were collected from a broad geographic area – Canada, Philadelphia, and Atlanta.

The results demonstrate the importance of lipid and fatty acid metabolism in ovarian cancer and serve as the foundation of a clinically significant diagnostic test, according to the Nature article.