This invention provides a non-invasive method to detect endometriosis in women through the use of novel diagnostic biomarkers that detect the presence, absence, and/or severity of endometriosis.
Endometriosis is a progressive inflammatory disorder characterized by pelvic pain and infertility. Currently, the only way to diagnose pelvic endometriosis is by laparoscopy or laparotomy, both are invasive surgical procedures. Additionally the average time from pelvic pain onset to surgical diagnosis is 6.7-11 years. Thus, there is an unmet medical need for a rapid, non-invasive diagnostic test not only to identify endometriosis but also to determine the stage/severity of the disease.
This novel invention provides the following advantages:
Scientists at the University of California, San Francisco have developed novel diagnostic classifiers for the detection of endometriosis in women. They compared the transcriptome of endometrium in women with and without minimal-mild and moderate-severe stage endometriosis using bioinformatic classifier analysis. The comparison found specific sets of genes that were dysregulated in women with endometriosis at different phases of the menstrual cycle including proliferative, early secretory, and mid secretory, which allowed identification of subjects with endometriosis. Their diagnostic classifiers are highly accurate (90-100%) and use a series of sequential binary decisions that are based on specific gene sets to distinguish between disease and healthy states and the stages of disease.
- Non-invasive diagnostic test for endometriosis
- Diagnostic tool to determine the severity or stage of endometriosis
- Used with gene expression technologies (microarray, qPCR), potential development for proteomic and immune-based technologies
- Development of therapeutics to these molecular targets for treatment of endometriosis
To develop and commercialize this technology as an effective diagnostic for endometriosis and other uterine/pelvic disorders.
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Additional Patent Pending
Endometriosis, Biomarker, Diagnostics, Prognostic, Fertility, Women, Pelvic Pain