Non-Invasive Detection and Phenotyping of Ovarian Cancer

University of Pittsburgh researchers have developed a novel approach for the non-invasive detection and phenotyping of ovarian cancer using liquid biopsy. This method leverages the analysis of DNA methylation signatures within cell-free DNA fragments found in plasma or other fluid reservoirs. The innovative algorithm developed can accurately identify changes in methylation patterns associated with ovarian cancer, offering a promising tool for early diagnosis and personalized treatment strategies.

Description

The technology involves the detection of DNA methylation signatures in cell-free DNA from plasma or other fluid reservoirs of ovarian cancer patients. The developed algorithm identifies changes in methylation patterns by modeling the plasma methylome as a mixture of various component methylomes. This approach allows for the accurate prediction of methylation patterns in new plasma samples, enhancing the sensitivity and specificity of ovarian cancer detection.

Applications

- Non-invasive cancer screening
- Cancer phenotyping
- Personalized treatment planning
- Early diagnosis of ovarian cancer

Advantages

This novel method provides a non-invasive, accurate, and practical approach to detecting and phenotyping ovarian cancer. The algorithm's ability to identify specific methylation changes in plasma samples offers high sensitivity and specificity. Additionally, the method can provide insights into both the presence and phenotype of the tumor, potentially leading to more effective and personalized treatment strategies.

Invention Readiness

The concept has been defined, proof-of-concept and in vivo data has been obtained. Preliminary analyses of plasma DNA samples from ovarian cancer patients and healthy controls demonstrate the ability to generate high-resolution epigenomic data. The algorithm has shown high sensitivity in detecting abnormal methylation patterns associated with ovarian cancer. Further validation and optimization are ongoing to enhance the robustness and applicability of the technology.