Highly Accurate Biomarkers Could Detect Ovarian Cancer Before Clinical Diagnosis

Ovarian cancer is a deadly and challenging disease, primarily because early detection is difficult. Most women (70-75%) are diagnosed only after the cancer has already spread, which significantly reduces their chances of survival to below 32%. Current screening methods, such as the measurement of Cancer Antigen 125 (CA125), only detect about 70% of early-stage cases. Despite this, additional biomarkers are needed to improve the sensitivity and detect cases that CA125 misses. The technological challenges in early detection arise mainly due to the limited sensitivity of available biomarkers and the lack of highly accurate markers capable of identifying the disease before it is clinically diagnosed. Researchers are now working to find early markers for ovarian cancer that would have improved sensitivity and detect cases missed by CA125.

Researchers at the University of Houston (Houston, TX, USA) and MD Anderson Cancer Center (Houston, TX, USA) are collaborating to discover autoantibodies that target the tumor suppressor gene often mutated in cancers and can be an early indicator of ovarian cancer development. To achieve this, they have developed a test capable of detecting thousands of immune reactions simultaneously, looking for immune complexes—clusters of antibodies and their targets. This research aims to improve the sensitivity of ovarian cancer detection, particularly by identifying new biomarkers that could complement the CA125 protein. By identifying immune complexes that are upregulated in ovarian cancer patients compared to healthy individuals, the team has made significant strides in creating a more accurate detection method.

The researchers tested more than 100 upregulated immune complexes in ovarian cancer patients, narrowing the list down to approximately 10 to 20 biomarker candidates for further evaluation. The next steps include further testing and validation of the biomarkers identified in this research. The team plans to assess the performance of these biomarkers in detecting early-stage ovarian cancer to improve the diagnostic process. The team will also use machine learning modeling to develop computer algorithms for data analysis and disease predictions. The advancements in early detection using immune complexes could revolutionize how ovarian cancer is diagnosed and lead to better survival outcomes.

"Advancing early detection methodologies is essential to improving patient prognosis and survival outcomes,” said Tianfu Wu, Associate Professor of Biomedical Engineering at the University of Houston. “The technological challenges in the early detection of ovarian cancer are multifaceted, primarily due to limited sensitivity of currently available biomarkers and the absence of highly accurate biomarkers that can detect the disease well before clinical diagnosis.”