Ovarian Cancer rendering courtesy: Mohammed Haneefa Nizamudeen/Getty Images
With a $1.2M grant from the Department of Defense, Tianfu Wu, associate professor of biomedical engineering, will lead a team of researchers, partnering with those from MD Anderson Cancer Center, in finding early markers for ovarian cancer.
Ovarian cancer is a deadly threat because it is difficult to detect early. Most women (70-75%) are diagnosed once the cancer has already spread, and their chances of survival are below 32%. Computational models estimate that detecting ovarian cancer earlier could reduce mortality by 10-30%.
Currently doctors screen for ovarian cancer by measuring the rising amount of a protein called Cancer Antigen 125, or CA125, produced by ovarian cancer cells, to detect 70% of early-stage cases, but still additional biomarkers are needed to improve sensitivity and to detect cases missed by CA125.
"Advancing early detection methodologies is essential to improving patient prognosis and survival outcomes," said Wu. "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."
So, Wu and team have set out to find better biomarkers, starting first with autoantibodies which target the tumor suppressor gene often mutated in cancers and can be an early indicator of ovarian cancer development.
Wu's partner in the research, Robert C. Bast, MD, at MD Anderson Cancer Center, has pioneered the practice of early detection of ovarian cancer.
"Given the fact that we have shown autoantibodies and antibody-antigen immune complex could improve the sensitivity and detect ovarian cancer earlier, we hypothesize that additional high-performance biomarkers, particularly novel immune complexes, could improve the sensitivity to detect early-stage ovarian cancer when combined with CA125," said Wu.
To discover new autoantibodies, Wu's team developed a test that detects thousands of immune reactions at once, looking for immune complexes (clusters of antibodies and their targets.) After finding more than 100 significantly upregulated immune complexes in ovarian cancer patients compared to healthy patients, the team will test approximately 10 to 20 of the biomarker candidates to assess their performance in the early detection of ovarian cancer.
The team will also use machine learning modeling to develop computer algorithms for data analysis and disease predictions in collaboration with Ying Lin, associate professor of industrial and systems engineering at UH. Zhen Lu, MD, at UT MD Anderson Cancer Center, rounds out the team.
