Serum Glycoproteomic Signatures as Early Biomarkers for Ovarian Cancer
Abstract
Early Detection of Ovarian Cancer – Significance and Critical Challenges: Ovarian cancer (ovCa) is the most deadly gynecological cancer among women in the United States. When ovCa is diagnosed early at either stage I or II, the disease is very curable and has a very encouraging 5-year survival rate of 94% and 70%, respectively. Unfortunately, only 20% of ovCa patients receive early diagnosis. This is partly because ovCa symptoms are non-specific (e.g., abdominal pain, bloating, etc.), and thus are often attributed to other diseases. To make things worse, when a patient is diagnosed with late-stage ovCa, even if she initially responds well to surgery and chemotherapy, there is a more than 80% chance that she will relapse in the future. Clearly, detecting ovCa at an early stage is tremendously important. Several different approaches have been developed in the hopes of detecting ovCa early, including (1) genetic testing for mutations (for example, in the genes BRCA1/2) and (2) testing the levels of ovCa-specific proteins (i.e., biomarker, the most established of which is CA-125) in patient serum samples. (1) Unfortunately, genetic tests can only inform of the risk of developing ovCa and is not a predictor of actual disease development. (2) Measuring the serum levels of only CA-125 is not adequate for early detection of ovCa, as its levels are elevated in no more than 60% of early-stage cases. Additional biomarkers are needed to detect patients with early-stage disease who would be missed with CA-125. At Venn Biosciences Corp. (InterVenn), we aim to address this critical need to identify additional biomarkers that can augment CA-125 in the early detection of ovCa by developing a novel strategy that combines advanced mass spectrometry (MS, a protein analysis method) with powerful machine learning algorithms. Recent scientific studies have shown that using MS to analyze the modifications of ovCa biomarkers, instead of just their levels in the blood, is a promising approach to early diagnosis. Protein modifications are known to be generally important in normal tissue development and function, and abnormal protein modifications are known to be involved in the progression and spread of several different types of cancer. As proof-of-concept, we performed a study in which we applied our strategy to a small number of serum samples from ovCa patients and healthy subject controls, from which our machine learning algorithm was able to identify several additional biomarkers that are specific to ovCa patients. In this research project, we propose several larger studies to validate the identities of these biomarkers. The success of our work would provide the ovCa community with the additional biomarkers needed to augment CA-125 serum level tests for the accurate early detection of ovCa. Early Detection of ovCa – New Insights, Paradigms, and Applications: Our unique approach to identifying new serum biomarkers for ovCa will lay the groundwork for developing a clinical blood test for the early detection of ovCa that would greatly improve the quality of life of all ovCa patients. Clinically, the availability of such a test would alter the clinical pathway of ovCa treatment. Clinical intervention of ovCa can now be performed at much earlier stages, when they are the most impactful. Finally, demonstration of the feasibility of our approach would provide the ovCa scientific community with a new, validated tool to further their research into the disease, which may lead to new, important insights and better therapeutics. Early Detection of ovCa – Its Relevance to the Vision and Mission of the Ovarian Cancer Research Program (OCRP): ovCa patient survival is intimately correlated with early detection, but only 20% of ovCa patients are diagnosed early. As ovCa is a leading cause of cancer-related mortalities among women in the United States, our proposed research would be transformational to many ovCa patients, including S
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Mar 10, 2021
- Source ID
- W81XWH2010414
Entities
People
- Klaus Lindpaintner
Organizations
- InterVenn Biosciences
- United States Army