DENVER, CO, October 23, 2025: AOA Dx (AOA), a company pioneering blood-based diagnostics for early cancer detection, announced the publication of peer-reviewed clinical evidence in Diagnostics demonstrating that its novel multi-omic machine-learning model delivers high sensitivity and specificity for the detection of early-stage ovarian cancer (OC) among symptomatic women.
The clinical evaluation builds on biological discovery and modelling work published earlier this year in Cancer Research Communications. Whereas the earlier study established the scientific basis for integrating lipidomic and proteomic biomarkers, the new analysis moves into practical, real-world evaluation against standards of care, with a focus on clinical endpoints.
In a large and clinically diverse cohort of more than 500 women, the AOA multi-omic model achieved 94.8% sensitivity in early-stage OC and 94.4% across all stages and subtypes, a significant improvement over the current standard of care biomarker, CA125. Importantly, the study population included diagnostically challenging cases, underscoring the robustness of the test in clinical settings.
Exploratory analyses also showed that the multi-omic approach could distinguish between treated and untreated patient profiles, suggesting potential applications beyond early detection, including treatment response monitoring. These findings represent a significant improvement over current detection methods, demonstrating the power of the multi-omic approach. Clinically, these gains could not only improve patient outcomes but also reduce the healthcare burden through earlier intervention and more targeted use of healthcare resources.
Ovarian cancer is the fifth leading cause of cancer-related deaths among women, largely due to late-stage diagnosis. Over 90% of women experience symptoms in Stage I, yet only 20% of cases are diagnosed in Stage I or II, as symptoms like bloating, abdominal pain, and digestive issues often resemble benign conditions. Existing diagnostic methods, which rely on invasive procedures or less reliable markers, frequently fail to identify early-stage disease. An accurate early detection test available to women when they first visit a physician with symptoms could revolutionize the detection of ovarian cancer.
About AOA Dx:
AOA Dx is transforming early cancer detection with its proprietary GlycoLocate™ platform, a first-of-its-kind, multi-omics liquid biopsy that integrates gangliosides, lipids, proteins, and clinical data using advanced machine learning. The company’s lead test, AKRIVIS GD™, is designed to detect ovarian cancer early in symptomatic women, where no other diagnostic currently exists. Based in Denver, Colorado, AOA Dx is led by an experienced team of scientists and industry veterans and operates a dedicated lipidomics lab focused on pioneering the future of cancer diagnostics.
Media contact: ariel@klovercommunications.com