DENVER, CO, April 29, 2025: AOA Dx, a company pioneering blood-based diagnostics for early cancer detection, announced a significant advancement in the early detection of ovarian cancer. In the most recent data presented at the 2025 American Association for Cancer Research (AACR) Annual Meeting, AOA Dx’s AI-powered multi-omic platform demonstrated high accuracy in detecting ovarian cancer in symptomatic women, a population where early diagnosis is critical but often delayed.
In a first-of-its-kind study, AOA Dx’s platform demonstrated high diagnostic accuracy, outperforming traditional markers like CA125. The research incorporated collaborations with two world-renowned institutions, the University of Colorado Anschutz Medical Campus, Ovarian Cancer Innovations Group (OCIG) and The University of Manchester, UK. Approximately 1,000 patient samples representing the real-world clinical population were analyzed, demonstrating promising test performance in this important group.
In addition to these data from collaborations with the academic institutions, previous work conducted at AOA Dx also demonstrated the potential clinical utility of lipidomics for early detection of ovarian cancer (OC).
“Our platform detects ovarian cancer at early stages and with greater accuracy than current tools,” said Oriana Papin-Zoghbi CEO and Co-Founder of AOA Dx. “These findings show its potential to aid clinicians in making faster, more informed decisions for women who need clarity during a challenging diagnostic process.”
AOA Dx conducted two independent studies on clinically similar populations. Cohort 1 was used for model training and included samples from CU Anschutz Ovarian Cancer Innovations Group (OCIG). In Cohort 1, the model achieved an area under the curve (AUC) of 93% when distinguishing all stages of ovarian cancer from all controls, and 92% for early-stage (stage I/II) disease. Cohort 2 was used as an independent testing set and included prospectively collected symptomatic samples in AOA’s intended use population from The University of Manchester. AOA’s data demonstrated that in Cohort 2, the model maintained strong performance with an AUC of 92% for ovarian cancer and 89% for early-stage disease. These results highlight the reliability of AOA Dx’s machine learning algorithms in identifying cancer-specific biomarker patterns.
The strength of AOA Dx’s platform lies in its integration of multi-omic data, combining lipid, ganglioside, and protein biomarkers from a small blood sample using liquid chromatography mass spectrometry (LC-MS) and immunoassays. Machine learning algorithms analyze these complex multi-omic datasets to help uncover disease-specific signatures, delivering results that surpass models using single biomarker types. This approach positions the test as a valuable tool for clinical diagnostics.
“By using machine learning to combine multiple biomarker types, we’ve developed a diagnostic tool that detects ovarian cancer across the molecular complexity of the disease in sub-types and stages” said Dr. Abigail McElhinny, Chief Science Officer of AOA Dx. “This platform offers a clear path toward earlier detection and better patient outcomes.”
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. This current challenge is compounded by the shortage of gynecologic oncologists in the U.S., which limits timely access to specialized care. An accurate early detection test for symptomatic women is therefore necessary.
Partnering with the CU Anschutz Ovarian Cancer Innovations Group (OCIG) and The University of Manchester provided access to clinically annotated patient samples and expertise in study design, ensuring rigorous validation in real-world populations.
Professor Emma Crosbie, Professor at The University of Manchester and Honorary Consultant in Gynecological Oncology, Manchester University NHS Foundation Trust (MFT), said: “AOA Dx’s platform shows significant promise for ovarian cancer early detection, offering a practical solution for symptomatic women.”
Professor Crosbie, who is also National Institute for Health and Care Research (NIHR) Manchester Biomedical Research Centre (BRC) Cancer Prevention and Early Detection Co-Theme Lead continued: “AOA Dx’s platform has the potential to significantly improve patient care and outcomes for women diagnosed with ovarian cancer. We are eager to continue advancing this important research through additional prospective trials to further validate and expand our understanding of how this could be integrated into existing healthcare systems.”
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.
About the University of Colorado Anschutz Medical Campus, Ovarian Cancer Innovations Group (OCIG)
The CU Ovarian Cancer Innovations Group (OCIG) at the University of Colorado Anschutz Medical Campus is a multidisciplinary team dedicated to advancing the research, prevention, early detection, and treatment of ovarian cancer. Located within the Department of Obstetrics and Gynecology and Division of Gynecologic Oncology, the OCIG brings together a diverse group of world-class researchers, clinicians, and innovators committed to developing innovative solutions for one of the most challenging gynecologic cancers. Through cutting-edge research and a collaborative approach, OCIG strives to transform ovarian cancer care and improve outcomes for women worldwide. Learn more at https://medschool.cuanschutz.edu/ob-gyn/ocig.
About Manchester University NHS Foundation Trust
Manchester University NHS Foundation Trust is the largest NHS Trust in the country and a leading provider of specialist healthcare services. Its ten hospitals are home to 28,000 staff including world class clinicians and academic staff committed to finding patients the best care and treatments. Its 10 hospitals are Manchester Royal Infirmary, Saint Mary’s Managed Clinical Service, Royal Manchester Children’s Hospital, Manchester Royal Eye Hospital, University Dental Hospital of Manchester, Trafford General Hospital, Altrincham Hospital, Wythenshawe Hospital, Withington Hospital and North Manchester General Hospital. More information is available at www.mft.nhs.uk
About the National Institute for Health and Care Research (NIHR)
The mission of the National Institute for Health and Care Research (NIHR) is to improve the health and wealth of the nation through research. We do this by:
- Funding high quality, timely research that benefits the NHS, public health and social care;
- Investing in world-class expertise, facilities and a skilled delivery workforce to translate discoveries into improved treatments and services;
- Partnering with patients, service users, carers and communities, improving the relevance, quality and impact of our research;
- Attracting, training and supporting the best researchers to tackle complex health and social care challenges;
- Collaborating with other public funders, charities and industry to help shape a cohesive and globally competitive research system;
- Funding applied global health research and training to meet the needs of the poorest people in low and middle income countries.
NIHR is funded by the Department of Health and Social Care. Its work in low and middle income countries is principally funded through UK international development funding from the UK government.