Did you know that May 8th is Ovarian Cancer Awareness Day? Ovarian cancer remains one of the most challenging women’s cancers to detect and treat effectively, particularly because it often does not present clear symptoms until its advanced stages. This can lead to diagnoses at a time when treatment options are more limited and less likely to result in a cure. In light of Ovarian Cancer Awareness Day, let us explore some important information about this disease, and highlight a study being carried out to develop a new blood test for ovarian cancer.
Ovarian Cancer: References
Learn more about ovarian cancer, its symptoms, stages, diagnosis and tumour marker tests, treatments, risk factors and prevention in the articles linked below.
A Recent Development in the Fight Against Ovarian Cancer
In a significant advancement for ovarian cancer detection, researchers are developing a promising blood test for ovarian cancer that could identify the disease earlier than ever before. It is discussed in an article by Cancer Health. This new approach combines advanced machine learning algorithms with liquid biopsy technologies to analyse blood samples noninvasively.
Research by the American Association for Cancer Research
According to a study presented at the American Association for Cancer Research (AACR) Annual Meeting 2024, a novel blood test has been developed that could significantly improve the early detection of ovarian cancer. The research, conducted by a team from Johns Hopkins Kimmel Cancer Center, utilises a combination of cell-free DNA (cfDNA) fragment patterns and the levels of two known protein biomarkers for ovarian cancer, specifically CA125 and HE4.
This innovative test, known as DELFI (DNA Evaluation of Fragments for early Interception), employs a technique called fragmentomics, which assesses the size and distribution of cfDNA fragments across the genome. Cancer cells, due to their rapid growth and chaotic genetic makeup, release DNA fragments into the blood that differ in pattern from those of healthy cells.
The Study’s Findings
The researchers, led by Jamie Medina, PhD, and Akshaya Annapragada, an MD/PhD student, analysed the fragmentomes from individuals diagnosed with ovarian cancer as well as those without the disease. By training a machine learning algorithm to integrate the fragmentome data with plasma levels of CA125 and HE4, they developed a robust model that could distinguish ovarian cancer from benign ovarian masses with high accuracy.
The test showed impressive results, identifying a significant percentage of ovarian cancer cases across different stages—with a specificity of over 99%, meaning almost no false positives were recorded. For instance, in early-stage (stage I) ovarian cancer, the test identified 69%, 76%, 85%, and 100% of ovarian cancer stages I-IV, respectively.
Future Directions and Study Limitations
The findings of this study are encouraging, but the researchers plan to further validate their models in larger cohorts to confirm these results. The study’s limitations include its retrospective nature and the relatively small and geographically limited sample size.
Funding for this crucial research was provided by various foundations and institutions, including the Dr. Miriam and Sheldon G. Adelson Medical Research Foundation, Stand Up To Cancer, and the U.S. National Institutes of Health, among others.
This breakthrough represents a significant step forward in the fight against ovarian cancer, potentially leading to earlier detection and more effective treatment options for women worldwide. As we observe Ovarian Cancer Awareness Day, it is vital to remain hopeful and supportive of continued advancements in this important field.
Screening For Ovarian Cancer
Ovarian Cancer is one that is currently difficult to accurately screen for. Common tests as of 2024 include transvaginal ultrasound and CA-125 tumour marker blood test. If you are at higher risk for the cancer and wish to undergo screening, contact us through the button below.
You may also browse health screening packages from our healthcare partners here.
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This article is informative only and is not intended to be a substitute for professional medical advice, diagnosis, or treatment, and should never be relied upon for specific medical advice.