Only one tube of blood is needed to accurately detect ovarian cancer at an early stage
Release date: 2017-11-03
Most women are at advanced stages of diagnosis of ovarian cancer, and only a quarter of patients can survive for more than five years. However, if found early, the 5-year survival rate of patients with ovarian cancer can be increased from 20-30% to 60-70%.
Current early detection methods (such as ultrasound detection or CA125 detection) have higher false positives. Clinical trials have shown that the use of these methods to detect early ovarian cancer has no significant effect on survival. As a result, the research team at the Dana-Farber Cancer Institute and the Bregen Women's Hospital hopes to find a more sensitive and specific detection tool.
The researchers turned their attention to the non-coding region of the genome, the microRNA (miRNA). Dr. Kevin Elias of the Brigham and Women's Hospital called it a copy editor for the genome. "Before the gene is transcribed into a protein, the miRNA modifies the information and adds proofreading notes to the genome." Elias is the first author of the paper. .
In this study, Elias and colleagues found that ovarian cancer cells and normal cells have different miRNA expression profiles. At the same time, the miRNA circulates in the blood so that it can be detected using serum samples. The researchers sequenced miRNAs from blood samples from 135 women (before surgery or chemotherapy) to create a "training sample set." They used this to train computer programs to find miRNA differences between ovarian cancer, benign tumors, and healthy tissue.
With this machine learning approach, researchers can take advantage of large amounts of miRNA data and develop different predictive models. A model that accurately distinguishes between ovarian cancer and benign tissue is called a neural network model, which reflects the complex interactions between miRNAs.
The team then tested the model among 44 women to determine the accuracy of the test. After the accuracy was confirmed, they used 859 patient samples to determine the sensitivity and specificity of the model. The results show that this new technique is much better than ultrasound in the prediction of ovarian cancer. When using ultrasound, less than 5% of abnormal results are ovarian cancer, and when using miRNA detection, almost 100% of abnormal results represent ovarian cancer.
Finally, the researchers applied this model to practice. They used miRNA testing to predict the diagnosis of 51 surgical patients in Poland. In this population, 91.3% of abnormal findings were ovarian cancer, meaning that the false positive rate was very low. Negative test results reliably predict the absence of cancer at 80% of the time, which is comparable to the accuracy of Pap smear.
"The key is that this test is unlikely to misdiagnose ovarian cancer. In the absence of a malignant tumor, it does not give a positive signal. This is a sign that the diagnostic test is effective," Corresponding author, Dana-Farber Cancer Institute Dipanjan Chowdhury said.
In order for this diagnostic tool to enter the clinic, researchers need to verify how the miRNA characteristics will change as the risk of ovarian cancer increases over time. In order to achieve this, they need to continuously collect longitudinal samples. They are also interested in knowing whether this tool is useful for high-risk women and the general population.
Original title
Diagnostic potential for a serum miRNA neural network for detection of ovarian cancer
Source: Biopass
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