New Blood Test Developed to Diagnose Ovarian Cancer

New Blood Test Developed to Diagnose Ovarian Cancer
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Brigham and Women's Hospital


Brigham and Women's Hospital (click to view)

Brigham and Women's Hospital

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Investigators from Brigham and Women’s Hospital and Dana-Farber Cancer Institute are leveraging the power of artificial intelligence to develop a new technique to detect ovarian cancer early and accurately. The team has identified a network of circulating microRNAs — small, non-coding pieces of genetic material — that are associated with risk of ovarian cancer and can be detected from a blood sample. Their findings are published online in eLife.

The team looked at a set of molecules called microRNAs — non-coding regions of the genome that help control where and when genes are activated.

“microRNAs are the copywrite editors of the genome: Before a gene gets transcribed into a protein, they modify the message, adding proofreading notes to the genome,” said lead author Kevin Elias, MD, of BWH’s Department of Obstetrics and Gynecology.

“This project exemplifies the synergy of the two institutes DFCI and BWH and the power of clinicians working closely with lab-based scientists. My lab has been working on miRNAs for a decade and when Kevin came to us with the patient samples, it was a no-brainer to initiate this project” said the senior author Dipanjan Chowdhury, PhD, Chief of the Division of Radiation and Genomic Stability in the Department of Radiation Oncology at DFCI.

In the lab, Elias and Chowdhury and their colleagues determined that ovarian cancer cells and normal cells have different microRNA profiles. Unlike other parts of the genetic code, microRNAs circulate in the blood, making it possible to measure their levels from a serum sample. The team sequenced the microRNAs in blood samples from 135 women (prior to surgery or chemotherapy) to create a “training set” with which to train a computer program to look for microRNA differences between cases of ovarian cancer and cases of benign tumors, non-invasive tumors and healthy tissue. Using this machine-learning approach, the team could leverage large amounts of microRNA data and develop different predictive models. The model that most accurately distinguished ovarian cancer from benign tissue is known as a neural network model, which reflects the complex interactions between microRNAs.

Click here to read more about this study.

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