A micrograph of a mucinous ovarian tumour. Credit: Nephron / commons.wikimedia.org.

Aspira Women’s Health has entered an exclusive licensing deal with the Dana-Farber Cancer Institute (DFCI) to design and develop a new microRNA-based ovarian cancer diagnostic test.

The new test will use circulating microRNAs, either alone or along with proteins or other factors, for identifying ovarian cancer in women with adnexal masses.

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It will be developed using a proprietary DFCI microRNA ovarian cancer signature along with the company’s proprietary AI/machine learning algorithms for evaluating the risk of ovarian cancer.

Micro-RNAs are a kind of RNA originating in the cell nucleus with a small amount leaking into the bloodstream.

These can be easily isolated from the blood and can be amplified to detect using PCR-based or next-generation sequencing technologies.

Aspira Women’s Health chief scientific and operating officer Dr Ryan Phan said: “Diagnosing ovarian cancer at its earliest stages is crucial to improving survival outcomes.

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“MicroRNA offers a tremendous opportunity to make this a reality as it appears earlier compared with other testing targets, like circulating tumour cells or proteins. Use of microRNA may help address a significant unmet diagnostic need in ovarian cancer.”

The company stated that the new non-invasive test will be offered as an expansion of its OvaSuite portfolio to healthcare providers.

Aspira Women’s Health president and CEO Nicole Sandford said: “We are thrilled to be on the cutting edge of development with this prestigious partner as we develop and expand upon our OvaSuite of products.

“Adding this new non-invasive, molecular-based risk assessment tool to our portfolio will be another significant step forward in arming physicians with information to determine an appropriate treatment pathway.”

The company noted that the deal is in addition to the existing sponsored research agreement with a consortium of institutions led by DCFI to develop a non-invasive test, which will help in identifying endometriosis using circulating microRNAs and proteins.