Rosetta Genomics Advances Development of its Differentiated Thyroid Cancer Assay

Molecular diagnostics company, Rosetta Genomics Ltd., announced that its novel assay for differential diagnosis of thyroid neoplasia is on track for launch by the end of the third quarter of 2015.

The company provided an update on the development of its microRNA-based assay for the differential diagnosis of thyroid neoplasia, which has the potential to improve clinical management in a cost-effective way by reducing the number of unnecessary surgeries.

According to Rosetta Genomics, an estimated 4-7 percent of the general population develops nodules in the thyroid, which can be felt upon examination, however fewer than ten percent are malignant. Currently, the standard technique for detecting cancer is to obtain tissue from the thyroid nodule for analysis with a Fine Needle Aspirate (FNA). It is estimated that each year nearly 500,000 FNAs are performed in the US and approximately 740,000 are performed in Europe. Interpreting FNA samples is not always straightforward. Up to 30 percent of the samples are deemed indeterminate. Among those with indeterminate results, approximately 75 percent undergo surgery, despite the fact that less than 50 percent of these tumors are malignant. This exposes patients to unnecessary risks and costs associated with surgery.

For the past two years, Rosetta Genomics has been developing this assay and has conducted multiple discovery studies, with more than 500 thyroid FNA and surgical samples, representing various histological subtypes. The company analyzed the samples utilizing its proprietary microarray and qPCR platforms, and a subset of the samples were also analyzed by deep sequencing. This approach led to the discovery of new microRNA biomarkers and allows the profiling of small cytological samples to ensure low failure rates.

In these studies, several microRNAs differentially expressed between benign and malignant tumors, as well as microRNA biomarkers of medullary thyroid carcinoma, one of the deadliest forms of thyroid cancer, were identified. These studies also showed the ability to extract and profile microRNAs from thyroid FNAs in various sample types, and in a manner that is consistent with common clinical practices. Rosetta is now conducting training studies and will soon start blinded validation studies at several leading medical centers where the company already has ready-to-test samples.

“We are very pleased with the progress we’ve made in advancing the development of our thyroid cancer assay. Upon commercialization we believe our thyroid assay will be competitive with current testing methods as we expect it to have the best combination of Negative Predictive Values and Positive Predictive Values, our turnaround time is expected to be less than half that of the market leaders and our process will spare patients unnecessary needle passages. Our specimen collection process is a competitive differentiator because it can utilize the smear used by the cytologist as part of their standard process rather than taking additional FNAs and preserving them in specialized tubes. The latter process of taking additional FNAs through another needle passage into the patient’s neck is required by some currently marketed thyroid tests, also creates the potential for misdiagnosis,” said Kenneth A. Berlin, President and Chief Executive Officer of Rosetta Genomics.

The test brings an innovative new way to help patients and physicians deal with indeterminate results in thyroid nodules utilizing microRNAs. The company expects to complete their training and validation studies soon, and remain on track to launch the assay by the end of the third quarter of 2015.

“With nearly 30% of FNAs yielding indeterminate results, the annual opportunity in the U.S. alone exceeds $350 million and currently available tests in this space generated approximately $50 million in revenues in 2014. We are looking forward to launching our novel and differentiated assay into this fast converting market,” said Berlin.

Source: Rosetta Genomics

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