A new device rapidly measures the efficacy of targeted cancer drugs. And, that could be a game-changer. The ability to figure out early if targeted drugs are working in individual patients is critical to the attempts to provide precision therapy. A description of the new device appears in the 15 July 2019 issue of Microsystems & Nanoengineering.
“We built a portable platform that can predict whether patients will respond positively to targeted cancer therapy,” said senior author Mehdi Javanmard, an assistant professor of electrical and computer engineering at Rutgers University-New Brunswick, in a press release. “Our technology combines artificial intelligence and sophisticated biosensors that handle tiny amounts of fluids to see if cancer cells are sensitive or resistant to chemotherapy drugs.”
In the paper, researchers reported that the new device can distinguish live from dead cancer cells with 95.9 percent accuracy in as little as 20 microliters of fluid, equivalent to less than half of a drop blood.
According to the researchers, the new approachimproves on current histological methods which can take days or weeks and rely on staining samples. The new device provides immediate results and, because it does not rely on staining, allows for further molecular analysis and characterization of cell surface features.
The current push for targeted chemotherapy is driven by the desires to improve care and reduce costs. Traditional chemotherapy drugs kill cancer cells, but, because they also kill normal cells, cause debilitating side effects, including hair and weight loss. And sometimes those therapies do not work, which causes unnecessary suffering and waste of resources.
Targeted therapies, on the other hand, combine antibody-specific molecules that bind only to receptor proteins on cancer cells, delivering their lethal payload to just those cells and minimizing side effects. But even targeted therapies may not work for each patient. Knowing that sooner rather than later is key.
“Novel technologies like this can really have a positive impact on the standard-of-care and result in cost-savings for both healthcare providers and patients,” said Joseph Bertino, M.D., a resident researcher at Rutgers Cancer Institute of New Jersey and professor at the Rutgers Robert Wood Johnson Medical School in the press release.
The new device uses multifrequency impedance spectroscopy/cytometry in combination with supervised machine learning for enhanced classification accuracy.Electrical impedance spectroscopy/cytometry measures the electrical properties of particles in suspension. Live cells have different electrical properties than dead ones. Microfluidics allows for measuring these differences one cell at a time. Machine learning algorithms are used to analyze the data and classify each cell.
Activated matriptase, a membrane-bound enzyme, is overexpressed on the surface of various kinds of cancer cells, which makes it an ideal drug target. Researchers used their new device to measure cancer cell response to anti-matriptase-conjugated drugs. They used commercially available breast cancer cell lines grown in culture in their tests.
If they found a larger proportion of dead cells in a sample, that was indicative that the cells express activated matriptase. In clinical practice, that result would indicate that the targeted therapy would be effective, the authors explained.
“We envision using this device as a point-of-care diagnostic for assessing patient response and personalization of therapeutics,” the authors wrote. Although beyond the scope of their study, they also wrote that they envision this device being used in a clinical setting and that their future studies will focus on clinical samples.
