CPHG’s Kristen Naegle, PhD and Colleagues Develop Shining ‘KSTAR’ to Guide Cancer Treatments

October 26, 2022 by jmd9fe@virginia.edu   |   Leave a Comment

Kristen Naegle, PhD

Kristen Naegle, PhD

UVA Cancer Center researchers have developed an algorithm that will improve cancer care by quickly and easily identifying patients who will benefit from powerful cancer drugs called kinase inhibitors. The algorithm may have other diagnostic benefits for patients as well.

Kinase inhibitors are the most common cancer drugs approved by the federal Food and Drug Administration. They can be hugely effective for the right patients, but they don’t work for everyone. UVA’s new algorithm offers a new and better way to pinpoint patients who will benefit – an important step forward in precision medicine tailored to the individual.

“We are really excited about this algorithm, which performs better than existing approaches with fewer requirements and assumptions – making it more applicable to understanding a cancer state from a single snapshot of the tumor.”  said researcher Kristen M. Naegle, PhD, of UVA’s Department of Biomedical Engineering, a joint program of UVA’s School of Medicine and School of Engineering. “Combining this approach with existing biomarkers for cancer diagnosis may help us to better tailor therapies, design new combination therapies, anticipate response to treatment and design better clinical trials.”

KSTAR for Better Cancer Care

Naegle and colleagues set out to overcome the limitations of existing methods to identify patients who may benefit from kinase inhibitors. Most of these methods require difficult-to-obtain and sometimes unreliable information quantifying “phosphorylation sites” within cells. UVA’s new approach, however, does not need all this complex measurement. Instead, it can “infer,” or predict, key information based on other available data. This allows the algorithm to produce a specific “KSTAR score” for individual enzymes called kinases. Doctors can use these scores to determine which patients will respond to kinase inhibitors, helping guide the best treatment choices.

In testing their new algorithm, Naegle and her collaborators found that it worked reliably across different tissue types, suggesting it is useful for many types of cancer. (Kinase inhibitors are widely used for certain types of blood, breast and lung cancers, among others. These drugs are often used in conjunction with other treatments, such as chemotherapy, radiation or surgery.)

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