Masonic Cancer Center’s Stefani Thomas on providing critical insight into patients’ ovarian cancer treatment—before the treatment even starts
Every day, Masonic Cancer Center, University of Minnesota researchers make innovative discoveries about cancer and its effects on Minnesotans and beyond, thanks in large part to generous funding from a number of partners. Those discoveries include new targeted treatment approaches to diseases like ovarian cancer, which, in 2022 alone, saw at least 300 new diagnoses in Minnesota.
Ovarian cancer is the eleventh most common cancer and the fifth leading cause of cancer-related death among women—making it the deadliest of gynecologic cancers. Researchers are uncovering new targeted treatments that help many patients, but nearly three-quarters of women with advanced ovarian cancer will still relapse within five years. Typical targeted treatments for ovarian cancer involve doctors examining the genes within a tumor to match patients to whichever drug is mostly likely to work for their particular tumor—with no way of knowing whether the patient might develop a sensitivity or resistance to that treatment in the future and have to be switched to another therapy, wasting crucial time.
Masonic Cancer Center member Stefani Thomas, PhD, a clinical chemist, assistant professor in the University of Minnesota Medical School’s Department of Laboratory Medicine & Pathology, and associate medical director of the University of Minnesota West Bank Laboratory—whose work is funded by The V Foundation for Cancer Research—is currently studying protein expression to better predict treatment sensitivity and resistance for patients with an ovarian cancer diagnosis. Protein expression refers to the production of proteins by cells, and studying it in cancer cells can help researchers gather information about a specific type of cancer, the best treatment to use for it, and how well a particular treatment works.
How could Stefani’s research one day lead to a blood-based diagnostic test that would give doctors incredible new insight into how a patient might respond to a particular treatment, before that treatment even starts?
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