September 07, 2018 PHILADELPHIA— Despina Kontos, PhD, an associate professor of Radiology, and Lewis A. Chodosh, MD, PhD, chair of Cancer Biology and associate director of the Abramson Cancer Center, both in the Perelman School of Medicine at the University of Pennsylvania, have received a five-year, $4 million grant from the National Institutes of Health (NIH) to develop more effective, personalized approaches for selecting tailored treatment options for breast cancer patients.
Kontos and Chodosh aim to determine whether certain tumor-imaging characteristics, combined with artificial intelligence, can provide additional information to established imaging tools and genetic methods for predicting risk for breast cancer recurrence.
For many years, a key tool in treating breast cancer has been determining the presence of certain tissue-based biomarkers, usually genes or proteins that are turned ‘on’ or ‘off’ in particular cancers. These individual molecular signatures can identify patients most likely to benefit from treatment, predict the most effective therapies, or measure responses to treatment. However, these biomarkers cannot show the variety of response within breast cancer cells. In addition, during therapy, certain cells may become resistant to treatment, rendering the information obtained from biopsies prior to treatment inaccurate. In such cases, opportunities may be lost for identifying the best course of treatment.
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Imaging is increasingly used for screening, diagnosing, and treating breast cancer, with different procedures offering complementary information. Traditional examples include breast ultrasound, breast magnetic resonance imaging (MRI), computerized tomography (CT), and positron emission tomography (PET), while newer techniques include molecular breast imaging, a test that uses a radioactive tracer and special camera to track down breast cancer. Imaging techniques allow for noninvasive assessment of disease at a single time or multiple times, which provide a potential for capturing vital aspects of tumor variety and mutation.
The team will “back-test” their model by using a cohort of women with imaging and tumor-tissue biomarker data from an NIH trial completed at Penn, from which ten-year follow-up data from initial diagnosis and treatment are available.
With this grant, the Penn researchers have several goals: to better capture the variety of primary tumors in size, shape and molecular composition; to determine the value of the imaging features in combination with molecular profiling in predicting risk of recurrence; and to perform an independent validation of their model with prospectively collected data at Penn.
Considering that multiple types of imaging are increasingly used as part of routine clinical care, the study could provide new imaging biomarkers to improve treatment decisions, at a minimal additional cost.
