A USC-led research team announced Thursday they have developed a new way to identify molecular markers of breast cancer tumors, which could potentially lead to better treatment for millions of patients worldwide.
The researchers taught a computer to rapidly sort images of breast tumors to identify which ones had estrogen receptors, a key to determining prognosis and treatment options. For more than a century, doctors have relied on microscopes and cell biopsies to classify tumors and select treatments.
“While machine learning has been used before for cancer detection, the USC study adapted the technology to more clearly focus on telltale markers of a cell’s nucleus,” according to a USC statement. “The key was to extract parameters describing the shape of nuclei, and feeding these into a large neural network that could learn relationships between nucleus shape and molecular markers.”
Their work — outlined this week in Nature Partner Journals/Breast Cancer — opens a new pathway for breast cancer treatment that promises faster results for less cost for more people worldwide, according to David B. Agus, a professor at the Keck School of Medicine of USC and the USC Viterbi School of Engineering, and CEO of the Lawrence J. Ellison Institute for Transformative Medicine of USC.
“It’s the beginning of a revolution to use machine learning to get new information about breast cancer to the physician,” Agus said. “We can use it to detect better treatments, get information to patients faster and help more people. We’re unleashing this power to give new information to physicians and help treat cancer.”
About 237,000 cases of breast cancer are diagnosed in U.S. women and about 41,000 die from the disease each year, according to the U.S. Centers for Disease Control and Prevention, making it the second-leading cause of cancer death among women, behind skin cancer.
“If you’re diagnosed with cancer, it’ll be a few weeks before you get a call from the doctor saying they’ve identified a marker,” said Dan Ruderman, one of the study authors and an assistant professor of research medicine at the Keck School of Medicine. “With machine learning technology, we can tell you the same day, so there’s less delay, less stress and potentially better outcomes. It’s going to enable us to identify the right drug and dose more quickly. It’s a big step toward personalized medicine.”
Rishi Rawat, a graduate student in the Keck School of Medicine and first author of the study, said the team’s findings demonstrate that the new technology has the potential to improve clinical care. Validation studies are under way — an important step before it is ready for use in the doctor’s office.
The other co-authors of the study were Paul Macklin of Intelligent Systems Engineering at Indiana University and David L. Rimm of the Department of Pathology at the Yale University School of Medicine.
The research was supported by a grant from the Breast Cancer Research Foundation.