“When you’re young and you experience your friends and your relatives getting cancer…you know you want to make an impact. It makes you feel like, ‘This is the field I want to be in.’ That is the real reason why I decided to go to medical school and pursue a career in cancer research.”
- Shaoguang Li, on why he pursued a career in cancer research
In this month’s Chair’s Spotlight, we highlight Shaoguang Li, MD, PhD, a scientist and professor of medicine in the Division of Hematology/Oncology. Last month we sat down with Dr. Li to learn more about his work and how he is studying artificial intelligence and its applications in other specialties to develop new, cutting-edge therapies for blood cancers.
Dr. Li’s research focuses on identifying important signaling molecules and pathways, and unique cellular features in blood cancer cells, to develop therapies by targeting major pathways and for detecting various types of leukemia cells to predict therapy response and treatment outcomes. Specifically, his work focuses on studying the molecular basis of leukemogenesis and the biology of leukemia stem cells. Most recently, his laboratory’s focus has been expanded to include a new area of research on utilizing emerging artificial intelligence (AI) technologies to investigate and develop diagnostics and therapies that are more effective in the treatment of blood cancers.
Dr. Li and his team study leukemia stem cells—a population of cells largely resistant to almost all available chemotherapies or targeted therapies. In studying this unique population, they have aimed to identify key drivers in the stem cells and determine whether they can be targeted specifically. Because a majority of blood cancers are stem cell derived (the genetic lesion that causes the cancer occurs in a stem cell), monitoring stem cells would be an effective way of assessing whether a patient is responding to therapy. For example, when a patient receives therapy and is in cellular or even molecular remission, it does not necessarily mean the cancer stem cell has been eradicated. In fact, in many cases, PCR testing does not show a detectable level of oncogene gene expression, but subsequent follow-up tests show a reappearance of oncogene transcripts in patients, indicating that the cancer stem cell has not been eradicated. The reduction of the cancer stem cell quantity would serve as an indicator of a good therapy response and treatment prognosis. Up to now, no research group has been able to quantify cancer stem cells. This is mainly because available methods are not able to distinguish cancer stem cells from their normal stem cell counterparts.
Dr. Li and his team believe that AI technology provides a powerful tool for recognizing various leukemia cell populations, including cancer stem cells. Using a preparatory computational algorithm (core voting algorithm), he and his team recently developed an AI platform that utilizes deep learning to diagnose human lymphoma by reading pathological images (published in Nature Communications). They have also done additional work to test the power of their AI deep learning platform in its ability to diagnose COVID-19-induced pneumonia and distinguish it from pneumonia caused by other viruses and bacteria through reading chest X-ray images of patients. In their study, “An artificial intelligence deep learning platform achieves high diagnostic accuracy for COVID-19 pneumonia by reading chest x-ray images” (published in iScience), a high diagnostic accuracy of greater than 99% was achieved. In another study, recently accepted for publication to iGIE, Dr. Li and his collaborators utilized their AI platform to enhance the diagnostic accuracy of video capsule endoscopy. Using their method, they achieved an overall diagnostic accuracy of greater than 99% and an accuracy of 100% for identifying bleeding and foreign bodies. Dr. Li and his team are now preparing to integrate their AI platform into research aiming to identify leukemia stem cells and distinguish them from their normal stem cell counterparts for assessing therapy response and disease prognosis.
Dr. Li earned his medical degree at the China Medical University in China and his PhD in cell and molecular biology at Tulane University in New Orleans, Louisiana. He completed his postdoctoral studies in genetics at Harvard Medical School in Boston. Dr. Li joined UMass Chan Medical School in 2008. Before coming to UMass Chan, he held an academic appointment and led his independent research team at The Jackson Laboratory in Bar Harbor, Maine.
Dr. Li is a valued member of our faculty and we thank him for his contributions to the Department of Medicine.