KSTAR: An Algorithm for Inferring Kinase Activity from Patient Phosphoproteomic Data

About this Class

Register/Join Kinase inhibitors have been intensively studied and used effectively for cancer treatment for decades. Yet, despite our progress in understanding kinases in oncology, more needs to be known to better predict how and which inhibitor will work best in any given patient at any given time. Phosphoproteomic data could hold the key for such precision medicine, allowing clinicians to select the best medication for each patient. Dr. Kristin Naegle of the University of Virginia will describe a statistical and graph-theoretic approach to predicting kinase activity. She will show how her team developed an algorithm using phosphoproteomic data from breast cancer tumor biopsies and PDX models. They found that HER2-negative patients were more likely to respond to therapy due to HER2 activity; whereas HER2-positive patients, which lacked net HER2-activity, did not respond to therapy. Dr. Naegle will describe these findings and other work in the field of phosphoproteomics. Dr. Kristen Naegle is an associate professor of biomedical engineering, computer science and engineering, and resident member of the Center for Public Health Genomics at the University of Virginia. She received her doctorate from the Massachusetts Institute of Technology in biological engineering and was subsequently trained as a postdoctoral associate at the Koch Institute for Integrative Cancer Research.