Aiguo Li
National Institutes of Health
USA
Title: Glioma classification and translational application in clinics
Biography
Biography: Aiguo Li
Abstract
Gliomas are the most common type of primary brain tumors in adults and a significant cause of cancer-related mortality. Defining glioma sub-types based on objective genetic and molecular signatures may allow for a more rational, patient specific approach to therapy in the future. Applying two unsupervised machine-learning methods to 159 glioma patient gene expression profiles ranging from low to high grade gliomas, we have established a glioma classification model containing six distinct sub-types. These sub-types are validated using three additional data-sets and annotated for underlying molecular functions. To translate this glioma classification model into clinical application, we developed a web-based tool, GliomaPredict, for assigning new patients into our molecular sub-types. The classification model also facilitates us to study glioma progression mechanism in cohered and to design targeted clinical trials.
