Glioblastoma (GBM) is the most common and most aggressive adult primary brain tumor. GBM is known for the patient's poor survival of less than 16 months despite surgical resection, radiation, and/or chemotherapy. There is a growing awareness that the interaction of tumors with their microenvironment, together with metabolic factors, is responsible for altering gene expression patterns, which enable the tumor to adapt and escape tumor treatment. This collaborative research project aims to develop novel biophotonic methods to recognize genome-wide epigenetic mutations in GBM. This methodology will not only permit the early diagnosis of GBM, it will also lead to a combination of mechanic and metabolic stimuli, which will be able to restore apoptosis (programmed cell death) in GBM and other solid tumors.
This proposal has the following aims: 1: Development of Nanoscale Technologies for Visualization and Characterization of Chromatin Alteration Induced by Mechano-metabolic Cues; Aim 2: Chromatin Level Epigenetic Engineering; and Aim 3: A Deep Hybrid Learning Model to Recognize and Predict Mechano-metabolic Conditions for Introducing Programmed Cell Death in GBM. The development of a new toolbox for reprogramming of transformed cells will have implications that will reach far beyond glioblastoma. It will apply to virtually all diseases with epigenetic drivers, among them numerous cancers, neurodegenerative diseases, cardiovascular diseases, obesity and metabolic syndrome.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
