GLIA-Deep: Glioblastoma Image Analysis using Deep Learning Convolutional Neural Networks to Accurately Classify Gene Methylation and Predict Drug Effectiveness Viraj Mehta

Overview

Glioblastoma Multiforme is a deadly brain tumor, with a median patient survival time of 18-24 months, despite aggressive treatments. This limited success is due to aggressive tumor behavior and resistance to therapy. Temozolomide is a commercially approved alkylating agent used to treat glioblastoma, but around 50% of Temozolomide-treated patients do not respond to it due to the over-expression of O6- methylguanine methyltransferase (MGMT). MGMT is a DNA repair enzyme that rescues tumor cells from alkylating agent-induced damage, leading to resistance to chemotherapy drugs. Epigenetic silencing of the MGMT gene by promoter methylation results in decreased MGMT protein expression, reduced DNA repair activity, increased sensitivity to TMZ, and longer survival time. Thus, it is paramount that clinicians determine the methylation status of patients to provide a personalized therapeutic recommendation. However, current methods for determining this through invasive biopsies or manually curated features from MRI scans are time- and cost- intensive, and have a very low accuracy. The author presents a novel approach of using convolutional neural networks to analyze brain MRI scans from TCIA and genomic data from TCGA to predict MGMT methylation status. The author developed a web-app www.GLIA- Deep.com to perform tumor identification using a U-Net architecture and predict methylation status using Resnet- 50. This real-time analysis gives results within seconds, eliminating huge time and cost investment of invasive biopsies. Using computational modelling, the analysis further recommends microRNAs that modulate MGMT gene expression by translational repression to make glioma cells TMZ sensitive, thereby improving the survival of glioma patients with “unmethylated MGMT”.

Competition history

• JSHS 2020 Category not listed

Resources

• National JSHS abstract book ↗

Related projects

Closest projects by meaning, across every fair and year in the corpus.