Introduction: To create mappings between quantitative image and genomic features for glioblastoma multiforme (GBM) and to assess the prognostic association of significant correlations.
Methods: We obtained multi-omics data from 251 patients and MR image data from a subset of 55 patients in the Cancer Genome Atlas (TCGA) and The Cancer Imaging Archive (TCIA) GBM databases. A board certified neuroradiologist traced 2D regions of interest (ROI) around necrotic and enhanced parts of the largest lesion in a selected slice from a T1 post-contrast MR, and around the region of hyperintensity obtained from the enhancement on the matched T2 FLAIR slice. These ROIs were used to compute quantitative image features from their shapes and pixel values. We used a module network algorithm that integrates copy number, DNA methylation and gene expression data into 100 co-expressed gene modules. We established a radiogenomics map by correlating these modules with the quantitative image features, and used significant module-image feature correlation for survival analysis using Cox proportional hazards modeling.
Results: A total of 28 quantitative image features were extracted for each of the necrosis, enhancement and edema ROIs in each patient. The radiogenomics map between modules and quantitative image features revealed 14, 10 and 16 significant gene-module associations with necrosis, enhancement and edema ROIs respectively. For example we found a significant correlation between Module 64, enriched with genes in neuronal differentiation, and the compactness of the necrosis (p=0.0145). Also, we found that the amount of necrosis vs. enhancement or edema is correlated with Module 74, enriched in metabolism related genes (p<0.01). Finally, we found e.g. that the compactness of the necrosis ROI is correlated with poor survival (p=0.037).
Conclusions: Creating radiogenomics maps provides multi-scale insights by associating image features with molecular function. Moreover, these maps may provide additional insight for image features with prognostic correlations.
Patient Care: Associating activation of molecular pathways with image features may allow non-invasive assessment of the molecular properties of a tumor at the time of diagnosis.
Learning Objectives: 1) Learn how image features are defined and extracted from Glioblastoma Multiforme MR images, 2) Learn the complexity and dimensionality reduction of gene expression data, 3) Learn how to correlate image features with gene expression data and establishing a radiogenomics map