NI-84 * MOLECULAR CHARACTERISTICS UNDERLYING THE RADIOLOGICALLY-DEFINED VOLUMETRIC COMPOSITION OF GLIOBLASTOMA

Abstract

PURPOSE: To characterize the biological and clinical correlates of radiologically-defined tumor composition. Further, to investigate if tumor volumetric and heterogeneity features can predict up- and down-regulation of biologically relevant gene-sets. METHODS: Genetic (mRNA expression) and radiographic scans (T1,T2,T1-post Gd and FLAIR) for 45 GBM cases was extracted from TCGA (Cancer Genome Atlas) and The Cancer Imaging Archive. Volumetric segmentation of the tumor region into 4 sub-compartments(necrosis, edema, enhancing and non-enhancing regions) was performed with an automated segmentation tool. Tumor-heterogeneity characteristics were measured from segmented post-contrast T1 and T2-FLAIR images. Information about biological pathways (including inflammation-associated gene signatures) were obtained from the Broad-website (MSigDb). Pathway up- or down-regulation scores were obtained using Gene Set Enrichment Analysis (GSEA). Multiple-response regression was used to identify pathways associated with the volumetric composition (percentage amounts of necrosis, edema, enhancing and non-enhancing regions) of the tumor. Four-dimensional volumetric composition was used to predict survival as well as status of key inflammatory pathways via a classification approach. True-positive (TPR) and false-positive rates (FPR) were obtained using cross-validation. RESULTS: Percentage of edema, necrosis and enhancing regions in the tumor are significantly associated with mesenchymal-subtype (p-value < 0.05). Examination of the molecular correlates underlying the volumetric composition of the tumor reveals several key pathways such as nitric-oxide biosynthesis, cAMP metabolism, microenvironmental remodeling and cellular differentiation are associated with(p-value < 0.05) and likely drive tumor composition. Further, prediction of 12 month survival using volumetrics and texture features is feasible: TPR of ∼90% and FPR of ∼20%. This study also reveals that volumetric features can be used for prediction of gene-set status (immune/inflammatory gene signatures in this case); with TPR and FPR of 80% and 10% respectively. CONCLUSION: Here, we characterized the molecular correlates of the radiologically-derived volumetric composition of glioblastoma. We show that combining image-derived volumetric and tumor heterogeneity features can predict gene-set status (specifically inflammation status) and overall survival.