Nimg-66. radiogenomics-based heterogeneity analysis of glioblastoma reveals mr imaging phenotypical characterization of egfr mutations

Abstract PURPOSE EGFR is one of the most frequently altered genes in glioblastoma, while also being an attractive therapeutic target for treatment. Having in vivo, imaging-based markers of EGFR mutations, and as well as characterizing molecular heterogeneity, are important for patient management and stratification into trials. Toward this end, we present deep learning models of imaging signatures of EGFR mutations built from MRI. METHODS Our cohort consists of 286 glioblastoma patients with multi-parametric MRI (mpMRI) scans (T1, T1-Gd, T2, T2-FLAIR, DSC, DTI). Radiomics features, including histograms, morphologic and textural descriptors, were first derived. Genomic information was determined from a targeted next generation sequencing (NGS) panel. We jointly trained a variational auto-encoder and a multi-label classifier for predicting 4 key driver genes EGFR, NF1, PTEN, TP53 to learn a latent representation reflecting the molecular heterogeneity in the dataset. The latent representation was analyzed using principal component analysis (PCA). We calculated the Euclidean distance between imaging features of tumors with EGFR plus 1 of the other 3 mutations, from those of tumors having only EGFR mutations. We also analyzed the imaging signatures and variant allele frequencies (VAFs) of individuals with multiple (typically 3-4) resected tissue samples (n=49). RESULTS Co-occurrence of mutations in any of NF1, PTEN, TP53 genes with EGFR mutations resulted into dominance of imaging signatures of these other mutations over that of EGFR mutations. Interestingly, tumors with homogeneously high VAFs in EGFR mutations (n=3) exhibit strong phenotypical difference from the exclusive EGFR mutated group, while individuals with heterogeneous EGFR mutations (n=4) do not show such differences. CONCLUSION Our preliminary findings suggest EGFR imaging characteristics can be dominated by those of other co-occurring mutations. Tumors with homogeneously high VAFs, potentially indicating presence of germline EGFR mutations, lead to relatively distinct imaging phenotypes.