NIMG-12. RADIOGENOMICS ON VENUS AND MARS: IMPACT OF SEX-DIFFERENCES ON MRI AND GENETIC CORRELATIONS IN GLIOBLASTOMA. (5th November 2018) - Hu, Leland; Gaw, Nathan; Yoon, Hyunsoo; Eschbacher, Jennifer; C. Baxter, Leslie; A. Smith, Kris; Nakaji, Peter; P. Karis, John; Whitmire, Paula; Hawkins-Daarud, Andrea; Singleton, Kyle; Jackson, Pamela; et al.
Abstract: BACKGROUND: MRI-based modeling can help characterize the intratumoral genetic heterogeneity of Glioblastoma (GBM). Yet, published models to date have neglected the potential impact of sex-differences on the accuracy of MRI-genetic correlations. Specifically, there is growing awareness that female GBM patients can display different genetic/molecular aberrations and phenotypic expression compared to male counterparts. In this exploratory study, we compare MRI signal and key GBM driver alterations across a cohort of male and female GBM patients, using image-guided biopsies and spatially matched multi-parametric MRI. METHODS: We collected 61 image-guided biopsies from 18 primary GBM patients (9/9 male/female). For each biopsy, we analyzed DNA copy number variants (CNV) for 6 core GBM driver genes reported by TCGA: amplifications (++) for EGFR and PDGFRA and deletions (--) for PTEN, CDKN2A, RB1, TP53. We compared regional CNV status with spatially matched MRI texture measurements from co-registered biopsy locations. Advanced MRI features included relative cerebral blood volume (rCBV) on DSC-perfusion, mean diffusivity (MD) and fractional anisotropy (FA) on diffusion tensor imaging. We identified univariate correlations for combined and sex-specific (male, female) subgroups. We also built multivariate predictive decision-tree models for each GBM driver gene and used leave-one-out-cross-validation (LOOCV) to determine area-under-curve (AUC) on ROC analysis to compare accuracies across combined and sex-specific models. RESULTS: We identified multiple univariate correlations between regional CNV status and spatially matched MRI texture features that were specific to either male or female GBM tumors. For instance, EGFR++ specifically correlated with T2W image textures in male biopsies but rCBV textures in female biopsies. In general, sex-specific analyses on decision-tree modeling improved predictive accuracies (AUC) compared to combined (male+female) modeling, particularly for EGFR++ (p<0.05), PTEN--(p<0.025), and TP53-- (p<0.025). CONCLUSION: Sex-differences impact MRI-genetic correlations and warrant further study in larger GBM cohorts.