Grove, Rebecca

Abstract Purpose: Glioblastoma is the most frequent and lethal primary brain tumor. Development of novel therapies relies on the availability of relevant preclinical models. We have established a panel of 96 glioblastoma patient-derived xenografts (PDX) and undertaken its genomic and phenotypic characterization. Experimental Design: PDXs were established from glioblastoma, IDH-wildtype (n = 93), glioblastoma, IDH-mutant (n = 2), diffuse midline glioma, H3 K27M-mutant (n = 1), and both primary (n = 60) and recurrent (n = 34) tumors. Tumor growth rates, histopathology, and treatment response were characterized. Integrated molecular profiling was performed by whole-exome sequencing (WES, n = 83), RNA-sequencing (n = 68), and genome-wide methylation profiling (n = 76). WES data from 24 patient tumors was compared with derivative models. Results: PDXs recapitulate many key phenotypic and molecular features of patient tumors. Orthotopic PDXs show characteristic tumor morphology and invasion patterns, but largely lack microvascular proliferation and necrosis. PDXs capture common and rare molecular drivers, including alterations of TERT, EGFR, PTEN, TP53, BRAF, and IDH1, most at frequencies comparable with human glioblastoma. However, PDGFRA amplification was absent. RNA-sequencing and genome-wide methylation profiling demonstrated broad representation of glioblastoma molecular subtypes. MGMT promoter methylation correlated with increased survival in response to temozolomide. WES of 24 matched patient tumors showed preservation of most genetic driver alterations, including EGFR amplification. However, in four patient–PDX pairs, driver alterations were gained or lost on engraftment, consistent with clonal selection. Conclusions: Our PDX panel captures the molecular heterogeneity of glioblastoma and recapitulates many salient genetic and phenotypic features. All models and genomic data are openly available to investigators.

Abstract INTRODUCTION Neuroimaging dogma for glioblastoma asserts that hyperintensity on T1Gd MRI reveals the bulk of the tumor, while T2/FLAIR signal indicates edema. However, it is unclear whether this edema results from immune response or increased tumor cells. Further, one significant driver of the known sex differences in glioblastoma may be differences in immune response, due to the X-linkage of many immune genes. Based on this, we hypothesized that assumptions regarding tumor cellularity in T2/FLAIR images should be tailored to the biological sex of the patient. METHODS Using a retrospective cohort of 18 primary glioblastoma patients receiving multiple image-localized biopsies (82 total) and standard MRI, we assessed: distance of biopsy from T1Gd and T2 areas; a pathologist’s score of percent tumor cell density; and an imaging-based invasion metric, D/ρ. This metric is derived from the biomathematical Proliferation-Invasion model of glioma growth, which features two parameters, net growth rate (ρ) and net invasion rate (D). Their ratio D/ρ is related to degree of invasion, and is estimated from volumetric measurements of MRI abnormalities. Additionally, 25 patient-derived xenograft models implanted in females were grown until moribund, at which point brains were excised and stained for DAPI (to show all cells) and Lamin (to highlight tumor cells). Image processing of lamin-stained sections defines contours of intensity correlating with cell density. RESULTS Outside both the T1Gd and T2 region, male patient biopsies had higher tumor cell densities than females. Males also tended to have higher invasion metrics. Although each set derived from different patients, preclinical metrics of invasion were positively correlated with clinical invasion in females but negatively correlated in males. CONCLUSION Our preliminary finding that cell distribution patterns correlate with imaging metrics differently between the sexes supports the hypothesis that the degree of tumor cell density represented on certain MRI sequences may be sex-specific.