Somatic mutations associated with MRI-derived volumetric features in glioblastoma.

David A Gutman,Chad A Holder,Patrick Grossmann,Keith L Ligon,Lee A D Cooper,William D Dunn,Brian M Alexander,Hugo J W L Aerts

doi:10.1007/s00234-015-1576-7

Abstract

IntroductionMR imaging can noninvasively visualize tumor phenotype characteristics at the macroscopic level. Here, we investigated whether somatic mutations are associated with and can be predicted by MRI-derived tumor imaging features of glioblastoma (GBM).MethodsSeventy-six GBM patients were identified from The Cancer Imaging Archive for whom preoperative T1-contrast (T1C) and T2-FLAIR MR images were available. For each tumor, a set of volumetric imaging features and their ratios were measured, including necrosis, contrast enhancing, and edema volumes. Imaging genomics analysis assessed the association of these features with mutation status of nine genes frequently altered in adult GBM. Finally, area under the curve (AUC) analysis was conducted to evaluate the predictive performance of imaging features for mutational status.ResultsOur results demonstrate that MR imaging features are strongly associated with mutation status. For example, TP53-mutated tumors had significantly smaller contrast enhancing and necrosis volumes (p = 0.012 and 0.017, respectively) and RB1-mutated tumors had significantly smaller edema volumes (p = 0.015) compared to wild-type tumors. MRI volumetric features were also found to significantly predict mutational status. For example, AUC analysis results indicated that TP53, RB1, NF1, EGFR, and PDGFRA mutations could each be significantly predicted by at least one imaging feature.ConclusionMRI-derived volumetric features are significantly associated with and predictive of several cancer-relevant, drug-targetable DNA mutations in glioblastoma. These results may shed insight into unique growth characteristics of individual tumors at the macroscopic level resulting from molecular events as well as increase the use of noninvasive imaging in personalized medicine.Electronic supplementary materialThe online version of this article (doi:10.1007/s00234-015-1576-7) contains supplementary material, which is available to authorized users.

Highlights

MR imaging can noninvasively visualize tumor phenotype characteristics at the macroscopic level
area under the curve (AUC) analysis results indicated that TP53, RB1, NF1, EGFR, and PDGFRA mutations could each be significantly predicted by at least one imaging feature
We limited our analysis to an a priori selected gene set as described by Verhaak et al [4], as well as showing mutations in at least five patients included in our cohort, resulting in the following: TP53, PTEN, NF1, EGFR, IDH1, PIK3R1, RB1, PIK3CA, and PDGFRA

Summary

Introduction

MR imaging can noninvasively visualize tumor phenotype characteristics at the macroscopic level. We investigated whether somatic mutations are associated with and can be predicted by MRI-derived tumor imaging features of glioblastoma (GBM). Methods Seventy-six GBM patients were identified from The Cancer Imaging Archive for whom preoperative T1-contrast (T1C) and T2-FLAIR MR images were available. A set of volumetric imaging features and their ratios were measured, including necrosis, contrast enhancing, and edema volumes. Imaging genomics analysis assessed the association of these features with mutation status of nine genes frequently altered in adult GBM. Area under the curve (AUC) analysis was conducted to evaluate the predictive performance of imaging features for mutational status. Integrated analyses using gene expression, copy number, methylation, and somatic mutation patterns have identified distinct GBM subtypes, some of which associated with distinct responses to treatment [4]

Methods

Results

Conclusion