In vivo evaluation of EGFRvIII mutation in primary glioblastoma patients via complex multiparametric MRI signature

Abstract

Background: Epidermal growth factor receptor variant III (EGFRvIII) is a driver mutation and potential therapeutic target in glioblastoma. Non-invasive in vivo EGFRvIII determination, using clinically acquired multiparametric MRI sequences, could assist in assessing spatial heterogeneity related to EGFRvIII, currently not captured via single-specimen analyses. We hypothesize that integration of subtle, yet distinctive, quantitative imaging/radiomic patterns using machine learning may lead to non-invasively determining molecular characteristics, and particularly the EGFRvIII mutation.

Methods: We integrated diverse imaging features, including the tumor's spatial distribution pattern, via support vector machines, to construct an imaging signature of EGFRvIII. This signature was evaluated in independent discovery (n = 75) and replication (n = 54) cohorts of de novo glioblastoma, and compared with the EGFRvIII status obtained through an assay based on next-generation sequencing.

Results: The cross-validated accuracy of the EGFRvIII signature in classifying the mutation status in individual patients of the independent discovery and replication cohorts was 85.3% (specificity = 86.3%, sensitivity = 83.3%, area under the curve [AUC] = 0.85) and 87% (specificity = 90%, sensitivity = 78.6%, AUC = 0.86), respectively. The signature was consistent with EGFRvIII+ tumors having increased neovascularization and cell density, as well as a distinctive spatial pattern involving relatively more frontal and parietal regions compared with EGFRvIII- tumors.

Conclusions: An imaging signature of EGFRvIII was found, revealing a complex, yet distinct macroscopic glioblastoma phenotype. By non-invasively capturing the tumor in its entirety, the proposed methodology can assist in evaluating the tumor's spatial heterogeneity, hence overcoming common spatial sampling limitations of tissue-based analyses. This signature can preoperatively stratify patients for EGFRvIII-targeted therapies, and potentially monitor dynamic mutational changes during treatment.

Fig. 1

(A) Two examples of the imaging modalities and the segmentations in EGFRvIII+ and EGFRvIII− patients. (B) Illustrated histograms of the most distinctive features, according to EGFRvIII status. The histograms in the first column are divided (by the dashed lines) into 3 partitions, to allow for deeper analysis. In the histogram of rCBV in ET, the first partition shows higher population for EGFRvIII+, likely representing pre-necrotic areas (ie, with very low rCBV). For this partition, ADC suggests higher cellularity in EGFRvIII+. The remainder of the rCBV in ET histogram suggests that EGFRvIII+ is hypervascular, with the second partition suggesting lower water concentration for the EGFRvIII+ tumors through the T2-FLAIR signal. In the histogram of T2-FLAIR for non-ET, the first partition might represent more CSF-like fluid in EGFRvIII− and the third partition represents higher water concentration for EGFRvIII−. ADC and FA measures, for the third partition, suggest higher cellularity and different microstructure, respectively, in EGFRvIII+ tumors. Note that the histograms were created using information from all patients, whereas a single patient example is shown at the bottom of the figure for visualization purposes.

Fig. 2

Spatial configuration of primary glioblastomas according to their EGFRvIII status. (A) EGFRvIII− on top panel, (B) EGFRvIII+ middle lower panel, and (C) frequency distribution on lower panel. The color look-up tables show the frequencies in percent. All images were displayed in the radiological convention orientation. The basal ganglia label consists of putamen, caudate nucleus, globus pallidus, subthalamic nucleus, nucleus accumbens, internal capsule, and thalamus.

Fig. 3

ROC curves in discovery (left panel) and replication (right panel) cohorts. Red points display the optimal cutoff points, revealing accuracies of 85.3% (AUC = 0.85, specificity = 86.3%, sensitivity = 83.3%) and 87% (AUC = 0.86, specificity = 90%, sensitivity = 78.6%) for discovery and replication cohorts, respectively.

Fig. 4

Summary of descriptive characteristics of EGFRvIII+ glioblastoma.

Fig. 5

Representative slices from VLSM maps computed for EGFRvIII mutation status of 129 glioblastoma patients. The top panel shows the voxel-based map of power across the brain, calculated for alpha = 0.05. The bottom maps are illustrations of t-test results. Patients with tumor in a given voxel were compared with those without tumor in that voxel on measures of EGFRvIII status. High t-scores (red) indicate that tumors to these voxels have a highly significant effect on EGFRvIII mutation status. Dark blue voxels indicate regions where the presence of a tumor had relatively lower impact on the EGFRvIII mutation status. T map includes only significant voxels (P = 0.05), considering thresholding based on cluster size and 1000 permutations.

Fig. 6

EGFRvIII associated molecular pathways, biological changes, and expected radiological changes. Note that multiple biological changes within the tumor have one or more imaging correlates on different MRI sequences.