Magnetic resonance image features identify glioblastoma phenotypic subtypes with distinct molecular pathway activities

Abstract

Glioblastoma (GBM) is the most common and highly lethal primary malignant brain tumor in adults. There is a dire need for easily accessible, noninvasive biomarkers that can delineate underlying molecular activities and predict response to therapy. To this end, we sought to identify subtypes of GBM, differentiated solely by quantitative magnetic resonance (MR) imaging features, that could be used for better management of GBM patients. Quantitative image features capturing the shape, texture, and edge sharpness of each lesion were extracted from MR images of 121 single-institution patients with de novo, solitary, unilateral GBM. Three distinct phenotypic "clusters" emerged in the development cohort using consensus clustering with 10,000 iterations on these image features. These three clusters--pre-multifocal, spherical, and rim-enhancing, names reflecting their image features--were validated in an independent cohort consisting of 144 multi-institution patients with similar tumor characteristics from The Cancer Genome Atlas (TCGA). Each cluster mapped to a unique set of molecular signaling pathways using pathway activity estimates derived from the analysis of TCGA tumor copy number and gene expression data with the PARADIGM (Pathway Recognition Algorithm Using Data Integration on Genomic Models) algorithm. Distinct pathways, such as c-Kit and FOXA, were enriched in each cluster, indicating differential molecular activities as determined by the image features. Each cluster also demonstrated differential probabilities of survival, indicating prognostic importance. Our imaging method offers a noninvasive approach to stratify GBM patients and also provides unique sets of molecular signatures to inform targeted therapy and personalized treatment of GBM.

Figure 1. Consensus matrix, cumulative distribution function curve, and delta curve for all clusters

(A to C) The Stanford cohort as the development cohort and the TCGA cohort as the validation cohort. (D to F) The TCGA cohort as the development cohort and the Stanford cohort as the validation cohort. (A and D) Consensus matrices represented as heat maps for k=3 (Clusters 1, 2, and 3). Subjects are both rows and columns and consensus values range from 0 (never clustered together, white) to 1 (always clustered together, dark blue). The matrices are ordered by consensus-clustered groups, depicted as a dendrogram above the heatmap. (B and E) Cumulative distribution function (CDF) curve was one diagnostic tool used to select the optimal number of clusters in consensus clustering. The bottom left of the graph represents sample pairs rarely clustered together, whereas the upper right contains those almost always paired together. The middle segment represents sample pairs with ambiguous assignments across different clustering runs. The goal was to identify the lowest rate of ambiguous assignments (flat middle segment). (C and F) The delta curve depicts the CDF progression graph, plotting the relative change in area under the CDF curve, comparing k with k+1. The goal was to select the largest k that induced the smallest incremental change in the AUC. Data for the TCGA cohort as development, Stanford as validation, are in fig. S2.

Figure 2. GBM subtypes cluster by phenotypic MRI characteristics, correlate with survival, and associate with molecular pathways

Three distinct image-based subtypes were derived from a development cohort (Stanford cohort) and validated in an independent validation cohort (TCGA cohort). (A) Imaging phenotypes are illustrated as simplified, representative pictograms for each cluster, although the multivariate combination of quantitative images features that characterize each cluster (table S4) cannot be fully visually exemplified. (B) Aggregate multi-slice 2D renditions of the three imaging subtypes (clusters). (C) Kaplan-Meier survival curves (solid lines) with 95% confidence intervals (dotted lines) derived from TCGA survival data are shown for each cluster in the TCGA cohort. Survival differences across the clusters: P = 0.004, Logrank test (n = 37 subjects across the clusters who underwent the same Stupp protocol treatment regimen). Cluster 1 was characterized by the least favorable survival (n=6), whereas Cluster 3 was marked by the most favorable survival (n=9); Cluster 2 was intermediate (n=22). (D) Molecular changes associated with each cluster. Arrows indicate up- or downregulation of sample pathways identified using PARADIGM. Table S6 provides a comprehensive list of significant regulatory pathways associated with each cluster at FDR < 5%.

Figure 3. Tumor volumes demonstrated by cluster for the development cohort (Stanford cohort)

The largest tumors were in Cluster 3, and the smallest in Cluster 2 (P < 0.001, Kruskal-Wallis test). An overlap in tumor volume is observed between Clusters 1 and 2.