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Quantitative imaging stratifies glioblastoma into three different phenotypes with distinct molecular activities independent of established molecular markers and clinical status. Brain images create cancer clusters When directing therapies toward tumors, a sample of the cancerous tissue is needed to identify molecular targets. For patients with glioblastoma, however, it is invasive to biopsy the brain. Itakura et al. sought to identify noninvasive determinants of tumor phenotype that would potentially correlate with molecular pathways, thus allowing for targeted therapy without such brain invasion. The authors used magnetic resonance imaging to look at solitary, unilateral tumors from 121 glioblastoma patients and then generated nearly 400 unique image features that could be used to describe each tumor. The tumors could be grouped into three different phenotypes or “clusters”: pre-multifocal cluster, with highly irregular tumor shapes; spherical cluster, with defined edges; and rim-enhancing cluster, with a hypointense center ringed by hyperintensity. The distinct clusters were further validated in a separate cohort of 144 patients. These clusters could be used to stratify patients not only according to molecular pathways for targeted therapy but also by survival, indicating the potential for such noninvasive image-based quantitative biomarkers to be used for patient prognosis. 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.

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