Magnetic resonance image features identify glioblastoma phenotypic subtypes with distinct molecular pathway activities.
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.
Division of Biomedical Informatics, Department of Medicine, Stanford University, Stanford, CA 94305, USA.
Haruka
H
Itakura
Department of Neurosurgery, Stanford University, Stanford, CA 94305, USA.
Achal S
AS
Achrol
Department of Radiology, Stanford University, Stanford, CA 94305, USA.
Lex A
LA
Mitchell
Department of Neurosurgery, Stanford University, Stanford, CA 94305, USA.
Joshua J
JJ
Loya
Division of Biomedical Informatics, Department of Medicine, Stanford University, Stanford, CA 94305, USA.
Tiffany
T
Liu
Department of Neurosurgery, Johns Hopkins University, Baltimore, MD 21218, USA.
Erick M
EM
Westbroek
Department of Neurosurgery, Stanford University, Stanford, CA 94305, USA.
Abdullah H
AH
Feroze
Department of Neurosurgery, Stanford University, Stanford, CA 94305, USA.
Scott
S
Rodriguez
Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA.
Sebastian
S
Echegaray
Department of Neurosurgery, Stanford University, Stanford, CA 94305, USA.
Tej D
TD
Azad
Department of Radiology, Stanford University, Stanford, CA 94305, USA.
Kristen W
KW
Yeom
Department of Radiology, Stanford University, Stanford, CA 94305, USA.
Sandy
S
Napel
Division of Biomedical Informatics, Department of Medicine, Stanford University, Stanford, CA 94305, USA. Department of Radiology, Stanford University, Stanford, CA 94305, USA.
Daniel L
DL
Rubin
Department of Neurosurgery, Stanford University, Stanford, CA 94305, USA.
Steven D
SD
Chang
Department of Neurosurgery, Stanford University, Stanford, CA 94305, USA.
Griffith R
GR
Harsh
Division of Biomedical Informatics, Department of Medicine, Stanford University, Stanford, CA 94305, USA. olivier.gevaert@stanford.edu.
Olivier
O
Gevaert
D001932:Brain Neoplasms / Q000145:classification / Q000473:pathology*; D015331:Cohort Studies; D005909:Glioblastoma / Q000145:classification / Q000473:pathology*; D008279:Magnetic Resonance Imaging