title: A combinatorial radiographic phenotype may stratify patient survival and be associated with invasion and proliferation characteristics in glioblastoma.
abstract: OBJECTIVE
  Individual MRI characteristics (e.g., volume) are routinely used to identify survival-associated phenotypes for glioblastoma (GBM). This study investigated whether combinations of MRI features can also stratify survival. Furthermore, the molecular differences between phenotype-induced groups were investigated.
  
  
  METHODS
  Ninety-two patients with imaging, molecular, and survival data from the TCGA (The Cancer Genome Atlas)-GBM collection were included in this study. For combinatorial phenotype analysis, hierarchical clustering was used. Groups were defined based on a cutpoint obtained via tree-based partitioning. Furthermore, differential expression analysis of microRNA (miRNA) and mRNA expression data was performed using GenePattern Suite. Functional analysis of the resulting genes and miRNAs was performed using Ingenuity Pathway Analysis. Pathway analysis was performed using Gene Set Enrichment Analysis.
  
  
  RESULTS
  Clustering analysis reveals that image-based grouping of the patients is driven by 3 features: volume-class, hemorrhage, and T1/FLAIR-envelope ratio. A combination of these features stratifies survival in a statistically significant manner. A cutpoint analysis yields a significant survival difference in the training set (median survival difference: 12 months, p = 0.004) as well as a validation set (p = 0.0001). Specifically, a low value for any of these 3 features indicates favorable survival characteristics. Differential expression analysis between cutpoint-induced groups suggests that several immune-associated (natural killer cell activity, T-cell lymphocyte differentiation) and metabolism-associated (mitochondrial activity, oxidative phosphorylation) pathways underlie the transition of this phenotype. Integrating data for mRNA and miRNA suggests the roles of several genes regulating proliferation and invasion.
  
  
  CONCLUSIONS
  A 3-way combination of MRI phenotypes may be capable of stratifying survival in GBM. Examination of molecular processes associated with groups created by this combinatorial phenotype suggests the role of biological processes associated with growth and invasion characteristics.
authors.affiliation: Departments of 1 Bioinformatics and Computational Biology.
authors.forename: Arvind
authors.initials: A
authors.lastname: Rao
authors.affiliation: Neurosurgery, and.
authors.forename: Ganesh
authors.initials: G
authors.lastname: Rao
authors.affiliation: Department of Biomedical Informatics, Emory University, Atlanta, Georgia;
authors.forename: David A
authors.initials: DA
authors.lastname: Gutman
authors.affiliation: Department of Radiology, Thomas Jefferson University Hospital, Philadelphia, Pennsylvania;
authors.forename: Adam E
authors.initials: AE
authors.lastname: Flanders
authors.affiliation: Department of Diagnostic Imaging, St. Jude Children's Research Hospital, Memphis, Tennessee;
authors.forename: Scott N
authors.initials: SN
authors.lastname: Hwang
authors.affiliation: Department of Radiology, Stanford University, Stanford, California;
authors.forename: Daniel L
authors.initials: DL
authors.lastname: Rubin
authors.affiliation: Diagnostic Radiology, University of Texas M.D. Anderson Cancer Center, Houston, Texas;
authors.forename: Rivka R
authors.initials: RR
authors.lastname: Colen
authors.affiliation: Department of Neurosurgery, Baylor College of Medicine, Houston, Texas;
authors.forename: Pascal O
authors.initials: PO
authors.lastname: Zinn
authors.affiliation: Department of Radiology, New York University School of Medicine, New York, New York;
authors.forename: Rajan
authors.initials: R
authors.lastname: Jain
authors.affiliation: Department of Interventional Neuroradiology, Stanford Medical Center, Stanford, California;
authors.forename: Max
authors.initials: M
authors.lastname: Wintermark
authors.affiliation: Leidos Biomedical Research, Inc., Frederick National Laboratory, Frederick, Maryland, and.
authors.forename: Justin S
authors.initials: JS
authors.lastname: Kirby
authors.affiliation: Department of Radiology, Boston University School of Medicine, Boston, Massachusetts.
authors.forename: C Carl
authors.initials: CC
authors.lastname: Jaffe
authors.affiliation: Leidos Biomedical Research, Inc., Frederick National Laboratory, Frederick, Maryland, and.
authors.forename: John
authors.initials: J
authors.lastname: Freymann
mesh_terms: D000465:Algorithms; D001932:Brain Neoplasms / Q000000981:diagnostic imaging* / Q000235:genetics / Q000601:surgery; D016000:Cluster Analysis; D023381:Endpoint Determination; D005260:Female; D005909:Glioblastoma / Q000000981:diagnostic imaging* / Q000235:genetics / Q000601:surgery; D006801:Humans; D008297:Male; D035683:MicroRNAs / Q000096:biosynthesis / Q000235:genetics; D057054:Molecular Imaging; D064847:Multimodal Imaging; D009361:Neoplasm Invasiveness; D010641:Phenotype; D011237:Predictive Value of Tests; D011859:Radiography; D016019:Survival Analysis; D016896:Treatment Outcome
chemical_list: D035683:MicroRNAs
keywords: AUC = area under the curve; EMT = epithelial-to-mesenchymal transition; FDR = false discovery rate; GBM = glioblastoma; GSEA = Gene Set Enrichment Analysis; IPA = Ingenuity Pathway Analysis; OXPHOS = oxidative phosphorylation; PFS = progression-free survival; ROC = receiver operating characteristic; TCGA = The Cancer Genome Atlas; TCIA = The Cancer Imaging Archive; VASARI = Visually AcceSAble Rembrandt Images; clustering; combinatorial phenotype analysis; differential expression analysis; glioblastoma; imaging genomics; miRNA = microRNA; oncology; radiogenomics