title: Integrated morphologic analysis for the identification and characterization of disease subtypes.
abstract: OBJECTIVE
  Morphologic variations of disease are often linked to underlying molecular events and patient outcome, suggesting that quantitative morphometric analysis may provide further insight into disease mechanisms. In this paper a methodology for the subclassification of disease is developed using image analysis techniques. Morphologic signatures that represent patient-specific tumor morphology are derived from the analysis of hundreds of millions of cells in digitized whole slide images. Clustering these signatures aggregates tumors into groups with cohesive morphologic characteristics. This methodology is demonstrated with an analysis of glioblastoma, using data from The Cancer Genome Atlas to identify a prognostically significant morphology-driven subclassification, in which clusters are correlated with transcriptional, genetic, and epigenetic events.
  
  
  METHODS
  Methodology was applied to 162 glioblastomas from The Cancer Genome Atlas to identify morphology-driven clusters and their clinical and molecular correlates. Signatures of patient-specific tumor morphology were generated from analysis of 200 million cells in 462 whole slide images. Morphology-driven clusters were interrogated for associations with patient outcome, response to therapy, molecular classifications, and genetic alterations. An additional layer of deep, genome-wide analysis identified characteristic transcriptional, epigenetic, and copy number variation events.
  
  
  CONCLUSIONS
  Analysis of glioblastoma identified three prognostically significant patient clusters (median survival 15.3, 10.7, and 13.0 months, log rank p=1.4e-3). Clustering results were validated in a separate dataset. Clusters were characterized by molecular events in nuclear compartment signaling including developmental and cell cycle checkpoint pathways. This analysis demonstrates the potential of high-throughput morphometrics for the subclassification of disease, establishing an approach that complements genomics.
authors.affiliation: Center for Comprehensive Informatics, Emory University, Atlanta, Georgia 30322, USA. lee.cooper@emory.edu
authors.forename: Lee A D
authors.initials: LA
authors.lastname: Cooper
authors.forename: Jun
authors.initials: J
authors.lastname: Kong
authors.forename: David A
authors.initials: DA
authors.lastname: Gutman
authors.forename: Fusheng
authors.initials: F
authors.lastname: Wang
authors.forename: Jingjing
authors.initials: J
authors.lastname: Gao
authors.forename: Christina
authors.initials: C
authors.lastname: Appin
authors.forename: Sharath
authors.initials: S
authors.lastname: Cholleti
authors.forename: Tony
authors.initials: T
authors.lastname: Pan
authors.forename: Ashish
authors.initials: A
authors.lastname: Sharma
authors.forename: Lisa
authors.initials: L
authors.lastname: Scarpace
authors.forename: Tom
authors.initials: T
authors.lastname: Mikkelsen
authors.forename: Tahsin
authors.initials: T
authors.lastname: Kurc
authors.forename: Carlos S
authors.initials: CS
authors.lastname: Moreno
authors.forename: Daniel J
authors.initials: DJ
authors.lastname: Brat
authors.forename: Joel H
authors.initials: JH
authors.lastname: Saltz
mesh_terms: D002477:Cells / Q000473:pathology*; D015972:Gene Expression Regulation, Neoplastic; D055106:Genome-Wide Association Study; D005909:Glioblastoma / Q000145:classification / Q000235:genetics* / Q000401:mortality / Q000473:pathology*; D006801:Humans; D011379:Prognosis