Integrated morphologic analysis for the identification and characterization of disease subtypes

• Type of Material: Article
• Authors: Lee A D Cooper, Emory University; Jun Kong, Emory University; David Andrew Gutman, Emory University; Fusheng Wang, Emory University; Jingjing (Doris) Gao, Emory University; Christina Appin, Emory University; Sharath R Cholleti, Emory University; Tony Pan, Emory University; Ashish Sharma, Emory University; Lisa Scarpace, Henry Ford Hospital; Tom Mikkelsen, Henry Ford Hospital; Tahsin Kurc, Emory University; Carlos S Moreno, Emory University; Daniel J Brat, Emory University; Joel H Saltz, Emory University
• Language: English
• Date: 2012-03
• Publisher: Oxford University Press (OUP): Policy B - Oxford Open Option B - CC-BY
• Publication Version: Final Published Version
• Copyright Statement: © 2012, Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.
• License: Creative Commons Attribution-NonCommercial 2.0 Generic
• Final Published Version (URL): http://jamia.oxfordjournals.org/content/19/2/317
• Title of Journal or Parent Work: Journal of the American Medical Informatics Association
• ISSN: 1067-5027
• Volume: 19
• Issue: 2
• Start Page: 317
• End Page: 323
• Grant/Funding Information: This work was supported in part by PHS Grant UL 1RR025008 from the Clinical and Translational Science Award Program, National Institutes of Health, Grant numbers R01LM009239 and R01LM011119 from the National Library of Medicine, Contract No. HHSN261200800001E from the National Cancer Institute, National Institutes of Health, and the Georgia Cancer Coalition.
• Keywords: computer-assisted image analysis; temporal database; genomics; pathology; emory; glioblastomabrain tumor; glioma; high end computing; imaging; cell morphology; cancer; biomedical informatics; Digital pathology; data integration; bioinformatics; microarray; middleware; image cytometry; spatial database; RFID; data management; transcription
• Research Categories: Biology, Bioinformatics; Health Sciences, Pathology

Abstract

Background and 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. Materials and 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. Results and discussion: 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.