Integrated morphologic analysis for the identification and characterization of disease subtypes

• 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
• Journal Title: Journal of the American Medical Informatics Association
• Volume: Volume 19, Number 2
• Publisher: Oxford University Press (OUP): Policy B - Oxford Open Option B - CC-BY | 2012-03, Pages 317-323
• Type of Work: Article | Final Publisher PDF
• Publisher DOI: http://doi.org/10.1136/amiajnl-2011-000700
• Subjects: Biology, Bioinformatics; Health Sciences, Pathology
• Keywords: Digital pathology; computer-assisted image analysis; cell morphology; image cytometry; cancer; data management; data integration; RFID; temporal database; spatial database; glioma; glioblastomabrain tumor; emory; bioinformatics; transcription; genomics; microarray; biomedical informatics; imaging; high end computing; middleware; 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.