Morphometic analysis of TCGA glioblastoma multiforme

Hang Chang, Gerald V. Fontenay, Ju Han, Ge Cong, Frederick L. Baehner, Joe Gray, Paul Spellman, Bahram Parvin

Research output: Contribution to journalArticle

33 Citations (Scopus)

Abstract

Background: Our goals are to develop a computational histopathology pipeline for characterizing tumor types that are being generated by The Cancer Genome Atlas (TCGA) for genomic association. TCGA is a national collaborative program where different tumor types are being collected, and each tumor is being characterized using a variety of genome-wide platforms. Here, we have developed a tumor-centric analytical pipeline to process tissue sections stained with hematoxylin and eosin (H&E) for visualization and cell-by-cell quantitative analysis. Thus far, analysis is limited to Glioblastoma Multiforme (GBM) and kidney renal clear cell carcinoma tissue sections. The final results are being distributed for subtyping and linking the histology sections to the genomic data.Results: A computational pipeline has been designed to continuously update a local image database, with limited clinical information, from an NIH repository. Each image is partitioned into blocks, where each cell in the block is characterized through a multidimensional representation (e.g., nuclear size, cellularity). A subset of morphometric indices, representing potential underlying biological processes, can then be selected for subtyping and genomic association. Simultaneously, these subtypes can also be predictive of the outcome as a result of clinical treatments. Using the cellularity index and nuclear size, the computational pipeline has revealed five subtypes, and one subtype, corresponding to the extreme high cellularity, has shown to be a predictor of survival as a result of a more aggressive therapeutic regime. Further association of this subtype with the corresponding gene expression data has identified enrichment of (i) the immune response and AP-1 signaling pathways, and (ii) IFNG, TGFB1, PKC, Cytokine, and MAPK14 hubs.Conclusion: While subtyping is often performed with genome-wide molecular data, we have shown that it can also be applied to categorizing histology sections. Accordingly, we have identified a subtype that is a predictor of the outcome as a result of a therapeutic regime. Computed representation has become publicly available through our Web site.

Original languageEnglish (US)
Article number484
JournalBMC Bioinformatics
Volume12
DOIs
StatePublished - Dec 20 2011

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Atlases
Atlas
Glioblastoma
Cellularity
Tumors
Cancer
Genome
Pipelines
Genes
Tumor
Histology
Association reactions
Genomics
Cell
Neoplasms
Mitogen-Activated Protein Kinase 14
Tissue
Predictors
Transcription Factor AP-1
Hematoxylin

ASJC Scopus subject areas

  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Applied Mathematics
  • Structural Biology

Cite this

Chang, H., Fontenay, G. V., Han, J., Cong, G., Baehner, F. L., Gray, J., ... Parvin, B. (2011). Morphometic analysis of TCGA glioblastoma multiforme. BMC Bioinformatics, 12, [484]. https://doi.org/10.1186/1471-2105-12-484

Morphometic analysis of TCGA glioblastoma multiforme. / Chang, Hang; Fontenay, Gerald V.; Han, Ju; Cong, Ge; Baehner, Frederick L.; Gray, Joe; Spellman, Paul; Parvin, Bahram.

In: BMC Bioinformatics, Vol. 12, 484, 20.12.2011.

Research output: Contribution to journalArticle

Chang, H, Fontenay, GV, Han, J, Cong, G, Baehner, FL, Gray, J, Spellman, P & Parvin, B 2011, 'Morphometic analysis of TCGA glioblastoma multiforme', BMC Bioinformatics, vol. 12, 484. https://doi.org/10.1186/1471-2105-12-484
Chang H, Fontenay GV, Han J, Cong G, Baehner FL, Gray J et al. Morphometic analysis of TCGA glioblastoma multiforme. BMC Bioinformatics. 2011 Dec 20;12. 484. https://doi.org/10.1186/1471-2105-12-484
Chang, Hang ; Fontenay, Gerald V. ; Han, Ju ; Cong, Ge ; Baehner, Frederick L. ; Gray, Joe ; Spellman, Paul ; Parvin, Bahram. / Morphometic analysis of TCGA glioblastoma multiforme. In: BMC Bioinformatics. 2011 ; Vol. 12.
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