DocumentCode :
3510706
Title :
Morphological signatures and genomic correlates in glioblastoma
Author :
Cooper, Lee A D ; Kong, Jun ; Wang, Fusheng ; Kurc, Tahsin ; Moreno, Carlos S. ; Brat, Daniel J. ; Saltz, Joel H.
Author_Institution :
Center for Comprehensive Inf., Emory Univ., Atlanta, GA, USA
fYear :
2011
fDate :
March 30 2011-April 2 2011
Firstpage :
1624
Lastpage :
1627
Abstract :
Large multimodal datasets such as The Cancer Genome Atlas present an opportunity to perform correlative studies of tissue morphology and genomics to explore the morphological phenotypes associated with gene expression and genetic alterations. In this paper we present an investigation of Cancer Genome Atlas data that correlates morphology with recently discovered molecular subtypes of glioblastoma. Using image analysis to segment and extract features from millions of cells, we calculate high-dimensional morphological signatures to describe trends of nuclear morphology and cytoplasmic staining in whole-slide images. We illustrate the similarities between the analysis of these signatures and predictive studies of gene expression, both in terms of limited sample size and high-dimensionality. Our top-down analysis demonstrates the power of morphological signatures to predict clinically-relevant molecular tumor subtypes, with 85.4% recognition of the proneural subtype. A complementary bottom-up analysis shows that self-aggregating clusters have statistically significant associations with tumor subtype and reveals the existence of remarkable structure in the morphological signature space of glioblastomas.
Keywords :
brain; cancer; cellular biophysics; feature extraction; genetics; genomics; image segmentation; medical image processing; molecular biophysics; tumours; Cancer Genome Atlas; biological cells; cytoplasmic staining; feature extraction; gene expression; genetic alterations; genomic correlates; glioblastoma; image analysis; image segmentation; molecular tumor subtypes; morphological signatures; multimodal datasets; nuclear morphology; self-aggregating clusters; tissue morphology; Bioinformatics; Cancer; Feature extraction; Gene expression; Genomics; Morphology; Tumors; bioinformatics; digital pathology; image analysis; in silico; microscopy;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Biomedical Imaging: From Nano to Macro, 2011 IEEE International Symposium on
Conference_Location :
Chicago, IL
ISSN :
1945-7928
Print_ISBN :
978-1-4244-4127-3
Electronic_ISBN :
1945-7928
Type :
conf
DOI :
10.1109/ISBI.2011.5872714
Filename :
5872714
Link To Document :
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