Molecular Classification and Prognostication of Adrenocortical Tumors by Transcriptome Profiling
Purpose: Our understanding of adrenocortical carcinoma (ACC) has improved considerably, yet many unanswered questions remain. For instance, can molecular subtypes of ACC be identified? If so, what is their underlying pathogenetic basis and do they possess clinical significance? Experimental Design:...
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Published in | Clinical cancer research Vol. 15; no. 2; pp. 668 - 676 |
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Main Authors | , , , , , , , , , |
Format | Journal Article |
Language | English |
Published |
Philadelphia, PA
American Association for Cancer Research
15.01.2009
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Subjects | |
Online Access | Get full text |
ISSN | 1078-0432 1557-3265 |
DOI | 10.1158/1078-0432.CCR-08-1067 |
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Summary: | Purpose: Our understanding of adrenocortical carcinoma (ACC) has improved considerably, yet many unanswered questions remain. For
instance, can molecular subtypes of ACC be identified? If so, what is their underlying pathogenetic basis and do they possess
clinical significance?
Experimental Design: We did a whole genome gene expression study of a large cohort of adrenocortical tissues annotated with clinicopathologic
data. Using Affymetrix Human Genome U133 Plus 2.0 oligonucleotide arrays, transcriptional profiles were generated for 10 normal
adrenal cortices (NC), 22 adrenocortical adenomas (ACA), and 33 ACCs.
Results: The overall classification of adrenocortical tumors was recapitulated using principal component analysis of the entire data
set. The NC and ACA cohorts showed little intragroup variation, whereas the ACC cohort revealed much greater variation in
gene expression. A robust list of 2,875 differentially expressed genes in ACC compared with both NC and ACA was generated
and used in functional enrichment analysis to find pathways and attributes of biological significance. Cluster analysis of
the ACCs revealed two subtypes that reflected tumor proliferation, as measured by mitotic counts and cell cycle genes. Kaplan-Meier
analysis of these ACC clusters showed a significant difference in survival ( P < 0.020). Multivariate Cox modeling using stage, mitotic rate, and gene expression data as measured by the first principal
component for ACC samples showed that gene expression data contains significant independent prognostic information ( P < 0.017).
Conclusions: This study lays the foundation for the molecular classification and prognostication of adrenocortical tumors and also provides
a rich source of potential diagnostic and prognostic markers. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Juliane Bauersfeld contributed to this study as a visiting medical student from the Christian-Albrechts University, Kiel, Germany. |
ISSN: | 1078-0432 1557-3265 |
DOI: | 10.1158/1078-0432.CCR-08-1067 |