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User name	José Felipe Golib Dzib
Email	golib@ihes.fr
Phone / Fax number	+33(0)160926665 / +33(0)160926609
Location	I.H.E.S. (Systems Epigenomics Group) - 35 route de chartres - 91440 Bures sur Yvette, France

Scientific description

As taken from original publication and GEO submission:

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.

Technical description

As taken from original publication and GEO submission:

Human samples of 33 adrenocortical carcinomas, 22 adrenocortical adenomas, and 10 normal adrenal cortex samples, each from a different patient, had mRNA assays performed using Affymetrix HG_U133_plus_2 arrays, with 54675 probe-sets.
Each probe set usually contains 11 perfectly matched 25-base long probes (PMs) as well as 11 mismatch probes that differ by a central base (MMs). A representative tumor (ADR016) was selected as the standard and probe-pairs for which the standard had PM-MM < -100 were excluded from study (a total of 33,581 probe-pairs). Trimmed-means for each probe-set on each chip were computed as the average of the PMâ€“MMs for a probe-set after discarding the largest and smallest 20% of the PMâ€“MM differences. ). the standard was scaled to give an average trimmed-mean of 1000 U. A quantile normalization procedure was used to adjust for differences in the probe intensity distribution across different chips. We applied a monotone linear spline to each chip that mapped quantiles 0.01 up to 0.99 (in increments of 0.01) exactly to the corresponding quantiles of the standard. Next, the data from each chip were log transformed using the log transform y=log(max(x+50,0)+50. We fit one-way ANOVA models to test for gene differences between ACCs vs. ACAs and ACCs vs. NCs, and used two-sample T-tests to test for differences between subgroups of ACCs.

Reference

Giordano TJ, Kuick R, Else T, Gauger PG et al. Molecular classification and prognostication of adrenocortical tumors by transcriptome profiling. Clin Cancer Res 2009 Jan 15;15(2):668-76.

Pubmed : http://www.ncbi.nlm.nih.gov/pubmed/19147773