Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Subtype classification, grading, and outcome prediction of urothelial carcinomas by combined mRNA profiling and aCGH

ABSTRACT: [original title] Combined gene expression and genomic profiling define two intrinsic molecular subtypes of urothelial carcinoma and gene signatures for molecular grading and outcome. In the present investigation we sought to refine the classification of urothelial carcinoma by combining information on gene expression, genomic, and gene mutation levels. For these purposes we performed gene expression analysis of 144 carcinomas, and whole genome array-CGH analysis and mutation analyses of FGFR3, PIK3CA, KRAS, HRAS, NRAS, TP53, CDKN2A, and TSC1, in 103 of these cases. Hierarchical cluster analysis identified two intrinsic molecular subtypes, MS1 and MS2, which were validated and defined by the same set of genes in three independent bladder cancer data sets. The two subtypes differed with respect to gene expression and mutation profiles, as well as with the level of genomic instability. The data shows that genomic instability was the most distinguishing genomic feature of MS2 tumors, and that this trait was not dependent on TP53/MDM2 alterations. By combining molecular and pathological data it was possible to distinguish two molecular subtypes of Ta and T1 tumors, respectively. In addition, we define gene signatures validated in two independent data sets that classify urothelial carcinoma into low (G1/G2) and high grade (G3) tumors as well as non-muscle and muscle-invasive tumors with high precisions and sensitivities, suggesting molecular grading as a relevant complement to standard pathological grading. We also present a gene expression signature with independent prognostic impact on metastasis and disease specific survival. We conclude that the combination of molecular and histopathological classification systems may provide a strong improvement for bladder cancer classification and produce new insights into the development of this tumor type. 144 bladder cancer tumor samples and 12 normal samples were analyzed on 2-color cDNA or oligo microarrays using the Stratagene Universal Human Reference RNA as the common reference sample. 24 samples are hybridized to both the cDNA and oligo platform and these were used for merging of data from the two different gene expression platforms into a single expression matrix and for subsequent evaluation steps. The merged gene expression matrix used for analyses is supplied as a supplementary file (at the foot of this record). 103 of the samples were also analyzed on a BAC array containing ~32 000 BAC clones. Arrays were produced at the Swegene Centre for Integrative Biology at Lund University (SCIBLU).

ORGANISM(S): Homo sapiens

SUBMITTER: David Lindgren

PROVIDER: E-GEOD-19915 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Publications

Combined gene expression and genomic profiling define two intrinsic molecular subtypes of urothelial carcinoma and gene signatures for molecular grading and outcome.

Lindgren David D Frigyesi Attila A Gudjonsson Sigurdur S Sjödahl Gottfrid G Hallden Christer C Chebil Gunilla G Veerla Srinivas S Ryden Tobias T Månsson Wiking W Liedberg Fredrik F Höglund Mattias M

Cancer research 20100420 9

In the present investigation, we sought to refine the classification of urothelial carcinoma by combining information on gene expression, genomic, and gene mutation levels. For these purposes, we performed gene expression analysis of 144 carcinomas, and whole genome array-CGH analysis and mutation analyses of FGFR3, PIK3CA, KRAS, HRAS, NRAS, TP53, CDKN2A, and TSC1 in 103 of these cases. Hierarchical cluster analysis identified two intrinsic molecular subtypes, MS1 and MS2, which were validated a ...[more]

PMID: 20406976

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Project description:Even though urothelial cancer (UC) is the fourth common tumor type among males, progress in treatment development has been deficient. Pathological assessment provides the urologists with only a broad classification, complicated by frequent disagreement among pathologist and the co-existence of different grading systems. Consequently, there is a great need for an objective, reproducible and biologically relevant classification system to make treatment more efficient. In the present investigation we present a molecular taxonomy for UC stratification based on integrated genomics. We used gene expression profiles from 308 UC to define seven molecular subtypes using step-by-step partitions and a bootstrap approach. Results were validated in three independent and publically available data sets. The subtypes differ significantly with respect to expression of cell cycle genes, of receptor tyrosine kinases particularity FGFR3, ERBB2 (HER2), and EGFR, of an FGFR3 associated gene expression signature, of cytokaratins, and of cell adhesion genes. The subtypes also differ significantly with respect to FGFR3, PIK3CA, and TP53 mutations. The expression of key proteins was validated by IHC on TMA. A further inspection indicated that the subtypes could be reduced to four major types of UC; Urobasal/D-driven, Genomically unstable/E-driven, Evolved urobasal, and Basal/SCC like, with characteristic and highly divergent molecular phenotypes. We show that the molecular subtypes cut across pathological classification and that tumors classified as one subtype maintain their characteristic molecular phenotype irrespective of pathological stage and grade. Available data from the Drugbank database and the Cochrane central registry of controlled trials indicate that susceptibility to specific drugs is more likely to be associated with the molecular stratification than with pathological classification. The presented molecular taxonomy stratifies UC into subtypes with distinct molecular phenotypes and biological properties. We anticipate that the molecular taxonomy will be a useful tool in future clinical investigations. Total RNA from fresh-frozen resection samples of 308 urothelial carcinomas was hybridized to the Illumina HumanHT-12 V3.0 expression beadchip arrays (Illumina Inc) at the SCIBLU Genomics Centre at Lund University Sweden (http://www.lth.se/sciblu). Supplementary files: GSE32894_non-normalized_308UCsamples.txt file = Raw intensity values for 308 UC (urothelial tumor) samples subjected only to background correction. GSE32894_reps_normals_preprocess*.txt files = Descriptive details and non-normalized data for technical replicates and normal samples that were used only in the preprocessing of the data. This dataset partly overlaps with Series GSE32549. Names of the overlapping sample names are the same, but the title of each sample is unique to the hybridization.

Dataset Information

Subtype classification, grading, and outcome prediction of urothelial carcinomas by combined mRNA profiling and aCGH

Publications

Combined gene expression and genomic profiling define two intrinsic molecular subtypes of urothelial carcinoma and gene signatures for molecular grading and outcome.

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