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Molecular classification of bladder cancer: global mRNA classification versus tumor cell phenotype classification.

ABSTRACT: In this study gene expression profiles for 307 cases of advanced bladder cancers were compared to molecular phenotype at the tumor cell level. TUR-B tissue for RNA extraction was macrodissected from the close vicinity of the tissue sampled for immunohistochemistry to ensure high-quality sampling and to minimize the effects of intra-tumor heterogeneity. Despite excellent agreement between gene expression values and IHC-score at the single marker level, broad differences emerge when samples are clustered at the global mRNA versus tumor cell (IHC) levels. Classification at the different levels give different results in a systematic fashion, which implicates that analysis at both levels is required for optimal subtype-classification of bladder cancer.

ORGANISM(S): Homo sapiens

PROVIDER: GSE83586 | GEO | 2017/03/13

SECONDARY ACCESSION(S): PRJNA326434

REPOSITORIES: GEO

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Project description:[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).

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Molecular classification of bladder cancer: global mRNA classification versus tumor cell phenotype classification.

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