Please note that we have stopped the regular imports of Gene Expression Omnibus (GEO) data into ArrayExpress. This may not be the latest version of this experiment.
E-GEOD-50451 - Microarray analysis of Merkel cell carcinoma (MCC) tumors, small cell lung cancer (SCLC) tumors, and MCC cell lines
Released on 23 December 2014, last updated on 2 January 2015
When using cell lines to study cancer, phenotypic similarity to the original tumor is paramount. Yet, little has been done to characterize how closely Merkel cell carcinoma (MCC) cell lines model native tumors. To determine their similarity to MCC tumor samples, we characterized MCC cell lines via gene expression microarrays. Using whole transcriptome gene expression signatures and a computational bioinformatic approach, we identified significant differences between variant cell lines (UISO, MCC13, and MCC26) and fresh frozen MCC tumors. Conversely, the classic WaGa and Mkl-1 cell lines more closely represented the global transcriptome of MCC tumors. When compared to publicly available cancer lines, WaGa and Mkl-1 cells were similar to other neuroendocrine tumors, but the variant cell lines were not. WaGa and Mkl-1 cells grown as xenografts in mice had histological and immunophenotypical features consistent with MCC, while UISO xenograft tumors were atypical for MCC. Spectral karyotyping and short tandem repeat analysis of the UISO cells matched the original cell line's description, ruling out contamination. Our results validate the use of transcriptome analysis to assess the cancer cell line representativeness and indicate that UISO, MCC13, and MCC26 cell lines are not representative of MCC tumors, whereas WaGa and Mkl-1 more closely model MCC. RNA was extracted from MCC cell lines and MCC and SCLC tumor samples and hybridized to Affymetrix microarrays for transcriptome profiling.
transcription profiling by array
Isaac Brownell <email@example.com>, Kenneth Daily
Assessment of Cancer Cell Line Representativeness using Microarrays for Merkel Cell Carcinoma. Daily K, Coxon A, Williams JS, Lee C, Coit DG, Busam KJ, Brownell I. , PMID:25521454