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Microarray analysis of Merkel cell carcinoma (MCC) tumors, small cell lung cancer (SCLC) tumors, and MCC cell lines

ABSTRACT: 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.

ORGANISM(S): Homo sapiens

PROVIDER: GSE50451 | GEO | 2014/12/23

SECONDARY ACCESSION(S): PRJNA217507

REPOSITORIES: GEO

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Microarray analysis of Merkel cell carcinoma (MCC) tumors, small cell lung cancer (SCLC) tumors, and MCC cell lines

Project description: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.

2014-12-23 | E-GEOD-50451 | biostudies-arrayexpress

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Project description:Solid tumors are complex organs comprising neoplastic cells and stroma, yet cancer cell lines remain widely used to study tumor biology, biomarkers and experimental therapy. Here, we performed a fully integrative analysis of global proteomic data comparing human colorectal cancer (CRC) cell lines to primary tumors and normal tissues. We found a significant, systematic difference between cell line and tumor proteomes, with a major contribution from tumor stroma proteomes. Nevertheless, cell lines overall mirrored the proteomic differences observed between tumors and normal tissues, in particular for genetic information processing and metabolic pathways, indicating that cell lines provide a system for the study of the intrinsic molecular programs in cancer cells. Intersection of cell line data with tumor data provided insights into tumor cell specific proteome alterations driven by genomic alterations. Our integration of cell line proteogenomic data with drug sensitivity data highlights the potential of proteomic data in predicting therapeutic response. We identified representative cell lines for the proteomic subtypes of primary tumors, and linked these to drug sensitivity data to identify subtype-specific drug candidates.

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