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

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.

在利用细胞系开展癌症研究时，其与原发肿瘤的表型相似性是核心考量要素。然而，目前针对默克尔细胞癌（Merkel cell carcinoma, MCC）细胞系对原生肿瘤的模拟贴合度的表征研究仍较为匮乏。为明确这些细胞系与MCC肿瘤样本的相似性，我们通过基因表达微阵列技术对MCC细胞系进行了表征分析。借助全转录组基因表达特征与计算生物信息学方法，我们发现变异型细胞系（UISO、MCC13与MCC26）与新鲜冷冻的MCC肿瘤样本之间存在显著差异。与之相反，经典的WaGa与Mkl-1细胞系则更贴合MCC肿瘤的整体转录组特征。将其与公开可用的其他癌细胞系对比后发现，WaGa与Mkl-1细胞与其他神经内分泌肿瘤的特征相似，但变异型细胞系则无此特征。以小鼠异种移植模型培养的WaGa与Mkl-1细胞，其组织学与免疫表型特征均符合MCC的典型表现；而UISO细胞形成的异种移植肿瘤则不符合MCC的典型特征。对UISO细胞进行的光谱核型分析与短串联重复序列（short tandem repeat, STR）分析结果与该细胞系的原始描述一致，排除了细胞污染的可能。本研究结果证实了转录组分析用于评估癌细胞系代表性的有效性，并表明UISO、MCC13与MCC26细胞系无法代表MCC肿瘤，而WaGa与Mkl-1细胞系则能更精准地模拟MCC肿瘤。本研究从MCC细胞系、MCC肿瘤样本与小细胞肺癌（small cell lung cancer, SCLC）肿瘤样本中提取RNA，将其与Affymetrix微阵列杂交以完成转录组谱分析。