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Comprehensive transcriptomic analysis of cell lines as models of primary tumors across 22 tumor types.

ABSTRACT: Cancer cell lines are a cornerstone of cancer research but previous studies have shown that not all cell lines are equal in their ability to model primary tumors. Here we present a comprehensive pan-cancer analysis utilizing transcriptomic profiles from The Cancer Genome Atlas and the Cancer Cell Line Encyclopedia to evaluate cell lines as models of primary tumors across 22 tumor types. We perform correlation analysis and gene set enrichment analysis to understand the differences between cell lines and primary tumors. Additionally, we classify cell lines into tumor subtypes in 9 tumor types. We present our pancreatic cancer results as a case study and find that the commonly used cell line MIA PaCa-2 is transcriptionally unrepresentative of primary pancreatic adenocarcinomas. Lastly, we propose a new cell line panel, the TCGA-110-CL, for pan-cancer studies. This study provides a resource to help researchers select more representative cell line models.

SUBMITTER: Yu K

PROVIDER: S-EPMC6687785 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature

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Comprehensive transcriptomic analysis of cell lines as models of primary tumors across 22 tumor types.

Yu K K Chen B B Aran D D Charalel J J Yau C C Wolf D M DM van 't Veer L J LJ Butte A J AJ Goldstein T T Sirota M M

Nature communications 20190808 1

Cancer cell lines are a cornerstone of cancer research but previous studies have shown that not all cell lines are equal in their ability to model primary tumors. Here we present a comprehensive pan-cancer analysis utilizing transcriptomic profiles from The Cancer Genome Atlas and the Cancer Cell Line Encyclopedia to evaluate cell lines as models of primary tumors across 22 tumor types. We perform correlation analysis and gene set enrichment analysis to understand the differences between cell li ...[more]

PMID: 31395879

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Project description:Background: Understanding how tumors subvert immune destruction is essential to the development of cancer immunotherapies. New evidence suggests that tumors limit anti-tumor immunity by exploiting transcriptional programs that regulate intratumoral trafficking and accumulation of effector cells. Here, we investigated the gene expression profiles that distinguish immunologically "cold" and "hot" tumors across diverse tumor types. Methods: RNAseq profiles of tumors (n = 8,920) representing 23 solid tumor types were analyzed using immune gene signatures that quantify CD8+ T cell abundance. Genes and pathways associated with a low CD8+ T cell infiltration profile (CD8-Low) were identified by correlation, differential expression, and statistical ranking methods. Gene subsets were evaluated in immunotherapy treatment cohorts and functionally characterized in cell lines and mouse tumor models. Results: Among different cancer types, we observed highly significant overlap of genes enriched in CD8-Low tumors, which included known immunomodulatory genes (e.g., BMP7, CMTM4, KDM5B, RCOR2) and exhibited significant associations with Wnt signaling, neurogenesis, cell-cell junctions, lipid biosynthesis, epidermal development, and cancer-testis antigens. Analysis of mutually exclusive gene clusters demonstrated that different transcriptional programs may converge on the T cell-cold phenotype as well as predict for response and survival of patients to Nivo treatment. Furthermore, we confirmed that a top-ranking candidate belonging to the TGF-? superfamily, BMP7, negatively regulates CD8+ T cell abundance in immunocompetent murine tumor models, with and without anti-PD-L1 treatment. Conclusions: This study presents the first evidence that solid tumors of diverse anatomical origin acquire conserved transcriptional alterations that may be operative in the T cell-cold state. Our findings demonstrate the potential clinical utility of CD8-Low tumor-associated genes for predicting patient immunotherapy outcomes and point to novel mechanisms with potential for broad therapeutic exploitation.

Project description:Despite the wide use of cell lines in cancer research, the extent to which their surface properties correspond to those of primary tumors is poorly characterized. The present study addresses this problem from a transcriptional standpoint, analyzing the expression of membrane protein genes--the Membranome--in primary tumors and immortalized in-vitro cultured tumor cells. 409 human samples, deriving from ten independent studies, were analyzed. These comprise normal tissues, primary tumors and tumor derived cell lines deriving from eight different tissues: brain, breast, colon, kidney, leukemia, lung, melanoma, and ovary. We demonstrated that the Membranome has greater power than the remainder of the transcriptome when used as input for the automatic classification of tumor samples. This feature is maintained in tumor derived cell lines. In most cases primary tumors show maximal similarity in Membranome expression with cell lines of same tissue origin. Differences in Membranome expression between tumors and cell lines were analyzed also at the pathway level and biological themes were identified that were differentially regulated in the two settings. Moreover, by including normal samples in the analysis, we quantified the degree to which cell lines retain the Membranome up- and down-regulations observed in primary tumors with respect to their normal counterparts. We showed that most of the Membranome up-regulations observed in primary tumors are lost in the in-vitro cultured cells. Conversely, the majority of Membranome genes down-regulated upon tumor transformation maintain lower expression levels also in the cell lines. This study points towards a central role of Membranome genes in the definition of the tumor phenotype. The comparative analysis of primary tumors and cell lines identifies the limits of cell lines as a model for the study of cancer-related processes mediated by the cell surface. Results presented allow for a more rational use of the cell lines as a model of cancer.

Project description:BACKGROUND:Proper cell models for breast cancer primary tumors have long been the focal point in the cancer's research. The genomic comparison between cell lines and tumors can investigate the similarity and dissimilarity and help to select right cell model to mimic tumor tissues to properly evaluate the drug reaction in vitro. In this paper, a comprehensive comparison in copy number variation (CNV), mutation, mRNA expression and protein expression between 68 breast cancer cell lines and 1375 primary breast tumors is conducted and presented. RESULTS:Using whole genome expression arrays, strong correlations were observed between cells and tumors. PAM50 gene expression differentiated them into four major breast cancer subtypes: Luminal A and B, HER2amp, and Basal-like in both cells and tumors partially. Genomic CNVs patterns were observed between tumors and cells across chromosomes in general. High C?>?T and C?>?G trans-version rates were observed in both cells and tumors, while the cells had slightly higher somatic mutation rates than tumors. Clustering analysis on protein expression data can reasonably recover the breast cancer subtypes in cell lines and tumors. Although the drug-targeted proteins ER/PR and interesting mTOR/GSK3/TS2/PDK1/ER_P118 cluster had shown the consistent patterns between cells and tumor, low protein-based correlations were observed between cells and tumors. The expression consistency of mRNA verse protein between cell line and tumors reaches 0.7076. These important drug targets in breast cancer, ESR1, PGR, HER2, EGFR and AR have a high similarity in mRNA and protein variation in both tumors and cell lines. GATA3 and RP56KB1 are two promising drug targets for breast cancer. A total score developed from the four correlations among four molecular profiles suggests that cell lines, BT483, T47D and MDAMB453 have the highest similarity with tumors. CONCLUSIONS:The integrated data from across these multiple platforms demonstrates the existence of the similarity and dissimilarity of molecular features between breast cancer tumors and cell lines. The cell lines only mirror some but not all of the molecular properties of primary tumors. The study results add more evidence in selecting cell line models for breast cancer research.

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Comprehensive transcriptomic analysis of cell lines as models of primary tumors across 22 tumor types.

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Comprehensive transcriptomic analysis of cell lines as models of primary tumors across 22 tumor types.

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