Evaluating cell lines as tumour models by comparison of genomic profiles

1] Computational Biology Center, Memorial Sloan-Kettering Cancer Center, 1275 York Avenue, Box 460, New York, New York 10065, USA [2] Department of Chemistry, Technische Universität München, Lichtenbergstraße 4, 85747 Garching bei München, Germany [3].
Nature Communications (Impact Factor: 11.47). 07/2013; 4:2126. DOI: 10.1038/ncomms3126
Source: PubMed


Cancer cell lines are frequently used as in vitro tumour models. Recent molecular profiles of hundreds of cell lines from The Cancer Cell Line Encyclopedia and thousands of tumour samples from the Cancer Genome Atlas now allow a systematic genomic comparison of cell lines and tumours. Here we analyse a panel of 47 ovarian cancer cell lines and identify those that have the highest genetic similarity to ovarian tumours. Our comparison of copy-number changes, mutations and mRNA expression profiles reveals pronounced differences in molecular profiles between commonly used ovarian cancer cell lines and high-grade serous ovarian cancer tumour samples. We identify several rarely used cell lines that more closely resemble cognate tumour profiles than commonly used cell lines, and we propose these lines as the most suitable models of ovarian cancer. Our results indicate that the gap between cell lines and tumours can be bridged by genomically informed choices of cell line models for all tumour types.

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Article: Evaluating cell lines as tumour models by comparison of genomic profiles

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    • "Differential gene expression analyses have identified differences , but not core similarities, between cell lines and tumors for particular cancers. Comparisons of cell lines and tumors on this basis are uninformative, as they simply separate in vivo and in vitro samples (Domcke et al., 2013). Supervised gene lists can be used to identify suitable tumor models from cancer cell lines (Dancik et al., 2011; Gillet et al., 2011; Uva et al., 2010), or similarities between cancer cell lines and their tumors of origin can be scored with a tissue similarity index (TSI). "
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    ABSTRACT: Molecular signatures specific to particular tumor types are required to design treatments for resistant tumors. However, it remains unclear whether tumors and corresponding cell lines used for drug development share such signatures. We developed similarity core analysis (SCA), a universal and unsupervised computational framework for extracting core molecular features common to tumors and cell lines. We applied SCA to mRNA/miRNA expression data from various sources, comparing melanoma cell lines and metastases. The signature obtained was associated with phenotypic characteristics in vitro, and the core genes CAPN3 and TRIM63 were implicated in melanoma cell migration/invasion. About 90% of the melanoma signature genes belong to an intrinsic network of transcription factors governing neural development (TFAP2A, DLX2, ALX1, MITF, PAX3, SOX10, LEF1, and GAS7) and miRNAs (211-5p, 221-3p, and 10a-5p). The SCA signature effectively discriminated between two subpopulations of melanoma patients differing in overall survival, and classified MEKi/BRAFi-resistant and -sensitive melanoma cell lines.
    Cell Reports 10/2015; 13(4). DOI:10.1016/j.celrep.2015.09.037 · 8.36 Impact Factor
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    • "See also Figure S1. Cell 162, 974–986, August 27, 2015 ª2015 Elsevier Inc. 975 Domcke et al., 2013). Aza induced partial IRF7 demethylation and increased expression in this cell line at days 7 and 10 while carboplatin did not (Figures 1B, 1C, and S3A), and IRF7 knockdown significantly reduced the Aza interferon response (Figures S3B and S3C). "
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    ABSTRACT: We show that DNA methyltransferase inhibitors (DNMTis) upregulate immune signaling in cancer through the viral defense pathway. In ovarian cancer (OC), DNMTis trigger cytosolic sensing of double-stranded RNA (dsRNA) causing a type I interferon response and apoptosis. Knocking down dsRNA sensors TLR3 and MAVS reduces this response 2-fold and blocking interferon beta or its receptor abrogates it. Upregulation of hypermethylated endogenous retrovirus (ERV) genes accompanies the response and ERV overexpression activates the response. Basal levels of ERV and viral defense gene expression significantly correlate in primary OC and the latter signature separates primary samples for multiple tumor types from The Cancer Genome Atlas into low versus high expression groups. In melanoma patients treated with an immune checkpoint therapy, high viral defense signature expression in tumors significantly associates with durable clinical response and DNMTi treatment sensitizes to anti-CTLA4 therapy in a pre-clinical melanoma model. Copyright © 2015 Elsevier Inc. All rights reserved.
    Cell 08/2015; 162(5):974-986. DOI:10.1016/j.cell.2015.07.011 · 32.24 Impact Factor
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    • "In the laboratory, the breast cancer is often modelled using established breast cancer cell lines due to their ease of being acquired and used [1]. However, accumulated evidences have pointed out the genomic differences between cancer cell lines and tissue samples in the past decades [2] [3] [4]. In the review of Holliday and Speirs [1], they demonstrated that cell lines are prone to genotypic and phenotypic drift during their continual culture . "
    Yi Sun · Qi Liu ·
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    ABSTRACT: Breast cancer is one of the most common cancers with high incident rate and high mortality rate worldwide. Although different breast cancer cell lines were widely used in laboratory investigations, accumulated evidences have indicated that genomic differences exist between cancer cell lines and tissue samples in the past decades. The abundant molecular profiles of cancer cell lines and tumor samples deposited in the Cancer Cell Line Encyclopedia and The Cancer Genome Atlas now allow a systematical comparison of the breast cancer cell lines with breast tumors. We depicted the genomic characteristics of breast primary tumors based on the copy number variation and gene expression profiles and the breast cancer cell lines were compared to different subgroups of breast tumors. We identified that some of the breast cancer cell lines show high correlation with the tumor group that agrees with previous knowledge, while a big part of them do not, including the most used MCF7, MDA-MB-231, and T-47D. We presented a computational framework to identify cell lines that mostly resemble a certain tumor group for the breast tumor study. Our investigation presents a useful guide to bridge the gap between cell lines and tumors and helps to select the most suitable cell line models for personalized cancer studies.
    08/2015; 2015(3, article r65):901303. DOI:10.1155/2015/901303
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