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      Analysis of renal cancer cell lines from two major resources enables genomics-guided cell line selection

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          Abstract

          The utility of cancer cell lines is affected by the similarity to endogenous tumour cells. Here we compare genomic data from 65 kidney-derived cell lines from the Cancer Cell Line Encyclopedia and the COSMIC Cell Lines Project to three renal cancer subtypes from The Cancer Genome Atlas: clear cell renal cell carcinoma (ccRCC, also known as kidney renal clear cell carcinoma), papillary (pRCC, also known as kidney papillary) and chromophobe (chRCC, also known as kidney chromophobe) renal cell carcinoma. Clustering copy number alterations shows that most cell lines resemble ccRCC, a few (including some often used as models of ccRCC) resemble pRCC, and none resemble chRCC. Human ccRCC tumours clustering with cell lines display clinical and genomic features of more aggressive disease, suggesting that cell lines best represent aggressive tumours. We stratify mutations and copy number alterations for important kidney cancer genes by the consistency between databases, and classify cell lines into established gene expression-based indolent and aggressive subtypes. Our results could aid investigators in analysing appropriate renal cancer cell lines.

          Abstract

          Cell lines are central to cancer research, but knowing which cell lines are the best representative of actual tumours is a major challenge. Here the authors provide a resource assessment of 65 renal cell lines to assist researchers in selecting suitable lines for studying specific renal carcinoma subtypes.

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          Most cited references26

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          The somatic genomic landscape of chromophobe renal cell carcinoma.

          We describe the landscape of somatic genomic alterations of 66 chromophobe renal cell carcinomas (ChRCCs) on the basis of multidimensional and comprehensive characterization, including mtDNA and whole-genome sequencing. The result is consistent that ChRCC originates from the distal nephron compared with other kidney cancers with more proximal origins. Combined mtDNA and gene expression analysis implicates changes in mitochondrial function as a component of the disease biology, while suggesting alternative roles for mtDNA mutations in cancers relying on oxidative phosphorylation. Genomic rearrangements lead to recurrent structural breakpoints within TERT promoter region, which correlates with highly elevated TERT expression and manifestation of kataegis, representing a mechanism of TERT upregulation in cancer distinct from previously observed amplifications and point mutations.
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            2004 WHO classification of the renal tumors of the adults.

            The recently introduced 2004 World Health Organisation (WHO) classification of the adult renal epithelial neoplasms is meant to replace the previous 1998 WHO classification. The 2004 WHO classification is based on pathology and genetic abnormalities. The description of categories has been expanded to improve their recognition and new diagnostic categories are included. Emphasis has been placed on defining familial renal cancer, carcinoma associated with Xp11 translocations, carcinoma associated with neuroblastoma, multilocular cystic renal cell carcinoma, tubular, mucinous and spindle cells carcinoma; and mixed epithelial and stromal tumour. The potentially aggressive epithelioid angiomyolipoma is recognised. Recognising these categories may have important implications in patients' clinical management.
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              Redefining the relevance of established cancer cell lines to the study of mechanisms of clinical anti-cancer drug resistance.

              Although in vitro models have been a cornerstone of anti-cancer drug development, their direct applicability to clinical cancer research has been uncertain. Using a state-of-the-art Taqman-based quantitative RT-PCR assay, we investigated the multidrug resistance (MDR) transcriptome of six cancer types, in established cancer cell lines (grown in monolayer, 3D scaffold, or in xenograft) and clinical samples, either containing >75% tumor cells or microdissected. The MDR transcriptome was determined a priori based on an extensive curation of the literature published during the last three decades, which led to the enumeration of 380 genes. No correlation was found between clinical samples and established cancer cell lines. As expected, we found up-regulation of genes that would facilitate survival across all cultured cancer cell lines evaluated. More troubling, however, were data showing that all of the cell lines, grown either in vitro or in vivo, bear more resemblance to each other, regardless of the tissue of origin, than to the clinical samples they are supposed to model. Although cultured cells can be used to study many aspects of cancer biology and response of cells to drugs, this study emphasizes the necessity for new in vitro cancer models and the use of primary tumor models in which gene expression can be manipulated and small molecules tested in a setting that more closely mimics the in vivo cancer microenvironment so as to avoid radical changes in gene expression profiles brought on by extended periods of cell culture.
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                Author and article information

                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group
                2041-1723
                10 May 2017
                2017
                : 8
                : 15165
                Affiliations
                [1 ]Department of Computational Biology, Memorial Sloan-Kettering Cancer Center , 417 E 68th St, New York, New York 10065, USA
                [2 ]Department of Genetics and Genomic Sciences, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai , One Gustave L. Levy Place, Box 1498, New York, New York 10029, USA
                [3 ]Urology Service, Department of Surgery, Memorial Sloan-Kettering Cancer Center , 417 E 68th St, New York, New York 10065, USA
                [4 ]Department of Pathology, Memorial Sloan-Kettering Cancer Center , 417 E 68th St, New York, New York 10065, USA
                [5 ]Human Oncology and Pathogenesis Program, Memorial Sloan-Kettering Cancer Center , 417 E 68th St, New York, New York 10065, USA
                [6 ]cBio Center, Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Compbio Collaboratory, Department of Cell Biology, Harvard Medical School , 450 Brookline Avenue, Boston, Massachusetts 02215-5450, USA
                [7 ]Molecular Oncology, Department of Medicine, Siteman Cancer Center, Washington University , St. Louis, Missouri 63110, USA
                Author notes
                Author information
                http://orcid.org/0000-0001-5497-5055
                http://orcid.org/0000-0002-9628-7282
                http://orcid.org/0000-0001-5207-3648
                http://orcid.org/0000-0002-5391-4936
                http://orcid.org/0000-0003-3595-0551
                Article
                ncomms15165
                10.1038/ncomms15165
                5436135
                28489074
                ea6d73a2-fecc-4d34-a4f1-d9b94a8a3024
                Copyright © 2017, The Author(s)

                This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

                History
                : 11 November 2016
                : 06 March 2017
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