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      The secret lives of cancer cell lines

      editorial

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          ABSTRACT

          The extent of genetic and epigenetic diversity between and within patient tumors is being mapped in ever more detail. It is clear that cancer is an evolutionary process in which tumor cell intrinsic and extrinsic forces shape clonal selection. The pre-clinical oncology pipeline uses model systems of human cancer – including mouse models, cell lines, patient-derived organoids and patient-derived xenografts – to study tumor biology and assess the efficacy of putative therapeutic agents. Model systems cannot completely replicate the environment of human tumors and, even within the same cancer model, data are often irreproducible between laboratories. One hypothesis is that ongoing evolutionary processes remain relevant in laboratory models, leading to divergence over time. In a recent edition of Nature, Ben-David and colleagues showed that different stocks of widely used cancer cell lines – a staple of cancer research over many decades – are highly heterogeneous in terms of their genetics, transcriptomics and responses to therapies. The authors find compelling evidence of positive selection based on ongoing mutational processes and chromosomal instability. Thus, the origin, culture conditions and cumulative number of population doublings of cell lines likely influence experimental outcomes. Here, we summarize the key findings of this important study and discuss the practical implications of this work for researchers using cell lines in the laboratory.

          Abstract

          Summary: A recent study in Nature demonstrated that ongoing mutational processes cause significant heterogeneity of widely used cancer cell lines. In this Editorial, the authors discuss the implications of these findings for the cancer and cell biology fields.

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          Most cited references28

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          Organoids in cancer research

          The recent advances in in vitro 3D culture technologies, such as organoids, have opened new avenues for the development of novel, more physiological human cancer models. Such preclinical models are essential for more efficient translation of basic cancer research into novel treatment regimens for patients with cancer. Wild-type organoids can be grown from embryonic and adult stem cells and display self-organizing capacities, phenocopying essential aspects of the organs they are derived from. Genetic modification of organoids allows disease modelling in a setting that approaches the physiological environment. Additionally, organoids can be grown with high efficiency from patient-derived healthy and tumour tissues, potentially enabling patient-specific drug testing and the development of individualized treatment regimens. In this Review, we evaluate tumour organoid protocols and how they can be utilized as an alternative model for cancer research.
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            Genome Remodeling in a Basal-like Breast Cancer Metastasis and Xenograft

            Massively parallel DNA sequencing technologies provide an unprecedented ability to screen entire genomes for genetic changes associated with tumor progression. Here we describe the genomic analyses of four DNA samples from an African-American patient with basal-like breast cancer: peripheral blood, the primary tumor, a brain metastasis, and a xenograft derived from the primary tumor. The metastasis contained two de novo mutations and a large deletion not present in the primary tumor, and was significantly enriched for 20 shared mutations. The xenograft retained all primary tumor mutations, and displayed a mutation enrichment pattern that paralleled the metastasis (16 of 20 genes). Two overlapping large deletions, encompassing CTNNA1, were present in all three tumor samples. The differential mutation frequencies and structural variation patterns in metastasis and xenograft compared to the primary tumor suggest that secondary tumors may arise from a minority of cells within the primary.
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              Patient-derived xenograft models: an emerging platform for translational cancer research.

              Recently, there has been an increasing interest in the development and characterization of patient-derived tumor xenograft (PDX) models for cancer research. PDX models mostly retain the principal histologic and genetic characteristics of their donor tumor and remain stable across passages. These models have been shown to be predictive of clinical outcomes and are being used for preclinical drug evaluation, biomarker identification, biologic studies, and personalized medicine strategies. This article summarizes the current state of the art in this field, including methodologic issues, available collections, practical applications, challenges and shortcomings, and future directions, and introduces a European consortium of PDX models. PDX models are increasingly used in translational cancer research. These models are useful for drug screening, biomarker development, and the preclinical evaluation of personalized medicine strategies. This review provides a timely overview of the key characteristics of PDX models and a detailed discussion of future directions in the field. ©2014 American Association for Cancer Research.
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                Author and article information

                Journal
                Dis Model Mech
                Dis Model Mech
                DMM
                dmm
                Disease Models & Mechanisms
                The Company of Biologists Ltd
                1754-8403
                1754-8411
                1 November 2018
                16 November 2018
                16 November 2018
                : 11
                : 11
                : dmm037366
                Affiliations
                [1 ]CRUK Lung Cancer Centre of Excellence, UCL Cancer Institute, University College London , London WC1E 6JD, UK
                [2 ]Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute , London NW1 1AT, UK
                [3 ]Lungs for Living Research Centre, UCL Respiratory, University College London , London WC1E 6JF, UK
                Author notes
                [* ]Author for correspondence ( rob.hynds@ 123456ucl.ac.uk )
                Author information
                http://orcid.org/0000-0002-2170-8791
                http://orcid.org/0000-0002-6350-819X
                http://orcid.org/0000-0002-6634-5939
                Article
                DMM037366
                10.1242/dmm.037366
                6262811
                30459183
                e9264917-375d-4497-a76b-96ec19bbc19a
                © 2018. Published by The Company of Biologists Ltd

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution and reproduction in any medium provided that the original work is properly attributed.

                History
                Funding
                Funded by: Wellcome Trust, http://dx.doi.org/10.13039/100010269;
                Award ID: WT209199/Z/17/Z
                Award ID: WT107963/Z/15/Z
                Funded by: Cancer Research UK, http://dx.doi.org/10.13039/501100000289;
                Funded by: Roy Castle Lung Cancer Foundation, http://dx.doi.org/10.13039/100009855;
                Funded by: Biotechnology and Biological Sciences Research Council, http://dx.doi.org/10.13039/501100000268;
                Funded by: Roy Castle Lung Cancer Foundation, http://dx.doi.org/10.13039/100009855;
                Funded by: Rosetrees Trust, http://dx.doi.org/10.13039/501100000833;
                Funded by: UCL Hospitals Charitable Foundation, http://dx.doi.org/10.13039/100011088;
                Funded by: UCLH Biomedical Research Centre;
                Categories
                304
                Dd
                Editorial

                Molecular medicine
                cancer,cell line,tissue culture,tumor cell line,genetic heterogeneity
                Molecular medicine
                cancer, cell line, tissue culture, tumor cell line, genetic heterogeneity

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