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      TCLP: an online cancer cell line catalogue integrating HLA type, predicted neo-epitopes, virus and gene expression

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          Abstract

          Human cancer cell lines are an important resource for research and drug development. However, the available annotations of cell lines are sparse, incomplete, and distributed in multiple repositories. Re-analyzing publicly available raw RNA-Seq data, we determined the human leukocyte antigen (HLA) type and abundance, identified expressed viruses and calculated gene expression of 1,082 cancer cell lines. Using the determined HLA types, public databases of cell line mutations, and existing HLA binding prediction algorithms, we predicted antigenic mutations in each cell line. We integrated the results into a comprehensive knowledgebase. Using the Django web framework, we provide an interactive user interface with advanced search capabilities to find and explore cell lines and an application programming interface to extract cell line information. The portal is available at http://celllines.tron-mainz.de.

          Electronic supplementary material

          The online version of this article (doi:10.1186/s13073-015-0240-5) contains supplementary material, which is available to authorized users.

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          The UCSC Known Genes.

          Fan Hsu, W. Kent, Hiram Clawson … (2006)
          The University of California Santa Cruz (UCSC) Known Genes dataset is constructed by a fully automated process, based on protein data from Swiss-Prot/TrEMBL (UniProt) and the associated mRNA data from Genbank. The detailed steps of this process are described. Extensive cross-references from this dataset to other genomic and proteomic data were constructed. For each known gene, a details page is provided containing rich information about the gene, together with extensive links to other relevant genomic, proteomic and pathway data. As of July 2005, the UCSC Known Genes are available for human, mouse and rat genomes. The Known Genes serves as a foundation to support several key programs: the Genome Browser, Proteome Browser, Gene Sorter and Table Browser offered at the UCSC website. All the associated data files and program source code are also available. They can be accessed at http://genome.ucsc.edu. The genomic coverage of UCSC Known Genes, RefSeq, Ensembl Genes, H-Invitational and CCDS is analyzed. Although UCSC Known Genes offers the highest genomic and CDS coverage among major human and mouse gene sets, more detailed analysis suggests all of them could be further improved.
          Article 0 comments Cited 258 times – based on 0 reviews     Review now
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            Immunomic, genomic and transcriptomic characterization of CT26 colorectal carcinoma

            John C Castle, Martin Loewer, Sebastian Boegel … (2014)
            Background Tumor models are critical for our understanding of cancer and the development of cancer therapeutics. Here, we present an integrated map of the genome, transcriptome and immunome of an epithelial mouse tumor, the CT26 colon carcinoma cell line. Results We found that Kras is homozygously mutated at p.G12D, Apc and Tp53 are not mutated, and Cdkn2a is homozygously deleted. Proliferation and stem-cell markers, including Top2a, Birc5 (Survivin), Cldn6 and Mki67, are highly expressed while differentiation and top-crypt markers Muc2, Ms4a8a (MS4A8B) and Epcam are not. Myc, Trp53 (tp53), Mdm2, Hif1a, and Nras are highly expressed while Egfr and Flt1 are not. MHC class I but not MHC class II is expressed. Several known cancer-testis antigens are expressed, including Atad2, Cep55, and Pbk. The highest expressed gene is a mutated form of the mouse tumor antigen gp70. Of the 1,688 non-synonymous point variations, 154 are both in expressed genes and in peptides predicted to bind MHC and thus potential targets for immunotherapy development. Based on its molecular signature, we predicted that CT26 is refractory to anti-EGFR mAbs and sensitive to MEK and MET inhibitors, as have been previously reported. Conclusions CT26 cells share molecular features with aggressive, undifferentiated, refractory human colorectal carcinoma cells. As CT26 is one of the most extensively used syngeneic mouse tumor models, our data provide a map for the rationale design of mode-of-action studies for pre-clinical evaluation of targeted- and immunotherapies.
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              Accurate pan-specific prediction of peptide-MHC class II binding affinity with improved binding core identification.

              Massimo Andreatta, Edita Karosiene, Michael H. Rasmussen … (2015)
              A key event in the generation of a cellular response against malicious organisms through the endocytic pathway is binding of peptidic antigens by major histocompatibility complex class II (MHC class II) molecules. The bound peptide is then presented on the cell surface where it can be recognized by T helper lymphocytes. NetMHCIIpan is a state-of-the-art method for the quantitative prediction of peptide binding to any human or mouse MHC class II molecule of known sequence. In this paper, we describe an updated version of the method with improved peptide binding register identification. Binding register prediction is concerned with determining the minimal core region of nine residues directly in contact with the MHC binding cleft, a crucial piece of information both for the identification and design of CD4(+) T cell antigens. When applied to a set of 51 crystal structures of peptide-MHC complexes with known binding registers, the new method NetMHCIIpan-3.1 significantly outperformed the earlier 3.0 version. We illustrate the impact of accurate binding core identification for the interpretation of T cell cross-reactivity using tetramer double staining with a CMV epitope and its variants mapped to the epitope binding core. NetMHCIIpan is publicly available at http://www.cbs.dtu.dk/services/NetMHCIIpan-3.1 .
              Article 0 comments Cited 151 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Sebastian Boegel: boegels@uni-mainz.de
                Journal
                Journal ID (nlm-ta): Genome Med
                Journal ID (iso-abbrev): Genome Med
                Title: Genome Medicine
                Publisher: BioMed Central (London )
                ISSN (Electronic): 1756-994X
                Publication date (Electronic): 20 November 2015
                Publication date PMC-release: 20 November 2015
                Publication date Collection: 2015
                Volume: 7
                Electronic Location Identifier: 118
                Affiliations
                [ ]TRON – Translational Oncology at the University Medical Center of Johannes Gutenberg University, Freiligrathstrasse 12, 55131 Mainz, Germany
                [ ]University Medical Center of the Johannes Gutenberg-University Mainz, 55131 Mainz, Germany
                [ ]Biopharmaceutical New Technologies (BioNTech) Corporation, An der Goldgrube 12, 55131 Mainz, Germany
                [ ]Present address: European Molecular Biology Laboratory, Meyerhofstraße 1, 69117 Heidelberg, Germany
                [ ]Present address: Agenus and 4-Antibody AG, Hochbergerstrasse 60C, CH-4057 Basel, Switzerland
                Article
                Publisher ID: 240
                DOI: 10.1186/s13073-015-0240-5
                PMC ID: 4653878
                PubMed ID: 26589293
                SO-VID: 9fda959e-94a0-4d08-b754-49e94efa4bff
                Copyright © © Scholtalbers et al. 2015
                License:

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                Date received : 31 July 2015
                Date accepted : 9 November 2015
                Categories
                Subject: Database
                Custom metadata
                issue-copyright-statement © The Author(s) 2015

                ScienceOpen disciplines: Molecular medicine
                Data availability:
                ScienceOpen disciplines: Molecular medicine

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