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      The marine triterpene glycoside frondoside A induces p53-independent apoptosis and inhibits autophagy in urothelial carcinoma cells

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          Abstract

          Background

          Advanced urothelial carcinomas represent a considerable clinical challenge as they are difficult to treat. Platinum-based combination regimens obtain response rates ranging from 40 to 70% in first-line therapy of advanced urothelial carcinoma. In the majority of cases, however, the duration of these responses is limited, and when progression occurs, the outcome is generally poor. Therefore, novel therapeutic strategies are urgently needed. The purpose of the current research is to investigate the anticancer effects and the mode of action of the marine triterpene glycoside frondoside A in p53-wild type and p53-deficient human urothelial carcinoma cells.

          Methods

          Activity of frondoside A was examined in the human urothelial carcinoma cell lines RT112, RT4, HT-1197, TCC-SUP, T-24, and 486p. Effects of frondoside A on cell viability, either alone or in combination with standard cytotoxic agents were investigated, and synergistic effects were analyzed. Pro-apoptotic activity was assessed by Western blotting and FACS, alone and in combination with a caspases-inhibitor. The impact of functional p53 was investigated by siRNA gene silencing and the p53 inhibitor pifithrin-α. Effects on autophagy were studied using LC3B-I/II and SQSTM/p62 as markers. The unpaired Student’s t-test was used for comparison of the data sets.

          Results

          Frondoside A shows high cytotoxicity in urothelial carcinoma cells with IC 50s ranging from 0.55 to 2.33 μM while higher concentrations of cisplatin are required for comparable effects (IC 50 = 2.03 ~ 5.88 μM). Induction of apoptosis by frondoside A was associated with the regulation of several pro-apoptotic factors, like caspase-3, -8, and -9, PARP, Bax, p21, DNA fragmentation, and externalization of phosphatidylserine. Remarkably, inhibition of p53 by gene silencing or pifithrin-α pretreatment, as well as caspase inhibition, did not suppress apoptotic activity of frondoside A, while cisplatin activity, in contrast, was significantly decreased. Frondoside A inhibited pro-survival autophagy, a known mechanism of drug resistance in urothelial carcinoma and showed synergistic activity with cisplatin and gemcitabine.

          Conclusions

          A unique combination of properties makes marine compound frondoside A a promising candidate for the treatment of human urothelial carcinomas.

          Electronic supplementary material

          The online version of this article (doi:10.1186/s12885-017-3085-z) contains supplementary material, which is available to authorized users.

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          Most cited references 27

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          The COSMIC (Catalogue of Somatic Mutations in Cancer) database and website

          Approximately one in three individuals in Europe and North America develops one of the approximately 200 different classes of cancer and it is the cause of death of one in five (Higginson, 1992). All cancers arise as a result of the acquisition of a series of fixed DNA sequence abnormalities, each of which ultimately confers growth advantage upon the clone of cells in which it has occurred (Vogelstein and Kinzler, 1998). These abnormalities include base substitutions, deletions, amplifications and rearrangements. The extent to which each of these mechanisms contributes to cancer varies markedly between different genes, and probably also between different cancer types. Identification of the genes that are mutated in cancer is a central aim of cancer research. Over the past 25 years, approximately 300 genes have been shown to be somatically mutated in cancer (Futreal et al, 2004). This work forms the foundation for understanding the biological abnormalities within neoplastic cells, provides information on the function of gene products and sheds light on more complex questions such as the relationships between genes and biochemical pathways. Current strategies for the development of new therapeutic and preventive agents in cancer are increasingly dependent upon modulation of these critical molecular targets. The scientific literature is a rich source of mutation data that, in general, is published in a piecemeal fashion. More comprehensive data sources do exist, such as Online Mendelian Inheritance in Man (OMIM, Wheeler et al, 2004), HGVbase (Fredman et al, 2002) and the Human Gene Mutation Database (HGMD, Stenson et al, 2003). These databases give overviews of the genetics and biology of many genes and associated diseases (OMIM), genome variants and associated genotype–phenotype relationships (HGVbase) or germline mutation data (HGMD). For somatic mutations in cancer, there are many locus-specific web resources, such as those for p53 (Olivier et al, 2002; Béroud and Soussi, 2003), that cover a single gene in depth. The value of these various databases should not be underestimated; however, none of them offer a comprehensive view of all previously reported somatic mutations in cancer. Looking to the future, the volume of somatic mutation data will continue to expand and the scientific community will be better served if this data is provided in a coherent fashion. A public, comprehensive, intuitive, accessible and integrated database is required to maximise the benefit from this rich data set. The Catalogue of Somatic Mutations in Cancer (COSMIC), (http://www.sanger.ac.uk/cosmic) is a database that holds somatic mutation data and associated information, and can be interrogated through a series of web pages to provide a graphical or tabular view of the data along with various export options. To date, the database has been populated with data from four genes: HRAS, KRAS2, NRAS and BRAF. DATA CURATION Gene selection The genes that have been selected for curation are taken from the list of cancer genes assembled in the Cancer Gene Census (Futreal et al, 2004). In the first instance, data was obtained for four genes that are known to be somatically mutated in cancer: HRAS (Reddy et al, 1982), KRAS2 (McCoy et al, 1983), NRAS (Hall et al, 1983) and BRAF (Davies et al, 2002). Data extraction from the literature PubMed (Wheeler et al, 2004) is broadly searched for references containing relevant somatic mutation data in cancer (example search: (ras OR genes, ras) AND human AND mutation). In the first instance, the abstract is read to identify, and select for inclusion in the database, papers that are likely to include somatic mutation information relating to cancer or precancerous conditions. Primary research papers are read and information about the samples, mutations and experimental methods (see Table 1 Table 1 Data entered in COSMIC Reference Sample Title Gene Authors Experimental information Journal Sample ID Year Mutation status Volume Normal tissue tested Page start and stop Site primary PubMed ID Site subtype 1 Experimental information Site subtype 2 Gene Histology   Histology subtype 1 Mutation Histology subtype 2 Mutation ID Stage Mutation type Grade DNA location Source tissue DNA change Loss of heterozygosity DNA evidence Gender Is somatic Age RNA label Other mutations RNA change Ethnicity RNA region Geographical location RNA location Parent tested RNA evidence Family ID Amino-acid label Remark Amino-acid location Reference Amino-acid change Environmental variables Amino-acid evidence   Gene Gene Sequence Name Remark Symbol   Other names Experimental information Chromosome Primary detection method Chromosome band Secondary detection method cDNA sequence accession Confirmation method cDNA sequence version Exons/codons screened Ensembl gene start and stop Whole gene screened Swissprot accession Remark OMIM accession Section heading for the data in COSMIC are in bold. ) is extracted and entered into the database. Reviews are also selected if thought to be specific to a gene of interest. In order to avoid duplication of data, this source is used to identify the relevant primary literature and not as the source of the mutation data. Any references containing incomplete data (e.g. mutations reported but not fully described) or data of insufficient quality (e.g. errors identified in the data) are not fully curated but are added to a list of additional references containing somatic mutation information. Simple mutations are fed through Mutation Checker (Stajich et al, 2002) before being imported to COSMIC, while more complex alterations are manually annotated. COSMIC DATABASE The COSMIC database is implemented in an Oracle relational database and has five sections each containing multiple tables. Gene information A static version of each gene is maintained in COSMIC. The genomic structure of each gene and chromosome location is derived from Ensembl (Birney et al, 2004) and cDNA sequence and protein sequence from the RefSeq project (Wheeler et al, 2004). Other information is held to provide links to web resources such as Ensembl (Birney et al, 2004), Pfam (Bateman et al, 2004), InterPro (Mulder et al, 2003) and OMIM (Wheeler et al, 2004). Paper information The details of the papers that have been curated are maintained in the paper section and include title, journal, author lists and links to PubMed. There are currently 1483 papers in COSMIC, 865 of these have been curated for mutations, while 618 either have no relevant data or incomplete data that could not accurately be extracted. By gene 30, 249, 718 and 303 papers report BRAF, HRAS, KRAS2 and NRAS mutations, respectively. Of the 865 papers reporting mutations, 615 report data on only one gene, while 72, 174 and four contain data on two, three or all four genes, respectively. Mutation information COSMIC can accommodate information on base substitutions, insertions and deletions, translocations and changes in copy number. For the four genes presently in COSMIC, there are 147 unique mutations (36 for BRAF, 27 for HRAS, 52 for KRAS2 and 32 for NRAS). In the tumours that have been analysed, there are a total of 10 647 mutations, 736 in BRAF, 477 in HRAS, 8302 in KRAS2 and 1132 in NRAS. Tumour classification system The tissue site and histology data is taken from the curated papers and entered into COSMIC (this forms the ‘paper definition’). Tumour classification is a continually evolving field and there is no standard nomenclature adhered to for the purposes of publication in the various journals. Identical tissues and histologies can have different labels depending on the origin and age of the study. To overcome difficulties caused by these alternate nomenclatures, a standardised system of definitions has been developed (the ‘COSMIC definitions’) through consultation with experts in the field. This groups data from the same tissue types and histologies and can be used to translate the ‘paper definitions’ to ‘COSMIC definitions’. Every sample has up to eight definitions; primary tissue, tissue subtype 1, 2 and 3, primary histology and histology subtypes 1, 2 and 3. If there is no data for any of these definitions, COSMIC records an entry of NS, not specified. A total of 513 tissue definitions have been noted in the papers in COSMIC and have been translated to 372 COSMIC tissue definitions. Likewise, a total of 1150 histology definitions were found in the papers in COSMIC that were translated to 425 COSMIC histology definitions. This unified classification system is presented through the web pages to present a normalised browsing tool. Individual/tumour/sample data The sample data is taken from the curated papers and linked to the appropriate gene, paper, classification and when present a mutation. This forms the core of the COSMIC database. An individual can have many tumours and each tumour can have many samples. However in the COSMIC scheme, each sample is unique and could be considered as a single experiment. There are 66 634 sample records in COSMIC (5158, 11 876, 35 716 and 13 884 for BRAF, HRAS, KRAS2 and NRAS, respectively). These samples are derived from 57 444 tumours of which 51 988 were analysed in one gene, 2353 in two genes, 2930 in three genes and 173 in all four genes. COSMIC WEBSITE A series of web pages provides query tools to interrogate COSMIC and produces graphical (Figure 1 Figure 1 The initial output from COSMIC is a graphical view of the mutations distributed along the linear amino-acid sequence of the gene. The scale bar incorporates a zoom function to generate a more detailed view of the protein to the point where individual amino acids are named (when there are fewer than 31 amino acids displayed). When a Pfam or Interpro domain is present, a link is provided to these resources (adjacent to the Domain label) while links to the papers that were curated are positioned beneath the mutations (in red) with an option of either viewing the papers that have data for a particular location in the protein or all of the papers for the selected gene. ) and tabular (Table 2 Table 2 Mutation Details from COSMIC   Details for BRAF Tissue Mutations (% of All Samples) All Samples Mutation Data NS 0 3 More Details adrenal gland 0 2 More Details autonomic ganglia 0 27 More Details bile duct 16 (23%) 70 More Details bladder 0 37 More Details bone 1 (3%) 31 More Details brain 4 (7%) 56 More Details breast 1 (1%) 78 More Details cervix 0 49 More Details endometrium 0 5 More Details eye 0 31 More Details haematopoietic and lymphoid tissue 4 (1%) 322 More Details head neck 6 (4%) 152 More Details kidney 0 12 More Details large intestine 148 (13%) 1135 More Details larynx 0 25 More Details liver 1 (3%) 32 More Details lung 15 (2%) 829 More Details mouth 0 13 More Details ovary 57 (20%) 282 More Details pancreas 5 (4%) 114 More Details pharynx 3 (6%) 51 More Details placenta 0 1 More Details pleura 0 3 More Details prostate 0 43 More Details skin 282 (61%) 460 More Details small intestine 0 1 More Details soft tissue 5 (2%) 211 More Details stomach 7 (2%) 407 More Details testis 0 7 More Details thyroid 181 (27%) 669 More Details The mutations from COSMIC are presented by tissue and where selected by histology with a figure for the number of samples analysed for each tissue (All Samples) and the number of mutations reported (Mutated). The ‘More Details’ column gives further navigation options to view data for the selected tissue, view data for the same tissue in other genes or provide more details on the mutations for the selected tissue. ) displays of the data. Currently the output is provided at the amino-acid level based on the protein structure of each gene. Browse by gene Immediate access to the data is provided through the Browse by Gene link. This gives an instant overview of the mutation data for one or more genes and gives links to display data for individual tissues. Browse by tissue More complex queries can be constructed using the Browse by Tissue link. The user has the option to select one or more tissues, then one or more histologies, and finally one or more genes. If only one tissue or histology is selected, it is possible to select one or more tissue or histology subtypes before making a gene selection. All of the tissues present in the COSMIC classification scheme are available from the first page; however, subsequent pages only show the relevant options and not the entire list of options, for example having selected eye, the tissue subtype options are retina and uveal tract. Data display After querying the database, the results are displayed as a figure (Figure 1) and as a series of tables (Table 2) for each gene that was selected. The figure shows the linear amino-acid sequence derived from the gene with the mutations positioned along its length. Further information and links are provided as appropriate to the protein sequence. The table gives a summary of the mutations stratified by tissue and histology. The depth of the stratification relates to the depth of the original query. If only tissue was selected, the data will be stratified by tissue; however, if tissue, subtissue, histology and subhistology are selected, the data will be broken down further. Links from this table reload the figure to display a subset of the data and provide more details of the specific mutations. Two other tables provide a summary of the statistics in COSMIC for the selected gene and a summary of the mutations shown in the figure. Exports and downloads Having displayed the results from a query, the data can be formatted in simple text, Excel or HTML that can be downloaded from the COSMIC site. The cDNA and protein sequences are available through the Additional Info. link on the COSMIC home page as is the Classification Scheme. FUTURE DIRECTIONS There is a continuing effort to enter additional somatic mutation data in to COSMIC. In order to keep the data in COSMIC up-to-date, we regularly monitor the literature for new reports of mutations in the genes that exist in COSMIC. In addition, further cancer genes will be taken from the Cancer Gene Census (Futreal et al, 2004) and curated. The COSMIC website will be developed further to make use of the underlying data. This will include a DNA view of the mutations and methods to display insertions and deletions. In addition, we will display other data that has already been captured such as the patient sex and age for the samples and the experimental methods used to screen for the mutations. There are however limitations to this data as we can only collect data that is described in the original work. Even with this caveat the data provides a direct summary of the somatic mutation literature. Considering the data set as a whole it will be possible to analyse, in greater detail, the wider aspects of the biology underlying the genetic changes that take place in cancer.
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            Cell death independent of caspases: a review.

            Patterns of cell death have been divided into apoptosis, which is actively executed by specific proteases, the caspases, and accidental necrosis. However, there is now accumulating evidence indicating that cell death can occur in a programmed fashion but in complete absence and independent of caspase activation. Alternative models of programmed cell death (PCD) have therefore been proposed, including autophagy, paraptosis, mitotic catastrophe, and the descriptive model of apoptosis-like and necrosis-like PCD. Caspase-independent cell death pathways are important safeguard mechanisms to protect the organism against unwanted and potential harmful cells when caspase-mediated routes fail but can also be triggered in response to cytotoxic agents or other death stimuli. As in apoptosis, the mitochondrion can play a key role but also other organelles such as lysosomes and the endoplasmic reticulum have an important function in the release and activation of death factors such as cathepsins, calpains, and other proteases. Here we review the various models of PCD and their death pathways at molecular and organelle level and discuss the relevance of the growing knowledge of caspase-independent cell death pathways for cancer.
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              Role of JNK activation in apoptosis: a double-edged sword.

               Jing Liu,  Anning Lin (2004)
              JNK is a key regulator of many cellular events, including programmed cell death (apoptosis). In the absence of NF-kB activation, prolonged JNK activation contributes to TNF-a induced apoptosis. JNK is also essential for UV induced apoptosis. However, recent studies reveal that JNK can suppress apoptosis in IL-3-dependent hematopoietic cells via phosphorylation of the proapoptotic Bcl-2 family protein BAD. Thus, JNK has pro- or antiapoptotic functions, depending on cell type, nature of the death stimulus, duration of its activation and the activity of other signaling pathways.
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                Author and article information

                Contributors
                +49 40 7410 51950 , +7 423 2311168 , dyshlovoy@gmail.com
                r.mandanchi@uke.de
                j.hauschild@uke.de
                k.otte@uke.de
                w.alsdorf@uke.de
                u.schumacher@uke.de
                kalininv@piboc.dvo.ru
                sialexandra@mail.ru
                avilov-1957@mail.ru
                friedemann.honecker@zetup.ch
                stonik@piboc.dvo.ru
                c.bokemeyer@uke.de
                g.von-amsberg@uke.de
                Journal
                BMC Cancer
                BMC Cancer
                BMC Cancer
                BioMed Central (London )
                1471-2407
                1 February 2017
                1 February 2017
                2017
                : 17
                Affiliations
                [1 ]ISNI 0000 0001 2180 3484, GRID grid.13648.38, Laboratory of Experimental Oncology, Department of Oncology, Hematology and Bone Marrow Transplantation with Section Pneumology, , University Medical Center Hamburg-Eppendorf, ; Martinistr. 52, Hamburg, 20246 Germany
                [2 ]ISNI 0000 0001 2192 9124, GRID grid.4886.2, Laboratory of Marine Natural Products Chemistry, G.B. Elyakov Pacific Institute of Bioorganic Chemistry, , Far-East Branch, Russian Academy of Sciences, ; Prospekt 100-let Vladivostoku 159, Vladivostok, 690022 Russian Federation
                [3 ]ISNI 0000 0004 0637 7917, GRID grid.440624.0, School of Natural Sciences, , Far Eastern Federal University, ; Sukhanova Street 8, Vladivostok, 690091 Russian Federation
                [4 ]GRID grid.412315.0, Institute of Anatomy and Experimental Morphology, , University Cancer Center Hamburg, University Medical Center Hamburg-Eppendorf, ; Martinistr. 52, Hamburg, 20246 Germany
                [5 ]Tumor and Breast Center ZeTuP St. Gallen, Rorschacher Strasse 150, St. Gallen, 9006 Switzerland
                Article
                3085
                10.1186/s12885-017-3085-z
                5286817
                28143426
                © The Author(s). 2017

                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.

                Funding
                Funded by: Hamburger Krebsgesellschaft
                Funded by: Erich and Gertrud Roggenbuckstiftung
                Funded by: Scholarship of the Department of Oncology, Hematology, Bone Marrow Transplantation with section Pneumology, Department of Medicine, University Hospital Hamburg-Eppendorf
                Funded by: Russian Foundation for Basic Research (RU)
                Award ID: 16-03-00553а
                Funded by: Ministry of Oceans and Fisheries of Korea
                Award ID: 20140513
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2017

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