GLORIA

GEOMAR Library Ocean Research Information Access

Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
  • 1
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 80, No. 16_Supplement ( 2020-08-15), p. 6221-6221
    Abstract: Background: Engagement of Tumor Necrosis Factor-α (TNF-α) with its receptor can lead to dramatically different cellular outcomes ranging from regulating cell survival and inflammation to induction of programmed forms of cell death. A critical proximal checkpoint determining the nature of TNF-α signaling is put in place by the cellular inhibitor of apoptosis proteins (cIAPs). In the context of cancer therapy these constitute an attractive target as they (1) block the TNF-α induced activation of apoptotic/necroptotic cues and (2) are negatively regulated by a highly selective endogenous ligand (i.e. SMAC), which served as a blueprint for the development of small molecule inhibitors of IAP (so called SMAC mimetics). Methods: Here we investigated the efficacy of SMAC mimetic BI891065 in enhancing targeted and chemotherapeutic approaches in preclinical mouse cancer models and describe immune-modulatory effects in syngeneic settings. To identify responding indications, a large pan-cancer cell line panel screening comprising 246 cell lines was performed (Eurofins). Proliferation of cells treated with increasing concentrations of BI 891065 combined with a fixed concentration of TNF-α was assessed by high-content screening. Furthermore, to gain a better understanding of the molecular determinants associated with sensitivity to SMAC mimetic treatment, genome-wide CRISPR/Cas9 drug modifier screens were performed. Results: Here we present key data demonstrating antitumor activity of BI891065 in preclinical models, our efforts towards understanding of genetic determinants of SMAC sensitivity and of potential responsive indications. By using genome-wide CRISPR/Cas9 drug modifier screens we not only demonstrated the feasibility of such unbiased approaches, as we identified many known (e.g. TNF Receptor 1, RIPK1, Caspase 8 and members of the NFκB signaling pathways) - but also potentially novel - regulators of TNF-α/SMAC mimetic induced cell death. In addition, to identify potential responsive indications to BI891065, extensive profiling of in vitro drug sensitivity across a large set of cancer cell types was performed. As a result of this, colorectal cancer (n=56) was identified as a promising indication: 5% of cell lines were found to be sensitive to BI 891065 single treatment. This could be further extended by the exogenous supply of TNF-α to BI 891065, increasing the number of sensitive cells to 21%. Conclusion: The presented data demonstrate the potential of BI 891065 to facilitate tumor cell death and to enhance anti-tumor immune responses, and nominate the compound as an attractive combination partner in cancer therapy. Our results led to the identification of potentially novel modulators of SMAC mimetic sensitivity via genome-wide CRISPR/Cas9 drug sensitizer screens and suggest colorectal cancer as a promising indication for clinical positioning. Citation Format: Martin Aichinger, Valeria Santoro, Ksenija Slavic-Obradovic, Stefanie Ruhland, Andreas Wernitznig, Andrea Neudolt, Markus Schaefer, Sabine Kallenda, Daniel Zach, Sabine Olt, Carina Salomon, Sarah Rieser, Martina Weissenboeck, Florian Ebner, Andreas Schlattl, Melanie Talata De Almeida, Rebecca Langlois, Martina Sykora, Markus Reschke, Thomas Zichner, Daniel Gerlach, Julian Jude, Michaela Fellner, Dirk Scharn, Norbert Kraut, Juergen Moll, Johannes Zuber, Sebastian Carotta, Maria Antonietta Impagnatiello, Ulrike Tontsch-Grunt. Targeting IAP in cancer: BI 891065 a potent small molecule SMAC mimetic that synergizes with immune checkpoint inhibition [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 6221.
    Type of Medium: Online Resource
    ISSN: 0008-5472 , 1538-7445
    RVK:
    RVK:
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2020
    detail.hit.zdb_id: 2036785-5
    detail.hit.zdb_id: 1432-1
    detail.hit.zdb_id: 410466-3
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
  • 2
    Online Resource
    Online Resource
    Oxford University Press (OUP) ; 2021
    In:  Bioinformatics Vol. 37, No. 23 ( 2021-12-07), p. 4559-4561
    In: Bioinformatics, Oxford University Press (OUP), Vol. 37, No. 23 ( 2021-12-07), p. 4559-4561
    Abstract: A main task in computational cancer analysis is the identification of patient subgroups (i.e. cohorts) based on metadata attributes (patient stratification) or genomic markers of response (biomarkers). Coral is a web-based cohort analysis tool that is designed to support this task: Users can interactively create and refine cohorts, which can then be compared, characterized and inspected down to the level of single items. Coral visualizes the evolution of cohorts and also provides intuitive access to prevalence information. Furthermore, findings can be stored, shared and reproduced via the integrated session management. Coral is pre-loaded with data from over 128 000 samples from the AACR Project GENIE, the Cancer Genome Atlas and the Cell Line Encyclopedia. Availability and implementation Coral is publicly available at https://coral.caleydoapp.org. The source code is released at https://github.com/Caleydo/coral. Supplementary information Supplementary data are available at Bioinformatics online.
    Type of Medium: Online Resource
    ISSN: 1367-4803 , 1367-4811
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2021
    detail.hit.zdb_id: 1468345-3
    SSG: 12
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
  • 3
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 80, No. 16_Supplement ( 2020-08-15), p. 4522-4522
    Abstract: Introduction: Stimulator of interferon genes (STING) is activated by cyclic dinucleotides that are generated by cyclic GMP-AMP synthase in the presence of cytosolic DNA, resulting in a type 1 interferon response and the secretion of pro-inflammatory cytokines. BI-STING mimics the natural ligand of STING and hijacks the STING signaling pathway to trigger a potent anti-tumor immune response. Here we report the pre-clinical characterization of BI-STING in human cells lines in vitro, in human ex vivo systems and in vivo mouse models. Methods: Activity and specificity of BI-STING were tested in vitro in the human monocytic leukemia cell line THP1. Target engagement was confirmed using ex vivo studies in human whole blood and in human precision cut colorectal cancer slices. For in vivo studies, BI-STING was administered intratumorally (i.tu.) in a range of subcutaneous, syngeneic murine tumor models. The modulation of cytokine secretion over time was recorded and the induction of a tumor-specific immune response demonstrated. Results: Using THP1 cells, activation of down-stream signaling events by BI-STING was seen specifically in STING wild type, but not STING knock out cells. Ex vivo treatment of human whole blood and human precision cut colorectal cancer slices with BI-STING resulted in the significant induction of cytokine secretion (including IFNβ) and in an array of transcriptional changes associated with the activation of STING signaling. Treatment of tumor-bearing mice with a single dose of BI-STING i.tu. led to a transient increase of cytokine levels in tumor and plasma. In all tested models, i.tu. administration of BI-STING resulted in dose-dependent local tumor control. Importantly, animals whose primary tumor was cured by BI-STING treatment did not develop tumors when re-challenged with the same tumor cell line and an abscopal delay in tumor growth was seen for non-injected (or anenestic) lesions in mice carrying bilateral tumors. Abscopal tumor control was further improved when combined with anti-PD-1. Finally, ELISpot analysis resulted in a significantly increased number of immunospots in splenocytes from BI-STING treated animals as compared to vehicle control mice, confirming the induction of a tumor-specific immune response. Conclusions: BI-STING is a potent and specific inducer of the STING signaling pathway. In addition to local tumor control, i.tu. treatment with BI-STING results in a systemic anti-tumor immune response in mice, which was enhanced upon anti-PD-1 combination. A clinical trial to evaluate i.tu. administration of BI-STING alone and in combination with anti-PD-1 in patients with advanced solid tumors is ongoing. Citation Format: Gabriela Gremel, Maria A. Impagnatiello, Sebastian Carotta, Otmar Schaaf, Paolo M. Chetta, Thorsten Oost, Thomas Zichner, Marco Hofmann, Sophia Blake, Tom Bretschneider, Martin Fleck, Achim Grube, Herbert Nar, Georg Rast, Esther Schmidt, Ute Klinkhardt, Kirsten Arndt-Schmitz, Thorsten Laux, Vittoria Zinzalla, Jonathon Sedgwick, Norbert Kraut. Potent induction of a tumor-specific immune response by a cyclic dinucleotide STING agonist [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 4522.
    Type of Medium: Online Resource
    ISSN: 0008-5472 , 1538-7445
    RVK:
    RVK:
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2020
    detail.hit.zdb_id: 2036785-5
    detail.hit.zdb_id: 1432-1
    detail.hit.zdb_id: 410466-3
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
  • 4
    Online Resource
    Online Resource
    SAGE Publications ; 2020
    In:  Information Visualization Vol. 19, No. 2 ( 2020-04), p. 114-136
    In: Information Visualization, SAGE Publications, Vol. 19, No. 2 ( 2020-04), p. 114-136
    Abstract: Most tabular data visualization techniques focus on overviews, yet many practical analysis tasks are concerned with investigating individual items of interest. At the same time, relating an item to the rest of a potentially large table is important. In this work, we present Taggle, a tabular visualization technique for exploring and presenting large and complex tables. Taggle takes an item-centric, spreadsheet-like approach, visualizing each row in the source data individually using visual encodings for the cells. At the same time, Taggle introduces data-driven aggregation of data subsets. The aggregation strategy is complemented by interaction methods tailored to answer specific analysis questions, such as sorting based on multiple columns and rich data selection and filtering capabilities. We demonstrate Taggle by a case study conducted by a domain expert on complex genomics data analysis for the purpose of drug discovery.
    Type of Medium: Online Resource
    ISSN: 1473-8716 , 1473-8724
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2020
    detail.hit.zdb_id: 2083606-5
    detail.hit.zdb_id: 2078513-6
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
  • 5
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 80, No. 16_Supplement ( 2020-08-15), p. 3227-3227
    Abstract: Cancer cell lines are important models for drug tests and viability screens. For the evaluation and understanding of differences between two groups of cell lines, we created a research software application called CLIFF (cell line differences). CLIFF finds differences in mutational pattern or DNA copy number, lists and visualizes the significantly differentially expressed genes or proteins or performs Gene Set Enrichment Analysis (GSEA) to name a few. Today we have data from large panels of cancer cell lines consisting of gene-, protein- and microRNA expression, DNA copy number, DNA mutations, methylation, histone H3 modification, and concentration of metabolites, in addition to annotations of the tumor of origin. A number of cell line properties have been derived from these data, like microsatellite-(in)stability (MSS/MSI), mutation pattern classifications, or mutational burden. Many drugs have been tested and genes have been knocked in and out to study changes in molecular processes and proliferation. Even though cell lines in vitro do not represent the growth-properties and -conditions in a multi-cell-type tumor environment very well, they are indispensable cancer models in research and drug discovery. After testing or evaluating various properties of cell lines, like e.g. the viability dependency on short hairpin RNA (shRNA) knockdown or CRISPR-Cas9 knockout, as well as the expression of a specific gene, the presence or absence of a DNA mutation, or any other of the above-mentioned annotations, cell lines can be classified into two classes. In addition, users can upload cell line classifications based on external data, e.g. sensitivity or resistance to drug treatments or growth media formulations. Here are two examples of the application of CLIFF: (1) the association of knockdown or knockout of WRN in colorectal cell lines with microsatellite instability (MSI) could be confirmed. (2) The growth media has an important impact on the dependency on certain gene knockouts. It could be verified, that low concentrations of L-asparagine in the growth medium is associated with increased dependency on the asparagine producing enzyme asparagine synthetase (ASNS). In summary, we integrated various cell line data into CLIFF to provide sophisticated statistical analysis to researchers without deeper bioinformatics knowledge. This supports the understanding of molecular mechanisms of tumor growth, helps defining drug targets for treating cancer within defined patient populations, and accelerates our understanding of the molecular drivers of cancer. Citation Format: Andreas Wernitznig, Jesse J. Lipp, Thomas Zichner, Daniel Gerlach, Markus J. Bauer, Tilman Voss, Andreas Schlattl, Christian Haslinger, Philip G. Montgomery, Mahdi Zamanighomi, William R. Sellers, Norbert Kraut. CLIFF, a bioinformatics software tool to explore molecular differences between two sets of cancer cell lines [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 3227.
    Type of Medium: Online Resource
    ISSN: 0008-5472 , 1538-7445
    RVK:
    RVK:
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2020
    detail.hit.zdb_id: 2036785-5
    detail.hit.zdb_id: 1432-1
    detail.hit.zdb_id: 410466-3
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
Close ⊗
This website uses cookies and the analysis tool Matomo. More information can be found here...