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Bendell J, Ulahannan SV, Chu Q, Patel M, George B, Auguste A, Leo-Kress T, Stadermann KB, Kraemer N, Elgadi M, Johnson M. Abstract 779: A phase I study of BI 754111, an anti-LAG-3 monoclonal antibody (mAb), in combination with BI 754091, an anti-PD-1 mAb: Biomarker analyses from the microsatellite stable metastatic colorectal cancer (MSS mCRC) cohort. Cancer Res 2020. [DOI: 10.1158/1538-7445.am2020-779] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Abstract
Introduction: LAG3 is an immune checkpoint receptor, often co-expressed with PD-1 on immune cell surfaces. PD-1 and LAG-3 signaling contribute to immune cell exhaustion in the tumor microenvironment. Dual blockade of PD-1 and LAG-3 is thus expected to improve response versus PD-1 blockade alone. A dose escalation/expansion trial (NCT03156114) is evaluating BI 754111, an anti-LAG-3 mAb, plus BI 754091, an anti-PD-1 mAb, in patients (pts) with advanced solid tumors. Here, we report data in pts with MSS mCRC, for whom checkpoint inhibitors (CPIs) have shown limited activity. We broadly evaluated biomarkers to provide information on mode of action and for their potential to predict responses.
Methods: In this dose expansion cohort, pts with PD-(L)1-naïve, previously treated MSS mCRC tumors were enrolled. Pre- and on-treatment tumor biopsies and blood samples for biomarker analyses were collected. Cytokines, including interferon-gamma (IFN-γ), were quantified in plasma samples by multiplexed and high-sensitivity immunoassays. Activated effector memory T cell and other peripheral blood mononuclear cell counts were determined by flow cytometry. PD-L1, LAG-3 and CD8 expression was determined by immunohistochemistry (IHC). Exploratory gene expression analyses to determine immuno-oncology (IO)-related markers were conducted.
Results: 40 pts with MSS mCRC received BI 754111 600 mg in combination with BI 754091 240 mg every 3 weeks. Pts had received a median (range) of 3.5 (1–10) prior treatments. To date (Sep 2019), 3 (7.5%) pts achieved a partial response (PR) and 11 (27.5%) had stable disease (SD) as best response. Peripheral blood analysis showed treatment led to coordinated upregulation of pro-inflammatory cytokines in some pts. Pts with greater cytokine induction were more likely to have SD. Also, upregulation of activated CD8 effector memory T cells in peripheral blood was observed in many pts. IHC analysis indicated that, in tumors that had CD8 T cells and PD-L1 expression at the tumor periphery at baseline, treatment led to infiltration of CD8 T cells into the tumor and an increase in PD-L1 expression. Analyses of pre-treatment tumors suggested that high PD-L1 gene expression was associated with clinical benefit. In addition, a treatment-associated increase of the IFN-γ gene signature scores was observed, supporting a treatment-induced activation of the immune system in the tumor; the effect was more pronounced in pts with PR/SD versus those with progressive disease. LAG-3 IHC levels at baseline were not predictive of outcome in this anti-PD-(L)1 naïve setting.
Conclusion: BI 754111 plus BI 754091 showed encouraging results in this IO-refractory MSS mCRC population. Biomarker analyses showed activation of the immune system in peripheral blood and the tumor, consistent with other CPIs.
Citation Format: Johanna Bendell, Susanna V. Ulahannan, Quincy Chu, Manish Patel, Ben George, Aurélie Auguste, Theresia Leo-Kress, Kai Bernd Stadermann, Nicole Kraemer, Mabrouk Elgadi, Melissa Johnson. A phase I study of BI 754111, an anti-LAG-3 monoclonal antibody (mAb), in combination with BI 754091, an anti-PD-1 mAb: Biomarker analyses from the microsatellite stable metastatic colorectal cancer (MSS mCRC) cohort [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 779.
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Affiliation(s)
- Johanna Bendell
- 1Sarah Cannon Research Institute, Nashville, TN, USA; Tennessee Oncology, Nashville, TN
| | | | - Quincy Chu
- 3University of Alberta, Edmonton, Alberta, Canada
| | - Manish Patel
- 4Sarah Cannon Research Institute, Nashville, TN, USA; Florida Cancer Specialists & Research Institute, Sarasota, FL
| | - Ben George
- 5Medical College of Wisconsin, Milwaukee, WI
| | - Aurélie Auguste
- 6Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an der Riß, Germany
| | - Theresia Leo-Kress
- 6Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an der Riß, Germany
| | | | - Nicole Kraemer
- 7Staburo GmBH, on behalf of Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an der Riß, Germany, Munich, Germany
| | - Mabrouk Elgadi
- 8Boehringer Ingelheim (Canada) Ltd./Ltee, Burlington, Ontario, Canada
| | - Melissa Johnson
- 1Sarah Cannon Research Institute, Nashville, TN, USA; Tennessee Oncology, Nashville, TN
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Pucker B, Holtgräwe D, Stadermann KB, Frey K, Huettel B, Reinhardt R, Weisshaar B. A chromosome-level sequence assembly reveals the structure of the Arabidopsis thaliana Nd-1 genome and its gene set. PLoS One 2019; 14:e0216233. [PMID: 31112551 PMCID: PMC6529160 DOI: 10.1371/journal.pone.0216233] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2018] [Accepted: 04/16/2019] [Indexed: 01/27/2023] Open
Abstract
In addition to the BAC-based reference sequence of the accession Columbia-0 from the year 2000, several short read assemblies of THE plant model organism Arabidopsis thaliana were published during the last years. Also, a SMRT-based assembly of Landsberg erecta has been generated that identified translocation and inversion polymorphisms between two genotypes of the species. Here we provide a chromosome-arm level assembly of the A. thaliana accession Niederzenz-1 (AthNd-1_v2c) based on SMRT sequencing data. The best assembly comprises 69 nucleome sequences and displays a contig length of up to 16 Mbp. Compared to an earlier Illumina short read-based NGS assembly (AthNd-1_v1), a 75 fold increase in contiguity was observed for AthNd-1_v2c. To assign contig locations independent from the Col-0 gold standard reference sequence, we used genetic anchoring to generate a de novo assembly. In addition, we assembled the chondrome and plastome sequences. Detailed analyses of AthNd-1_v2c allowed reliable identification of large genomic rearrangements between A. thaliana accessions contributing to differences in the gene sets that distinguish the genotypes. One of the differences detected identified a gene that is lacking from the Col-0 gold standard sequence. This de novo assembly extends the known proportion of the A. thaliana pan-genome.
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Affiliation(s)
- Boas Pucker
- Bielefeld University, Faculty of Biology & Center for Biotechnology, Bielefeld, Germany
| | - Daniela Holtgräwe
- Bielefeld University, Faculty of Biology & Center for Biotechnology, Bielefeld, Germany
| | - Kai Bernd Stadermann
- Bielefeld University, Faculty of Biology & Center for Biotechnology, Bielefeld, Germany
| | - Katharina Frey
- Bielefeld University, Faculty of Biology & Center for Biotechnology, Bielefeld, Germany
| | - Bruno Huettel
- Max Planck Genome Centre Cologne, Max Planck Institute for Plant Breeding Research, Cologne, Germany
| | - Richard Reinhardt
- Max Planck Genome Centre Cologne, Max Planck Institute for Plant Breeding Research, Cologne, Germany
| | - Bernd Weisshaar
- Bielefeld University, Faculty of Biology & Center for Biotechnology, Bielefeld, Germany
- * E-mail:
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Stadermann KB, Blom J, Borgmeier C, Sciberras N, Herbold S, Kipker M, Meurer G, Molck S, Petri D, Pelzer S, Schneider J. First complete genome sequence of Bacillus glycinifermentans B-27. J Biotechnol 2017; 257:187-191. [PMID: 28438580 DOI: 10.1016/j.jbiotec.2017.04.021] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2016] [Revised: 04/18/2017] [Accepted: 04/18/2017] [Indexed: 01/20/2023]
Abstract
The first complete genome sequence of Bacillus glycinifermentans B-27 was determined by SMRT sequencing generating a genome sequence with a total length of 4,607,442 bases. Based on this sequence 4738 protein-coding sequences were predicted and used to identify gene clusters that are related to the production of secondary metabolites such as Lichenysin, Bacillibactin and Bacitracin. This genomic potential combined with the ability of B. glycinifermentans B-27 to grown in bile containing media might contribute to a future application of this strain as probiotic in productive livestock potentially inhibiting competing and pathogenic organisms.
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Affiliation(s)
- Kai Bernd Stadermann
- Evonik Nutrition and Care GmbH, Halle (Westf.), Germany; Genome Research, Faculty of Biology, Bielefeld University, Bielefeld, Germany; Bioinformatics Resource Facility, Centre for Biotechnology, Bielefeld University, Bielefeld, Germany
| | - Jochen Blom
- Bioinformatics and Systems Biology, Justus-Liebig-University Giessen, Giessen, Germany
| | | | | | | | - Maike Kipker
- Evonik Nutrition and Care GmbH, Halle (Westf.), Germany
| | | | - Stella Molck
- Evonik Nutrition and Care GmbH, Halle (Westf.), Germany
| | - Daniel Petri
- Evonik Nutrition and Care GmbH, Halle (Westf.), Germany
| | - Stefan Pelzer
- Evonik Nutrition and Care GmbH, Halle (Westf.), Germany
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Hilker R, Stadermann KB, Schwengers O, Anisiforov E, Jaenicke S, Weisshaar B, Zimmermann T, Goesmann A. ReadXplorer 2-detailed read mapping analysis and visualization from one single source. Bioinformatics 2016; 32:3702-3708. [PMID: 27540267 PMCID: PMC5167064 DOI: 10.1093/bioinformatics/btw541] [Citation(s) in RCA: 69] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2016] [Revised: 08/02/2016] [Accepted: 08/15/2016] [Indexed: 01/29/2023] Open
Abstract
MOTIVATION The vast amount of already available and currently generated read mapping data requires comprehensive visualization, and should benefit from bioinformatics tools offering a wide spectrum of analysis functionality from just one source. Appropriate handling of multiple mapped reads during mapping analyses remains an issue that demands improvement. RESULTS The capabilities of the read mapping analysis and visualization tool ReadXplorer were vastly enhanced. Here, we present an even finer granulated read mapping classification, improving the level of detail for analyses and visualizations. The spectrum of automatic analysis functions has been broadened to include genome rearrangement detection as well as correlation analysis between two mapping data sets. Existing functions were refined and enhanced, namely the computation of differentially expressed genes, the read count and normalization analysis and the transcription start site detection. Additionally, ReadXplorer 2 features a highly improved support for large eukaryotic data sets and a command line version, enabling its integration into workflows. Finally, the new version is now able to display any kind of tabular results from other bioinformatics tools. AVAILABILITY AND IMPLEMENTATION http://www.readxplorer.org CONTACT: readxplorer@computational.bio.uni-giessen.deSupplementary information: Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Rolf Hilker
- Bioinformatics and Systems Biology, Faculty of Biology and Chemistry, Justus-Liebig-University, Giessen 35392, Germany
| | - Kai Bernd Stadermann
- Faculty of Biology, Chair of Genome Research, Bielefeld University, Bielefeld 33615, Germany
| | - Oliver Schwengers
- Bioinformatics and Systems Biology, Faculty of Biology and Chemistry, Justus-Liebig-University, Giessen 35392, Germany
| | - Evgeny Anisiforov
- Bioinformatics and Systems Biology, Faculty of Biology and Chemistry, Justus-Liebig-University, Giessen 35392, Germany
| | - Sebastian Jaenicke
- Bioinformatics and Systems Biology, Faculty of Biology and Chemistry, Justus-Liebig-University, Giessen 35392, Germany
| | - Bernd Weisshaar
- Faculty of Biology, Chair of Genome Research, Bielefeld University, Bielefeld 33615, Germany
| | - Tobias Zimmermann
- Bioinformatics and Systems Biology, Faculty of Biology and Chemistry, Justus-Liebig-University, Giessen 35392, Germany
| | - Alexander Goesmann
- Bioinformatics and Systems Biology, Faculty of Biology and Chemistry, Justus-Liebig-University, Giessen 35392, Germany
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Stadermann KB, Weisshaar B, Holtgräwe D. SMRT sequencing only de novo assembly of the sugar beet (Beta vulgaris) chloroplast genome. BMC Bioinformatics 2015; 16:295. [PMID: 26377912 PMCID: PMC4573686 DOI: 10.1186/s12859-015-0726-6] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2015] [Accepted: 09/06/2015] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Third generation sequencing methods, like SMRT (Single Molecule, Real-Time) sequencing developed by Pacific Biosciences, offer much longer read length in comparison to Next Generation Sequencing (NGS) methods. Hence, they are well suited for de novo- or re-sequencing projects. Sequences generated for these purposes will not only contain reads originating from the nuclear genome, but also a significant amount of reads originating from the organelles of the target organism. These reads are usually discarded but they can also be used for an assembly of organellar replicons. The long read length supports resolution of repetitive regions and repeats within the organelles genome which might be problematic when just using short read data. Additionally, SMRT sequencing is less influenced by GC rich areas and by long stretches of the same base. RESULTS We describe a workflow for a de novo assembly of the sugar beet (Beta vulgaris ssp. vulgaris) chloroplast genome sequence only based on data originating from a SMRT sequencing dataset targeted on its nuclear genome. We show that the data obtained from such an experiment are sufficient to create a high quality assembly with a higher reliability than assemblies derived from e.g. Illumina reads only. The chloroplast genome is especially challenging for de novo assembling as it contains two large inverted repeat (IR) regions. We also describe some limitations that still apply even though long reads are used for the assembly. CONCLUSIONS SMRT sequencing reads extracted from a dataset created for nuclear genome (re)sequencing can be used to obtain a high quality de novo assembly of the chloroplast of the sequenced organism. Even with a relatively small overall coverage for the nuclear genome it is possible to collect more than enough reads to generate a high quality assembly that outperforms short read based assemblies. However, even with long reads it is not always possible to clarify the order of elements of a chloroplast genome sequence reliantly which we could demonstrate with Fosmid End Sequences (FES) generated with Sanger technology. Nevertheless, this limitation also applies to short read sequencing data but is reached in this case at a much earlier stage during finishing.
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Affiliation(s)
- Kai Bernd Stadermann
- Chair of Genome Research, Faculty of Biology, Bielefeld University, Bielefeld, Germany. .,Bioinformatics Resource Facility, Centre for Biotechnology, Bielefeld University, Bielefeld, Germany.
| | - Bernd Weisshaar
- Chair of Genome Research, Faculty of Biology, Bielefeld University, Bielefeld, Germany.
| | - Daniela Holtgräwe
- Chair of Genome Research, Faculty of Biology, Bielefeld University, Bielefeld, Germany.
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Hilker R, Stadermann KB, Doppmeier D, Kalinowski J, Stoye J, Straube J, Winnebald J, Goesmann A. ReadXplorer--visualization and analysis of mapped sequences. Bioinformatics 2014; 30:2247-54. [PMID: 24790157 PMCID: PMC4217279 DOI: 10.1093/bioinformatics/btu205] [Citation(s) in RCA: 92] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
MOTIVATION Fast algorithms and well-arranged visualizations are required for the comprehensive analysis of the ever-growing size of genomic and transcriptomic next-generation sequencing data. RESULTS ReadXplorer is a software offering straightforward visualization and extensive analysis functions for genomic and transcriptomic DNA sequences mapped on a reference. A unique specialty of ReadXplorer is the quality classification of the read mappings. It is incorporated in all analysis functions and displayed in ReadXplorer's various synchronized data viewers for (i) the reference sequence, its base coverage as (ii) normalizable plot and (iii) histogram, (iv) read alignments and (v) read pairs. ReadXplorer's analysis capability covers RNA secondary structure prediction, single nucleotide polymorphism and deletion-insertion polymorphism detection, genomic feature and general coverage analysis. Especially for RNA-Seq data, it offers differential gene expression analysis, transcription start site and operon detection as well as RPKM value and read count calculations. Furthermore, ReadXplorer can combine or superimpose coverage of different datasets. AVAILABILITY AND IMPLEMENTATION ReadXplorer is available as open-source software at http://www.readxplorer.org along with a detailed manual.
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Affiliation(s)
- Rolf Hilker
- Institute of Medical Microbiology, Justus-Liebig-University, 35392 Giessen, Germany, Faculty of Biology, Institute for Bioinformatics, Center for Biotechnology, Computational Genomics, Center for Biotechnology, Technology Platform Genomics, Center for Biotechnology, Genome Informatics, Faculty of Technology, Bielefeld University, 33615 Bielefeld, Germany and Bioinformatics and Systems Biology, Faculty of Biology and Chemistry, Justus-Liebig-University, 35392 Giessen, Germany
| | - Kai Bernd Stadermann
- Institute of Medical Microbiology, Justus-Liebig-University, 35392 Giessen, Germany, Faculty of Biology, Institute for Bioinformatics, Center for Biotechnology, Computational Genomics, Center for Biotechnology, Technology Platform Genomics, Center for Biotechnology, Genome Informatics, Faculty of Technology, Bielefeld University, 33615 Bielefeld, Germany and Bioinformatics and Systems Biology, Faculty of Biology and Chemistry, Justus-Liebig-University, 35392 Giessen, GermanyInstitute of Medical Microbiology, Justus-Liebig-University, 35392 Giessen, Germany, Faculty of Biology, Institute for Bioinformatics, Center for Biotechnology, Computational Genomics, Center for Biotechnology, Technology Platform Genomics, Center for Biotechnology, Genome Informatics, Faculty of Technology, Bielefeld University, 33615 Bielefeld, Germany and Bioinformatics and Systems Biology, Faculty of Biology and Chemistry, Justus-Liebig-University, 35392 Giessen, Germany
| | - Daniel Doppmeier
- Institute of Medical Microbiology, Justus-Liebig-University, 35392 Giessen, Germany, Faculty of Biology, Institute for Bioinformatics, Center for Biotechnology, Computational Genomics, Center for Biotechnology, Technology Platform Genomics, Center for Biotechnology, Genome Informatics, Faculty of Technology, Bielefeld University, 33615 Bielefeld, Germany and Bioinformatics and Systems Biology, Faculty of Biology and Chemistry, Justus-Liebig-University, 35392 Giessen, Germany
| | - Jörn Kalinowski
- Institute of Medical Microbiology, Justus-Liebig-University, 35392 Giessen, Germany, Faculty of Biology, Institute for Bioinformatics, Center for Biotechnology, Computational Genomics, Center for Biotechnology, Technology Platform Genomics, Center for Biotechnology, Genome Informatics, Faculty of Technology, Bielefeld University, 33615 Bielefeld, Germany and Bioinformatics and Systems Biology, Faculty of Biology and Chemistry, Justus-Liebig-University, 35392 Giessen, Germany
| | - Jens Stoye
- Institute of Medical Microbiology, Justus-Liebig-University, 35392 Giessen, Germany, Faculty of Biology, Institute for Bioinformatics, Center for Biotechnology, Computational Genomics, Center for Biotechnology, Technology Platform Genomics, Center for Biotechnology, Genome Informatics, Faculty of Technology, Bielefeld University, 33615 Bielefeld, Germany and Bioinformatics and Systems Biology, Faculty of Biology and Chemistry, Justus-Liebig-University, 35392 Giessen, GermanyInstitute of Medical Microbiology, Justus-Liebig-University, 35392 Giessen, Germany, Faculty of Biology, Institute for Bioinformatics, Center for Biotechnology, Computational Genomics, Center for Biotechnology, Technology Platform Genomics, Center for Biotechnology, Genome Informatics, Faculty of Technology, Bielefeld University, 33615 Bielefeld, Germany and Bioinformatics and Systems Biology, Faculty of Biology and Chemistry, Justus-Liebig-University, 35392 Giessen, Germany
| | - Jasmin Straube
- Institute of Medical Microbiology, Justus-Liebig-University, 35392 Giessen, Germany, Faculty of Biology, Institute for Bioinformatics, Center for Biotechnology, Computational Genomics, Center for Biotechnology, Technology Platform Genomics, Center for Biotechnology, Genome Informatics, Faculty of Technology, Bielefeld University, 33615 Bielefeld, Germany and Bioinformatics and Systems Biology, Faculty of Biology and Chemistry, Justus-Liebig-University, 35392 Giessen, Germany
| | - Jörn Winnebald
- Institute of Medical Microbiology, Justus-Liebig-University, 35392 Giessen, Germany, Faculty of Biology, Institute for Bioinformatics, Center for Biotechnology, Computational Genomics, Center for Biotechnology, Technology Platform Genomics, Center for Biotechnology, Genome Informatics, Faculty of Technology, Bielefeld University, 33615 Bielefeld, Germany and Bioinformatics and Systems Biology, Faculty of Biology and Chemistry, Justus-Liebig-University, 35392 Giessen, Germany
| | - Alexander Goesmann
- Institute of Medical Microbiology, Justus-Liebig-University, 35392 Giessen, Germany, Faculty of Biology, Institute for Bioinformatics, Center for Biotechnology, Computational Genomics, Center for Biotechnology, Technology Platform Genomics, Center for Biotechnology, Genome Informatics, Faculty of Technology, Bielefeld University, 33615 Bielefeld, Germany and Bioinformatics and Systems Biology, Faculty of Biology and Chemistry, Justus-Liebig-University, 35392 Giessen, Germany
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