The Cancer Genomics Cloud: Collaborative, Reproducible, and Democratized—A New Paradigm in Large-Scale Computational Research

Lau, Jessica W.; Lehnert, Erik; Sethi, Anurag; Malhotra, Raunaq; Kaushik, Gaurav; Onder, Zeynep; Groves-Kirkby, Nick; Mihajlović, Aleksandar; DiGiovanna, Jack; Srdic, Mladen; Bajcic, Dragan; Radenkovic, Jelena; Mladenovic, Vladimir; Krstanovic, Damir; Arsenijevic, V.; Klisic, Djordje; Mitrovic, Milan; Bogicevic, I.; Kural, Deniz; Davis‐Dusenbery, Brandi N.

doi:10.1158/0008-5472.can-17-0387

Cited by 146 publications

(131 citation statements)

References 14 publications

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“…The frequencies ( Fig 1A) and total numbers (Fig EV1A) of IFNc-positive and IFNc/TNF double-positive CD8 + T cells specific for the LCMV glycoprotein (GP) epitope GP [33][34][35][36][37][38][39][40][41] were increased in spleens and livers of Trail À/À mice compared with control animals. No differences in CD4 + T cells specific for the GP epitope GP [61][62][63][64][65][66][67][68][69][70][71][72][73][74][75][76][77][78][79][80] were detected (Fig EV1C), indicating that TRAIL exerts a CD8 + T cellrestricted effect on the LCMV-specific T-cell response. No differences in CD4 + T cells specific for the GP epitope GP [61][62][63][64][65][66][67][68][69][70][71][72][73][74][75]…”

Section: Resultsmentioning

confidence: 99%

“…We also observed higher frequencies of cytokineproducing CD8 + T cells specific for the H-2D b -restricted nucleoprotein (NP) epitope NP [396][397][398][399][400][401][402][403][404] in Trail À/À mice ( Fig EV1B). No differences in CD4 + T cells specific for the GP epitope GP [61][62][63][64][65][66][67][68][69][70][71][72][73][74][75][76][77][78][79][80] were detected ( Fig EV1C), indicating that TRAIL exerts a CD8 + T cellrestricted effect on the LCMV-specific T-cell response. Trail À/À mice also showed reduced CD8 + T cell-mediated liver immunopathology, as measured by lower levels of serum alanine transaminase (ALT) ( Fig 1B).…”

Section: Resultsmentioning

confidence: 99%

“…All fluorescent-labeled antibodies used in this study are indicated in Appendix Table S1. LCMV-specific peptides GP [33][34][35][36][37][38][39][40][41] (KAVYNFATC), GP [61][62][63][64][65][66][67][68][69][70][71][72][73][74][75][76][77][78][79][80] (GLKGPDIYKGVYQFKSVEFD), and NP 396-404 (FQPQNGQFI) were purchased from Eurogentec (Lüttich, Belgium). Cell proliferation dye eFluor 670 was obtained from eBioscience (Santa Clara, CA, USA).…”

Section: Antibodies and Reagentsmentioning

confidence: 99%

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Non‐apoptotic TRAIL function modulates NK cell activity during viral infection

et al. 2019

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The role of death receptor signaling for pathogen control and infection-associated pathogenesis is multifaceted and controversial. Here, we show that during viral infection, tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) modulates NK cell activity independently of its pro-apoptotic function. In mice infected with lymphocytic choriomeningitis virus (LCMV), Trail deficiency led to improved specific CD8 + T-cell responses, resulting in faster pathogen clearance and reduced liver pathology. Depletion experiments indicated that this effect was mediated by NK cells. Mechanistically, TRAIL expressed by immune cells positively and dose-dependently modulates IL-15 signaling-induced granzyme B production in NK cells, leading to enhanced NK cell-mediated T cell killing. TRAIL also regulates the signaling downstream of IL-15 receptor in human NK cells. In addition, TRAIL restricts NK1.1-triggered IFNc production by NK cells. Our study reveals a hitherto unappreciated immunoregulatory role of TRAIL signaling on NK cells for the granzyme B-dependent elimination of antiviral T cells.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Antibodies and Reagentsmentioning

confidence: 99%

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Non‐apoptotic TRAIL function modulates NK cell activity during viral infection

et al. 2019

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“…All analyses were performed on the CGC (https://cgc.sbgenomics.com/) 35 with workflows and tools implemented using Common Workflow Language. Human and mouse data were aligned to GRCh38 and mm10 assemblies, respectively.…”

Section: Computational Workflowsmentioning

confidence: 99%

Systematic Establishment of Robustness and Standards in Patient-Derived Xenograft Experiments and Analysis

Srivastava

Randjelović

Arunachalam

et al. 2019

Preprint

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Patient-Derived Xenografts (PDXs) are tumor-in-mouse models for cancer. PDX collections, such as those supported by the NCI PDXNet program, are powerful resources for preclinical therapeutic testing. However, variations in experimental design and analysis procedures have limited interpretability. To determine the robustness of PDX studies, the PDXNet tested temozolomide drug response for three pre-validated PDX models (sensitive, resistant, and intermediate) across four blinded PDX Development and Trial Centers (PDTCs) using independently selected SOPs. Each PDTC was able to correctly identify the sensitive, resistant, and intermediate models, and statistical evaluations were concordant across all groups. We also developed and benchmarked optimized PDX informatics pipelines, and these yielded robust assessments across xenograft biological replicates. These studies show that PDX drug responses and sequence results are reproducible across diverse experimental protocols. Here we share the range of experimental procedures that maintained robustness, as well as standardized cloudbased workflows for PDX exome-seq and RNA-Seq analysis and for evaluating growth.

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“…A similar approach to sequencing analysis was later implemented by Galaxy (5), GenomicScape (6), and other platforms (3,(7)(8)(9)(10)(11)(12)(13)(14). Each of these platforms has a similar paradigm: Users upload data to a central server and apply a diverse, heterogeneous set of programs through a standardized user interface.…”

Section: Introductionmentioning

confidence: 99%

DolphinNext: A graphical user interface for creating, deploying and executing Nextflow pipelines

Yukselen

Turkyilmaz

Öztürk

et al. 2019

Preprint

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Several platforms currently exist for the design and execution of complex pipelines (e.g. Galaxy, GenePattern, GeneProf). Unfortunately, these platforms lack the necessary combination of parallelism, portability, flexibility and/or reproducibility that are required by the current research environment. To address these shortcomings, Nextflow was implemented to simplify portable, scalable, and reproducible scientific pipelines using containers. We have used Nextflow capabilities as leverage and developed a user interface, DolphinNext, for creating, deploying, and executing complex Nextflow pipelines for high throughput data processing. The guiding principle of DolphinNext is to facilitate the building and deployment of complex pipelines using a modular approach implemented in a graphical interface. DolphinNext provides: 1. A drag and drop user interface that abstracts pipelines and allows users to create pipelines without familiarity in underlying programming languages. 2. A user interface to monitor pipeline execution that allows the re-initiation of pipelines at intermediate steps 3. Reproducible pipelines with version tracking and stand-alone versions that can be run independently. 4. Seamless portability to distributed computational environments such as high-performance clusters or cloud computing environments.

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The Cancer Genomics Cloud: Collaborative, Reproducible, and Democratized—A New Paradigm in Large-Scale Computational Research

Cited by 146 publications

References 14 publications

Non‐apoptotic TRAIL function modulates NK cell activity during viral infection

Non‐apoptotic TRAIL function modulates NK cell activity during viral infection

Systematic Establishment of Robustness and Standards in Patient-Derived Xenograft Experiments and Analysis

DolphinNext: A graphical user interface for creating, deploying and executing Nextflow pipelines

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