Purpose: Relapsed or refractory diffuse large B-cell lymphoma (rrDLBCL) is fatal in 90% of patients, and yet little is known about its biology.Experimental Design: Using exome sequencing, we characterized the mutation profiles of 38 rrDLBCL biopsies obtained at the time of progression after immunochemotherapy. To identify genes that may be associated with relapse, we compared the mutation frequency in samples obtained at relapse to an unrelated cohort of 138 diagnostic DLBCLs and separately amplified specific mutations in their matched diagnostic samples to identify clonal expansions.Results: On the basis of a higher frequency at relapse and evidence for clonal selection, TP53, FOXO1, MLL3 (KMT2C), CCND3, NFKBIZ, and STAT6 emerged as top candidate genes implicated in therapeutic resistance. We observed individual examples of clonal expansions affecting genes whose mutations had not been previously associated with DLBCL including two regulators of NF-kB: NFKBIE and NFKBIZ. We detected mutations that may be affect sensitivity to novel therapeutics, such as MYD88 and CD79B mutations, in 31% and 23% of patients with activated B-cell-type of rrDLBCL, respectively. We also identified recurrent STAT6 mutations affecting D419 in 36% of patients with the germinal center B (GCB) cell rrDLBCL. These were associated with activated JAK/STAT signaling, increased phospho-STAT6 protein expression and increased expression of STAT6 target genes.Conclusions: This work improves our understanding of therapeutic resistance in rrDLBCL and has identified novel therapeutic opportunities especially for the high-risk patients with GCB-type rrDLBCL.

Key Points• Panobinostat induces responses in 28% of patients with relapsed and refractory DLBCL that are typically durable off therapy.• MEF2B mutations predicted for response whereas early increase in ctDNA abundance was a strong predictor of subsequent treatment failure.The majority of diffuse large B-cell lymphoma (DLBCL) tumors contain mutations in histone-modifying enzymes (HMEs), indicating a potential therapeutic benefit of histone deacetylase inhibitors (HDIs), and preclinical data suggest that HDIs augment the effect of rituximab. In this randomized phase 2 study, we evaluated the response rate and toxicity of panobinostat, a pan-HDI administered 30 mg orally 3 times weekly, with or without rituximab, in 40 patients with relapsed or refractory de novo (n 5 27) or transformed (n 5 13) DLBCL. Candidate genes and whole exomes were sequenced in relapse tumor biopsies to search for molecular correlates, and these data were used to quantify circulating tumor DNA (ctDNA) in serial plasma samples. Eleven of 40 patients (28%) responded to panobinostat (95% confidence interval [CI] 14.6-43.9) and rituximab did not increase responses. The median duration of response was 14.5 months (95% CI 9.4 to "not reached"). At time of data censoring, 6 of 11 patients had not progressed. Of the genes tested for mutations, only those in MEF2B were significantly associated with response. We detected ctDNA in at least 1 plasma sample from 96% of tested patients. A significant increase in ctDNA at day 15 relative to baseline was strongly associated with lack of response (sensitivity 71.4%, specificity 100%). We conclude that panobinostat induces very durable responses in some patients with relapsed DLBCL, and early responses can be predicted by mutations in MEF2B or a significant change in ctDNA level at 15 days after treatment initiation. This clinical trial was registered at www.ClinicalTrials.gov (#NCT01238692). (Blood. 2016;128(2):185-194)

BackgroundWe introduce BPG, a framework for generating publication-quality, highly-customizable plots in the R statistical environment.ResultsThis open-source package includes multiple methods of displaying high-dimensional datasets and facilitates generation of complex multi-panel figures, making it suitable for complex datasets. A web-based interactive tool allows online figure customization, from which R code can be downloaded for integration with computational pipelines.ConclusionBPG provides a new approach for linking interactive and scripted data visualization and is available at http://labs.oicr.on.ca/boutros-lab/software/bpg or via CRAN at https://cran.r-project.org/web/packages/BoutrosLab.plotting.generalElectronic supplementary materialThe online version of this article (10.1186/s12859-019-2610-2) contains supplementary material, which is available to authorized users.

Ultrasensitive methods for rare allele detection are critical to leverage the full potential offered by liquid biopsies. Here, we describe a novel molecular barcoding method for the precise detection and quantification of circulating tumor DNA (ctDNA). The major benefits of our design include straightforward and cost-effective production of barcoded adapters to tag individual DNA molecules before PCR and sequencing, and better control over cross-contamination between experiments. We validated our approach in a cohort of 24 patients with a broad spectrum of cancer diagnoses by targeting and quantifying single-nucleotide variants (SNVs), indels and genomic rearrangements in plasma samples. By using personalized panels targeting a priori known mutations, we demonstrate comprehensive error-suppression capabilities for SNVs and detection thresholds for ctDNA below 0.1%. We also show that our semi-degenerate barcoded adapters hold promise for noninvasive genotyping in the absence of tumor biopsies and monitoring of minimal residual disease in longitudinal plasma samples. The benefits demonstrated here include broad applicability, flexibility, affordability and reproducibility in the research and clinical settings.

We introduce BPG, an easy-to-use framework for generating publication-quality, highly-customizable plots in the R statistical environment. This open-source package includes novel methods of displaying high-dimensional datasets and facilitates generation of complex multi-panel figures, making it ideal for complex datasets. A webbased interactive tool allows online figure customization, from which R code can be downloaded for seamless integration with computational pipelines. BPG is available at http://labs.oicr.on.ca/boutros-lab/software/bpg . CC-BY 4.0 International license peer-reviewed) is the author/funder. It is made available under a The copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/156067 doi: bioRxiv preprint first posted online Jun. 26, 2017; BPG P'ng et al. Page 3 of 8Biological experiments are increasingly generating large, multifaceted datasets. Exploring such data and communicating observations is, in turn, growing more difficult and the need for robust scientific datavisualization is growing rapidly 1,2,3,4 . Myriad data visualization tools exist, particularly as web-based interfaces and non-R-based local software packages. Unfortunately these do not integrate easily into R-based statistical pipelines such as the widely used Bioconductor 5 . Within R, visualization packages exist, including base graphics 6 , ggplot2 7 , lattice 8 , Sushi 9 , circlize 10 , multiDimBio 11 , NetBioV 12 , GenomeGraphs 13 and ggbio 14 . These lack publication-quality defaults, contain limited plot types, provide limited scope for automatic generation of multi-panel figures, are constrained to specific data-types and do not allow interactive visualization.Good visualization software must create a wide variety of chart-types in order to match the diversity of datatypes available. It should provide flexible parametrization for highly customized figures and allow for multiple output formats while employing reasonable, publication-appropriate default settings, such as producing high resolution output. In addition, it should integrate seamlessly with existing computational pipelines while also providing an easily intuitive, interactive mode. There should be an ability to transition between pipeline and interactive mode, allowing cyclical development. Finally, good design principles should be encouraged, such as suggesting appropriate color choices and layouts for specific use-cases. To help users quickly gain proficiency, detailed examples, tutorials and an application programming interface (API) are required. To date, no existing visualization suite fills these needs.To address this gap, we have created the BPG library, which is implemented in R using the grid graphics system and lattice framework. It generates a broad suite of chart-types, ranging from common plots such as bar charts and box plots to more specialized plots, such as Manhattan plots (Figure 1; code is in Supplementary File 1).

As high-throughput sequencing continues to increase in speed and throughput, routine clinical and industrial application draws closer. These 'production' settings will require enhanced quality monitoring and quality control to optimize output and reduce costs. We developed SeqControl, a framework for predicting sequencing quality and coverage using a set of 15 metrics describing overall coverage, coverage distribution, basewise coverage and basewise quality. Using whole-genome sequences of 27 prostate cancers and 26 normal references, we derived multivariate models that predict sequencing quality and depth. SeqControl robustly predicted how much sequencing was required to reach a given coverage depth (area under the curve (AUC) = 0.993), accurately classified clinically relevant formalin-fixed, paraffin-embedded samples, and made predictions from as little as one-eighth of a sequencing lane (AUC = 0.967). These techniques can be immediately incorporated into existing sequencing pipelines to monitor data quality in real time. SeqControl is available at http://labs.oicr.on.ca/Boutros-lab/software/SeqControl/.

The field of cancer genomics has demonstrated the power of massively parallel sequencing techniques to inform on the genes and specific alterations that drive tumor onset and progression. Although large comprehensive sequence data sets continue to be made increasingly available, data analysis remains an ongoing challenge, particularly for laboratories lacking dedicated resources and bioinformatics expertise. To address this, we have produced a collection of Galaxy tools that represent many popular algorithms for detecting somatic genetic alterations from cancer genome and exome data. We developed new methods for parallelization of these tools within Galaxy to accelerate runtime and have demonstrated their usability and summarized their runtimes on multiple cloud service providers. Some tools represent extensions or refinement of existing toolkits to yield visualizations suited to cohort-wide cancer genomic analysis. For example, we present Oncocircos and Oncoprintplus, which generate data-rich summaries of exome-derived somatic mutation. Workflows that integrate these to achieve data integration and visualizations are demonstrated on a cohort of 96 diffuse large B-cell lymphomas and enabled the discovery of multiple candidate lymphoma-related genes. Our toolkit is available from our GitHub repository as Galaxy tool and dependency definitions and has been deployed using virtualization on multiple platforms including Docker.

The Systemic Theoretical Process Analysis (STPA) model is used for hazard analysis and accident prevention, based on systemic thinking and the identification of causal scenarios, created by Professor Nancy Leveson of the Institute of Technology of Massachusetts (MIT). The purpose of this article is to perform a bibliometric and patent analysis of the STPA model. Since bibliometry is an important tool in the analysis of scientific production, this method is used as a descriptive statistic, for the purposes of this study, the concepts of Goffman's Epidemic Theory were highlighted, under a mainly qualitative analysis, for a study of decline and ascent scientific method. For the bibliometric analysis, the main page of Professor Nancy Leveson was used in MIT's Web site, besides the Web of Science, Mendeley, ResearchGate, Village of Engineering and Scientific Electronic Library Online (SciELO). Aiming to cover the patents analysis it was used the Derwent, IHS and Orbit research bases. Defining as search term “Analysis of the Theoretical Process of Systems” and “STPA”, searched in the title, abstract and keywords. A total of 171 publications (1990 to 2017) were found, with 89 specific references of the STPA model (2002 to 2017), addressing several subjects such as: definitions, steps, complements to the model, areas of application and use of the model with another risk analysis tool. At the end of this article are highlighted the main works of the STPA model, tools of support and analysis, serving as base and favoring future works.