Enhancing knowledge discovery from cancer genomics data with Galaxy

Albuquerque, Marco A.; Grande, Bruno M.; Ritch, Elie; Pararajalingam, Prasath; Jessa, Selin; Krzywinski, Martin; Grewal, Jasleen K.; Shah, Sohrab P.; Boutros, Paul C.; Morin, Ryan D.

doi:10.1093/gigascience/gix015

Cited by 6 publications

(4 citation statements)

References 44 publications

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“…We used the largest cohort, consisting of WES data from over 1000 DLBCL cases 10 as our external validation cohort. Analysis of the relapsed/treatment refractory DLBCLs and the TCGA cohort was recently described by our group 47 .…”

Section: Methodsmentioning

confidence: 99%

Genome-wide discovery of somatic regulatory variants in diffuse large B-cell lymphoma

et al. 2018

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Diffuse large B-cell lymphoma (DLBCL) is an aggressive cancer originating from mature B-cells. Prognosis is strongly associated with molecular subgroup, although the driver mutations that distinguish the two main subgroups remain poorly defined. Through an integrative analysis of whole genomes, exomes, and transcriptomes, we have uncovered genes and non-coding loci that are commonly mutated in DLBCL. Our analysis has identified novel cis-regulatory sites, and implicates recurrent mutations in the 3′ UTR of NFKBIZ as a novel mechanism of oncogene deregulation and NF-κB pathway activation in the activated B-cell (ABC) subgroup. Small amplifications associated with over-expression of FCGR2B (the Fcγ receptor protein IIB), primarily in the germinal centre B-cell (GCB) subgroup, correlate with poor patient outcomes suggestive of a novel oncogene. These results expand the list of subgroup driver mutations that may facilitate implementation of improved diagnostic assays and could offer new avenues for the development of targeted therapeutics.

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Section: Methodsmentioning

confidence: 99%

Genome-wide discovery of somatic regulatory variants in diffuse large B-cell lymphoma

et al. 2018

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Section: Discussionmentioning

confidence: 99%

“…13,36,37 Efficient tools, that support the visual stratification of a tumour genomic profiles and that highlight their relationships to know drugs or treatments, will be more useful than the existing research-oriented tools. 13,38 Researchers and doctors usually combine different visualisation methods in a typical analysis procedure to assist their work. For example, they need first to normalise experimental and batch differences between samples and then to identify differentially regulated genes based on a fold-change level when comparing across samples, such as between a healthy and a non-healthy tissue.…”

Section: Comparison Of Traditional and New Methods For Genomic Data Vmentioning

confidence: 99%

Visual Analytics of Genomic and Cancer Data: A Systematic Review

Lau

Nguyễn

et al. 2019

Cancer Inform

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Visual analytics and visualisation can leverage the human perceptual system to interpret and uncover hidden patterns in big data. The advent of next-generation sequencing technologies has allowed the rapid production of massive amounts of genomic data and created a corresponding need for new tools and methods for visualising and interpreting these data. Visualising genomic data requires not only simply plotting of data but should also offer a decision or a choice about what the message should be conveyed in the particular plot; which methodologies should be used to represent the results must provide an easy, clear, and accurate way to the clinicians, experts, or researchers to interact with the data. Genomic data visual analytics is rapidly evolving in parallel with advances in high-throughput technologies such as artificial intelligence (AI) and virtual reality (VR). Personalised medicine requires new genomic visualisation tools, which can efficiently extract knowledge from the genomic data and speed up expert decisions about the best treatment of individual patient’s needs. However, meaningful visual analytics of such large genomic data remains a serious challenge. This article provides a comprehensive systematic review and discussion on the tools, methods, and trends for visual analytics of cancer-related genomic data. We reviewed methods for genomic data visualisation including traditional approaches such as scatter plots, heatmaps, coordinates, and networks, as well as emerging technologies using AI and VR. We also demonstrate the development of genomic data visualisation tools over time and analyse the evolution of visualising genomic data.

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“…Galaxy workflow was used to identify somatic mutations in genes, including the top five genes mutated in CCRCC, as reported in Cosmic (https://cancer.sanger.ac.uk/cosmic) VHL, PBRM1, SETD2, BAP1 and KDM5C, as well as NFE2L2, which is mutated in papillary RCC, and TP53 (associated with carcinogenesis in general) by comparing the tumor samples with sequencing obtained from cells in the blood of the same patients. 18…”

Section: Polymerase Chain Reaction and Sequencingmentioning

confidence: 99%

Molecular characteristics and markers of advanced clear cell renal cell carcinoma: Pitfalls due to intratumoral heterogeneity and identification of genetic alterations associated with metastasis

et al. 2020

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Objectives To identify clear cell renal cell carcinoma‐related gene mutations potentially associated with aggressive disease, sarcomatoid differentiation or poor prognosis. Methods We carried out genomic analysis of 217 tumor foci from 25 patients with conventional clear cell renal cell carcinoma (14 patients), clear cell renal cell carcinoma with sarcomatoid differentiation (six patients) and non‐clear cell renal cell carcinoma (five patients). Each tumor nodule on the tissue block that corresponded to the same focus on the slide was separated from the normal parenchyma and other histologically distinct areas of tumor. The isolated tumor foci were used for subsequent analyses and sequencing. Deoxyribonucleic acid from the formalin‐fixed paraffin‐embedded tissues was extracted. Multiplex bar‐coded polymerase chain reaction amplification was carried out using next‐generation sequencing libraries. Results Overall, 67 protein alterations, including amino acid alterations, frame shifts and splice site mutations in seven genes were identified in the cohort of renal cell carcinoma tumors included in this study. Fewer patients with clear cell renal cell carcinoma with sarcomatoid differentiation had clear cell renal cell carcinoma‐related mutations in comparison with patients with conventional clear cell renal cell carcinoma. Additionally, the average number of unique clear cell renal cell carcinoma‐related protein alterations per patient was significantly lower in clear cell renal cell carcinoma with sarcomatoid differentiation than in conventional clear cell renal cell carcinoma. Mutations in PBRM1 were identified in a higher proportion of patients with high‐grade tumors (World Health Organization/International Society of Urological Pathology grade 4) and in the primary tumors of six of 10 (60%) patients with metastatic disease. Conclusions Although there are pitfalls due to intratumoral heterogeneity and sampling bias, mutations in PBRM1 may be associated with metastasis and aggressive disease in clear cell renal cell carcinoma.

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Enhancing knowledge discovery from cancer genomics data with Galaxy

Cited by 6 publications

References 44 publications

Genome-wide discovery of somatic regulatory variants in diffuse large B-cell lymphoma

Genome-wide discovery of somatic regulatory variants in diffuse large B-cell lymphoma

Visual Analytics of Genomic and Cancer Data: A Systematic Review

Molecular characteristics and markers of advanced clear cell renal cell carcinoma: Pitfalls due to intratumoral heterogeneity and identification of genetic alterations associated with metastasis

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