MAGI: visualization and collaborative annotation of genomic aberrations

Leiserson, Mark D.M.; Gramazio, Connor; Hu, Jason; Wu, Hsin Ta; Laidlaw, David H.; Raphael, Benjamin J.

doi:10.1038/nmeth.3412

Cited by 27 publications

(17 citation statements)

References 6 publications

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“…RREB1 is a positive regulator of the ZIP3 zinc transporter, and thus recurrent mutations in RREB1 may suggest an important role for zinc homeostasis in PDAC pathogenesis. Comparison of missense mutations in our cohort of patients to those reported in the literature using the Mutation Annotation and Genome Interpretation (MAGI) tool (Leiserson et al, 2015) highlighted mutations in CTNBB1 , PIK3CA , ERBB2 , POLE , SF3B1 and additional genes that have been identified in other cancer types (Table S2). …”

Section: Resultsmentioning

confidence: 87%

Integrated Genomic Characterization of Pancreatic Ductal Adenocarcinoma

Raphael¹,

Hruban²,

Aguirre³

et al. 2017

Cancer Cell

1,486

1,072

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SUMMARY We performed integrated genomic, transcriptomic and proteomic profiling of 150 pancreatic ductal adenocarcinoma (PDAC) specimens, including samples with characteristic low neoplastic cellularity. Deep whole-exome sequencing revealed recurrent somatic mutations in KRAS, TP53, CDKN2A, SMAD4, RNF43, ARID1A, TGFβR2, GNAS, RREB1 and PBRM1. KRAS wild-type tumors harbored alterations in other oncogenic drivers, including GNAS, BRAF, CTNNB1 and additional RAS pathway genes. A subset of tumors harbored multiple KRAS mutations, with some showing evidence of biallelic mutations. Protein profiling identified a favorable prognosis subset with low epithelial-mesenchymal transition and high MTOR pathway scores. Associations of non-coding RNAs with tumor-specific mRNA subtypes were also identified. Our integrated multi-platform analysis reveals a complex molecular landscape of PDAC and provides a roadmap for precision medicine.

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Section: Resultsmentioning

confidence: 87%

Integrated Genomic Characterization of Pancreatic Ductal Adenocarcinoma

Raphael¹,

Hruban²,

Aguirre³

et al. 2017

Cancer Cell

1,486

1,072

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show abstract

“…These include web tools that provide data and text summaries of the frequency, mechanisms, and druggable targets of known driver mutations [110]. Multiple tools now include “interpretations” or summaries of the driver mutations written by clinicians – including the Precision Medicine Knowledgebase (at Weill Cornell) and the Personalized Cancer Therapy knowledge base (at MD Anderson) – or by the “crowd” [111, 112] (see list of references in Table S2C). A related approach recently explored leveraging existing ‘omics datasets for the interpretation of variants in newly sequenced samples, in acute myeloid leukemia[113].…”

Section: Analysis Approaches To Determine Molecular Subtypes and Cancmentioning

confidence: 99%

Precision Oncology: The Road Ahead

Senft

Leiserson

Ruppin

et al. 2017

Trends in Molecular Medicine

150

123

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Current efforts in precision oncology largely focus on the benefit of genomics-guided therapy. Yet, advances in sequencing techniques provide an unprecedented view of the complex genetic and non-genetic heterogeneity within individual tumors. Herein, we outline the benefits of integrating genomic and transcriptomic analyses for advanced precision oncology. We summarize relevant computational approaches to detect novel drivers and genetic vulnerabilities, suitable for therapeutic exploration. Clinically relevant platforms to functionally test predicted drugs/drug combinations for individual patients are reviewed. Finally, we highlight the technological advances in single-cell analysis of tumor specimens. These may ultimately lead to the development of next generation cancer drugs, capable of tackling the hurdles imposed by genetic and phenotypic heterogeneity on current anticancer therapies.

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“…However, researchers wishing to use a data portal to explore their own data have to either a redeploy the entire platform, a difficult task even for bioinformaticians, or upload private data to a server outside the user's control, a non-starter for protected patient data such as germline variants (e.g. MAGI (Mutation Annotation and Genome Interpretation, Leiserson 2015), WebMeV (Wang 2017), Ordino (Streit 2018)). Desktop tools can view a user's own data securely (e.g.…”

Section: Introductionmentioning

confidence: 99%

The UCSC Xena platform for public and private cancer genomics data visualization and interpretation

Goldman

Craft

Hastie³

et al. 2018

Preprint

543

534

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UCSC Xena is a visual exploration resource for both public and private omics data, supported through the web-based Xena Browser and multiple turn-key Xena Hubs. This unique archecture allows researchers to view their own data securely, using private Xena Hubs, simultaneously visualizing large public cancer genomics datasets, including TCGA and the GDC. Data integration occurs only within the Xena Browser, keeping private data private. Xena supports virtually any functional genomics data, including SNVs, INDELs, large structural variants, CNV, expression, DNA methylation, ATAC-seq signals, and phenotypic annotations. Browser features include the Visual Spreadsheet, survival analyses, powerful filtering and subgrouping, statistical analyses, genomic signatures, and bookmarks. Xena differentiates itself from other genomics tools, including its predecessor, the UCSC Cancer Genomics Browser, by its ability to easily and securely view public and private data, its high performance, its broad data type support, and many unique features.

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MAGI: visualization and collaborative annotation of genomic aberrations

Cited by 27 publications

References 6 publications

Integrated Genomic Characterization of Pancreatic Ductal Adenocarcinoma

Integrated Genomic Characterization of Pancreatic Ductal Adenocarcinoma

Precision Oncology: The Road Ahead

The UCSC Xena platform for public and private cancer genomics data visualization and interpretation

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