A framework for organizing cancer-related variations from existing databases, publications and NGS data using a High-performance Integrated Virtual Environment (HIVE)

Wu, Tsung-Jung; Shamsaddini, Amirhossein; Pan, Yang; Smith, Krista; Crichton, Daniel J.; Simonyan, Vahan; Mazumder, Raja

doi:10.1093/database/bau022

Cited by 61 publications

(73 citation statements)

References 52 publications

(66 reference statements)

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“…Clinical trial registries as well as reporting of outcome data should have an obligatory and standardized format, which would facilitate accruals and support clinical utility for predictive, prognostic, and diagnostic biomarkers. There needs to be a push for clinical trial registries to register trials in a systematic way, similar to metadata curation of big data (44,45 ). Tremendous and commendable effort has been made in requiring the registration of clinical trials, and journals have been critical in enforcement.…”

Section: Discussionmentioning

confidence: 99%

Clinical Trials in Precision Oncology

et al. 2016

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BACKGROUND Availability of genomic information used in the management of cancer treatment has outpaced both regulatory and reimbursement efforts. Many types of clinical trials are underway to validate the utility of emerging genome-based biomarkers for diagnostic, prognostic, and predictive applications. Clinical trials are a key source of evidence required for US Food and Drug Administration approval of therapies and companion diagnostics and for establishing the acceptance criteria for reimbursement. CONTENT Determining the eligibility of patients for molecular-based clinical trials and the interpretation of data emerging from clinical trials is significantly hampered by 2 primary factors: the lack of specific reporting standards for biomarkers in clinical trials and the lack of adherence to official gene and variant naming standards. Clinical trial registries need specifics on the mutation required for enrollment as opposed to allowing a generic mutation entry such as, “EGFR mutation.” The use of clinical trials data in bioinformatics analysis and reporting is also gated by the lack of robust, state of the art programmatic access support. An initiative is needed to develop community standards for clinical trial descriptions and outcome reporting that are modeled after similar efforts in the genomics research community. SUMMARY Systematic implementation of reporting standards is needed to insure consistency and specificity of biomarker data, which will in turn enable better comparison and assessment of clinical trial outcomes across multiple studies. Reporting standards will facilitate improved identification of relevant clinical trials, aggregation and comparison of information across independent trials, and programmatic access to clinical trials databases.

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Section: Discussionmentioning

confidence: 99%

Clinical Trials in Precision Oncology

et al. 2016

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“…34 The integration was performed using methods described earlier. 35 The final data set contains 1 705 286 somatic nsSNVs and 1 132 832 germline nsSNVs. Drug IDs were retrieved from DrugBank and literature to allow linking to detailed pharmacology and pharmaceutical knowledge and drug target information.…”

Section: Methodsmentioning

confidence: 99%

Impact of germline and somatic missense variations on drug binding sites

Cheng

Pattabiraman²,

Goecks

et al. 2016

Pharmacogenomics J

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Advancements in next-generation sequencing (NGS) technologies are generating a vast amount of data. This exacerbates the current challenge of translating NGS data into actionable clinical interpretations. We have comprehensively combined germline and somatic nonsynonymous single-nucleotide variations (nsSNVs) that affect drug binding sites in order to investigate their prevalence. The integrated data thus generated in conjunction with exome or whole-genome sequencing can be used to identify patients who may not respond to a specific drug because of alterations in drug binding efficacy due to nsSNVs in the target protein's gene. To identify the nsSNVs that may affect drug binding, protein–drug complex structures were retrieved from Protein Data Bank (PDB) followed by identification of amino acids in the protein–drug binding sites using an occluded surface method. Then, the germline and somatic mutations were mapped to these amino acids to identify which of these alter protein–drug binding sites. Using this method we identified 12 993 amino acid–drug binding sites across 253 unique proteins bound to 235 unique drugs. The integration of amino acid–drug binding sites data with both germline and somatic nsSNVs data sets revealed 3133 nsSNVs affecting amino acid–drug binding sites. In addition, a comprehensive drug target discovery was conducted based on protein structure similarity and conservation of amino acid–drug binding sites. Using this method, 81 paralogs were identified that could serve as alternative drug targets. In addition, non-human mammalian proteins bound to drugs were used to identify 142 homologs in humans that can potentially bind to drugs. In the current protein–drug pairs that contain somatic mutations within their binding site, we identified 85 proteins with significant differential gene expression changes associated with specific cancer types. Information on protein–drug binding predicted drug target proteins and prevalence of both somatic and germline nsSNVs that disrupt these binding sites can provide valuable knowledge for personalized medicine treatment. A web portal is available where nsSNVs from individual patient can be checked by scanning against DrugVar to determine whether any of the SNVs affect the binding of any drug in the database.

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“…As well, big data studies correlating disease-associated mutations and genetic variations with PTMs are emerging and should help frame studies of the glycoproteome. 398,399 …”

Section: Summary and Future Outlookmentioning

confidence: 99%

Chemical Glycoproteomics

Palaniappan

Bertozzi²

2016

Chem. Rev.

226

199

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Chemical tools have accelerated progress in glycoscience, reducing experimental barriers to studying protein glycosylation, the most widespread and complex form of posttranslational modification. For example, chemical glycoproteomics technologies have enabled the identification of specific glycosylation sites and glycan structures that modulate protein function in a number of biological processes. This field is now entering a stage of logarithmic growth, during which chemical innovations combined with mass spectrometry advances could make it possible to fully characterize the human glycoproteome. In this review, we describe the important role that chemical glycoproteomics methods are playing in such efforts. We summarize developments in four key areas: enrichment of glycoproteins and glycopeptides from complex mixtures, emphasizing methods that exploit unique chemical properties of glycans or introduce unnatural functional groups through metabolic labeling and chemoenzymatic tagging; identification of sites of protein glycosylation; targeted glycoproteomics; and functional glycoproteomics, with a focus on probing interactions between glycoproteins and glycan-binding proteins. Our goal with this survey is to provide a foundation on which continued technological advancements can be made to promote further explorations of protein glycosylation.

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A framework for organizing cancer-related variations from existing databases, publications and NGS data using a High-performance Integrated Virtual Environment (HIVE)

Cited by 61 publications

References 52 publications

Clinical Trials in Precision Oncology

Clinical Trials in Precision Oncology

Impact of germline and somatic missense variations on drug binding sites

Chemical Glycoproteomics

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