DGIdb: mining the druggable genome

Griffith, Malachi; Coffman, Adam; Weible, James V.; McMichael, Josh F; Spies, Nicholas C.; Koval, James; Das, Indraniel; Callaway, Matthew B.; Eldred, James M.; Miller, Christopher A.; Subramanian, Janakiraman; Govindan, Ramaswamy; Kumar, Runjun D.; Bose, Ron; Li, Ding; Walker, Jason; Larson, David E.; Dooling, David J.; Smith, Scott M.; Ley, Timothy J.; Mardis, Elaine R.; Wilson, Richard K.

doi:10.1038/nmeth.2689

Cited by 450 publications

(422 citation statements)

References 37 publications

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“…Cytoscape (v3.2.1) and the EnrichmentMap plug-in was used to generate networks for genesets enriched with an FDR cut-off of < 0.05. SE associated genes were also used to query the Washington University Drug Gene Interaction database restricted to expert curated drug-target interactions to identify novel and Druggable gene targets 17 .…”

Section: Methodsmentioning

confidence: 99%

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Therapeutic targeting of ependymoma as informed by oncogenic enhancer profiling

Mack¹,

Pajtler

Chávez

et al. 2017

Nature

178

218

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SUMMARY Genomic sequencing has driven precision-based oncology therapy; however, genetic drivers remain unknown or non-targetable for many malignancies, demanding alternative approaches to identify therapeutic leads. Ependymomas are chemotherapy-resistant brain tumours, which, despite genomic sequencing, lack effective molecular targets. Intracranial ependymomas are segregated based on anatomical location – supratentorial region (ST) or posterior fossa (PF) – and further divided into distinct molecular subgroups that reflect differences in age of onset, gender predominance, and response to therapy1–3. The most common and aggressive subgroup, Posterior Fossa Ependymoma Group A (PF-EPN-A), occurs in young children and appears to lack recurrent somatic mutations2. Conversely, Posterior Fossa Ependymoma Group B (PF-EPN-B) tumours display frequent large-scale copy number gains and losses yet favourable clinical outcomes1,3. Greater than 70% of supratentorial ependymomas are defined by highly recurrent gene fusions in the NFκB subunit RELA (ST-EPN-RELA), and less frequently involve fusion of the gene encoding the transcriptional activator YAP1 (ST-EPN-YAP1).1,3,4 Subependymomas, a distinct histologic variant, can also be found within the ST and PF compartments accounting for the majority of tumours in the molecular subgroups ST-EPN-SE and PF-EPN-SE, respectively1. Here, we mapped active chromatin landscapes in 42 primary ependymomas in two non-overlapping primary ependymoma cohorts with the goal of identifying essential super enhancer associated genes on which tumour cells were dependent. Enhancer regions revealed putative oncogenes, molecular targets, and pathways, which when subjected to small molecule inhibitor or shRNA treatment, diminished proliferation of patient-derived neurospheres and increased survival in mouse models of ependymomas. Through profiling of transcriptional enhancers, our study provides a framework for target and drug discovery in other cancers recalcitrant to therapeutic development because of their lack of known genetic drivers.

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Section: Methodsmentioning

confidence: 99%

“…4a, Supplementary Table 21). 17 HDAC7 , EPHA2 , FGFR1 , and CACNA1H were identified as candidate ependymoma gene dependencies that could be responsive to small molecule inhibitors (Fig. 4a).…”

mentioning

confidence: 99%

Therapeutic targeting of ependymoma as informed by oncogenic enhancer profiling

Mack¹,

Pajtler

Chávez

et al. 2017

Nature

178

218

View full text Add to dashboard Cite

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“…13,15 My Cancer Genome (mycancergenome.org) and MD Anderson Cancer Center personalized cancer therapy (https://pct.mdanderson.org) web resources deliver detailed, comprehensive summaries on selected driver mutations with therapeutic implications, including a list of potential clinical trials. The Drug-Gene Interaction Database 16 and Therapeutic Targeted Database 17 mine existing resources to generate known and potential gene target-drug associations. In addition, there are a few databases that incorporate in silico prediction tools to identify novel driver mutations in cancer.…”

Section: Discussionmentioning

confidence: 99%

Cancer Driver Log (CanDL)

Damodaran

Miya

Kautto

et al. 2015

The Journal of Molecular Diagnostics

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Massively parallel sequencing technologies have enabled characterization of genomic alterations across multiple tumor types. Efforts have focused on identifying driver mutations because they represent potential targets for therapy. However, because of the presence of driver and passenger mutations, it is often challenging to assign the clinical relevance of specific mutations observed in patients. Currently, there are multiple databases and tools that provide in silico assessment for potential drivers; however, there is no comprehensive resource for mutations with functional characterization. Therefore, we created an expert-curated database of potentially actionable driver mutations for molecular pathologists to facilitate annotation of cancer genomic testing. We reviewed scientific literature to identify variants that have been functionally characterized in vitro or in vivo as driver mutations. We obtained the chromosome location and all possible nucleotide positions for each amino acid change and uploaded them to the Cancer Driver Log (CanDL) database with associated literature reference indicating functional driver evidence. In addition to a simple interface, the database allows users to download all or selected genes as a comma-separated values file for incorporation into their own analysis pipeline. Furthermore, the database includes a mechanism for third-party contributions to support updates for novel driver mutations. Overall, this freely available database will facilitate rapid annotation of cancer genomic testing in molecular pathology laboratories for mutations. (J Mol Diagn 2015, 17: 554e559; http://dx

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“…Pathwaybased analysis revealed several associated with the disease, and intersecting these pathways with annotations from the Drug Gene Interaction database (22) highlighted candidates that could modulate pathways associated with the disease. Such analyses lay the groundwork for studies that aim to evaluate the repurposed treatments in models of the disease.…”

Section: Pathway and Network-based Drug Identification From Genetic Amentioning

confidence: 99%

Pathway and network-based strategies to translate genetic discoveries into effective therapies

Greene

Voight

2016

Hum. Mol. Genet.

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One way to design a drug is to attempt to phenocopy a genetic variant that is known to have the desired effect. In general, drugs that are supported by genetic associations progress further in the development pipeline. However, the number of associations that are candidates for development into drugs is limited because many associations are in non-coding regions or difficult to target genes. Approaches that overlay information from pathway databases or biological networks can expand the potential target list. In cases where the initial variant is not targetable or there is no variant with the desired effect, this may reveal new means to target a disease. In this review, we discuss recent examples in the domain of pathway and network-based drug repositioning from genetic associations. We highlight important caveats and challenges for the field, and we discuss opportunities for further development.

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DGIdb: mining the druggable genome

Cited by 450 publications

References 37 publications

Therapeutic targeting of ependymoma as informed by oncogenic enhancer profiling

Therapeutic targeting of ependymoma as informed by oncogenic enhancer profiling

Cancer Driver Log (CanDL)

Pathway and network-based strategies to translate genetic discoveries into effective therapies

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