PharmGKB: A worldwide resource for pharmacogenomic information

Barbarino, Julia M.; Whirl‐Carrillo, Michelle; Altman, Russ B.; Klein, Teri E.

doi:10.1002/wsbm.1417

Cited by 260 publications

(208 citation statements)

References 30 publications

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“…In our previous study [44], we revealed essential proteins associated with BC pathogenesis through deep analyses of genetic alterations, signaling pathways [52], protein-protein interaction networks [55, 56], protein expression [57, 58], dependency maps [57], and enrichment maps [49] of previously prioritized genes by the Consensus Strategy [59], the Pan-Cancer Atlas project [60–64], the Pharmacogenomics Knowledgebase (PharmGKB) [2], and the Cancer Genome Interpreter [65]. The druggable prediction of BC proteins through ML approaches will provide us relevant information regarding potential biomarkers, therapeutic targets and future clinical trials, avoiding ethnicity bias and improving cancer pharmacogenomics and precision medicine worldwide [66–76].…”

Section: Resultsmentioning

confidence: 99%

Prediction of druggable proteins using machine learning and functional enrichment analysis: a focus on cancer-related proteins and RNA-binding proteins

López‐Cortés

Cabrera-Andrade

Cruz-Segundo

et al. 2019

Preprint

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BackgroundDruggable proteins are a trending topic in drug design. The druggable proteome can be defined as the percentage of proteins that have the capacity to bind an antibody or small molecule with adequate chemical properties and affinity. The screening and in silico modeling are critical activities for the reduction of experimental costs.MethodsThe current work proposes a unique prediction model for druggable proteins using amino acid composition descriptors of protein sequences and 13 machine learning linear and non-linear classifiers. After feature selection, the best classifier was obtained using the support vector machine method and 200 tri-amino acid composition descriptors.ResultsThe high performance of the model is determined by an area under the receiver operating characteristics (AUROC) of 0.975 ± 0.003 and accuracy of 0.929 ± 0.006 (3-fold cross-validation). Regarding the prediction of cancer-associated proteins using this model, the best ranked druggable predicted proteins in the breast cancer protein set were CDK4, AP1S1, POLE, HMMR, RPL5, PALB2, TIMP1, RPL22, NFKB1 and TOP2A; in the cancer-driving protein set were TLL2, FAM47C, SAGE1, HTR1E, MACC1, ZFR2, VMA21, DUSP9, CTNNA3 and GABRG1; and in the RNA-binding protein set were PLA2G1B, CPEB2, NOL6, LRRC47, CTTN, CORO1A, SCAF11, KCTD12, DDX43 and TMPO.ConclusionsThis powerful model predicts several druggable proteins which should be deeply studied to find better therapeutic targets and thus improve clinical trials. The scripts are freely available at https://github.com/muntisa/machine-learning-for-druggable-proteins.

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Section: Resultsmentioning

confidence: 99%

Prediction of druggable proteins using machine learning and functional enrichment analysis: a focus on cancer-related proteins and RNA-binding proteins

López‐Cortés

Cabrera-Andrade

Cruz-Segundo

et al. 2019

Preprint

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“…Canonical pathways of scTPA contain 51,210 human and 1,762 mouse pathways from seven widely used pathway databases including BioCarta, HumanCyc (13), KEGG (14), PANTHER (15), PharmGKB (16), Reactome (17), SMPDB (18), which were retrieved from R package graphite (19). Notably, these literature-curated pathways were grouped into 6 different catalogs including general pathways, metabolic pathways, signaling and regulatory pathways, genome maintenance pathways, drug & small molecules pathways and cancer pathways (20).…”

Section: Biological Pathwaysmentioning

confidence: 99%

scTPA: A web tool for single-cell transcriptome analysis of pathway activation signatures

Zhang

et al. 2020

Preprint

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The most fundamental challenge in current single-cell RNA-seq data analysis is functional interpretation and annotation of cell clusters. The biological pathways in distinct cell types have different activation patterns, which facilitates understanding cell functions in single-cell transcriptomics. However, no effective web tool has been implemented for single-cell transcriptomic data analysis based on prior biological pathway knowledge. Here, we introduce scTPA (http://sctpa.bio-data.cn/sctpa), which is a web-based platform providing pathway-based analysis of single-cell RNA-seq data in human and mouse. scTPA incorporates four widely-used gene set enrichment methods to estimate the pathway activation scores of single cells based on a collection of available biological pathways with different functional and taxonomic classifications. The clustering analysis and cell-type-specific activation pathway identification were provided for the functional interpretation of cell types from pathway-oriented perspective. An intuitive interface allows users to conveniently visualize and download single-cell pathway signatures. Together, scTPA is a comprehensive tool to identify pathway activation signatures for dissecting single cell heterogeneity.

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“…The CGI flags genomic biomarkers of drug response with different levels of clinical relevance 38 . Lastly, PharmGKB is a comprehensive resource that curates and spreads knowledge of the impact of clinical annotations on BC drug response 37,40 . PharmGKB collects the precise guidelines for the application of pharmacogenomics in clinical practice published by the European Society for Medical Oncology (ESMO), the National Comprehensive Cancer Network (NCCN), the Royal Dutch Association for the Advancement of Pharmacy (DPWG), the Canadian Pharmacogenomics Network for Drug Safety (CPNDS) and the Clinical Pharmacogenetics Implementation Consortium (CPIC) 41–44 .…”

Section: Introductionmentioning

confidence: 99%

OncoOmics approaches to reveal essential genes in breast cancer: a panoramic view from pathogenesis to precision medicine

López‐Cortés

Paz‐y‐Miño

Guerrero

et al. 2019

Preprint

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SUMMARYBreast cancer (BC) is a heterogeneous disease where each OncoOmics approach needs to be fully understood as a part of a complex network. Therefore, the main objective of this study was to analyze genetic alterations, signaling pathways, protein-protein interaction networks, protein expression, dependency maps and enrichment maps in 230 previously prioritized genes by the Consensus Strategy, the Pan-Cancer Atlas, the Pharmacogenomics Knowledgebase and the Cancer Genome Interpreter, in order to reveal essential genes to accelerate the development of precision medicine in BC. The OncoOmics essential genes were rationally filtered to 144, 48 (33%) of which were hallmarks of cancer and 20 (14%) were significant in at least three OncoOmics approaches: RAC1, AKT1 CCND1, PIK3CA, ERBB2, CDH1, MAPK14, TP53, MAPK1, SRC, RAC3, PLCG1, GRB2, MED1, TOP2A, GATA3, BCL2, CTNNB1, EGFR and CDK2. According to the Open Targets Platform, there are 111 drugs that are currently being analyzed in 3151 clinical trials in 39 genes. Lastly, there are more than 800 clinical annotations associated with 94 genes in BC pharmacogenomics.

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PharmGKB: A worldwide resource for pharmacogenomic information

Cited by 260 publications

References 30 publications

Prediction of druggable proteins using machine learning and functional enrichment analysis: a focus on cancer-related proteins and RNA-binding proteins

Prediction of druggable proteins using machine learning and functional enrichment analysis: a focus on cancer-related proteins and RNA-binding proteins

scTPA: A web tool for single-cell transcriptome analysis of pathway activation signatures

OncoOmics approaches to reveal essential genes in breast cancer: a panoramic view from pathogenesis to precision medicine

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