OncoKB: A Precision Oncology Knowledge Base

By D. Chakravarty, Jianjiong Gao, Sarah M. Phillips, R. Kundra, Hongxin Zhang, Jiaojiao Wang, J. Rudolph, Rona Yaeger, Tara E. Soumerai, Moriah H. Nissan and 38 more. JCO Precision Oncology, 2017

2,318 citations recorded by the source corpus.

Executive Summary PURPOSE With prospective clinical sequencing of tumors emerging as a mainstay in cancer care, there is an urgent need for a clinical support tool that distills the clinical implications associated with specific mutation events into a standardized and easily interpretable format. To this end, we developed OncoKB, an expert-guided precision oncology knowledge base. METHODS OncoKB annotates the biological and oncogenic effect and the prognostic and predictive significance of somatic molecular alterations. Potential treatment implications are stratified by the level of evidence that a specific molecular alteration is predictive of drug response based on US Food and Drug Administration (FDA) labeling, National Comprehensive Cancer Network (NCCN) guidelines, disease-focused expert group recommendations and the scientific literature. RESULTS To date, over 3000 unique mutations, fusions, and copy number alterations in 418 cancer-associated genes have been annotated. To test the utility of OncoKB, we annotated all genomic events in 5983 primary tumor samples in 19 cancer types. Forty-one percent of samples harbored at least one potentially actionable alteration, of which 7.5% were predictive of clinical benefit from a standard treatment. OncoKB annotations are available through a public web resource (http://oncokb.org/) and are also incorporated into the cBioPortal for Cancer Genomics to facilitate the interpretation of genomic alterations by physicians and researchers. CONCLUSION OncoKB, a comprehensive and curated precision oncology knowledge base, offers oncologists detailed, evidence-based information about individual somatic mutations and structural alterations present in patient tumors with the goal of supporting optimal treatment decisions.

Subfields: Cancer Research, Genetics, Pulmonary and Respiratory Medicine

View Paper: https://www.semanticscholar.org/paper/3acbedf611ecc2fbddcbb4af106abef604d5fbf6

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Citations in the corpus

Genomic correlates of clinical outcome in advanced prostate cancer (2019, methodology): “Single-nucleotide variants (SNVs) in the most frequently altered genes were found to be likely oncogenic (16) in the majority of cases (Fig.”
DGIdb 3.0: a redesign and expansion of the drug–gene interaction database (2017, neutral): “These include the Precision Oncology Knowledge Base (OncoKB), cancer genome interpreter (CGI) and The Jackson Laboratory Clinical Knowledgebase (CKB) (Supplementary Table S1) (6,7).”
Comprehensive Molecular Characterization of Muscle-Invasive Bladder Cancer. (2017, neutral): “A uthor M anuscript A uthor M anuscript A uthor M anuscript A uthor M anuscript 3Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA 4PSMAR-IMIM Lab, Bladder Cancer Center, Department of Medicine, Dana-Farber Cancer Institute and Harvard University, Boston, MA 02”
Clinical sequencing defines the genomic landscape of metastatic colorectal cancer (2018, methodology): “…2013 RRID:SCR_010779; http://archive.broadinstitute.org/ cancer/cga/mutsig Other cBioPortal Memorial Sloan Kettering Cancer Center, New York, NY RRID:SCR_014555 http://www.cbioportal.org/ Cancer Hotspots Chang et al., 2016 http://cancerhotspots.org OncoKB Chakravarty et al., 2017 http://oncokb.org”
Oncogenic Signaling Pathways in The Cancer Genome Atlas (2018, methodology): “Using the OncoKB knowledge base of clinically actionable alterations (Chakravarty et al., 2017a), we systematically assessed all alterations in each sample of each cancer type, distinguishing between standard care actionability (Levels 1 or 2) and investigational therapies (Levels 3 and 4).”
Integration of the Drug–Gene Interaction Database (DGIdb 4.0) with open crowdsource efforts (2020, background): “We have also updated multiple sources including large, well-curated sources such as DrugBank (21), Guide to Pharmacology (22), Gene Ontology (23,24), OncoKB (25), PharmGKB (26), and the Therapeutic Target Database (27) (Figure 2, Supplementary Table S1).”
Analysis and Visualization of Longitudinal Genomic and Clinical Data from the AACR Project GENIE Biopharma Collaborative in cBioPortal (2023, methodology): “The cBioPortal is integrated with various other databases to help with interpretability of the genomic data, including OncoKB (5), CIVIC (6), MyCancerGenome (7), Cancer Hotspots (8), and gnomAD (9).”
A foundation model for clinical-grade computational pathology and rare cancers detection (2024, neutral): “The oncogenic status of PTEN mutation was determined based on MSK-IMPACT sequencing and OncoKB annotation 68 .”
The Genomic Landscape of Endocrine-Resistant Advanced Breast Cancers (2018, background): “Recurrent hotspot mutations were identified as previously described (Chakravarty et al., 2017; Chang et al., 2016).”
The long tail of oncogenic drivers in prostate cancer (2018, neutral): “Finally, genes with at least five oncogenic variants (according to OncoKB, http://oncokb.org), but were not previously included in the SMG list, were added to the SMG list 39 .”
Cancer therapy shapes the fitness landscape of clonal hematopoiesis (2020, neutral): “We annotated variants as oncogenic (CH-PD) if they fulfilled any of the following criteria: 1) any variant noted as oncogenic or likely oncogenic in OncoKB 25 ; 2) any truncating mutations (nonsense, essential splice site or frameshift indel) in known tumor suppressor genes as per the Cancer Gene Ce”
Comprehensive Characterization of Cancer Driver Genes and Mutations (2018, methodology): “…L.D., S.Q. Sun, R.J. Mashl, A.D. Scott, W. Wang, P. Batra, L.-B. Wang, and M.A. Wyczalkowski, unpublished data http://depo-dinglab.ddns.net OncoKB Chakravarty et al., 2017 http://oncokb.org Mutation Validation Ng et al., 2018 N/A Software and Algorithms 20/20+ Tokheim et al., 2016b…”
A saturation mutagenesis approach to understanding PTEN lipid phosphatase activity and genotype-phenotypes relationships (2018, methodology): “Similar to the germline analysis, to test whether fitness scores could discriminate somatic mutations that are likely pathogenic, we evaluated mutations from Onco-KB, a precision oncology database with expert annotation of somatic mutations (Table S5).(55)We found thatfitness scoresofPTENmutations c”