OncoKB: A Precision Oncology Knowledge Base

Abstract

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.

Fig 1.

OncoKB workflow. Data sources: Alterations are identified by their recurrence from public variant databases (cBioPortal, COSMIC [Catalogue of Somatic Mutations in Cancer], the Memorial Sloan Kettering [MSK] IMPACT internal clinical sequencing cohort), and by prior knowledge available in the literature. Biologic and clinical therapeutic implications of alterations are curated from several public resources, including disease-specific treatment guidelines; abstracts from major conference proceedings, such as ASCO, European Society of Medical Oncology, and American Association for Cancer Research; and the scientific literature through PubMed. Variant curation: This information is entered into the curation interface as structured data elements organized in a hierarchy of gene, alteration, and tumor type. Within each tumor type, clinical implications, including prevalence, prognostic implications, and standard or investigational therapeutic implications, are individually curated and stored. Clinical Genomics Annotation Committee (CGAC): OncoKB annotation is vetted by selected clinicians and physician-scientists across 22 disease management teams who make up the CGAC. Curation review occurs in the form of sample medical reports sent every 3 months to CGAC members and monthly e-mails that request feedback. CGAC recommendations and feedback are incorporated into OncoKB in real time. OncoKB access: OncoKB data are available for public use through an interactive Web site and the cBioPortal for Cancer Genomics and are used internally to annotate MSK clinical reports. API, application program interface; NCCN, National Comprehensive Cancer Network.

Fig 2.

Levels of evidence. Individual mutational events are annotated by the level of evidence that supports the use of a certain drug in an indication that harbors that mutation. Standard therapeutic implications include Food and Drug Administration (FDA)–recognized biomarkers that are predictive of response to an FDA-approved drug in a specific indication (level 1) and standard care biomarkers that are predictive of response to an FDA-approved drug in a specific indication (level 2A). Investigational therapeutic implications include FDA-approved biomarkers predictive of response to an FDA-approved drug detected in an off-label indication (level 2B), FDA- or non–FDA-recognized biomarkers that are predictive of response to novel targeted agents that have shown promising results in clinical trials (level 3A), and non–FDA-recognized biomarkers that are predictive of response to novel targeted agents on the basis of compelling biologic data (level 4). NCCN, National Comprehensive Cancer Network.

Fig 3.

Examples for the OncoKB levels of evidence system. Information in OncoKB is organized hierarchically by gene, alteration, indication, and level of evidence. Implicit in the designation of a level of evidence for each branch is whether the biomarker is Food and Drug Administration (FDA)–recognized standard care or investigational and whether it is predictive of response to a drug that is FDA approved or currently being tested in clinical trials. Examples shown are BRAF, EGFR, AKT1, and ERBB2. MAPK, mitogen-activated protein kinase; TKI, tyrosine kinase inhibitor.

Fig 4.

Frequencies of level of evidence 1 to 3 assignments in the Cancer Genome Atlas cohorts. Patient samples from 19 cancer types (The Cancer Genome Atlas) are classified by the alteration that carries the highest level of evidence. (A) Inset pie chart: Fraction of samples across all cancer types that carry a mutation considered actionable according to the levels of evidence, oncogenic but not actionable, or variants of unknown significance (VUS). Stacked bar graph: Similar analysis as inset pie chart. Tumor type–specific samples are analyzed by variants considered actionable, oncogenic but not actionable, or VUS. (B) Highest level of evidence by tumor type and gene. Cell color as shown in the key for the inset pie chart (A). Columns indicate sample tumor type, rows indicate gene alteration present in sample, and numbers indicate the percentage of samples per tumor type that harbor an alteration in each gene. (C) Each patient sample was classified by the number of oncogenic alterations or the number of actionable alterations. Shown is the mean number of actionable (black), oncogenic (dark gray), or total (gray) mutations per sample per tumor type. (D) Each tumor type was evaluated for the percentage of samples that carry zero, one, two, three, or four or more actionable mutations per sample (indicated in shades of blue). MAPK, mitogen-activated protein kinase; mTOR, mammalian target of rapamycin; PI3K, phosphatidylinositol 3-kinase.