oncokbR

{oncokbR} allows you to annotate genomic data sets using MSK’s OncoKB Precision Oncology Knowledge Base directly in R. The package wraps existing OncoKB API endpoints so R users can easily leverage the existing API to annotate data on mutations, copy number alterations and fusions.

This package is compatible with oncoKB data v3.16, but is subject to change as new versions are released. For more information on oncoKB, see Chakravarty et al. 2017.

Installation

You can install the development version of oncokbR with:

remotes::install_github('karissawhiting/oncokbR')

Authentication

In order to use this package, you will need to acquire an oncoKB API token and save it to your .Renviron file (or wherever you store credentials). You will need to register for an account on OncoKB Precision Oncology Knowledge Base. Once your registration is approved, you will see a token under your Account Settings. Save it in your .Renviron file as ONCOKB_TOKEN. This will save the token as an environmental variable called ONCOKB_TOKEN that can be recognized in all package functions.

Tip: The following {usethis} function can easily find and open the .Renviron for you:

usethis::edit_r_environ()
#> • Edit '/Users/kwhiting/.Renviron'
#> • Restart R for changes to take effect

Paste the token you were given (using the format below) in the .Renviron file and save the file changes. After saving you should restart your R session to ensure the token is saved and recognized.

ONCOKB_TOKEN = 'YOUR_TOKEN'

Annotate Data

Annotate Mutations:

library(oncokbR)
library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union

Annotate MAF data with tumor type indicated for annotations on oncogenicity and oncoKB treatment levels. This will show the highest sensitivity levels for bladder cancer specifically:

blca_mutation <- oncokbR::blca_mutation %>%
  mutate(tumor_type = "BLCA")

annotated_tt <- annotate_mutations(mutations = blca_mutation[1:50,])

annotated_tt$oncokb_oncogenic 
#>  [1] "Unknown"          "Unknown"          "Unknown"          "Unknown"         
#>  [5] "Unknown"          "Unknown"          "Unknown"          "Unknown"         
#>  [9] "Unknown"          "Oncogenic"        "Unknown"          "Unknown"         
#> [13] "Unknown"          "Unknown"          "Unknown"          "Unknown"         
#> [17] "Unknown"          "Unknown"          "Unknown"          "Unknown"         
#> [21] "Unknown"          "Unknown"          "Unknown"          "Unknown"         
#> [25] "Unknown"          "Unknown"          "Unknown"          "Unknown"         
#> [29] "Unknown"          "Unknown"          "Unknown"          "Unknown"         
#> [33] "Unknown"          "Likely Neutral"   "Likely Neutral"   "Unknown"         
#> [37] "Unknown"          "Unknown"          "Unknown"          "Unknown"         
#> [41] "Likely Oncogenic" "Unknown"          "Unknown"          "Unknown"         
#> [45] "Unknown"          "Likely Oncogenic" "Unknown"          "Unknown"         
#> [49] "Unknown"          "Unknown"

annotated_tt %>%
  select(oncokb_highest_sensitive_level) %>% 
  table()
#> oncokb_highest_sensitive_level
#> LEVEL_3B 
#>        1

You can also annotated with no tumor type data for oncogenicity. Here you’ll see the highest sensitive lever is higher than in the bladder specific annotation because it’s referring to a non bladder cancer type that has a higher level.

blca_mutation <- oncokbR::blca_mutation

annotated_no_tt <- annotate_mutations(mutations = blca_mutation[1:50,])
#> ℹ No "tumor_type" found in data. No treatment-level annotations will be returned.

annotated_no_tt$oncokb_oncogenic
#>  [1] "Unknown"          "Unknown"          "Unknown"          "Unknown"         
#>  [5] "Unknown"          "Unknown"          "Unknown"          "Unknown"         
#>  [9] "Unknown"          "Oncogenic"        "Unknown"          "Unknown"         
#> [13] "Unknown"          "Unknown"          "Unknown"          "Unknown"         
#> [17] "Unknown"          "Unknown"          "Unknown"          "Unknown"         
#> [21] "Unknown"          "Unknown"          "Unknown"          "Unknown"         
#> [25] "Unknown"          "Unknown"          "Unknown"          "Unknown"         
#> [29] "Unknown"          "Unknown"          "Unknown"          "Unknown"         
#> [33] "Unknown"          "Likely Neutral"   "Likely Neutral"   "Unknown"         
#> [37] "Unknown"          "Unknown"          "Unknown"          "Unknown"         
#> [41] "Likely Oncogenic" "Unknown"          "Unknown"          "Unknown"         
#> [45] "Unknown"          "Likely Oncogenic" "Unknown"          "Unknown"         
#> [49] "Unknown"          "Unknown"

annotated_no_tt %>%
  select(oncokb_highest_sensitive_level) %>% 
  table()
#> oncokb_highest_sensitive_level
#> LEVEL_1 
#>       1

Annotate CNA:

blca_cna <- blca_cna %>%
  mutate(tumor_type = "BLCA")


annotated <- annotate_cna(blca_cna[1:10,])
table(annotated$oncokb_oncogenic)
#> 
#> Likely Oncogenic        Oncogenic          Unknown 
#>                3                6                1

Annotate Structural Variants:

blca_sv <- blca_sv %>%
  mutate(tumor_type = "BLCA")


annotated <- annotate_sv(blca_sv[1:10,])
table(annotated$oncokb_oncogenic)
#> 
#> Likely Oncogenic          Unknown 
#>                5                5