The goal of mskcc.oncotree is to facilitate access to the OncoTree API.
The OncoTree is an open-source ontology that was developed at Memorial Sloan Kettering Cancer Center (MSK) for standardizing cancer type diagnosis from a clinical perspective by assigning each diagnosis a unique OncoTree code.
Currently, the functionality provided is the retrieval of tumor types and the mapping of tumor type codes across a few tumor type classification systems.
Installation
Install mskcc.oncotree from CRAN:
install.packages("mskcc.oncotree")You can install the development version of mskcc.oncotree with:
# install.packages("remotes")
remotes::install_github("maialab/mskcc.oncotree")OncoTree tumor types
Get the tumor types defined by OncoTree in their latest release:
library(mskcc.oncotree)
(tumor_types <- get_tumor_types())
#> # A tibble: 885 × 13
#> oncotree_version oncotree_code oncotree_name oncotree_main_t… tissue level
#> <chr> <chr> <chr> <chr> <chr> <int>
#> 1 oncotree_latest_… MMB Medullomyoblas… Embryonal Tumor CNS/B… 3
#> 2 oncotree_latest_… GCB Germinal Cente… Mature B-Cell N… Lymph… 6
#> 3 oncotree_latest_… SBLU Splenic B-Cell… Mature B-Cell N… Lymph… 5
#> 4 oncotree_latest_… OHNCA Head and Neck … Head and Neck C… Head … 2
#> 5 oncotree_latest_… PAOS Parosteal Oste… Bone Cancer Bone 3
#> 6 oncotree_latest_… TMDS Therapy-Relate… Leukemia Myelo… 5
#> 7 oncotree_latest_… ARMS Alveolar Rhabd… Soft Tissue Sar… Soft … 3
#> 8 oncotree_latest_… SCST Sex Cord Strom… Sex Cord Stroma… Ovary… 2
#> 9 oncotree_latest_… ITLPDGI Indolent T-Cel… Mature T and NK… Lymph… 5
#> 10 oncotree_latest_… MBC Metaplastic Br… Breast Cancer Breast 2
#> # … with 875 more rows, and 7 more variables: parent <chr>, umls_code <list>,
#> # nci_code <list>, history <list>, revocations <list>, precursors <list>,
#> # color <chr>The mapping to the corresponding tumor type identifier(s) in the Unified Medical Language System (UMLS) and in the National Cancer Institute (NCI) Thesaurus classification systems is provided in columns umls_code and nci_code.
tumor_types[c('oncotree_code', 'umls_code', 'nci_code')]
#> # A tibble: 885 × 3
#> oncotree_code umls_code nci_code
#> <chr> <list> <list>
#> 1 MMB <chr [1]> <chr [1]>
#> 2 GCB <NULL> <NULL>
#> 3 SBLU <NULL> <NULL>
#> 4 OHNCA <NULL> <NULL>
#> 5 PAOS <chr [1]> <chr [1]>
#> 6 TMDS <NULL> <NULL>
#> 7 ARMS <chr [1]> <chr [1]>
#> 8 SCST <chr [1]> <chr [1]>
#> 9 ITLPDGI <NULL> <NULL>
#> 10 MBC <chr [1]> <chr [1]>
#> # … with 875 more rowsumls_code and nci_code are list-columns because the mapping can be one-to-many.
If you want, you may convert these columns to type character by unnesting with tidyr::unnest(). This will have the side effect of creating more than one row per tumor type for cases where the mapping is one-to-many.
tumor_types[c('oncotree_code', 'umls_code')] %>%
tidyr::unnest(cols = 'umls_code', keep_empty = TRUE)
#> # A tibble: 886 × 2
#> oncotree_code umls_code
#> <chr> <chr>
#> 1 MMB C0205833
#> 2 GCB <NA>
#> 3 SBLU <NA>
#> 4 OHNCA <NA>
#> 5 PAOS C0206642
#> 6 TMDS <NA>
#> 7 ARMS C0206655
#> 8 SCST C0600113
#> 9 ITLPDGI <NA>
#> 10 MBC C1334708
#> # … with 876 more rowsOncoTree versions
The data provided by OncoTree is versioned. You may list the released versions so far with get_versions():
get_versions()
#> # A tibble: 28 × 4
#> oncotree_version description visible release_date
#> <chr> <chr> <lgl> <chr>
#> 1 oncotree_development Latest OncoTree under develo… TRUE 2021-11-04
#> 2 oncotree_candidate_release This version of the OncoTree… TRUE 2021-11-03
#> 3 oncotree_2021_11_02 Stable OncoTree released on … FALSE 2021-11-02
#> 4 oncotree_latest_stable This is the latest approved … TRUE 2021-11-02
#> 5 oncotree_2020_10_01 Stable OncoTree released on … FALSE 2020-10-01
#> 6 oncotree_2020_04_01 Stable OncoTree released on … FALSE 2020-04-01
#> 7 oncotree_2020_02_06 Stable OncoTree released on … FALSE 2020-02-06
#> 8 oncotree_2020_02_01 Stable OncoTree released on … FALSE 2020-02-01
#> 9 oncotree_2019_12_01 Stable OncoTree released on … FALSE 2019-12-01
#> 10 oncotree_2019_08_01 Stable OncoTree released on … FALSE 2019-08-01
#> # … with 18 more rowsThe function get_tumor_types() accepts a release version, allowing to get data for past versions of OncoTree:
get_tumor_types(oncotree_version = 'oncotree_2019_08_01')
#> # A tibble: 863 × 13
#> oncotree_version oncotree_code oncotree_name oncotree_main_t… tissue level
#> <chr> <chr> <chr> <chr> <chr> <int>
#> 1 oncotree_2019_08_01 MMB Medullomyobl… Embryonal Tumor CNS/B… 3
#> 2 oncotree_2019_08_01 GCB Germinal Cen… Mature B-Cell N… Lymph… 6
#> 3 oncotree_2019_08_01 SBLU Splenic B-Ce… Mature B-Cell N… Lymph… 5
#> 4 oncotree_2019_08_01 OHNCA Head and Nec… Head and Neck C… Head … 2
#> 5 oncotree_2019_08_01 PAOS Parosteal Os… Bone Cancer Bone 3
#> 6 oncotree_2019_08_01 TMDS Therapy-Rela… Leukemia Myelo… 5
#> 7 oncotree_2019_08_01 ARMS Alveolar Rha… Soft Tissue Sar… Soft … 3
#> 8 oncotree_2019_08_01 SCST Sex Cord Str… Sex Cord Stroma… Ovary… 2
#> 9 oncotree_2019_08_01 ITLPDGI Indolent T-C… Mature T and NK… Lymph… 5
#> 10 oncotree_2019_08_01 MBC Metaplastic … Breast Cancer Breast 2
#> # … with 853 more rows, and 7 more variables: parent <chr>, umls_code <list>,
#> # nci_code <list>, history <list>, revocations <list>, precursors <list>,
#> # color <chr>Mapping of tumor type codes
UMLS and NCIt
OncoTree provides a few mappings between their identifiers (codes) and other tumor classification systems. Currently, through their web API, only mappings between OncoTree codes and Unified Medical Language System (UMLS) and National Cancer Institute (NCI) Thesaurus (NCIt) codes are provided.
From mskcc.oncotree we provide four functions:
| Function | Description |
|---|---|
oncotree_to_nci() |
Map OncoTree to NCIt codes |
nci_to_oncotree() |
Map NCIt to OncoTree codes |
oncotree_to_umls() |
Map OncoTree to UMLS codes |
umls_to_oncotree() |
Map UMLS to OncoTree codes |
From and to NCIt
oncotree_to_nci(c('MMB', 'PAOS'), expand = TRUE)
#> # A tibble: 2 × 2
#> oncotree_code nci_code
#> <chr> <chr>
#> 1 MMB C3706
#> 2 PAOS C8969
nci_to_oncotree(c('C3706', 'C8969'), expand = TRUE)
#> # A tibble: 2 × 2
#> nci_code oncotree_code
#> <chr> <chr>
#> 1 C3706 MMB
#> 2 C8969 PAOSFrom and to UMLS
oncotree_to_umls(c('MMB', 'PAOS'), expand = TRUE)
#> # A tibble: 2 × 2
#> oncotree_code umls_code
#> <chr> <chr>
#> 1 MMB C0205833
#> 2 PAOS C0206642
umls_to_oncotree(c('C0205833', 'C0206642', 'C1334708'), expand = TRUE)
#> # A tibble: 3 × 2
#> umls_code oncotree_code
#> <chr> <chr>
#> 1 C0205833 MMB
#> 2 C0206642 PAOS
#> 3 C1334708 MBCOther mappings
Besides UMLS and NCIt, you may also map OncoTree codes to ICD-O topography and morphology, and HemeOnc codes. This functionality is based on the file ontology_mappings.txt. provided by OncoTree at their GitHub repository.
In OncoTree’s discussion forum the developers have warned that this data can’t be expected to be neither accurate nor complete. However, it might still be useful for some users, and hence we also provide access to these mappings with the function map_ontology_code().
Here are some usage examples map_ontology_code():
# Simple example
map_ontology_code(code = 'MMB', from = 'oncotree_code', to = 'nci_code')
#> # A tibble: 1 × 2
#> oncotree_code nci_code
#> <chr> <chr>
#> 1 MMB C3706
# Omit the `code` argument to get all possible mappings. Note that
# one-to-many mappings will generate more than one row per `from` code.
map_ontology_code(from = 'oncotree_code', to = 'nci_code')
#> # A tibble: 855 × 2
#> oncotree_code nci_code
#> <chr> <chr>
#> 1 MMB C3706
#> 2 AIS C4123
#> 3 AASTR C9477
#> 4 FL C3209
#> 5 VIMT C4286
#> 6 KIDNEY C12415
#> 7 MDEP C4327
#> 8 PAOS C8969
#> 9 PRSCC C6766
#> 10 DSTAD C9159
#> # … with 845 more rows
# Some mappings are one-to-many, e.g. "SRCCR", which means repeated rows for
# the same input code.
map_ontology_code(code = 'SRCCR', from = 'oncotree_code', to = 'nci_code')
#> # A tibble: 2 × 2
#> oncotree_code nci_code
#> <chr> <chr>
#> 1 SRCCR C9168
#> 2 SRCCR C7967
# Using the `collapse` argument to "collapse" one-to-many mappings makes sure
# that the output has as many rows as the `from` vector.
map_ontology_code(code = 'SRCCR',
from = 'oncotree_code',
to = 'nci_code',
collapse = toString)
#> # A tibble: 1 × 2
#> oncotree_code nci_code
#> <chr> <chr>
#> 1 SRCCR C9168, C7967
map_ontology_code(code = 'SRCCR',
from = 'oncotree_code',
to = 'nci_code',
collapse = list)
#> # A tibble: 1 × 2
#> oncotree_code nci_code
#> <chr> <list>
#> 1 SRCCR <chr [2]>
map_ontology_code(
code = 'SRCCR',
from = 'oncotree_code',
to = 'nci_code',
collapse = \(x) paste(x, collapse = ' ')
)
#> # A tibble: 1 × 2
#> oncotree_code nci_code
#> <chr> <chr>
#> 1 SRCCR C9168 C7967
# `map_ontology_code()` is vectorized over `code`
map_ontology_code(code = c('AASTR', 'MDEP'), from = 'oncotree_code', to = 'nci_code')
#> # A tibble: 2 × 2
#> oncotree_code nci_code
#> <chr> <chr>
#> 1 AASTR C9477
#> 2 MDEP C4327
# Map from ICDO topography to ICDO morphology codes
map_ontology_code(code = 'C72.9', from = 'icdo_topography_code', to = 'icdo_morphology_code')
#> # A tibble: 65 × 2
#> icdo_topography_code icdo_morphology_code
#> <chr> <chr>
#> 1 C72.9 9401/3
#> 2 C72.9 9501/3
#> 3 C72.9 9390/1
#> 4 C72.9 8000/3
#> 5 C72.9 9424/3
#> 6 C72.9 9391/3
#> 7 C72.9 9425/3
#> 8 C72.9 9383/1
#> 9 C72.9 9100/3
#> 10 C72.9 9522/3
#> # … with 55 more rows