if (!("remotes" %in% installed.packages())) {
install.packages("remotes")
}
remotes::install_github('sigven/phenOncoX')Installation
Core OncoTree terms
This shows how to retrieve cancer phenotype terms in, as defined in OncoTree (max tree depth = 2)
## load the data
download_dir <- tempdir()
oncotree <- phenOncoX::get_tree(
cache_dir = download_dir, max_tree_depth = 2)
## Number of records
nrow(oncotree$records)[1] 265
## Show metadata for underlying resources
oncotree$metadata source source_description
1 OncoTree A cancer classification system for precision oncology
source_url
1 http://oncotree.mskcc.org/#/home
source_citation source_version
1 Kundra et al., JCO Clin Cancer Inform, 2021; 33625877 2025_10_03
source_abbreviation source_license
1 oncotree CC BY 4.0
source_license_url
1 https://creativecommons.org/licenses/by/4.0/Cancer phenotype terms
## get all cancer phenotype terms - OncoTree-expanded
oncoterms <- phenOncoX::get_terms(
cache_dir = download_dir)
## Number of records
nrow(oncoterms$records)[1] 27694Term statistics per primary tumor type/tissue
## get all cancer phenotype terms
oncoterms <- phenOncoX::get_terms(
cache_dir = download_dir)
## Number of records
as.data.frame(oncoterms$records |>
dplyr::filter(!is.na(primary_site)) |>
dplyr::group_by(primary_site) |>
dplyr::summarise(num_terms = dplyr::n(),
.groups = "drop") |>
dplyr::arrange(dplyr::desc(num_terms))) primary_site num_terms
1 Lymphoid 4362
2 Soft Tissue 3061
3 Head and Neck 2608
4 CNS/Brain 2475
5 Myeloid 1955
6 Skin 1885
7 Lung 1197
8 Colon/Rectum 1122
9 Ovary/Fallopian Tube 938
10 Breast 821
11 Esophagus/Stomach 758
12 Bone 563
13 Bladder/Urinary Tract 497
14 Uterus 486
15 Liver 478
16 Kidney 474
17 Peripheral Nervous System 469
18 Pancreas 368
19 Biliary Tract 357
20 Cervix 288
21 Eye 274
22 Vulva/Vagina 269
23 Thyroid 259
24 Testis 243
25 Prostate 227
26 Other/Unknown 187
27 Pleura 135
28 Peritoneum 110
29 Thymus 95
30 Adrenal Gland 94
31 Penis 64
32 Ampulla of Vater 47Terms relevant for prostate cancer
## get all oncoterms for prostate cancer
oncoterms_prostate <- phenOncoX::get_terms(
cache_dir = download_dir,
site = "Prostate")
# ## Make as datatable
# prostate_terms_table <- DT::datatable(
# dplyr::select(
# oncoterms_prostate$records,
# primary_site, cui, cui_name,
# dplyr::everything()),
# escape = FALSE,
# extensions = c("Buttons", "Responsive"),
# width = "100%",
# options = list(
# buttons = c("csv", "excel"),
# dom = "Bfrtip"))
prostate_terms_table <- reactable::reactable(
dplyr::select(
oncoterms_prostate$records,
primary_site, cui, cui_name,
dplyr::everything()),
filterable = TRUE,
sortable = TRUE,
striped = TRUE,
compact = TRUE,
searchable = TRUE,
defaultPageSize = 10
#downloadable = TRUE
)Session Info
R version 4.6.1 (2026-06-24)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 24.04.4 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so; LAPACK version 3.12.0
locale:
[1] LC_CTYPE=C.UTF-8 LC_NUMERIC=C LC_TIME=C.UTF-8
[4] LC_COLLATE=C.UTF-8 LC_MONETARY=C.UTF-8 LC_MESSAGES=C.UTF-8
[7] LC_PAPER=C.UTF-8 LC_NAME=C LC_ADDRESS=C
[10] LC_TELEPHONE=C LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C
time zone: UTC
tzcode source: system (glibc)
attached base packages:
[1] stats graphics grDevices utils datasets methods base
loaded via a namespace (and not attached):
[1] jsonlite_2.0.0 dplyr_1.2.1 compiler_4.6.1 crayon_1.5.3
[5] tidyselect_1.2.1 phenOncoX_1.2.3 yaml_2.3.12 fastmap_1.2.0
[9] R6_2.6.1 generics_0.1.4 curl_7.1.0 knitr_1.51
[13] htmlwidgets_1.6.4 tibble_3.3.1 reactable_0.4.5 pillar_1.11.1
[17] rlang_1.3.0 lgr_0.5.2 reactR_0.6.1 xfun_0.59
[21] fs_2.1.0 otel_0.2.0 cli_3.6.6 withr_3.0.3
[25] magrittr_2.0.5 crosstalk_1.2.2 digest_0.6.39 lifecycle_1.0.5
[29] vctrs_0.7.3 evaluate_1.0.5 gargle_1.6.1 glue_1.8.1
[33] googledrive_2.1.2 rmarkdown_2.31 purrr_1.2.2 httr_1.4.8
[37] tools_4.6.1 pkgconfig_2.0.3 htmltools_0.5.9 References
Huang, Kuan-Lin, R Jay Mashl, Yige Wu, et al. 2018. “Pathogenic Germline Variants in 10,389 Adult Cancers.” Cell 173 (2): 355–370.e14. http://dx.doi.org/10.1016/j.cell.2018.03.039.
Kundra, Ritika, Hongxin Zhang, Robert Sheridan, et al. 2021. “OncoTree: A Cancer Classification System for Precision Oncology.” JCO Clin Cancer Inform 5 (February): 221–30. http://dx.doi.org/10.1200/CCI.20.00108.
Louden, Diana Nelson. 2020. “MedGen: NCBI’s Portal to Information on Medical Conditions with a Genetic Component.” Med. Ref. Serv. Q. 39 (2): 183–91. https://doi.org/10.1080/02763869.2020.1726152.
Malone, James, Ele Holloway, Tomasz Adamusiak, et al. 2010. “Modeling Sample Variables with an Experimental Factor Ontology.” Bioinformatics 26 (8): 1112–18. http://dx.doi.org/10.1093/bioinformatics/btq099.
Schriml, Lynn Marie, Cesar Arze, Suvarna Nadendla, et al. 2012. “Disease Ontology: A Backbone for Disease Semantic Integration.” Nucleic Acids Res. 40 (Database issue): D940–6. http://dx.doi.org/10.1093/nar/gkr972.