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Getting started

Source: vignettes/pharmOncoX.Rmd
pharmOncoX.Rmd



Installation 


if (!("remotes" %in% installed.packages())) {
  install.packages("remotes")
}

remotes::install_github('sigven/pharmOncoX')
#> Using github PAT from envvar GITHUB_PAT. Use `gitcreds::gitcreds_set()` and unset GITHUB_PAT in .Renviron (or elsewhere) if you want to use the more secure git credential store instead.
#> Downloading GitHub repo sigven/pharmOncoX@HEAD
#> 
#> ── R CMD build ─────────────────────────────────────────────────────────────────
#> * checking for file ‘/tmp/RtmpKThryo/remotes1c7662b4ff63/sigven-pharmOncoX-2aa11a3/DESCRIPTION’ ... OK
#> * preparing ‘pharmOncoX’:
#> * checking DESCRIPTION meta-information ... OK
#> * checking for LF line-endings in source and make files and shell scripts
#> * checking for empty or unneeded directories
#> Omitted ‘LazyData’ from DESCRIPTION
#> * building ‘pharmOncoX_1.8.8.tar.gz’
#> Installing package into '/home/runner/work/_temp/Library'
#> (as 'lib' is unspecified)

library(pharmOncoX)
cache_dir <- tempdir()



Cancer drug classifications

Targeted agents

Plotted below are key statistics with respect to drug classification numbers for targeted and chemotherapy agents found in pharmOncoX. Existing drug classifications have been retrieved from ATC, and these have been extended with manual addition/curation, and also through the establishment of multiple novel levels in the ATC tree, particularly for targeted therapies. Note that only drugs that are indicated for cancer conditions (as harvested from the Open Targets platform) are considered in the numbers plotted below.


p_targeted_classifications

Chemotherapy agents 

p_chemo_classifications

Retrieval of drugs - examples

Get BRAF-targeted drugs, list records per indication 


drugs <- get_drugs(
  cache_dir = cache_dir,
  treatment_category =  c("targeted_therapy_classified",
                          "targeted_therapy_unclassified"),
    drug_target = c('BRAF'))
#> INFO  [2024-12-21 15:23:00] Reading from cache_dir = '/tmp/RtmpKThryo', argument force_download = F
#> INFO  [2024-12-21 15:23:00] Object 'drug_map_name' sucessfully loaded
#> INFO  [2024-12-21 15:23:00] Retrieved n = 35388 records
#> INFO  [2024-12-21 15:23:01] Reading from cache_dir = '/tmp/RtmpKThryo', argument force_download = F
#> INFO  [2024-12-21 15:23:01] Object 'drug_map_alias' sucessfully loaded
#> INFO  [2024-12-21 15:23:01] Retrieved n = 772345 records
#> INFO  [2024-12-21 15:23:01] Reading from cache_dir = '/tmp/RtmpKThryo', argument force_download = F
#> INFO  [2024-12-21 15:23:01] Object 'drug_map_basic' sucessfully loaded
#> INFO  [2024-12-21 15:23:01] Retrieved n = 35548 records
#> INFO  [2024-12-21 15:23:01] Reading from cache_dir = '/tmp/RtmpKThryo', argument force_download = F
#> INFO  [2024-12-21 15:23:01] Object 'drug_map_target' sucessfully loaded
#> INFO  [2024-12-21 15:23:01] Retrieved n = 44513 records
#> INFO  [2024-12-21 15:23:01] Reading from cache_dir = '/tmp/RtmpKThryo', argument force_download = F
#> INFO  [2024-12-21 15:23:01] Object 'drug_map_indication' sucessfully loaded
#> INFO  [2024-12-21 15:23:01] Retrieved n = 71321 records
#> INFO  [2024-12-21 15:23:04] Record set satisfying user-defined criteria: n = 325
#> INFO  [2024-12-21 15:23:04] Collapsing record set - providing output on a 'per_drug_target_indication' resolution
#> INFO  [2024-12-21 15:23:04] Final record set: n = 275 records

## Number of drug records
nrow(drugs$records)
#> [1] 275

## Column names of drug records
colnames(drugs$records)
#>  [1] "drug_id"                      "drug_name"                   
#>  [3] "drug_type"                    "molecule_chembl_id"          
#>  [5] "drug_action_type"             "drug_alias"                  
#>  [7] "nci_concept_definition"       "opentargets"                 
#>  [9] "drug_cancer_relevance"        "inhibition_moa"              
#> [11] "is_salt"                      "is_adc"                      
#> [13] "drug_blackbox_warning"        "nci_t"                       
#> [15] "target_symbol"                "target_entrezgene"           
#> [17] "target_genename"              "target_ensembl_gene_id"      
#> [19] "target_type"                  "drug_max_phase_indication"   
#> [21] "drug_approved_indication"     "drug_frac_cancer_indications"
#> [23] "drug_approved_noncancer"      "drug_n_indications"          
#> [25] "drug_year_first_approval"     "drug_max_ct_phase"           
#> [27] "disease_efo_id"               "disease_efo_label"           
#> [29] "primary_site"                 "drug_clinical_id"            
#> [31] "drug_clinical_source"         "atc_code_level1"             
#> [33] "atc_level1"                   "atc_code_level2"             
#> [35] "atc_level2"                   "atc_code_level3"             
#> [37] "atc_level3"                   "atc_treatment_category"



Get RAS-targeted drugs, list per drug target 


drugs <- get_drugs(
  cache_dir = cache_dir, 
  treatment_category =  c("targeted_therapy_classified"),
  drug_action_inhibition = T,
  output_resolution = "drug2target")$records |>
  dplyr::filter(atc_level3 == "RAS inhibitors")

drugs <- drugs |>
  dplyr::select(
    -c("drug_alias",
       "disease_main_group",
       "drug_clinical_id")) |>
  dplyr::mutate(
    disease_indication = stringr::str_replace_all(
      disease_indication, "\\|",", ")
    ) |>
  dplyr::select(
    drug_id, drug_name, drug_type, molecule_chembl_id,
    drug_action_type, target_symbol, dplyr::everything()
  )

dt_drugtable_ras_inhibitors <- DT::datatable(
  drugs,
  escape = FALSE,
  extensions = c("Buttons", "Responsive"), 
  width = "100%",
  options = list(
    buttons = c("csv", "excel"), 
    dom = "Bfrtip")
)




Get MEK inhibitors, list per drug only 


drugs <- get_drugs(
  cache_dir = cache_dir, 
  treatment_category =  c("targeted_therapy_classified"),
  drug_action_inhibition = T,
  drug_source_opentargets = T, 
  output_resolution = "drug" )$records |>
  dplyr::filter(atc_level3 == "MEK inhibitors")

drugs <- drugs |>
  dplyr::select(
    -c("drug_alias",
       "disease_main_group",
       "drug_clinical_id")) |>
  dplyr::mutate(
    disease_indication = stringr::str_replace_all(
      disease_indication, "\\|",", ")
    )

dt_drugtable_mek_inhibitors <- DT::datatable(
  drugs,
  escape = FALSE,
  extensions = c("Buttons", "Responsive"), 
  width = "100%",
  options = list(
    buttons = c("csv", "excel"), 
    dom = "Bfrtip")
)




Get immune checkpoint inhibitors, list per drug target 


drugs <- get_drugs(
  cache_dir = cache_dir, 
  treatment_category =  c("targeted_therapy_classified"),
  drug_source_opentargets = F,
  drug_classified_cancer = T,
   output_resolution = "drug2target")

drugs$records <- drugs$records |>
  dplyr::filter(
    (!is.na(atc_level3) & 
       (atc_level3 == "PD-1/PDL-1 inhibitors" |
         atc_level3 == "Other ICIs - LAG3 inhibitors" | 
         atc_level3 == "Other ICIs - TIGIT inhibitors" | 
         atc_level3 == "Other ICIs - CTLA4 inhibitors" |
     atc_level3 == "Other ICIs")
  )) |>
  dplyr::select(
    -c("drug_alias",
       "disease_main_group",
       "drug_clinical_id")) |>
  dplyr::mutate(
    disease_indication = stringr::str_replace_all(
      disease_indication, "\\|",", ")
    ) |>
  dplyr::select(
    drug_id,
    drug_name,
    drug_type,
    target_symbol,
    target_genename,
    dplyr::everything()
  )

dt_drugtable_ici <- DT::datatable(
  drugs$records,
  escape = FALSE,
  extensions = c("Buttons", "Responsive"), 
  width = "100%",
  options = list(
    buttons = c("csv", "excel"), 
    dom = "Bfrtip")
)




Get immune checkpoint inhibitors indicated for lung cancer conditions, list per drug-target entry 


drugs <- get_drugs(
  cache_dir = cache_dir, 
  output_resolution = "drug2target", 
  treatment_category = c("targeted_therapy_classified"),
  drug_source_opentargets = T,
  drug_indication_main = "Lung")

drugs$records <- drugs$records |>
  dplyr::filter(
    (!is.na(atc_level3) & 
      (atc_level3 == "PD-1/PDL-1 inhibitors" |
         atc_level3 == "Other ICIs - LAG3 inhibitors" | 
         atc_level3 == "Other ICIs - TIGIT inhibitors" | 
         atc_level3 == "Other ICIs - CTLA4 inhibitors" |
     atc_level3 == "Other ICIs")
  )) |>
  dplyr::select(
    -c("drug_alias",
       "disease_main_group",
       "drug_clinical_id")) |>
  dplyr::mutate(
    disease_indication = stringr::str_replace_all(
      disease_indication, "\\|",", ")
    ) |>
  dplyr::select(
    drug_id,
    drug_name,
    drug_type,
    target_symbol,
    target_genename,
    dplyr::everything()
  )

dt_drugtable_ici_lung <- DT::datatable(
  drugs$records,
  escape = FALSE,
  extensions = c("Buttons", "Responsive"), 
  width = "100%",
  options = list(
    buttons = c("csv", "excel"), 
    dom = "Bfrtip")
)




Get antimetabolite drugs 


drugs <- get_drugs(
  cache_dir = cache_dir, 
  treatment_category = c("chemo_therapy_classified"),
  output_resolution = "drug")
#> INFO  [2024-12-21 15:23:24] Reading from cache_dir = '/tmp/RtmpKThryo', argument force_download = F
#> INFO  [2024-12-21 15:23:24] Object 'drug_map_name' sucessfully loaded
#> INFO  [2024-12-21 15:23:24] Retrieved n = 35388 records
#> INFO  [2024-12-21 15:23:25] Reading from cache_dir = '/tmp/RtmpKThryo', argument force_download = F
#> INFO  [2024-12-21 15:23:25] Object 'drug_map_alias' sucessfully loaded
#> INFO  [2024-12-21 15:23:25] Retrieved n = 772345 records
#> INFO  [2024-12-21 15:23:25] Reading from cache_dir = '/tmp/RtmpKThryo', argument force_download = F
#> INFO  [2024-12-21 15:23:25] Object 'drug_map_basic' sucessfully loaded
#> INFO  [2024-12-21 15:23:25] Retrieved n = 35548 records
#> INFO  [2024-12-21 15:23:25] Reading from cache_dir = '/tmp/RtmpKThryo', argument force_download = F
#> INFO  [2024-12-21 15:23:25] Object 'drug_map_target' sucessfully loaded
#> INFO  [2024-12-21 15:23:25] Retrieved n = 44513 records
#> INFO  [2024-12-21 15:23:25] Reading from cache_dir = '/tmp/RtmpKThryo', argument force_download = F
#> INFO  [2024-12-21 15:23:25] Object 'drug_map_indication' sucessfully loaded
#> INFO  [2024-12-21 15:23:25] Retrieved n = 71321 records
#> INFO  [2024-12-21 15:23:28] Record set satisfying user-defined criteria: n = 18087
#> INFO  [2024-12-21 15:23:28] Collapsing record set - providing output on a 'per_drug' resolution
#> INFO  [2024-12-21 15:23:28] Final record set: n = 191 records

drugs$records <- drugs$records |>
  dplyr::filter(
    !is.na(atc_level2) &
    stringr::str_detect(
      atc_level2, "ANTIMETABOLITES"
    )
  ) |>
  dplyr::select(
    -c("drug_alias",
       "disease_main_group",
       "drug_clinical_id")) |>
  dplyr::mutate(
    disease_indication = stringr::str_replace_all(
      disease_indication, "\\|",", ")
    )

dt_drugtable_metabolites <- DT::datatable(
  drugs$records,
  escape = FALSE,
  extensions = c("Buttons", "Responsive"), 
  width = "100%",
  options = list(
    buttons = c("csv", "excel"), 
    dom = "Bfrtip")
)




Get taxanes 


drugs <- get_drugs(
  cache_dir = cache_dir, 
  treatment_category = "chemo_therapy_classified",
  output_resolution = "drug")
#> INFO  [2024-12-21 15:23:28] Reading from cache_dir = '/tmp/RtmpKThryo', argument force_download = F
#> INFO  [2024-12-21 15:23:28] Object 'drug_map_name' sucessfully loaded
#> INFO  [2024-12-21 15:23:28] Retrieved n = 35388 records
#> INFO  [2024-12-21 15:23:29] Reading from cache_dir = '/tmp/RtmpKThryo', argument force_download = F
#> INFO  [2024-12-21 15:23:29] Object 'drug_map_alias' sucessfully loaded
#> INFO  [2024-12-21 15:23:29] Retrieved n = 772345 records
#> INFO  [2024-12-21 15:23:29] Reading from cache_dir = '/tmp/RtmpKThryo', argument force_download = F
#> INFO  [2024-12-21 15:23:29] Object 'drug_map_basic' sucessfully loaded
#> INFO  [2024-12-21 15:23:29] Retrieved n = 35548 records
#> INFO  [2024-12-21 15:23:29] Reading from cache_dir = '/tmp/RtmpKThryo', argument force_download = F
#> INFO  [2024-12-21 15:23:29] Object 'drug_map_target' sucessfully loaded
#> INFO  [2024-12-21 15:23:29] Retrieved n = 44513 records
#> INFO  [2024-12-21 15:23:29] Reading from cache_dir = '/tmp/RtmpKThryo', argument force_download = F
#> INFO  [2024-12-21 15:23:29] Object 'drug_map_indication' sucessfully loaded
#> INFO  [2024-12-21 15:23:29] Retrieved n = 71321 records
#> INFO  [2024-12-21 15:23:32] Record set satisfying user-defined criteria: n = 18087
#> INFO  [2024-12-21 15:23:32] Collapsing record set - providing output on a 'per_drug' resolution
#> INFO  [2024-12-21 15:23:32] Final record set: n = 191 records

drugs$records <- drugs$records |>
  dplyr::filter(
    stringr::str_detect(
      atc_level3, "Taxanes"
    )
  ) |>
  dplyr::select(
    -c("drug_alias",
       "disease_main_group",
       "drug_clinical_id")) |>
  dplyr::mutate(
    disease_indication = stringr::str_replace_all(
      disease_indication, "\\|",", ")
    )

dt_drugtable_taxanes <- DT::datatable(
  drugs$records,
  escape = FALSE,
  extensions = c("Buttons", "Responsive"), 
  width = "100%",
  options = list(
    buttons = c("csv", "excel"), 
    dom = "Bfrtip")
)




Get platinum compounds 


drugs <- get_drugs(
  cache_dir = cache_dir, 
  treatment_category = "chemo_therapy_classified",
  output_resolution = "drug")
#> INFO  [2024-12-21 15:23:32] Reading from cache_dir = '/tmp/RtmpKThryo', argument force_download = F
#> INFO  [2024-12-21 15:23:32] Object 'drug_map_name' sucessfully loaded
#> INFO  [2024-12-21 15:23:32] Retrieved n = 35388 records
#> INFO  [2024-12-21 15:23:33] Reading from cache_dir = '/tmp/RtmpKThryo', argument force_download = F
#> INFO  [2024-12-21 15:23:33] Object 'drug_map_alias' sucessfully loaded
#> INFO  [2024-12-21 15:23:33] Retrieved n = 772345 records
#> INFO  [2024-12-21 15:23:33] Reading from cache_dir = '/tmp/RtmpKThryo', argument force_download = F
#> INFO  [2024-12-21 15:23:33] Object 'drug_map_basic' sucessfully loaded
#> INFO  [2024-12-21 15:23:33] Retrieved n = 35548 records
#> INFO  [2024-12-21 15:23:33] Reading from cache_dir = '/tmp/RtmpKThryo', argument force_download = F
#> INFO  [2024-12-21 15:23:33] Object 'drug_map_target' sucessfully loaded
#> INFO  [2024-12-21 15:23:33] Retrieved n = 44513 records
#> INFO  [2024-12-21 15:23:33] Reading from cache_dir = '/tmp/RtmpKThryo', argument force_download = F
#> INFO  [2024-12-21 15:23:33] Object 'drug_map_indication' sucessfully loaded
#> INFO  [2024-12-21 15:23:33] Retrieved n = 71321 records
#> INFO  [2024-12-21 15:23:36] Record set satisfying user-defined criteria: n = 18087
#> INFO  [2024-12-21 15:23:36] Collapsing record set - providing output on a 'per_drug' resolution
#> INFO  [2024-12-21 15:23:36] Final record set: n = 191 records

drugs$records <- drugs$records |>
  dplyr::filter(
    stringr::str_detect(
      atc_level3, "Platinum compounds"
    )
  ) |>
  dplyr::select(
    -c("drug_alias",
       "disease_main_group",
       "drug_clinical_id")) |>
  dplyr::mutate(
    disease_indication = stringr::str_replace_all(
      disease_indication, "\\|",", ")
    ) |>
  dplyr::select(
    drug_id, drug_name, drug_type, molecule_chembl_id,
    drug_action_type, opentargets, dplyr::everything()
  )

dt_drugtable_platins <- DT::datatable(
  drugs$records,
  escape = FALSE,
  extensions = c("Buttons", "Responsive"), 
  width = "100%",
  options = list(
    buttons = c("csv", "excel"), 
    dom = "Bfrtip")
)




Retrieval of biomarkers

Reported associations between BRCA1/2 alterations and drug sensitivity

  • Get evidence from CIViC and CGI for cancer drug sensitivity of BRCA1/2 alterations (somatically (tumor) or inherited/germline)

biomarkers <- get_biomarkers(
  cache_dir = cache_dir)
#> INFO  [2024-12-21 15:23:36] Downloading remote dataset from Google Drive to cache_dir
#> INFO  [2024-12-21 15:23:40] Reading from cache_dir = ' (/tmp/RtmpKThryo'), argument force_download = F
#> INFO  [2024-12-21 15:23:40] Object 'biomarkers' sucessfully loaded
#> INFO  [2024-12-21 15:23:40] md5 checksum is valid: 6160ea68ee037495aa4d0ed9d475f023

brca1_biomarkers <- list()
for(source in c('civic','cgi')){
  brca1_biomarkers[[source]] <- 
    biomarkers$data[[source]]$variant |> 
    dplyr::filter(
      !is.na(symbol) & (symbol == "BRCA1" | symbol == "BRCA2")) |>
    dplyr::group_by(variant_id, variant_name_primary, variant_consequence) |>
    dplyr::summarise(
      variant_alias = paste(variant_alias, collapse=", "), 
      .groups = "drop") |> 
    dplyr::inner_join(
      biomarkers$data[[source]]$clinical, by = "variant_id") |>
    dplyr::select(
      variant_id, variant_name_primary, therapeutic_context,
      evidence_type, evidence_level, 
      biomarker_source, biomarker_source_datestamp,
      molecular_profile_name, evidence_id, variant_origin,
      primary_site, evidence_id, source_id,
      evidence_url, 
      evidence_description, 
      clinical_significance) |> 
    dplyr::distinct() |> 
    dplyr::rename(literature_id = source_id,
                  variant_name = variant_name_primary) |>
    dplyr::filter(evidence_type == "Predictive") |>
    dplyr::select(
      variant_name, 
      primary_site,
      therapeutic_context,
      molecular_profile_name, 
      evidence_level,
      dplyr::everything()
    )
}

brca1_biomarkers_all <- 
  dplyr::bind_rows(brca1_biomarkers[['civic']],
                   brca1_biomarkers[['cgi']]) |>
    dplyr::arrange(evidence_level)
  

dt_brca1_biomarkers <- DT::datatable(
  brca1_biomarkers_all,
  escape = FALSE,
  extensions = c("Buttons", "Responsive"), 
  width = "100%",
  options = list(
    buttons = c("csv", "excel"), 
    dom = "Bfrtip")
)




Session Info 

# set eval = FALSE if you don't want this info (useful for reproducibility) 
# to appear
sessionInfo()
#> R version 4.4.2 (2024-10-31)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.1 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     
#> 
#> other attached packages:
#> [1] pharmOncoX_1.8.8
#> 
#> loaded via a namespace (and not attached):
#>  [1] sass_0.4.9        generics_0.1.3    stringi_1.8.4     digest_0.6.37    
#>  [5] magrittr_2.0.3    evaluate_1.0.1    grid_4.4.2        fastmap_1.2.0    
#>  [9] jsonlite_1.8.9    processx_3.8.4    pkgbuild_1.4.5    googledrive_2.1.1
#> [13] ps_1.8.1          httr_1.4.7        purrr_1.0.2       crosstalk_1.2.1  
#> [17] scales_1.3.0      textshaping_0.4.1 jquerylib_0.1.4   cli_3.6.3        
#> [21] rlang_1.1.4       crayon_1.5.3      munsell_0.5.1     remotes_2.5.0    
#> [25] withr_3.0.2       cachem_1.1.0      yaml_2.3.10       tools_4.4.2      
#> [29] gargle_1.5.2      dplyr_1.1.4       colorspace_2.1-1  ggplot2_3.5.1    
#> [33] DT_0.33           curl_6.0.1        assertthat_0.2.1  vctrs_0.6.5      
#> [37] R6_2.5.1          lifecycle_1.0.4   stringr_1.5.1     fs_1.6.5         
#> [41] htmlwidgets_1.6.4 ragg_1.3.3        pkgconfig_2.0.3   desc_1.4.3       
#> [45] callr_3.7.6       pkgdown_2.1.1     pillar_1.10.0     bslib_0.8.0      
#> [49] gtable_0.3.6      glue_1.8.0        lgr_0.4.4         systemfonts_1.1.0
#> [53] xfun_0.49         tibble_3.2.1      tidyselect_1.2.1  knitr_1.49       
#> [57] farver_2.1.2      htmltools_0.5.8.1 rmarkdown_2.29    compiler_4.4.2



References

Griffith, Malachi, Nicholas C Spies, Kilannin Krysiak, Joshua F McMichael, Adam C Coffman, Arpad M Danos, Benjamin J Ainscough, et al. 2017. “CIViC Is a Community Knowledgebase for Expert Crowdsourcing the Clinical Interpretation of Variants in Cancer.” Nat. Genet. 49 (2): 170–74. http://dx.doi.org/10.1038/ng.3774.
Kim, Sunghwan, Jie Chen, Tiejun Cheng, Asta Gindulyte, Jia He, Siqian He, Qingliang Li, et al. 2021. “PubChem in 2021: New Data Content and Improved Web Interfaces.” Nucleic Acids Res. 49 (D1): D1388–95. http://dx.doi.org/10.1093/nar/gkaa971.
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