In sigven/oncoPharmaDB: Molecularly targeted cancer drugs and biomarkers
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
Installation
if (!("remotes" %in% installed.packages())) {
install.packages("remotes")
}
remotes::install_github('sigven/pharmOncoX')
library(pharmOncoX)
cache_dir <- tempdir()
Cancer drug classifications
Targeted agents
Plotted below are key statistics with respect to drug classification
numbers for targeted and chemotherapy agents found in pharmOncoX. Existing drug classifications have been retrieved from ATC, and these have been extended with manual addition/curation, and also through the establishment of multiple novel levels in the ATC tree, particularly for targeted therapies. Note that only drugs that are indicated for cancer conditions (as harvested from the Open Targets platform) are considered in the numbers plotted below.
drugs <- get_drugs(
cache_dir = cache_dir,
treatment_category = c("targeted_therapy_classified",
"hormone_therapy_classified"),
output_resolution = "drug",
drug_action_inhibition = T, drug_source_opentargets = T)
drug_classifications <-
drugs$records |>
dplyr::select(drug_name, atc_level3) |>
dplyr::mutate(
atc_level3 = stringr::str_replace_all(
atc_level3,
"\\|(Antineovascularisation agents|Selective immunosuppressants)",
""
)
) |>
dplyr::group_by(atc_level3) |>
dplyr::summarise(n = dplyr::n()) |>
dplyr::mutate(pct = round(n/sum(n) * 100, digits = 2)) |>
dplyr::arrange(dplyr::desc(pct)) |>
dplyr::filter(n >= 2)
p_targeted_classifications <- ggplot2::ggplot(
data = drug_classifications, ggplot2::aes(x = reorder(atc_level3, pct), y = pct)) +
ggplot2::geom_bar(stat = "identity",fill="steelblue") +
ggplot2::theme_classic() +
ggplot2::coord_flip() +
ggplot2::ylab("Percent of targeted agents") +
ggplot2::xlab("") +
ggplot2::scale_y_continuous(breaks=seq(0,12,2)) +
ggplot2::geom_text(
ggplot2::aes(label=n), hjust=1.4, vjust=0.50, color="white", size=4) +
ggplot2::theme(
legend.title = ggplot2::element_blank(),
axis.text.x = ggplot2::element_text(size = 14, vjust = 0.5),
legend.text = ggplot2::element_text(family = "Helvetica", size = 14),
axis.text.y = ggplot2::element_text(family = "Helvetica", size = 13),
axis.title.x = ggplot2::element_text(family = "Helvetica", size = 14),
axis.title.y = ggplot2::element_text(family = "Helvetica", size = 14)
)
p_targeted_classifications
Chemotherapy agents
drugs <- get_drugs(
cache_dir = cache_dir,
treatment_category = c("chemo_therapy_classified"),
output_resolution = "drug",drug_classified_cancer = T,
drug_source_opentargets = T)
drug_classifications_chemo <-
drugs$records |>
dplyr::select(drug_name, atc_level3) |>
dplyr::group_by(atc_level3) |>
dplyr::summarise(n = dplyr::n()) |>
dplyr::mutate(pct = round(n/sum(n) * 100, digits = 2)) |>
dplyr::arrange(dplyr::desc(pct))
p_chemo_classifications <- ggplot2::ggplot(
data = drug_classifications_chemo, ggplot2::aes(
x = reorder(atc_level3, pct), y = pct)) +
ggplot2::geom_bar(stat = "identity",fill="#E69F00") +
ggplot2::theme_classic() +
ggplot2::coord_flip() +
ggplot2::ylab("Percent of chemotherapy agents") +
ggplot2::xlab("") +
ggplot2::scale_y_continuous(breaks=seq(0,30,5)) +
ggplot2::geom_text(ggplot2::aes(label=n),
hjust=1.4, vjust=0.50, color="white", size=4) +
ggplot2::theme(
legend.title = ggplot2::element_blank(),
axis.text.x = ggplot2::element_text(size = 14, vjust = 0.5),
legend.text = ggplot2::element_text(family = "Helvetica", size = 14),
axis.text.y = ggplot2::element_text(family = "Helvetica", size = 13),
axis.title.x = ggplot2::element_text(family = "Helvetica", size = 14),
axis.title.y = ggplot2::element_text(family = "Helvetica", size = 14)
)
p_chemo_classifications
Retrieval of drugs - examples
Get BRAF-targeted drugs, list records per indication
drugs <- get_drugs(
cache_dir = cache_dir,
treatment_category = c("targeted_therapy_classified",
"targeted_therapy_unclassified"),
drug_target = c('BRAF'))
## Number of drug records
nrow(drugs$records)
## Column names of drug records
colnames(drugs$records)
Get RAS-targeted drugs, list per drug target
drugs <- get_drugs(
cache_dir = cache_dir,
treatment_category = c("targeted_therapy_classified"),
drug_action_inhibition = T,
output_resolution = "drug2target")$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")
)
dt_drugtable_ras_inhibitors
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" )$records |>
dplyr::filter(atc_level3 == "MEK inhibitors")
drugs <- drugs |>
dplyr::select(
-c("drug_alias",
"disease_main_group",
"drug_clinical_id")) |>
dplyr::mutate(
disease_indication = stringr::str_replace_all(
disease_indication, "\\|",", ")
)
dt_drugtable_mek_inhibitors <- DT::datatable(
drugs,
escape = FALSE,
extensions = c("Buttons", "Responsive"),
width = "100%",
options = list(
buttons = c("csv", "excel"),
dom = "Bfrtip")
)
dt_drugtable_mek_inhibitors
Get immune checkpoint inhibitors, list per drug target
drugs <- get_drugs(
cache_dir = cache_dir,
treatment_category = c("targeted_therapy_classified"),
drug_source_opentargets = F,
drug_classified_cancer = T,
output_resolution = "drug2target")
drugs$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")
)
dt_drugtable_ici
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$records,
escape = FALSE,
extensions = c("Buttons", "Responsive"),
width = "100%",
options = list(
buttons = c("csv", "excel"),
dom = "Bfrtip")
)
dt_drugtable_ici_lung
Get antimetabolite drugs
drugs <- get_drugs(
cache_dir = cache_dir,
treatment_category = c("chemo_therapy_classified"),
output_resolution = "drug")
drugs$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")
)
dt_drugtable_metabolites
Get taxanes
drugs <- get_drugs(
cache_dir = cache_dir,
treatment_category = "chemo_therapy_classified",
output_resolution = "drug")
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$records,
escape = FALSE,
extensions = c("Buttons", "Responsive"),
width = "100%",
options = list(
buttons = c("csv", "excel"),
dom = "Bfrtip")
)
dt_drugtable_taxanes
Get platinum compounds
drugs <- get_drugs(
cache_dir = cache_dir,
treatment_category = "chemo_therapy_classified",
output_resolution = "drug")
drugs$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")
)
dt_drugtable_platins
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)
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")
)
dt_brca1_biomarkers
Session Info
\vspace{5pt}
\footnotesize
# set eval = FALSE if you don't want this info (useful for reproducibility)
# to appear
sessionInfo()
\normalsize
References
sigven/oncoPharmaDB documentation built on Dec. 24, 2024, 9:46 p.m.
R Package Documentation
Browse R Packages
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
knitr::opts_chunk$set( collapse = TRUE, comment = "#>" )
Installation
if (!("remotes" %in% installed.packages())) { install.packages("remotes") } remotes::install_github('sigven/pharmOncoX') library(pharmOncoX) cache_dir <- tempdir()
Cancer drug classifications
Targeted agents
Plotted below are key statistics with respect to drug classification numbers for targeted and chemotherapy agents found in pharmOncoX. Existing drug classifications have been retrieved from ATC, and these have been extended with manual addition/curation, and also through the establishment of multiple novel levels in the ATC tree, particularly for targeted therapies. Note that only drugs that are indicated for cancer conditions (as harvested from the Open Targets platform) are considered in the numbers plotted below.
drugs <- get_drugs( cache_dir = cache_dir, treatment_category = c("targeted_therapy_classified", "hormone_therapy_classified"), output_resolution = "drug", drug_action_inhibition = T, drug_source_opentargets = T) drug_classifications <- drugs$records |> dplyr::select(drug_name, atc_level3) |> dplyr::mutate( atc_level3 = stringr::str_replace_all( atc_level3, "\\|(Antineovascularisation agents|Selective immunosuppressants)", "" ) ) |> dplyr::group_by(atc_level3) |> dplyr::summarise(n = dplyr::n()) |> dplyr::mutate(pct = round(n/sum(n) * 100, digits = 2)) |> dplyr::arrange(dplyr::desc(pct)) |> dplyr::filter(n >= 2) p_targeted_classifications <- ggplot2::ggplot( data = drug_classifications, ggplot2::aes(x = reorder(atc_level3, pct), y = pct)) + ggplot2::geom_bar(stat = "identity",fill="steelblue") + ggplot2::theme_classic() + ggplot2::coord_flip() + ggplot2::ylab("Percent of targeted agents") + ggplot2::xlab("") + ggplot2::scale_y_continuous(breaks=seq(0,12,2)) + ggplot2::geom_text( ggplot2::aes(label=n), hjust=1.4, vjust=0.50, color="white", size=4) + ggplot2::theme( legend.title = ggplot2::element_blank(), axis.text.x = ggplot2::element_text(size = 14, vjust = 0.5), legend.text = ggplot2::element_text(family = "Helvetica", size = 14), axis.text.y = ggplot2::element_text(family = "Helvetica", size = 13), axis.title.x = ggplot2::element_text(family = "Helvetica", size = 14), axis.title.y = ggplot2::element_text(family = "Helvetica", size = 14) )
p_targeted_classifications
Chemotherapy agents
drugs <- get_drugs( cache_dir = cache_dir, treatment_category = c("chemo_therapy_classified"), output_resolution = "drug",drug_classified_cancer = T, drug_source_opentargets = T) drug_classifications_chemo <- drugs$records |> dplyr::select(drug_name, atc_level3) |> dplyr::group_by(atc_level3) |> dplyr::summarise(n = dplyr::n()) |> dplyr::mutate(pct = round(n/sum(n) * 100, digits = 2)) |> dplyr::arrange(dplyr::desc(pct)) p_chemo_classifications <- ggplot2::ggplot( data = drug_classifications_chemo, ggplot2::aes( x = reorder(atc_level3, pct), y = pct)) + ggplot2::geom_bar(stat = "identity",fill="#E69F00") + ggplot2::theme_classic() + ggplot2::coord_flip() + ggplot2::ylab("Percent of chemotherapy agents") + ggplot2::xlab("") + ggplot2::scale_y_continuous(breaks=seq(0,30,5)) + ggplot2::geom_text(ggplot2::aes(label=n), hjust=1.4, vjust=0.50, color="white", size=4) + ggplot2::theme( legend.title = ggplot2::element_blank(), axis.text.x = ggplot2::element_text(size = 14, vjust = 0.5), legend.text = ggplot2::element_text(family = "Helvetica", size = 14), axis.text.y = ggplot2::element_text(family = "Helvetica", size = 13), axis.title.x = ggplot2::element_text(family = "Helvetica", size = 14), axis.title.y = ggplot2::element_text(family = "Helvetica", size = 14) )
p_chemo_classifications
Retrieval of drugs - examples
Get BRAF-targeted drugs, list records per indication
drugs <- get_drugs( cache_dir = cache_dir, treatment_category = c("targeted_therapy_classified", "targeted_therapy_unclassified"), drug_target = c('BRAF')) ## Number of drug records nrow(drugs$records) ## Column names of drug records colnames(drugs$records)
Get RAS-targeted drugs, list per drug target
drugs <- get_drugs( cache_dir = cache_dir, treatment_category = c("targeted_therapy_classified"), drug_action_inhibition = T, output_resolution = "drug2target")$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") )
dt_drugtable_ras_inhibitors
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" )$records |> dplyr::filter(atc_level3 == "MEK inhibitors") drugs <- drugs |> dplyr::select( -c("drug_alias", "disease_main_group", "drug_clinical_id")) |> dplyr::mutate( disease_indication = stringr::str_replace_all( disease_indication, "\\|",", ") ) dt_drugtable_mek_inhibitors <- DT::datatable( drugs, escape = FALSE, extensions = c("Buttons", "Responsive"), width = "100%", options = list( buttons = c("csv", "excel"), dom = "Bfrtip") )
dt_drugtable_mek_inhibitors
Get immune checkpoint inhibitors, list per drug target
drugs <- get_drugs( cache_dir = cache_dir, treatment_category = c("targeted_therapy_classified"), drug_source_opentargets = F, drug_classified_cancer = T, output_resolution = "drug2target") drugs$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") )
dt_drugtable_ici
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$records, escape = FALSE, extensions = c("Buttons", "Responsive"), width = "100%", options = list( buttons = c("csv", "excel"), dom = "Bfrtip") )
dt_drugtable_ici_lung
Get antimetabolite drugs
drugs <- get_drugs( cache_dir = cache_dir, treatment_category = c("chemo_therapy_classified"), output_resolution = "drug") drugs$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") )
dt_drugtable_metabolites
Get taxanes
drugs <- get_drugs( cache_dir = cache_dir, treatment_category = "chemo_therapy_classified", output_resolution = "drug") 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$records, escape = FALSE, extensions = c("Buttons", "Responsive"), width = "100%", options = list( buttons = c("csv", "excel"), dom = "Bfrtip") )
dt_drugtable_taxanes
Get platinum compounds
drugs <- get_drugs( cache_dir = cache_dir, treatment_category = "chemo_therapy_classified", output_resolution = "drug") drugs$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") )
dt_drugtable_platins
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) 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") )
dt_brca1_biomarkers
Session Info
\vspace{5pt} \footnotesize
# set eval = FALSE if you don't want this info (useful for reproducibility) # to appear sessionInfo()
\normalsize
References
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