Contents

Installation

Please use the devel version of the AnVIL Bioconductor package.

library(cBioPortalData)
library(AnVIL)

Introduction

The cBioPortal for Cancer Genomics website is a great resource for interactive exploration of study datasets. However, it does not easily allow the analyst to obtain and further analyze the data.

We’ve developed the cBioPortalData package to fill this need to programmatically access the data resources available on the cBioPortal.

The cBioPortalData package provides an R interface for accessing the cBioPortal study data within the Bioconductor ecosystem.

It downloads study data from the cBioPortal API (https://cbioportal.org/api) and uses Bioconductor infrastructure to cache and represent the data.

We use the MultiAssayExperiment (@Ramos2017-er) package to integrate, represent, and coordinate multiple experiments for the studies availble in the cBioPortal. This package in conjunction with curatedTCGAData give access to a large trove of publicly available bioinformatic data. Please see our publication here (@Ramos2020-ya).

We demonstrate common use cases of cBioPortalData and curatedTCGAData during Bioconductor conference workshops.

Overview

This vignette is for users / developers who would like to learn more about the available data in cBioPortalData and to learn how to hit other endpoints in the cBioPortal API implementation. The functionality demonstrated here is used internally by the package to create integrative representations of study datasets.

Note. To avoid overloading the API service, the API was designed to only query a part of the study data. Therefore, the user is required to enter either a set of genes of interest or a gene panel identifier.

API representation

Obtaining the cBioPortal API representation object

(cbio <- cBioPortal())
## service: cBioPortal
## tags(); use cbioportal$<tab completion>:
## # A tibble: 61 × 3
##    tag                 operation                                  summary       
##    <chr>               <chr>                                      <chr>         
##  1 Cancer Types        getAllCancerTypesUsingGET                  Get all cance…
##  2 Cancer Types        getCancerTypeUsingGET                      Get a cancer …
##  3 Clinical Attributes fetchClinicalAttributesUsingPOST           Fetch clinica…
##  4 Clinical Attributes getAllClinicalAttributesInStudyUsingGET    Get all clini…
##  5 Clinical Attributes getAllClinicalAttributesUsingGET           Get all clini…
##  6 Clinical Attributes getClinicalAttributeInStudyUsingGET        Get specified…
##  7 Clinical Data       fetchAllClinicalDataInStudyUsingPOST       Fetch clinica…
##  8 Clinical Data       fetchClinicalDataUsingPOST                 Fetch clinica…
##  9 Clinical Data       getAllClinicalDataInStudyUsingGET          Get all clini…
## 10 Clinical Data       getAllClinicalDataOfPatientInStudyUsingGET Get all clini…
## # … with 51 more rows
## tag values:
##   Cancer Types, Clinical Attributes, Clinical Data, Copy Number
##   Segments, Discrete Copy Number Alterations, Gene Panels, Generic
##   Assays, Genes, Molecular Data, Molecular Profiles, Mutations,
##   Patients, Sample Lists, Samples, Structural Variants, Studies,
##   Treatments
## schemas():
##   AlleleSpecificCopyNumber, AlterationFilter,
##   AndedPatientTreatmentFilters, AndedSampleTreatmentFilters,
##   CancerStudy
##   # ... with 58 more elements

Operations

Check available tags, operations, and descriptions as a tibble:

tags(cbio)
## # A tibble: 61 × 3
##    tag                 operation                                  summary       
##    <chr>               <chr>                                      <chr>         
##  1 Cancer Types        getAllCancerTypesUsingGET                  Get all cance…
##  2 Cancer Types        getCancerTypeUsingGET                      Get a cancer …
##  3 Clinical Attributes fetchClinicalAttributesUsingPOST           Fetch clinica…
##  4 Clinical Attributes getAllClinicalAttributesInStudyUsingGET    Get all clini…
##  5 Clinical Attributes getAllClinicalAttributesUsingGET           Get all clini…
##  6 Clinical Attributes getClinicalAttributeInStudyUsingGET        Get specified…
##  7 Clinical Data       fetchAllClinicalDataInStudyUsingPOST       Fetch clinica…
##  8 Clinical Data       fetchClinicalDataUsingPOST                 Fetch clinica…
##  9 Clinical Data       getAllClinicalDataInStudyUsingGET          Get all clini…
## 10 Clinical Data       getAllClinicalDataOfPatientInStudyUsingGET Get all clini…
## # … with 51 more rows
head(tags(cbio)$operation)
## [1] "getAllCancerTypesUsingGET"              
## [2] "getCancerTypeUsingGET"                  
## [3] "fetchClinicalAttributesUsingPOST"       
## [4] "getAllClinicalAttributesInStudyUsingGET"
## [5] "getAllClinicalAttributesUsingGET"       
## [6] "getClinicalAttributeInStudyUsingGET"

Searching through the API

searchOps(cbio, "clinical")
## [1] "getAllClinicalAttributesUsingGET"          
## [2] "fetchClinicalAttributesUsingPOST"          
## [3] "fetchClinicalDataUsingPOST"                
## [4] "getAllClinicalAttributesInStudyUsingGET"   
## [5] "getClinicalAttributeInStudyUsingGET"       
## [6] "getAllClinicalDataInStudyUsingGET"         
## [7] "fetchAllClinicalDataInStudyUsingPOST"      
## [8] "getAllClinicalDataOfPatientInStudyUsingGET"
## [9] "getAllClinicalDataOfSampleInStudyUsingGET"

Studies

Get the list of studies available:

getStudies(cbio)
## # A tibble: 345 × 13
##    name          description publicStudy groups status importDate allSampleCount
##    <chr>         <chr>       <lgl>       <chr>   <int> <chr>               <int>
##  1 Adrenocortic… "TCGA Adre… TRUE        "PUBL…      0 2022-03-0…             92
##  2 Bladder Canc… "Whole exo… TRUE        ""          0 2022-03-0…             34
##  3 Basal Cell C… "Whole-exo… TRUE        "PUBL…      0 2022-03-0…            293
##  4 Acute Lympho… "Comprehen… TRUE        "PUBL…      0 2022-03-0…             93
##  5 Ampullary Ca… "Exome seq… TRUE        "PUBL…      0 2022-03-0…            160
##  6 Bladder Urot… "Whole exo… TRUE        "PUBL…      0 2022-03-0…             50
##  7 Bladder Canc… "Comprehen… TRUE        "PUBL…      0 2022-03-0…             97
##  8 Bladder Urot… "Whole-exo… TRUE        "PUBL…      0 2022-03-0…             99
##  9 Bladder Canc… "Genomic P… TRUE        "PUBL…      0 2022-03-0…            109
## 10 Hypodiploid … "Whole gen… TRUE        ""          0 2022-03-0…             44
## # … with 335 more rows, and 6 more variables: readPermission <lgl>,
## #   studyId <chr>, cancerTypeId <chr>, referenceGenome <chr>, pmid <chr>,
## #   citation <chr>

Clinical Data

Obtain the clinical data for a particular study:

clinicalData(cbio, "acc_tcga")
## # A tibble: 92 × 85
##    patientId    AGE   AJCC_PATHOLOGIC_TUMOR_S… ATYPICAL_MITOTI… CAPSULAR_INVASI…
##    <chr>        <chr> <chr>                    <chr>            <chr>           
##  1 TCGA-OR-A5J1 58    Stage II                 Atypical Mitoti… Invasion of Tum…
##  2 TCGA-OR-A5J2 44    Stage IV                 Atypical Mitoti… Invasion of Tum…
##  3 TCGA-OR-A5J3 23    Stage III                Atypical Mitoti… Invasion of Tum…
##  4 TCGA-OR-A5J4 23    Stage IV                 Atypical Mitoti… Invasion of Tum…
##  5 TCGA-OR-A5J5 30    Stage III                Atypical Mitoti… Invasion of Tum…
##  6 TCGA-OR-A5J6 29    Stage II                 Atypical Mitoti… Invasion of Tum…
##  7 TCGA-OR-A5J7 30    Stage III                Atypical Mitoti… Invasion of Tum…
##  8 TCGA-OR-A5J8 66    Stage III                Atypical Mitoti… Invasion of Tum…
##  9 TCGA-OR-A5J9 22    Stage II                 Atypical Mitoti… Invasion of Tum…
## 10 TCGA-OR-A5JA 53    Stage IV                 Atypical Mitoti… Invasion of Tum…
## # … with 82 more rows, and 80 more variables: CLIN_M_STAGE <chr>,
## #   CT_SCAN_PREOP_RESULTS <chr>,
## #   CYTOPLASM_PRESENCE_LESS_THAN_EQUAL_25_PERCENT <chr>,
## #   DAYS_TO_INITIAL_PATHOLOGIC_DIAGNOSIS <chr>, DFS_MONTHS <chr>,
## #   DFS_STATUS <chr>, DIFFUSE_ARCHITECTURE <chr>, ETHNICITY <chr>,
## #   FORM_COMPLETION_DATE <chr>, HISTOLOGICAL_DIAGNOSIS <chr>,
## #   HISTORY_ADRENAL_HORMONE_EXCESS <chr>, …

Molecular Profiles

A table of molecular profiles for a particular study can be obtained by running the following:

mols <- molecularProfiles(cbio, "acc_tcga")
mols[["molecularProfileId"]]
## [1] "acc_tcga_rppa"                                     
## [2] "acc_tcga_rppa_Zscores"                             
## [3] "acc_tcga_gistic"                                   
## [4] "acc_tcga_linear_CNA"                               
## [5] "acc_tcga_mutations"                                
## [6] "acc_tcga_methylation_hm450"                        
## [7] "acc_tcga_rna_seq_v2_mrna"                          
## [8] "acc_tcga_rna_seq_v2_mrna_median_Zscores"           
## [9] "acc_tcga_rna_seq_v2_mrna_median_all_sample_Zscores"

Molecular Profile Data

The data for a molecular profile can be obtained with prior knowledge of available entrezGeneIds:

molecularData(cbio, molecularProfileId = "acc_tcga_rna_seq_v2_mrna",
    entrezGeneIds = c(1, 2),
    sampleIds = c("TCGA-OR-A5J1-01",  "TCGA-OR-A5J2-01")
)
## $acc_tcga_rna_seq_v2_mrna
## # A tibble: 4 × 8
##   uniqueSampleKey        uniquePatientKey entrezGeneId molecularProfil… sampleId
##   <chr>                  <chr>                   <int> <chr>            <chr>   
## 1 VENHQS1PUi1BNUoxLTAxO… VENHQS1PUi1BNUo…            1 acc_tcga_rna_se… TCGA-OR…
## 2 VENHQS1PUi1BNUoxLTAxO… VENHQS1PUi1BNUo…            2 acc_tcga_rna_se… TCGA-OR…
## 3 VENHQS1PUi1BNUoyLTAxO… VENHQS1PUi1BNUo…            1 acc_tcga_rna_se… TCGA-OR…
## 4 VENHQS1PUi1BNUoyLTAxO… VENHQS1PUi1BNUo…            2 acc_tcga_rna_se… TCGA-OR…
## # … with 3 more variables: patientId <chr>, studyId <chr>, value <dbl>

Genes

All available genes

A list of all the genes provided by the API service including hugo symbols, and entrez gene IDs can be obtained by using the geneTable function:

geneTable(cbio)
## # A tibble: 1,000 × 3
##    entrezGeneId hugoGeneSymbol  type 
##           <int> <chr>           <chr>
##  1        -3624 MIR-10A/10A     miRNA
##  2        -3712 MIR-559/559     miRNA
##  3        -3042 MIR-4315-2/4315 miRNA
##  4        -3204 MIR-4535/4535   miRNA
##  5        -3763 MIR-607/607     miRNA
##  6        -3457 MIR-1269A/1269A miRNA
##  7        -3286 MIR-4679-1/4679 miRNA
##  8        -3295 MIR-4686/4686   miRNA
##  9        -3054 MIR-4325/4325   miRNA
## 10        -3510 MIR-124A-1/5P   miRNA
## # … with 990 more rows

Gene Panels

genePanels(cbio)
## # A tibble: 54 × 2
##    description                                                       genePanelId
##    <chr>                                                             <chr>      
##  1 Targeted (27 cancer genes) sequencing of adenoid cystic carcinom… ACYC_FMI_27
##  2 Targeted panel of 232 genes.                                      Agilent    
##  3 Targeted panel of 8 genes.                                        AmpliSeq   
##  4 ARCHER-HEME gene panel (199 genes)                                ARCHER-HEM…
##  5 ARCHER-SOLID Gene Panel (62 genes)                                ARCHER-SOL…
##  6 Targeted sequencing of various tumor types via bait v3.           bait_v3    
##  7 Targeted sequencing of various tumor types via bait v5.           bait_v5    
##  8 Targeted panel of 387 cancer-related genes.                       bcc_unige_…
##  9 Research (CMO) IMPACT-Heme gene panel version 3.                  HemePACT_v3
## 10 Targeted sequencing of 503 cancer-associated genes on Illumina H… DFCI_504   
## # … with 44 more rows
getGenePanel(cbio, "IMPACT341")
## # A tibble: 341 × 2
##    entrezGeneId hugoGeneSymbol
##           <int> <chr>         
##  1           25 ABL1          
##  2        84142 ABRAXAS1      
##  3          207 AKT1          
##  4          208 AKT2          
##  5        10000 AKT3          
##  6          238 ALK           
##  7          242 ALOX12B       
##  8       139285 AMER1         
##  9          324 APC           
## 10          367 AR            
## # … with 331 more rows

Molecular Gene Panels

genePanelMolecular

gprppa <- genePanelMolecular(cbio,
    molecularProfileId = "acc_tcga_rppa",
    sampleListId = "acc_tcga_all")
gprppa
## # A tibble: 92 × 7
##    uniqueSampleKey  uniquePatientKey molecularProfil… sampleId patientId studyId
##    <chr>            <chr>            <chr>            <chr>    <chr>     <chr>  
##  1 VENHQS1PUi1BNUo… VENHQS1PUi1BNUo… acc_tcga_rppa    TCGA-OR… TCGA-OR-… acc_tc…
##  2 VENHQS1PUi1BNUo… VENHQS1PUi1BNUo… acc_tcga_rppa    TCGA-OR… TCGA-OR-… acc_tc…
##  3 VENHQS1PUi1BNUo… VENHQS1PUi1BNUo… acc_tcga_rppa    TCGA-OR… TCGA-OR-… acc_tc…
##  4 VENHQS1PUi1BNUo… VENHQS1PUi1BNUo… acc_tcga_rppa    TCGA-OR… TCGA-OR-… acc_tc…
##  5 VENHQS1PUi1BNUo… VENHQS1PUi1BNUo… acc_tcga_rppa    TCGA-OR… TCGA-OR-… acc_tc…
##  6 VENHQS1PUi1BNUo… VENHQS1PUi1BNUo… acc_tcga_rppa    TCGA-OR… TCGA-OR-… acc_tc…
##  7 VENHQS1PUi1BNUo… VENHQS1PUi1BNUo… acc_tcga_rppa    TCGA-OR… TCGA-OR-… acc_tc…
##  8 VENHQS1PUi1BNUo… VENHQS1PUi1BNUo… acc_tcga_rppa    TCGA-OR… TCGA-OR-… acc_tc…
##  9 VENHQS1PUi1BNUo… VENHQS1PUi1BNUo… acc_tcga_rppa    TCGA-OR… TCGA-OR-… acc_tc…
## 10 VENHQS1PUi1BNUp… VENHQS1PUi1BNUp… acc_tcga_rppa    TCGA-OR… TCGA-OR-… acc_tc…
## # … with 82 more rows, and 1 more variable: profiled <lgl>

getGenePanelMolecular

getGenePanelMolecular(cbio,
    molecularProfileIds = c("acc_tcga_rppa", "acc_tcga_gistic"),
    sampleIds = allSamples(cbio, "acc_tcga")$sampleId
)
## # A tibble: 184 × 7
##    uniqueSampleKey  uniquePatientKey molecularProfil… sampleId patientId studyId
##    <chr>            <chr>            <chr>            <chr>    <chr>     <chr>  
##  1 VENHQS1PUi1BNUo… VENHQS1PUi1BNUo… acc_tcga_gistic  TCGA-OR… TCGA-OR-… acc_tc…
##  2 VENHQS1PUi1BNUo… VENHQS1PUi1BNUo… acc_tcga_gistic  TCGA-OR… TCGA-OR-… acc_tc…
##  3 VENHQS1PUi1BNUo… VENHQS1PUi1BNUo… acc_tcga_gistic  TCGA-OR… TCGA-OR-… acc_tc…
##  4 VENHQS1PUi1BNUo… VENHQS1PUi1BNUo… acc_tcga_gistic  TCGA-OR… TCGA-OR-… acc_tc…
##  5 VENHQS1PUi1BNUo… VENHQS1PUi1BNUo… acc_tcga_gistic  TCGA-OR… TCGA-OR-… acc_tc…
##  6 VENHQS1PUi1BNUo… VENHQS1PUi1BNUo… acc_tcga_gistic  TCGA-OR… TCGA-OR-… acc_tc…
##  7 VENHQS1PUi1BNUo… VENHQS1PUi1BNUo… acc_tcga_gistic  TCGA-OR… TCGA-OR-… acc_tc…
##  8 VENHQS1PUi1BNUo… VENHQS1PUi1BNUo… acc_tcga_gistic  TCGA-OR… TCGA-OR-… acc_tc…
##  9 VENHQS1PUi1BNUo… VENHQS1PUi1BNUo… acc_tcga_gistic  TCGA-OR… TCGA-OR-… acc_tc…
## 10 VENHQS1PUi1BNUp… VENHQS1PUi1BNUp… acc_tcga_gistic  TCGA-OR… TCGA-OR-… acc_tc…
## # … with 174 more rows, and 1 more variable: profiled <lgl>

getDataByGenes

getDataByGenes(cbio, "acc_tcga", genePanelId = "IMPACT341",
    molecularProfileId = "acc_tcga_rppa", sampleListId = "acc_tcga_rppa")
## $acc_tcga_rppa
## # A tibble: 2,622 × 9
##    uniqueSampleKey       uniquePatientKey entrezGeneId molecularProfil… sampleId
##    <chr>                 <chr>                   <int> <chr>            <chr>   
##  1 VENHQS1PUi1BNUoyLTAx… VENHQS1PUi1BNUo…         5728 acc_tcga_rppa    TCGA-OR…
##  2 VENHQS1PUi1BNUoyLTAx… VENHQS1PUi1BNUo…          595 acc_tcga_rppa    TCGA-OR…
##  3 VENHQS1PUi1BNUoyLTAx… VENHQS1PUi1BNUo…          596 acc_tcga_rppa    TCGA-OR…
##  4 VENHQS1PUi1BNUoyLTAx… VENHQS1PUi1BNUo…        10413 acc_tcga_rppa    TCGA-OR…
##  5 VENHQS1PUi1BNUoyLTAx… VENHQS1PUi1BNUo…         3791 acc_tcga_rppa    TCGA-OR…
##  6 VENHQS1PUi1BNUoyLTAx… VENHQS1PUi1BNUo…         7157 acc_tcga_rppa    TCGA-OR…
##  7 VENHQS1PUi1BNUoyLTAx… VENHQS1PUi1BNUo…          207 acc_tcga_rppa    TCGA-OR…
##  8 VENHQS1PUi1BNUoyLTAx… VENHQS1PUi1BNUo…          208 acc_tcga_rppa    TCGA-OR…
##  9 VENHQS1PUi1BNUoyLTAx… VENHQS1PUi1BNUo…        57521 acc_tcga_rppa    TCGA-OR…
## 10 VENHQS1PUi1BNUoyLTAx… VENHQS1PUi1BNUo…         2064 acc_tcga_rppa    TCGA-OR…
## # … with 2,612 more rows, and 4 more variables: patientId <chr>, studyId <chr>,
## #   value <dbl>, hugoGeneSymbol <chr>

It uses the getAllGenesUsingGET function from the API.

Samples

Sample List Identifiers

To display all available sample list identifiers for a particular study ID, one can use the sampleLists function:

sampleLists(cbio, "acc_tcga")
## # A tibble: 9 × 5
##   category                                name  description sampleListId studyId
##   <chr>                                   <chr> <chr>       <chr>        <chr>  
## 1 all_cases_with_mrna_rnaseq_data         Samp… Samples wi… acc_tcga_rn… acc_tc…
## 2 all_cases_in_study                      All … All sample… acc_tcga_all acc_tc…
## 3 all_cases_with_cna_data                 Samp… Samples wi… acc_tcga_cna acc_tc…
## 4 all_cases_with_mutation_and_cna_data    Samp… Samples wi… acc_tcga_cn… acc_tc…
## 5 all_cases_with_mutation_and_cna_and_mr… Comp… Samples wi… acc_tcga_3w… acc_tc…
## 6 all_cases_with_methylation_data         Samp… Samples wi… acc_tcga_me… acc_tc…
## 7 all_cases_with_methylation_data         Samp… Samples wi… acc_tcga_me… acc_tc…
## 8 all_cases_with_rppa_data                Samp… Samples pr… acc_tcga_rp… acc_tc…
## 9 all_cases_with_mutation_data            Samp… Samples wi… acc_tcga_se… acc_tc…

Sample Identifiers

One can obtain the barcodes / identifiers for each sample using a specific sample list identifier, in this case we want all the copy number alteration samples:

samplesInSampleLists(cbio, "acc_tcga_cna")
## CharacterList of length 1
## [["acc_tcga_cna"]] TCGA-OR-A5J1-01 TCGA-OR-A5J2-01 ... TCGA-PK-A5HC-01

This returns a CharacterList of all identifiers for each sample list identifier input:

samplesInSampleLists(cbio, c("acc_tcga_cna", "acc_tcga_cnaseq"))
## CharacterList of length 2
## [["acc_tcga_cna"]] TCGA-OR-A5J1-01 TCGA-OR-A5J2-01 ... TCGA-PK-A5HC-01
## [["acc_tcga_cnaseq"]] TCGA-OR-A5J1-01 TCGA-OR-A5J2-01 ... TCGA-PK-A5HC-01

All samples within a study ID

allSamples(cbio, "acc_tcga")
## # A tibble: 92 × 6
##    uniqueSampleKey        uniquePatientKey sampleType sampleId patientId studyId
##    <chr>                  <chr>            <chr>      <chr>    <chr>     <chr>  
##  1 VENHQS1PUi1BNUoxLTAxO… VENHQS1PUi1BNUo… Primary S… TCGA-OR… TCGA-OR-… acc_tc…
##  2 VENHQS1PUi1BNUoyLTAxO… VENHQS1PUi1BNUo… Primary S… TCGA-OR… TCGA-OR-… acc_tc…
##  3 VENHQS1PUi1BNUozLTAxO… VENHQS1PUi1BNUo… Primary S… TCGA-OR… TCGA-OR-… acc_tc…
##  4 VENHQS1PUi1BNUo0LTAxO… VENHQS1PUi1BNUo… Primary S… TCGA-OR… TCGA-OR-… acc_tc…
##  5 VENHQS1PUi1BNUo1LTAxO… VENHQS1PUi1BNUo… Primary S… TCGA-OR… TCGA-OR-… acc_tc…
##  6 VENHQS1PUi1BNUo2LTAxO… VENHQS1PUi1BNUo… Primary S… TCGA-OR… TCGA-OR-… acc_tc…
##  7 VENHQS1PUi1BNUo3LTAxO… VENHQS1PUi1BNUo… Primary S… TCGA-OR… TCGA-OR-… acc_tc…
##  8 VENHQS1PUi1BNUo4LTAxO… VENHQS1PUi1BNUo… Primary S… TCGA-OR… TCGA-OR-… acc_tc…
##  9 VENHQS1PUi1BNUo5LTAxO… VENHQS1PUi1BNUo… Primary S… TCGA-OR… TCGA-OR-… acc_tc…
## 10 VENHQS1PUi1BNUpBLTAxO… VENHQS1PUi1BNUp… Primary S… TCGA-OR… TCGA-OR-… acc_tc…
## # … with 82 more rows

Info on Samples

getSampleInfo(cbio, studyId = "acc_tcga",
    sampleListIds = c("acc_tcga_rppa", "acc_tcga_gistic"))
## # A tibble: 1 × 1
##   message                               
##   <chr>                                 
## 1 Sample list not found: acc_tcga_gistic

Advanced Usage

The cBioPortal API representation is not limited to the functions provided in the package. Users who wish to make use of any of the endpoints provided by the API specification should use the dollar sign $ function to access the endpoints.

First the user should see the input for a particular endpoint as detailed in the API:

cbio$getGeneUsingGET
## getGeneUsingGET 
## Get a gene 
## 
## Parameters:
##   geneId (string)
##     Entrez Gene ID or Hugo Gene Symbol e.g. 1 or A1BG

Then the user can provide such input:

(resp <- cbio$getGeneUsingGET("BRCA1"))
## Response [https://www.cbioportal.org/api/genes/BRCA1]
##   Date: 2022-06-21 20:04
##   Status: 200
##   Content-Type: application/json
##   Size: 69 B

which will require the user to ‘translate’ the response using httr::content:

httr::content(resp)
## $entrezGeneId
## [1] 672
## 
## $hugoGeneSymbol
## [1] "BRCA1"
## 
## $type
## [1] "protein-coding"

Clearing the cache

For users who wish to clear the entire cBioPortalData cache, it is recommended that they use:

unlink("~/.cache/cBioPortalData/")

sessionInfo

sessionInfo()
## R version 4.2.0 Patched (2022-06-02 r82447)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.4 LTS
## 
## Matrix products: default
## BLAS:   /home/biocbuild/bbs-3.16-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.16-bioc/R/lib/libRlapack.so
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_GB              LC_COLLATE=C              
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] stats4    stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] survminer_0.4.9             ggpubr_0.4.0               
##  [3] ggplot2_3.3.6               survival_3.3-1             
##  [5] cBioPortalData_2.9.4        MultiAssayExperiment_1.23.1
##  [7] SummarizedExperiment_1.27.1 Biobase_2.57.1             
##  [9] GenomicRanges_1.49.0        GenomeInfoDb_1.33.3        
## [11] IRanges_2.31.0              S4Vectors_0.35.1           
## [13] BiocGenerics_0.43.0         MatrixGenerics_1.9.0       
## [15] matrixStats_0.62.0          AnVIL_1.9.5                
## [17] dplyr_1.0.9                 BiocStyle_2.25.0           
## 
## loaded via a namespace (and not attached):
##   [1] backports_1.4.1           BiocFileCache_2.5.0      
##   [3] RCircos_1.2.2             splines_4.2.0            
##   [5] BiocParallel_1.31.8       TCGAutils_1.17.2         
##   [7] digest_0.6.29             htmltools_0.5.2          
##   [9] magick_2.7.3              fansi_1.0.3              
##  [11] magrittr_2.0.3            memoise_2.0.1            
##  [13] tzdb_0.3.0                limma_3.53.3             
##  [15] Biostrings_2.65.1         readr_2.1.2              
##  [17] vroom_1.5.7               prettyunits_1.1.1        
##  [19] colorspace_2.0-3          blob_1.2.3               
##  [21] rvest_1.0.2               rappdirs_0.3.3           
##  [23] xfun_0.31                 crayon_1.5.1             
##  [25] RCurl_1.98-1.7            jsonlite_1.8.0           
##  [27] RaggedExperiment_1.21.0   zoo_1.8-10               
##  [29] glue_1.6.2                GenomicDataCommons_1.21.1
##  [31] gtable_0.3.0              zlibbioc_1.43.0          
##  [33] XVector_0.37.0            DelayedArray_0.23.0      
##  [35] car_3.1-0                 abind_1.4-5              
##  [37] scales_1.2.0              futile.options_1.0.1     
##  [39] DBI_1.1.3                 rstatix_0.7.0            
##  [41] Rcpp_1.0.8.3              xtable_1.8-4             
##  [43] progress_1.2.2            gridtext_0.1.4           
##  [45] bit_4.0.4                 km.ci_0.5-6              
##  [47] httr_1.4.3                ellipsis_0.3.2           
##  [49] pkgconfig_2.0.3           XML_3.99-0.10            
##  [51] rapiclient_0.1.3          farver_2.1.0             
##  [53] sass_0.4.1                dbplyr_2.2.0             
##  [55] utf8_1.2.2                RJSONIO_1.3-1.6          
##  [57] tidyselect_1.1.2          labeling_0.4.2           
##  [59] rlang_1.0.2               AnnotationDbi_1.59.1     
##  [61] munsell_0.5.0             tools_4.2.0              
##  [63] cachem_1.0.6              cli_3.3.0                
##  [65] generics_0.1.2            RSQLite_2.2.14           
##  [67] broom_0.8.0               evaluate_0.15            
##  [69] stringr_1.4.0             fastmap_1.1.0            
##  [71] yaml_2.3.5                knitr_1.39               
##  [73] bit64_4.0.5               survMisc_0.5.6           
##  [75] purrr_0.3.4               KEGGREST_1.37.2          
##  [77] formatR_1.12              xml2_1.3.3               
##  [79] biomaRt_2.53.2            compiler_4.2.0           
##  [81] filelock_1.0.2            curl_4.3.2               
##  [83] png_0.1-7                 ggsignif_0.6.3           
##  [85] tibble_3.1.7              bslib_0.3.1              
##  [87] stringi_1.7.6             highr_0.9                
##  [89] futile.logger_1.4.3       GenomicFeatures_1.49.5   
##  [91] lattice_0.20-45           Matrix_1.4-1             
##  [93] markdown_1.1              KMsurv_0.1-5             
##  [95] RTCGAToolbox_2.27.0       vctrs_0.4.1              
##  [97] pillar_1.7.0              lifecycle_1.0.1          
##  [99] BiocManager_1.30.18       jquerylib_0.1.4          
## [101] data.table_1.14.2         bitops_1.0-7             
## [103] rtracklayer_1.57.0        R6_2.5.1                 
## [105] BiocIO_1.7.1              bookdown_0.27            
## [107] gridExtra_2.3             codetools_0.2-18         
## [109] lambda.r_1.2.4            assertthat_0.2.1         
## [111] rjson_0.2.21              withr_2.5.0              
## [113] GenomicAlignments_1.33.0  Rsamtools_2.13.3         
## [115] GenomeInfoDbData_1.2.8    parallel_4.2.0           
## [117] hms_1.1.1                 ggtext_0.1.1             
## [119] grid_4.2.0                tidyr_1.2.0              
## [121] rmarkdown_2.14            carData_3.0-5            
## [123] restfulr_0.0.15