I'm not sure if this will be helpful to you, but the "sampleMap" files in ExperimentHub (created for use by curatedTCGAData) contain the aliquot and case barcodes, and the TCGAutils package provides a utility for filtering by sample type. There's a warning in the example below because some barcodes are truncated (these get kept in the sample type filtering):

library(ExperimentHub)
eh <- ExperimentHub()
mapsEH <- query(eh, "sampleMap")
suppressMessages(maps <- lapply(seq_along(mapsEH), function(i) mapsEH[[i]]))
levels(maps[[1]]$assay)
#> [1] "ACC_RNASeq2GeneNorm-20160128"         
#> [2] "ACC_miRNASeqGene-20160128"            
#> [3] "ACC_CNASNP-20160128"                  
#> [4] "ACC_CNVSNP-20160128"                  
#> [5] "ACC_Methylation-20160128"             
#> [6] "ACC_RPPAArray-20160128"               
#> [7] "ACC_Mutation-20160128"                
#> [8] "ACC_GISTIC_AllByGene-20160128"        
#> [9] "ACC_GISTIC_ThresholdedByGene-20160128"

mapdf <- do.call(rbind, maps)
mapdf <- mapdf[grepl("Mutation|Methylation|RNASeq2GeneNorm", mapdf$assay), ]

## TCGAutils to filter by sample type
suppressPackageStartupMessages(library(TCGAutils))
TCGAutils::sampleTypes
#>    Code                                        Definition
#> 1    01                               Primary Solid Tumor
#> 2    02                             Recurrent Solid Tumor
#> 3    03   Primary Blood Derived Cancer - Peripheral Blood
#> 4    04      Recurrent Blood Derived Cancer - Bone Marrow
#> 5    05                          Additional - New Primary
#> 6    06                                        Metastatic
#> 7    07                             Additional Metastatic
#> 8    08                        Human Tumor Original Cells
#> 9    09        Primary Blood Derived Cancer - Bone Marrow
#> 10   10                              Blood Derived Normal
#> 11   11                               Solid Tissue Normal
#> 12   12                                Buccal Cell Normal
#> 13   13                           EBV Immortalized Normal
#> 14   14                                Bone Marrow Normal
#> 15   15                                    sample type 15
#> 16   16                                    sample type 16
#> 17   20                                   Control Analyte
#> 18   40 Recurrent Blood Derived Cancer - Peripheral Blood
#> 19   50                                        Cell Lines
#> 20   60                          Primary Xenograft Tissue
#> 21   61                Cell Line Derived Xenograft Tissue
#> 22   99                                    sample type 99
#>    Short.Letter.Code
#> 1                 TP
#> 2                 TR
#> 3                 TB
#> 4               TRBM
#> 5                TAP
#> 6                 TM
#> 7                TAM
#> 8               THOC
#> 9                TBM
#> 10                NB
#> 11                NT
#> 12               NBC
#> 13              NEBV
#> 14               NBM
#> 15              15SH
#> 16              16SH
#> 17             CELLC
#> 18               TRB
#> 19              CELL
#> 20                XP
#> 21               XCL
#> 22              99SH
keepers <- TCGAutils::TCGAsampleSelect(mapdf$colname, c("01", "03", "10", "11"))
#> Warning in TCGAutils::TCGAsampleSelect(mapdf$colname, c("01", "03", "10", :
#> Inconsistent barcode lengths: 28, 15
#> Warning in if (!sampleCode %in% sampleTypes[["Code"]]) stop("'sampleCode'
#> not in look up table"): the condition has length > 1 and only the first
#> element will be used
#> Selecting 'Primary Solid TumorPrimary Blood Derived Cancer - Peripheral BloodBlood Derived NormalSolid Tissue Normal' samples
#> Warning in (function (..., deparse.level = 1) : number of columns of result
#> is not a multiple of vector length (arg 19456)
#> Warning in barcodeSamples == sampleCode: longer object length is not a
#> multiple of shorter object length
mapdf <- mapdf[keepers, ]
head(mapdf)
#> DataFrame with 6 rows and 3 columns
#>                          assay      primary                      colname
#>                       <factor>  <character>                  <character>
#> 1 ACC_RNASeq2GeneNorm-20160128 TCGA-OR-A5J1 TCGA-OR-A5J1-01A-11R-A29S-07
#> 2 ACC_RNASeq2GeneNorm-20160128 TCGA-OR-A5J6 TCGA-OR-A5J6-01A-31R-A29S-07
#> 3 ACC_RNASeq2GeneNorm-20160128 TCGA-OR-A5JA TCGA-OR-A5JA-01A-11R-A29S-07
#> 4 ACC_RNASeq2GeneNorm-20160128 TCGA-OR-A5JE TCGA-OR-A5JE-01A-11R-A29S-07
#> 5 ACC_RNASeq2GeneNorm-20160128 TCGA-OR-A5JJ TCGA-OR-A5JJ-01A-11R-A29S-07
#> 6 ACC_RNASeq2GeneNorm-20160128 TCGA-OR-A5JO TCGA-OR-A5JO-01A-11R-A29S-07

Thank you very much for both your answers, that was exactly what I needed!
Best