TARGET-NBL "Column `disease_response` doesn't exist."

[hberkayc]

NBQ <- GDCquery(
project = "TARGET-NBL",
data.category = "Transcriptome Profiling",
data.type = "Gene Expression Quantification"
)

I've build and downloaded my query but I can't convert the data because of this error:

NB.data <- GDCprepare(NBQ)
|=======================================================================================================================|100% Completed after 14 s
Starting to add information to samples
Adding description to TARGET samples
=> Add clinical information to samples
Error in dplyr::select():
! Can't select columns that don't exist.
✖ Column disease_response doesn't exist.
Run rlang::last_trace() to see where the error occurred.

rlang::last_trace()
<error/vctrs_error_subscript_oob>
Error in dplyr::select():
! Can't select columns that don't exist.
✖ Column disease_response doesn't exist.

---

Backtrace:
▆

1. ├─TCGAbiolinks::GDCprepare(NBQ)
2. │ └─TCGAbiolinks:::readTranscriptomeProfiling(...)
3. │ └─TCGAbiolinks:::makeSEfromTranscriptomeProfilingSTAR(...)
4. │ └─TCGAbiolinks::colDataPrepare(cases)
5. │ └─TCGAbiolinks:::splitAPICall(...)
6. │ └─base::tryCatch(...)
7. │ └─base (local) tryCatchList(expr, classes, parentenv, handlers)
8. │ └─base (local) tryCatchOne(expr, names, parentenv, handlers[[1L]])
9. │ └─value[3L]
10. │ └─TCGAbiolinks (local) FUN(items[start:end])
11. │ └─... %>% ...
12. ├─dplyr::rename(., days_to_last_follow_up = days_to_follow_up)
13. ├─dplyr::rename_at(...)
14. │ └─dplyr:::tbl_at_vars(.tbl, .vars, .include_group_vars = TRUE)
15. │ └─dplyr::tbl_vars(tbl)
16. │ ├─dplyr:::new_sel_vars(tbl_vars_dispatch(x), group_vars(x))
17. │ │ └─base::structure(...)
18. │ └─dplyr:::tbl_vars_dispatch(x)
19. ├─dplyr::ungroup(.)
20. ├─dplyr::filter(., dplyr::row_number() == which.max(days_to_follow_up))
21. ├─dplyr::group_by(., submitter_id)
22. ├─dplyr::filter(., !is.na(submitter_id), !is.na(days_to_follow_up))
23. ├─dplyr::select(., c(submitter_id, days_to_follow_up, disease_response))
24. └─dplyr:::select.data.frame(., c(submitter_id, days_to_follow_up, disease_response))

My session info:

R version 4.5.0 (2025-04-11)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 24.04.2 LTS

Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.12.0
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0 LAPACK version 3.12.0

locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8 LC_MONETARY=en_US.UTF-8
[6] LC_MESSAGES=en_US.UTF-8 LC_PAPER=en_US.UTF-8 LC_NAME=C LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C

time zone: Europe/Istanbul
tzcode source: system (glibc)

attached base packages:
[1] parallel stats4 stats graphics grDevices utils datasets methods base

other attached packages:
[1] biomaRt_2.64.0 survminer_0.5.0
[3] ggpubr_0.6.0 viper_1.42.0
[5] pathview_1.48.0 IlluminaHumanMethylation450kanno.ilmn12.hg19_0.6.1
[7] minfi_1.54.1 bumphunter_1.50.0
[9] locfit_1.5-9.12 iterators_1.0.14
[11] foreach_1.5.2 impute_1.82.0
[13] sesame_1.26.0 pheatmap_1.0.12
[15] enrichplot_1.28.2 org.Hs.eg.db_3.21.0
[17] AnnotationDbi_1.70.0 clusterProfiler_4.16.0
[19] ggplot2_3.5.2 pryr_0.1.6
[21] dplyr_1.1.4 sesameData_1.26.0
[23] ExperimentHub_2.16.0 AnnotationHub_3.16.0
[25] BiocFileCache_2.16.0 dbplyr_2.5.0
[27] gplots_3.2.0 EDASeq_2.42.0
[29] ShortRead_1.66.0 GenomicAlignments_1.44.0
[31] Rsamtools_2.24.0 Biostrings_2.76.0
[33] XVector_0.48.0 BiocParallel_1.42.0
[35] edgeR_4.6.2 limma_3.64.0
[37] TCGAbiolinks_2.36.0 BiocManager_1.30.25
[39] DESeq2_1.48.1 SummarizedExperiment_1.38.1
[41] Biobase_2.68.0 MatrixGenerics_1.20.0
[43] matrixStats_1.5.0 GenomicRanges_1.60.0
[45] GenomeInfoDb_1.44.0 IRanges_2.42.0
[47] S4Vectors_0.46.0 BiocGenerics_0.54.0
[49] generics_0.1.3 dorothea_1.20.0

loaded via a namespace (and not attached):
[1] R.methodsS3_1.8.2 progress_1.2.3 HDF5Array_1.36.0 vctrs_0.6.5 ggtangle_0.0.6
[6] digest_0.6.37 png_0.1-8 proxy_0.4-27 ggrepel_0.9.6 deldir_2.0-4
[11] bcellViper_1.44.0 MASS_7.3-65 reshape_0.8.9 reshape2_1.4.4 qvalue_2.40.0
[16] withr_3.0.2 xfun_0.52 ggfun_0.1.8 survival_3.8-3 doRNG_1.8.6.2
[21] memoise_2.0.1 gson_0.1.0 mixtools_2.0.0.1 tidytree_0.4.6 zoo_1.8-14
[26] gtools_3.9.5 KEGGgraph_1.68.0 R.oo_1.27.1 Formula_1.2-5 prettyunits_1.2.0
[31] KEGGREST_1.48.0 httr_1.4.7 downloader_0.4.1 rstatix_0.7.2 restfulr_0.0.15
[36] rhdf5filters_1.20.0 rhdf5_2.52.0 UCSC.utils_1.4.0 DOSE_4.2.0 curl_6.2.2
[41] h5mread_1.0.0 polyclip_1.10-7 GenomeInfoDbData_1.2.14 quadprog_1.5-8 SparseArray_1.8.0
[46] xtable_1.8-4 stringr_1.5.1 evaluate_1.0.3 S4Arrays_1.8.0 preprocessCore_1.70.0
[51] hms_1.1.3 colorspace_2.1-1 filelock_1.0.3 reticulate_1.42.0 magrittr_2.0.3
[56] readr_2.1.5 Rgraphviz_2.52.0 ggtree_3.16.0 lattice_0.22-5 genefilter_1.90.0
[61] XML_3.99-0.18 cowplot_1.1.3 class_7.3-23 pillar_1.10.2 nlme_3.1-168
[66] pwalign_1.4.0 caTools_1.18.3 compiler_4.5.0 RSpectra_0.16-2 stringi_1.8.7
[71] plyr_1.8.9 crayon_1.5.3 abind_1.4-8 BiocIO_1.18.0 gridGraphics_0.5-1
[76] bit_4.6.0 fastmatch_1.1-6 codetools_0.2-20 openssl_2.3.2 e1071_1.7-16
[81] plotly_4.10.4 multtest_2.64.0 splines_4.5.0 Rcpp_1.0.14 sparseMatrixStats_1.20.0
[86] TCGAbiolinksGUI.data_1.28.0 interp_1.1-6 knitr_1.50 blob_1.2.4 BiocVersion_3.21.1
[91] fs_1.6.4 DelayedMatrixStats_1.30.0 ggsignif_0.6.4 ggplotify_0.1.2 tibble_3.2.1
[96] Matrix_1.7-3 statmod_1.5.0 tzdb_0.5.0 tweenr_2.0.3 pkgconfig_2.0.3
[101] tools_4.5.0 cachem_1.1.0 RSQLite_2.3.11 viridisLite_0.4.2 rvest_1.0.4
[106] DBI_1.2.3 fastmap_1.2.0 rmarkdown_2.29 scales_1.4.0 grid_4.5.0
[111] broom_1.0.8 patchwork_1.3.0 graph_1.86.0 carData_3.0-5 scrime_1.3.5
[116] farver_2.1.2 yaml_2.3.10 latticeExtra_0.6-30 rtracklayer_1.68.0 illuminaio_0.50.0
[121] cli_3.6.5 purrr_1.0.4 siggenes_1.82.0 GEOquery_2.76.0 lifecycle_1.0.4
[126] askpass_1.2.1 kernlab_0.9-33 backports_1.5.0 annotate_1.86.0 gtable_0.3.6
[131] rjson_0.2.23 umap_0.2.10.0 ape_5.8-1 jsonlite_2.0.0 bitops_1.0-9
[136] bit64_4.6.0-1 yulab.utils_0.2.0 base64_2.0.2 GOSemSim_2.34.0 segmented_2.1-4
[141] survMisc_0.5.6 R.utils_2.13.0 lazyeval_0.2.2 htmltools_0.5.8.1 KMsurv_0.1-5
[146] GO.db_3.21.0 rappdirs_0.3.3 glue_1.8.0 httr2_1.1.2 RCurl_1.98-1.17
[151] treeio_1.32.0 mclust_6.1.1 jpeg_0.1-11 gridExtra_2.3 igraph_2.1.4
[156] R6_2.6.1 tidyr_1.3.1 km.ci_0.5-6 GenomicFeatures_1.60.0 rngtools_1.5.2
[161] Rhdf5lib_1.30.0 beanplot_1.3.1 aplot_0.2.5 DelayedArray_0.34.1 tidyselect_1.2.1
[166] ggforce_0.4.2 xml2_1.3.8 car_3.1-3 wheatmap_0.2.0 KernSmooth_2.23-26
[171] nor1mix_1.3-3 data.table_1.17.2 htmlwidgets_1.6.4 aroma.light_3.38.0 fgsea_1.34.0
[176] RColorBrewer_1.1-3 hwriter_1.3.2.1 rlang_1.1.6 rentrez_1.2.3

[DijkJel]

I run into the same issue when trying to download TCGA-COAD.

[emma-214]

I have the exact same issue, also when trying to download TCGA-COAD. Did you find out what the problem is?

[argyro-olympitis]

Having the same issue with TCGA-GBM

Starting to add information to samples => Add clinical information to samples Error in dplyr::select(): ! Can't select columns that don't exist. ✖ Column disease_response doesn't exist.

Updating biolinks from GitHub hasn't fixed the problem.

Any update on resolving this?

[akshaydinesh26]

I have also facing this issue with TCGA-GBM. any solution ?

[franpeters]

I have met the same error but have got out a workaround to prepare the data for differential gene expression:

library(TCGAbiolinks)
library(tidyverse)

proj <- paste0("TCGA-","GBM")
query <- GDCquery(
  project = proj,
  data.category = "Transcriptome Profiling", 
  data.type = "Gene Expression Quantification",
  workflow.type = "STAR - Counts"
)
GDCdownload(query)

#this command fails so need to use a workaround
#data <- GDCprepare(query)

#workaround solution:

clinical <- GDCquery_clinic(project = "TCGA-GBM", type = "clinical")
colnames(clinical)

data <- GDCprepare(query,summarizedExperiment = F)

data_subset = data %>% dplyr::select(starts_with("gene"),starts_with("unstranded"))
rm(data)
colnames(data_subset) <- gsub("unstranded_","",colnames(data_subset))


barcodes <- colnames(data_subset)[-c(1:3)]

tumor_samples <- TCGAquery_SampleTypes(barcodes, typesample = "TP")
normal_samples <- TCGAquery_SampleTypes(barcodes, typesample = "NT")

metadata = data.frame(sample_id = c(tumor_samples,normal_samples),
                      sample_type = c(rep("Tumour",length(tumor_samples)),
                                      rep("Normal",length(normal_samples))
                                      ))

[smallboy-code]

@argyro-olympitis @akshaydinesh26 are you solve this problem?