TCGAbiolinks retrieved molecular
subtypes information from TCGA samples. The functions
PanCancerAtlas_subtypes
and TCGAquery_subtype
can be used to get the information tables.
While the PanCancerAtlas_subtypes
function gives access
to a curated table retrieved from synapse (probably with the most
updated molecular subtypes) the TCGAquery_subtype
function
has the complete table also with sample information retrieved from the
TCGA marker papers.
PanCancerAtlas_subtypes
: Curated molecular
subtypes.Data and description retrieved from synapse (https://www.synapse.org/#!Synapse:syn8402849)
Synapse has published a single file with all available molecular
subtypes that have been described by TCGA (all tumor types and all
molecular platforms), which can be accessed using the
PanCancerAtlas_subtypes
function as below:
subtypes <- PanCancerAtlas_subtypes()
DT::datatable(
data = subtypes,
filter = 'top',
options = list(scrollX = TRUE, keys = TRUE, pageLength = 5),
rownames = FALSE
)
The columns “Subtype_Selected” was selected as most prominent subtype classification (from the other columns)
All available molecular data based-subtype | Selected subtype | Number of samples | Link to file | Reference | link to paper | |
---|---|---|---|---|---|---|
ACC | mRNA, DNAmeth, protein, miRNA, CNA, COC, C1A.C1B | DNAmeth | 91 | Link | Cancer Cell 2016 | Link |
AML | mRNA and miRNA | mRNA | 187 | Link | NEJM 2013 | Link |
BLCA | mRNA subtypes | mRNA | 129 | Link | Nature 2014 | Link |
BRCA | PAM50 (mRNA) | PAM50 | 1218 | Link | Nature 2012 | Link |
GBM/LGG* | mRNA, DNAmeth, protein, Supervised_DNAmeth | Supervised_DNAmeth | 1122 | Link | Cell 2016 | Link |
Pan-GI (preliminary) ESCA/STAD/COAD/READ | Molecular_Subtype | Molecular_Subtype | 1011 | Link | Cancer Cell 2018 | Link |
HNSC | mRNA, DNAmeth, RPPA, miRNA, CNA, Paradigm | mRNA | 279 | Link (TabS7.2) | Nature 2015 | Link |
KICH | Eosinophilic | Eosinophilic | 66 | Link | Cancer Cell 2014 | Link |
KIRC | mRNA, miRNA | mRNA | 442 | Link | Nature 2013 | Link |
KIRP | mRNA, DNAmeth, protein, miRNA, CNA, COC | COC | 161 | Link | NEJM 2015 | Link |
LIHC (preliminary) | mRNA, DNAmeth, protein, miRNA, CNA, Paradigma, iCluster | iCluster | 196 | Link (Table S1A) | not published | |
LUAD | DNAmeth, iCluster | iCluster | 230 | Link (Table S7) | Nature 2014 | Link |
LUSC | mRNA | mRNA | 178 | Link (Data file S7.5) | Nature 2012 | Link |
OVCA | mRNA | mRNA | 489 | Link | Nature 2011 | Link |
PCPG | mRNA, DNAmeth, protein, miRNA, CNA | mRNA | 178 | tableS2 | Cancer Cell 2017 | Link |
PRAD | mRNA, DNAmeth, protein, miRNA, CNA, icluster, mutation/fusion | mutation/fusion | 333 | Link | Cell 2015 | Link |
SKCM | mRNA, DNAmeth, protein, miRNA, mutation | mutation | 331 | Link (Table S1D) | Cell 2015 | Link |
THCA | mRNA, DNAmeth, protein, miRNA, CNA, histology | mRNA | 496 | Link (Table S2 - Tab1) | Cell 2014 | Link |
UCEC | iCluster, MSI, CNA, mRNA | iCluster - updated according to Pan-Gyne/Pathways groups | 538 | Link (datafile S1.1) | Nature 2013 | Link |
Link | ||||||
UCS (preliminary) | mRNA | mRNA | 57 | Link | not published |
TCGAquery_subtype
: Working with molecular subtypes
data.The Cancer Genome Atlas (TCGA) Research Network has reported integrated genome-wide studies of various diseases. We have added some of the subtypes defined by these report in our package:
TCGA dataset | Link | Paper | Journal |
---|---|---|---|
ACC | doi:10.1016/j.ccell.2016.04.002 | Comprehensive Pan-Genomic Characterization of Adrenocortical Carcinoma. | Cancer cell 2016 |
BRCA | https://www.cell.com/cancer-cell/fulltext/S1535-6108(18)30119-3 | A Comprehensive Pan-Cancer Molecular Study of Gynecologic and Breast Cancers | Cancer cell 2018 |
BLCA | http://www.cell.com/cell/fulltext/S0092-8674(17)31056-5 | Comprehensive Molecular Characterization of Muscle-Invasive Bladder Cancer Cell 2017 | |
CHOL | http://www.sciencedirect.com/science/article/pii/S2211124717302140?via%3Dihub | Integrative Genomic Analysis of Cholangiocarcinoma Identifies Distinct IDH-Mutant Molecular Profiles | Cell Reports 2017 |
COAD | http://www.nature.com/nature/journal/v487/n7407/abs/nature11252.html | Comprehensive molecular characterization of human colon and rectal cancer | Nature 2012 |
ESCA | https://www.nature.com/articles/nature20805 | Integrated genomic characterization of oesophageal carcinoma | Nature 2017 |
GBM | http://dx.doi.org/10.1016/j.cell.2015.12.028 | Molecular Profiling Reveals Biologically Discrete Subsets and Pathways of Progression in Diffuse Glioma | Cell 2016 |
HNSC | http://www.nature.com/nature/journal/v517/n7536/abs/nature14129.html | Comprehensive genomic characterization of head and neck squamous cell carcinomas | Nature 2015 |
KICH | http://www.sciencedirect.com/science/article/pii/S1535610814003043 | The Somatic Genomic Landscape of Chromophobe Renal Cell Carcinoma | Cancer cell 2014 |
KIRC | http://www.nature.com/nature/journal/v499/n7456/abs/nature12222.html | Comprehensive molecular characterization of clear cell renal cell carcinoma | Nature 2013 |
KIRP | http://www.nejm.org/doi/full/10.1056/NEJMoa1505917 | Comprehensive Molecular Characterization of Papillary Renal-Cell Carcinoma | NEJM 2016 |
LIHC | http://linkinghub.elsevier.com/retrieve/pii/S0092-8674(17)30639-6 | Comprehensive and Integrative Genomic Characterization of Hepatocellular Carcinoma | Cell 2017 |
LGG | http://dx.doi.org/10.1016/j.cell.2015.12.028 | Molecular Profiling Reveals Biologically Discrete Subsets and Pathways of Progression in Diffuse Glioma | Cell 2016 |
LUAD | http://www.nature.com/nature/journal/v511/n7511/abs/nature13385.html | Comprehensive molecular profiling of lung adenocarcinoma | Nature 2014 |
LUSC | http://www.nature.com/nature/journal/v489/n7417/abs/nature11404.html | Comprehensive genomic characterization of squamous cell lung cancers | Nature 2012 |
PAAD | http://www.cell.com/cancer-cell/fulltext/S1535-6108(17)30299-4 | Integrated Genomic Characterization of Pancreatic Ductal Adenocarcinoma | Cancer Cell 2017 |
PCPG | http://dx.doi.org/10.1016/j.ccell.2017.01.001 | Comprehensive Molecular Characterization of Pheochromocytoma and Paraganglioma | Cancer cell 2017 |
PRAD | http://www.sciencedirect.com/science/article/pii/S0092867415013392 | The Molecular Taxonomy of Primary Prostate Cancer | Cell 2015 |
READ | http://www.nature.com/nature/journal/v487/n7407/abs/nature11252.html | Comprehensive molecular characterization of human colon and rectal cancer | Nature 2012 |
SARC | http://www.cell.com/cell/fulltext/S0092-8674(17)31203-5 | Comprehensive and Integrated Genomic Characterization of Adult Soft Tissue Sarcomas | Cell 2017 |
SKCM | http://www.sciencedirect.com/science/article/pii/S0092867415006340 | Genomic Classification of Cutaneous Melanoma | Cell 2015 |
STAD | http://www.nature.com/nature/journal/v511/n7511/abs/nature13385.html | Comprehensive molecular characterization of gastric adenocarcinoma | Nature 2013 |
THCA | http://www.sciencedirect.com/science/article/pii/S0092867414012380 | Integrated Genomic Characterization of Papillary Thyroid Carcinoma | Cell 2014 |
UCEC | http://www.nature.com/nature/journal/v497/n7447/abs/nature12113.html | Integrated genomic characterization of endometrial carcinoma | Nature 2013 |
UCS | http://www.cell.com/cancer-cell/fulltext/S1535-6108(17)30053-3 | Integrated Molecular Characterization of Uterine Carcinosarcoma Cancer | Cell 2017 |
UVM | http://www.cell.com/cancer-cell/fulltext/S1535-6108(17)30295-7 | Integrative Analysis Identifies Four Molecular and Clinical Subsets in Uveal Melanoma | Cancer Cell 2017 |
These subtypes will be automatically added in the
summarizedExperiment object through GDCprepare. But you can also use the
TCGAquery_subtype
function to retrieve this
information.
## lgg subtype information from:doi:10.1016/j.cell.2015.12.028
A subset of the LGG subytpe is shown below:
## R version 4.4.1 (2024-06-14)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04 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=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 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
##
## time zone: Etc/UTC
## tzcode source: system (glibc)
##
## attached base packages:
## [1] grid stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] maftools_2.21.0 jpeg_0.1-10
## [3] png_0.1-8 DT_0.33
## [5] dplyr_1.1.4 SummarizedExperiment_1.35.1
## [7] Biobase_2.65.1 GenomicRanges_1.57.1
## [9] GenomeInfoDb_1.41.1 IRanges_2.39.2
## [11] S4Vectors_0.43.2 BiocGenerics_0.51.0
## [13] MatrixGenerics_1.17.0 matrixStats_1.3.0
## [15] TCGAbiolinks_2.33.0 testthat_3.2.1.1
##
## loaded via a namespace (and not attached):
## [1] RColorBrewer_1.1-3 sys_3.4.2
## [3] rstudioapi_0.16.0 jsonlite_1.8.8
## [5] magrittr_2.0.3 GenomicFeatures_1.57.0
## [7] rmarkdown_2.28 BiocIO_1.15.2
## [9] fs_1.6.4 zlibbioc_1.51.1
## [11] vctrs_0.6.5 Rsamtools_2.21.1
## [13] memoise_2.0.1 RCurl_1.98-1.16
## [15] htmltools_0.5.8.1 S4Arrays_1.5.7
## [17] usethis_3.0.0 progress_1.2.3
## [19] curl_5.2.2 SparseArray_1.5.31
## [21] sass_0.4.9 bslib_0.8.0
## [23] htmlwidgets_1.6.4 desc_1.4.3
## [25] plyr_1.8.9 httr2_1.0.3
## [27] cachem_1.1.0 GenomicAlignments_1.41.0
## [29] buildtools_1.0.0 mime_0.12
## [31] lifecycle_1.0.4 pkgconfig_2.0.3
## [33] Matrix_1.7-0 R6_2.5.1
## [35] fastmap_1.2.0 GenomeInfoDbData_1.2.12
## [37] shiny_1.9.1 digest_0.6.37
## [39] colorspace_2.1-1 ShortRead_1.63.1
## [41] AnnotationDbi_1.67.0 rprojroot_2.0.4
## [43] pkgload_1.4.0 crosstalk_1.2.1
## [45] RSQLite_2.3.7 hwriter_1.3.2.1
## [47] filelock_1.0.3 fansi_1.0.6
## [49] httr_1.4.7 abind_1.4-5
## [51] compiler_4.4.1 remotes_2.5.0
## [53] bit64_4.0.5 withr_3.0.1
## [55] downloader_0.4 BiocParallel_1.39.0
## [57] DBI_1.2.3 pkgbuild_1.4.4
## [59] highr_0.11 R.utils_2.12.3
## [61] biomaRt_2.61.3 rappdirs_0.3.3
## [63] DelayedArray_0.31.11 sessioninfo_1.2.2
## [65] rjson_0.2.22 DNAcopy_1.79.0
## [67] tools_4.4.1 httpuv_1.6.15
## [69] R.oo_1.26.0 glue_1.7.0
## [71] restfulr_0.0.15 promises_1.3.0
## [73] generics_0.1.3 gtable_0.3.5
## [75] tzdb_0.4.0 R.methodsS3_1.8.2
## [77] tidyr_1.3.1 data.table_1.16.0
## [79] hms_1.1.3 xml2_1.3.6
## [81] utf8_1.2.4 XVector_0.45.0
## [83] pillar_1.9.0 stringr_1.5.1
## [85] vroom_1.6.5 later_1.3.2
## [87] splines_4.4.1 BiocFileCache_2.13.0
## [89] lattice_0.22-6 deldir_2.0-4
## [91] rtracklayer_1.65.0 aroma.light_3.35.0
## [93] survival_3.7-0 bit_4.0.5
## [95] tidyselect_1.2.1 maketools_1.3.0
## [97] Biostrings_2.73.1 miniUI_0.1.1.1
## [99] knitr_1.48 xfun_0.47
## [101] devtools_2.4.5 brio_1.1.5
## [103] stringi_1.8.4 UCSC.utils_1.1.0
## [105] yaml_2.3.10 codetools_0.2-20
## [107] TCGAbiolinksGUI.data_1.25.0 evaluate_0.24.0
## [109] interp_1.1-6 EDASeq_2.39.0
## [111] tibble_3.2.1 BiocManager_1.30.25
## [113] cli_3.6.3 xtable_1.8-4
## [115] munsell_0.5.1 jquerylib_0.1.4
## [117] Rcpp_1.0.13 dbplyr_2.5.0
## [119] parallel_4.4.1 XML_3.99-0.17
## [121] ellipsis_0.3.2 ggplot2_3.5.1
## [123] readr_2.1.5 blob_1.2.4
## [125] prettyunits_1.2.0 latticeExtra_0.6-30
## [127] profvis_0.3.8 urlchecker_1.0.1
## [129] bitops_1.0-8 pwalign_1.1.0
## [131] scales_1.3.0 purrr_1.0.2
## [133] crayon_1.5.3 BiocStyle_2.33.1
## [135] rlang_1.1.4 KEGGREST_1.45.1
## [137] rvest_1.0.4