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 4.4.2 (2024-10-31)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 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_2.23.0 jpeg_0.1-10
## [3] png_0.1-8 DT_0.33
## [5] dplyr_1.1.4 SummarizedExperiment_1.37.0
## [7] Biobase_2.67.0 GenomicRanges_1.59.1
## [9] GenomeInfoDb_1.43.1 IRanges_2.41.1
## [11] S4Vectors_0.45.2 BiocGenerics_0.53.3
## [13] generics_0.1.3 MatrixGenerics_1.19.0
## [15] matrixStats_1.4.1 TCGAbiolinks_2.35.0
## [17] testthat_3.2.1.1
##
## loaded via a namespace (and not attached):
## [1] RColorBrewer_1.1-3 sys_3.4.3
## [3] rstudioapi_0.17.1 jsonlite_1.8.9
## [5] magrittr_2.0.3 GenomicFeatures_1.59.1
## [7] rmarkdown_2.29 BiocIO_1.17.0
## [9] fs_1.6.5 zlibbioc_1.52.0
## [11] vctrs_0.6.5 Rsamtools_2.23.0
## [13] memoise_2.0.1 RCurl_1.98-1.16
## [15] htmltools_0.5.8.1 S4Arrays_1.7.1
## [17] usethis_3.0.0 progress_1.2.3
## [19] curl_6.0.1 SparseArray_1.7.2
## [21] sass_0.4.9 bslib_0.8.0
## [23] htmlwidgets_1.6.4 desc_1.4.3
## [25] plyr_1.8.9 httr2_1.0.6
## [27] cachem_1.1.0 GenomicAlignments_1.43.0
## [29] buildtools_1.0.0 mime_0.12
## [31] lifecycle_1.0.4 pkgconfig_2.0.3
## [33] Matrix_1.7-1 R6_2.5.1
## [35] fastmap_1.2.0 GenomeInfoDbData_1.2.13
## [37] shiny_1.9.1 digest_0.6.37
## [39] colorspace_2.1-1 ShortRead_1.65.0
## [41] AnnotationDbi_1.69.0 rprojroot_2.0.4
## [43] pkgload_1.4.0 crosstalk_1.2.1
## [45] RSQLite_2.3.8 hwriter_1.3.2.1
## [47] filelock_1.0.3 fansi_1.0.6
## [49] httr_1.4.7 abind_1.4-8
## [51] compiler_4.4.2 remotes_2.5.0
## [53] bit64_4.5.2 withr_3.0.2
## [55] downloader_0.4 BiocParallel_1.41.0
## [57] DBI_1.2.3 pkgbuild_1.4.5
## [59] R.utils_2.12.3 biomaRt_2.63.0
## [61] rappdirs_0.3.3 DelayedArray_0.33.2
## [63] sessioninfo_1.2.2 rjson_0.2.23
## [65] DNAcopy_1.81.0 tools_4.4.2
## [67] httpuv_1.6.15 R.oo_1.27.0
## [69] glue_1.8.0 restfulr_0.0.15
## [71] promises_1.3.0 gtable_0.3.6
## [73] tzdb_0.4.0 R.methodsS3_1.8.2
## [75] tidyr_1.3.1 data.table_1.16.2
## [77] hms_1.1.3 xml2_1.3.6
## [79] utf8_1.2.4 XVector_0.47.0
## [81] pillar_1.9.0 stringr_1.5.1
## [83] vroom_1.6.5 later_1.3.2
## [85] splines_4.4.2 BiocFileCache_2.15.0
## [87] lattice_0.22-6 deldir_2.0-4
## [89] rtracklayer_1.67.0 aroma.light_3.37.0
## [91] survival_3.7-0 bit_4.5.0
## [93] tidyselect_1.2.1 maketools_1.3.1
## [95] Biostrings_2.75.1 miniUI_0.1.1.1
## [97] knitr_1.49 xfun_0.49
## [99] devtools_2.4.5 brio_1.1.5
## [101] stringi_1.8.4 UCSC.utils_1.3.0
## [103] yaml_2.3.10 codetools_0.2-20
## [105] TCGAbiolinksGUI.data_1.26.0 evaluate_1.0.1
## [107] interp_1.1-6 EDASeq_2.41.0
## [109] tibble_3.2.1 BiocManager_1.30.25
## [111] cli_3.6.3 xtable_1.8-4
## [113] munsell_0.5.1 jquerylib_0.1.4
## [115] Rcpp_1.0.13-1 dbplyr_2.5.0
## [117] XML_3.99-0.17 parallel_4.4.2
## [119] ellipsis_0.3.2 ggplot2_3.5.1
## [121] readr_2.1.5 blob_1.2.4
## [123] prettyunits_1.2.0 latticeExtra_0.6-30
## [125] profvis_0.4.0 urlchecker_1.0.1
## [127] bitops_1.0-9 pwalign_1.3.0
## [129] scales_1.3.0 purrr_1.0.2
## [131] crayon_1.5.3 BiocStyle_2.35.0
## [133] rlang_1.1.4 KEGGREST_1.47.0
## [135] rvest_1.0.4