#BiocManager::install("PharmacoGx")
library(PharmacoGx)
## 载入需要的程辑包：CoreGx
## 载入需要的程辑包：BiocGenerics
##
## 载入程辑包：'BiocGenerics'
## The following objects are masked from 'package:stats':
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## dirname, do.call, duplicated, eval, evalq, Filter, Find, get, grep,
## grepl, intersect, is.unsorted, lapply, Map, mapply, match, mget,
## order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,
## rbind, Reduce, rownames, sapply, setdiff, sort, table, tapply,
## union, unique, unsplit, which.max, which.min
## 载入需要的程辑包：SummarizedExperiment
## 载入需要的程辑包：MatrixGenerics
## 载入需要的程辑包：matrixStats
## Warning: 程辑包'matrixStats'是用R版本4.2.3 来建造的
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## 载入程辑包：'MatrixGenerics'
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## colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs,
## colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats,
## colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds,
## colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads,
## colWeightedMeans, colWeightedMedians, colWeightedSds,
## colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet,
## rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods,
## rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps,
## rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins,
## rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks,
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## 载入需要的程辑包：GenomicRanges
## 载入需要的程辑包：stats4
## 载入需要的程辑包：S4Vectors
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## 载入程辑包：'S4Vectors'
## The following objects are masked from 'package:base':
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## 载入需要的程辑包：IRanges
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## 载入程辑包：'IRanges'
## The following object is masked from 'package:grDevices':
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## windows
## 载入需要的程辑包：GenomeInfoDb
## 载入需要的程辑包：Biobase
## Welcome to Bioconductor
##
## Vignettes contain introductory material; view with
## 'browseVignettes()'. To cite Bioconductor, see
## 'citation("Biobase")', and for packages 'citation("pkgname")'.
##
## 载入程辑包：'Biobase'
## The following object is masked from 'package:MatrixGenerics':
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## rowMedians
## The following objects are masked from 'package:matrixStats':
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## anyMissing, rowMedians
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## 载入程辑包：'PharmacoGx'
## The following objects are masked from 'package:CoreGx':
##
## .parseToRoxygen, amcc, connectivityScore, cosinePerm, gwc, mcc
availablePSets()
## Dataset Name Date Created PSet Name version
## 1 GDSC 2021-12-16T19:58:28.388Z GDSC_2020(v2-8.2) 2020(v2-8.2)
## 2 FIMM 2020-06-24T14:39:26.588Z FIMM_2016 2016
## 3 Tavor 2021-03-05T17:05:08.535Z Tavor_2020 2020
## 4 NCI60 2021-08-18T16:28:45.207Z NCI60_2021 2021
## 5 UHNBreast 2020-06-24T14:39:26.588Z UHNBreast_2019 2019
## 6 GDSC 2021-12-16T19:10:35.091Z GDSC_2020(v1-8.2) 2020(v1-8.2)
## 7 PRISM 2021-08-18T16:28:45.207Z PRISM_2020 2020
## 8 BeatAML 2021-03-05T16:55:27.968Z BeatAML_2018 2018
## 9 gCSI 2021-06-11T21:58:16.390Z gCSI_2019 2019
## 10 CTRPv2 2020-06-24T14:39:26.588Z CTRPv2_2015 2015
## 11 GRAY 2021-02-23T14:39:26.588Z GRAY_2017 2017
## 12 CCLE 2020-06-24T14:39:26.588Z CCLE_2015 2015
## 13 PDTX 2022-01-07T00:00:00.000Z PDTX_2019 2019
## 14 GBM 2022-01-07T00:00:00.000Z GBM_scr2 2021
## 15 GBM 2022-01-07T00:00:00.000Z GBM_scr3 2021
## type
## 1 sensitivity
## 2 sensitivity
## 3 sensitivity
## 4 sensitivity
## 5 both
## 6 sensitivity
## 7 sensitivity
## 8 sensitivity
## 9 <NA>
## 10 sensitivity
## 11 sensitivity
## 12 sensitivity
## 13 <NA>
## 14 <NA>
## 15 <NA>
## publication
## 1 Yang, Wanjuan et al. “Genomics of Drug Sensitivity in Cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells.” Nucleic acids research vol. 41,Database issue (2013): D955-61. doi:10.1093/nar/gks1111, Iorio F, Knijnenburg TA, Vis DJ, et al. A Landscape of Pharmacogenomic Interactions in Cancer. Cell. 2016;166(3):740‐754. doi:10.1016/j.cell.2016.06.017, Garnett MJ, Edelman EJ, Heidorn SJ, et al. Systematic identification of genomic markers of drug sensitivity in cancer cells. Nature. 2012;483(7391):570‐575. Published 2012 Mar 28. doi:10.1038/nature11005, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3531057/, https://pubmed.ncbi.nlm.nih.gov/27397505/, https://pubmed.ncbi.nlm.nih.gov/22460902/
## 2 Mpindi, J., Yadav, B., <U+00D6>stling, P. et al. Consistency in drug response profiling. Nature 540, E5–E6 (2016) doi:10.1038/nature20171, https://www.nature.com/articles/nature20171
## 3 Sigal Tavor, Tali Shalit, Noa Chapal Ilani, Yoni Moskovitz, Nir Livnat, Yoram Groner, Haim Barr, Mark D. Minden, Alexander Plotnikov, Michael W. Deininger, Nathali Kaushansky, Liran I. Shlush. Dasatinib response in acute myeloid leukemia is correlated with FLT3/ITD, PTPN11 mutations and a unique gene expression signature. Haematologica 2020;105(12):2795-2804; https://doi.org/10.3324/haematol.2019.240705., https://haematologica.org/article/view/9762
## 4 Shoemaker, R. The NCI60 human tumour cell line anticancer drug screen. Nat Rev Cancer 6, 813–823 (2006). https://doi.org/10.1038/nrc1951, https://www.nature.com/articles/nrc1951
## 5 Mammoliti, Anthony et al. “Creating reproducible pharmacogenomic analysis pipelines.” Scientific data vol. 6,1 166. 3 Sep. 2019, doi:10.1038/s41597-019-0174-7, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6722117/
## 6 Yang, Wanjuan et al. “Genomics of Drug Sensitivity in Cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells.” Nucleic acids research vol. 41,Database issue (2013): D955-61. doi:10.1093/nar/gks1111, Iorio F, Knijnenburg TA, Vis DJ, et al. A Landscape of Pharmacogenomic Interactions in Cancer. Cell. 2016;166(3):740‐754. doi:10.1016/j.cell.2016.06.017, Garnett MJ, Edelman EJ, Heidorn SJ, et al. Systematic identification of genomic markers of drug sensitivity in cancer cells. Nature. 2012;483(7391):570‐575. Published 2012 Mar 28. doi:10.1038/nature11005, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3531057/, https://pubmed.ncbi.nlm.nih.gov/27397505/, https://pubmed.ncbi.nlm.nih.gov/22460902/
## 7 Corsello SM, Nagari RT, Spangler RD, et al. Discovering the anti-cancer potential of non-oncology drugs by systematic viability profiling. Nat Cancer. 2020;1(2):235-248. doi:10.1038/s43018-019-0018-6, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7328899/
## 8 Tyner, J.W., Tognon, C.E., Bottomly, D. et al. Functional genomic landscape of acute myeloid leukaemia. Nature 562, 526–531 (2018). https://doi.org/10.1038/s41586-018-0623-z, https://www.nature.com/articles/s41586-018-0623-z#citeas
## 9 Petr Smirnov, Ian Smith, Zhaleh Safikhani, Wail Ba-alawi, Farnoosh Khodakarami, Eva Lin, Yihong Yu, Scott Martin, Janosch Ortmann, Tero Aittokallio, Marc Hafner, Benjamin Haibe-Kains. Evaluation of statistical approaches for association testing in noisy drug screening data. arXiv (2021), https://arxiv.org/abs/2104.14036
## 10 Rees, Matthew G et al. “Correlating chemical sensitivity and basal gene expression reveals mechanism of action.” Nature chemical biology vol. 12,2 (2016): 109-16. doi:10.1038/nchembio.1986, Seashore-Ludlow, Brinton et al. “Harnessing Connectivity in a Large-Scale Small-Molecule Sensitivity Dataset.” Cancer discovery vol. 5,11 (2015): 1210-23. doi:10.1158/2159-8290.CD-15-0235, Basu, Amrita et al. “An interactive resource to identify cancer genetic and lineage dependencies targeted by small molecules.” Cell vol. 154,5 (2013): 1151-1161. doi:10.1016/j.cell.2013.08.003, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4718762/, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4631646/, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3954635/
## 11 Hafner, Marc et al. “Quantification of sensitivity and resistance of breast cancer cell lines to anti-cancer drugs using GR metrics.” Scientific data vol. 4 170166. 7 Nov. 2017, doi:10.1038/sdata.2017.166, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5674849/
## 12 Barretina, Jordi et al. “The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity.” Nature vol. 483,7391 603-7. 28 Mar. 2012, doi:10.1038/nature11003, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3320027/
## 13 Bruna, A. et al. (2016). A biobank of breast cancer explants with preserved intra-tumor heterogeneity to screen anticancer compounds. Cell. 167(1):260-274., https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5037319/
## 14 NULL
## 15 NULL
## DOI
## 1 10.5281/zenodo.7829919
## 2 10.5281/zenodo.7823755
## 3 10.5281/zenodo.5979590
## 4 10.5281/zenodo.7893032
## 5 10.5281/zenodo.7826860
## 6 10.5281/zenodo.7829915
## 7 10.5281/zenodo.7826864
## 8 10.5281/zenodo.7829853
## 9 10.5281/zenodo.7829857
## 10 10.5281/zenodo.7826870
## 11 10.5281/zenodo.7826847
## 12 10.5281/zenodo.3905461
## 13 10.5281/zenodo.7826875
## 14 10.5281/zenodo.7829873
## 15 10.5281/zenodo.7829873
## Download
## 1 https://zenodo.org/record/7829919/files/PSet_GDSC2020.rds?download=1
## 2 https://zenodo.org/record/7823755/files/PSet_FIMM.rds?download=1
## 3 https://zenodo.org/record/5979590/files/Tavor.rds?download=1
## 4 https://zenodo.org/record/7893032/files/PSet_NCI60.rds?download=1
## 5 https://zenodo.org/record/7826860/files/PSet_UHNBreast.rds?download=1
## 6 https://zenodo.org/record/7829915/files/PSet_GDSC2020.rds?download=1
## 7 https://zenodo.org/record/7826864/files/PSet_PRISM.rds?download=1
## 8 https://zenodo.org/record/7829853/files/PSet_BeatAML.rds?download=1
## 9 https://zenodo.org/record/7829857/files/PSet_gCSI2019.rds?download=1
## 10 https://zenodo.org/record/7826870/files/PSet_CTRPv2.rds?download=1
## 11 https://zenodo.org/record/7826847/files/PSet_GRAY2017.rds?download=1
## 12 https://zenodo.org/record/3905462/files/CCLE.rds?download=1
## 13 https://zenodo.org/record/7826875/files/PSet_PDTXBreast.rds?download=1
## 14 https://zenodo.org/record/7829873/files/PSet_GBM_scr2.rds?download=1
## 15 https://zenodo.org/record/7829873/files/PSet_GBM_scr3.rds?download=1
#?downloadPSet
GDSC <- downloadPSet("GDSC_2020(v1-8.2)")
dim(GDSC)
## Cells Drugs
## 1084 343
head(GDSC@sample)
## Sample.Name COSMIC.identifier Whole.Exome.Sequencing..WES.
## COLO 205 COLO-205 905961 Y
## NCI-H2369 H2369 1290808 Y
## NCI-H2373 H2373 1290809 Y
## NCI-H2461 H2461 1290810 Y
## NCI-H2591 H2591 1240131 Y
## NCI-H2595 H2595 1240132 Y
## Copy.Number.Alterations..CNA. Gene.Expression Methylation
## COLO 205 Y Y Y
## NCI-H2369 Y Y Y
## NCI-H2373 Y Y Y
## NCI-H2461 Y Y Y
## NCI-H2591 Y Y Y
## NCI-H2595 Y Y Y
## Drug..Response GDSC..Tissue.descriptor.1 GDSC..Tissue..descriptor.2
## COLO 205 Y large_intestine large_intestine
## NCI-H2369 Y lung mesothelioma
## NCI-H2373 Y lung mesothelioma
## NCI-H2461 Y lung mesothelioma
## NCI-H2591 Y lung mesothelioma
## NCI-H2595 Y lung mesothelioma
## Cancer.Type...matching.TCGA.label.
## COLO 205 COAD/READ
## NCI-H2369 MESO
## NCI-H2373 MESO
## NCI-H2461 MESO
## NCI-H2591 MESO
## NCI-H2595 MESO
## Microsatellite...instability.Status..MSI. Screen.Medium
## COLO 205 MSS/MSI-L R
## NCI-H2369 MSS/MSI-L R
## NCI-H2373 MSS/MSI-L R
## NCI-H2461 MSS/MSI-L R
## NCI-H2591 MSS/MSI-L R
## NCI-H2595 MSS/MSI-L R
## Growth.Properties sampleid tissueid Cellosaurus.Disease.Type
## COLO 205 Suspension COLO 205 Bowel Colon adenocarcinoma
## NCI-H2369 Adherent NCI-H2369 Pleura Pleural malignant mesothelioma
## NCI-H2373 Adherent NCI-H2373 Pleura Pleural sarcomatoid mesothelioma
## NCI-H2461 Adherent NCI-H2461 Pleura Pleural epithelioid mesothelioma
## NCI-H2591 Adherent NCI-H2591 Pleura Pleural epithelioid mesothelioma
## NCI-H2595 Adherent NCI-H2595 Pleura Pleural epithelioid mesothelioma
## Cellosaurus.Accession.id PharmacoDB.id unique.tissueid.fromstudies
## COLO 205 CVCL_0218 COLO205_221_2019 large_intestine
## NCI-H2369 CVCL_A532 H2369_420_2019 pleura
## NCI-H2373 CVCL_A533 H2373_421_2019 lung
## NCI-H2461 CVCL_A536 H2461_422_2019 pleura
## NCI-H2591 CVCL_A543 H2591_423_2019 lung
## NCI-H2595 CVCL_A545 H2595_424_2019 lung
## CellLine.Type Metastatic
## COLO 205 Cancer cell line TRUE
## NCI-H2369 Cancer cell line NA
## NCI-H2373 Cancer cell line NA
## NCI-H2461 Cancer cell line NA
## NCI-H2591 Cancer cell line NA
## NCI-H2595 Cancer cell line NA
head(GDSC@treatment)
## DRUG_ID SCREENING_SITE DRUG_NAME
## (-)-Parthenolide 89 MGH Parthenolide
## (5Z)-7-Oxozeaenol 1242 SANGER (5Z)-7-Oxozeaenol
## 5-Fluorouracil 179///1073 MGH///SANGER 5-Fluorouracil
## 681640 1046 SANGER Wee1 Inhibitor
## A-443654 86 MGH A-443654
## A-484954 409 MGH eEF2K Inhibitor, A-484954
## SYNONYMS TARGET
## (-)-Parthenolide HDAC1
## (5Z)-7-Oxozeaenol 5Z-7-Oxozeaenol, LL-Z1640-2 TAK1
## 5-Fluorouracil 5-FU Antimetabolite (DNA & RNA)
## 681640 681640, Wee1 Inhibitor WEE1, CHEK1
## A-443654 KIN001-139 AKT1, AKT2, AKT3
## A-484954 eEF2K
## TARGET_PATHWAY treatmentid
## (-)-Parthenolide Chromatin histone acetylation (-)-Parthenolide
## (5Z)-7-Oxozeaenol Other, kinases (5Z)-7-Oxozeaenol
## 5-Fluorouracil Other 5-Fluorouracil
## 681640 Cell cycle 681640
## A-443654 PI3K/MTOR signaling A-443654
## A-484954 Other, kinases A-484954
## smiles
## (-)-Parthenolide CC1=CCCC2(C(O2)C3C(CC1)C(=C)C(=O)O3)C
## (5Z)-7-Oxozeaenol <NA>
## 5-Fluorouracil C1=C(C(=O)NC(=O)N1)F
## 681640 <NA>
## A-443654 CC1=C2C=C(C=CC2=NN1)C3=CC(=CN=C3)OCC(CC4=CNC5=CC=CC=C54)N
## A-484954 CCN1C(=O)C2=CC(=C(N=C2N(C1=O)C3CC3)N)C(=O)N
## inchikey cid FDA
## (-)-Parthenolide KTEXNACQROZXEV-ZRPLFPEYSA-N 5420804 NA
## (5Z)-7-Oxozeaenol <NA> 9863776 NA
## 5-Fluorouracil GHASVSINZRGABV-UHFFFAOYSA-N 3385 NA
## 681640 <NA> NA NA
## A-443654 YWTBGJGMTBHQTM-IBGZPJMESA-N 10172943 NA
## A-484954 HWODCHXORCTEGU-UHFFFAOYSA-N 14998470 FALSE
head(GDSC@annotation)
## $name
## [1] "GDSC_v1"
##
## $dateCreated
## [1] "Tue Apr 11 22:30:47 2023"
##
## $sessionInfo
## R version 4.1.2 (2021-11-01)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Big Sur 10.16
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] parallel stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] dplyr_1.0.10 reshape2_1.4.4
## [3] data.table_1.14.2 PharmacoGx_2.6.0
## [5] CoreGx_1.6.0 SummarizedExperiment_1.24.0
## [7] Biobase_2.54.0 GenomicRanges_1.46.1
## [9] GenomeInfoDb_1.30.1 IRanges_2.28.0
## [11] S4Vectors_0.32.4 MatrixGenerics_1.6.0
## [13] matrixStats_0.62.0 BiocGenerics_0.40.0
##
## loaded via a namespace (and not attached):
## [1] fgsea_1.20.0 colorspace_2.0-3
## [3] deldir_1.0-6 ellipsis_0.3.2
## [5] htmlTable_2.4.1 lsa_0.73.3
## [7] XVector_0.34.0 base64enc_0.1-3
## [9] rstudioapi_0.14 SnowballC_0.7.0
## [11] MultiAssayExperiment_1.20.0 DT_0.25
## [13] fansi_1.0.3 splines_4.1.2
## [15] knitr_1.40 Formula_1.2-4
## [17] jsonlite_1.8.2 magicaxis_2.2.14
## [19] cluster_2.1.4 png_0.1-7
## [21] shinydashboard_0.7.2 shiny_1.7.3
## [23] mapproj_1.2.8 compiler_4.1.2
## [25] backports_1.4.1 assertthat_0.2.1
## [27] Matrix_1.5-1 fastmap_1.1.0
## [29] limma_3.50.3 cli_3.4.1
## [31] later_1.3.0 visNetwork_2.1.2
## [33] htmltools_0.5.3 tools_4.1.2
## [35] igraph_1.3.5 gtable_0.3.1
## [37] glue_1.6.2 GenomeInfoDbData_1.2.7
## [39] RANN_2.6.1 maps_3.4.0
## [41] fastmatch_1.1-3 Rcpp_1.0.9
## [43] slam_0.1-50 vctrs_0.4.2
## [45] BumpyMatrix_1.2.0 xfun_0.33
## [47] stringr_1.4.1 mime_0.12
## [49] lifecycle_1.0.2 gtools_3.9.3
## [51] zlibbioc_1.40.0 MASS_7.3-58.1
## [53] scales_1.2.1 promises_1.2.0.1
## [55] relations_0.6-12 RColorBrewer_1.1-3
## [57] sets_1.0-21 gridExtra_2.3
## [59] ggplot2_3.3.6 downloader_0.4
## [61] rpart_4.1.16 latticeExtra_0.6-30
## [63] stringi_1.7.8 NISTunits_1.0.1
## [65] plotrix_3.8-2 checkmate_2.1.0
## [67] caTools_1.18.2 BiocParallel_1.28.3
## [69] rlang_1.0.6 pkgconfig_2.0.3
## [71] bitops_1.0-7 pracma_2.4.2
## [73] lattice_0.20-45 purrr_0.3.5
## [75] htmlwidgets_1.5.4 tidyselect_1.1.2
## [77] plyr_1.8.7 magrittr_2.0.3
## [79] R6_2.5.1 gplots_3.1.3
## [81] generics_0.1.3 Hmisc_4.7-1
## [83] DelayedArray_0.20.0 DBI_1.1.3
## [85] sm_2.2-5.7.1 foreign_0.8-83
## [87] pillar_1.8.1 nnet_7.3-18
## [89] survival_3.4-0 RCurl_1.98-1.9
## [91] tibble_3.1.8 crayon_1.5.2
## [93] interp_1.1-3 KernSmooth_2.23-20
## [95] utf8_1.2.2 jpeg_0.1-9
## [97] grid_4.1.2 marray_1.72.0
## [99] piano_2.10.1 digest_0.6.29
## [101] xtable_1.8-4 httpuv_1.6.6
## [103] munsell_0.5.0 celestial_1.4.6
## [105] shinyjs_2.1.0
##
## $call
## PharmacoGx::PharmacoSet(name = paste("GDSC", version, sep = "_"),
## molecularProfiles = z, cell = cell.info, drug = drug.info,
## sensitivityInfo = sens.info, sensitivityRaw = sens.raw, sensitivityProfiles = sens.profiles,
## sensitivityN = NULL, curationDrug = curationDrug, curationCell = curationCell,
## curationTissue = curationTissue, datasetType = "sensitivity")
##
## $version
## [1] 2
#https://bioc.ism.ac.jp/packages/3.10/bioc/vignettes/PharmacoGx/inst/doc/PharmacoGx.pdf
#https://mp.weixin.qq.com/s/zCImy_TPiI-FfVnH_WtNMg
#high AUC implying high sensitivity to the drug
dose <- c(0.0025,0.008,0.025,0.08,0.25,0.8,2.53,8)
viability <- c(108.67,111,102.16,100.27,90,87,74,57)
computeAUC(dose, viability)
## Warning in CoreGx::.sanitizeInput(x = conc, y = viability, x_as_log =
## conc_as_log, : Warning: y data exceeds negative control.
## [1] 3.69127