BloodCancerMultiOmics2017 - data overview
Prerequisites
Introduction
Primary tumor samples from blood cancer patients underwent functional and molecular characterization. BloodCancerMultiOmics2017 includes the resulting preprocessed data. A quick overview of the available data is provided below. For the details on experimental settings please refer to:
S Dietrich*, M Oleś*, J Lu* et al. Drug-perturbation-based
stratification of blood cancer
J. Clin. Invest.
(2018); 128(1):427–445. doi:10.1172/JCI93801.
* equal contribution
Data overview
Load all of the available data.
data("conctab", "drpar", "lpdAll", "patmeta", "day23rep", "drugs",
"methData", "validateExp", "dds", "exprTreat", "mutCOM",
"cytokineViab")The data sets are objects of different classes
(data.frame, ExpressionSet,
NChannelSet, RangedSummarizedExperiment,
DESeqDataSet), and include data for either all studied
patient samples or only a subset of these. The overview below shortly
describes and summarizes the data available. Please note that the
presence of a given patient sample ID within the data set doesn’t
necessarily mean that the data is available for this sample (the slot
could be filled with NAs).
Patient samples per data set.
samplesPerData = list(
drpar = colnames(drpar),
lpdAll = colnames(lpdAll),
day23rep = colnames(day23rep),
methData = colnames(methData),
patmeta = rownames(patmeta),
validateExp = unique(validateExp$patientID),
dds = colData(dds)$PatID,
exprTreat = unique(pData(exprTreat)$PatientID),
mutCOM = rownames(mutCOM),
cytokineViab = unique(cytokineViab$Patient)
)List of all samples present in data sets.
## [1] "H001" "H002" "H003" "H005" "H006" "H007" "H008" "H009" "H010" "H011"
## [11] "H012" "H013" "H014" "H015" "H016" "H017" "H018" "H019" "H020" "H021"
## [21] "H022" "H023" "H024" "H025" "H026" "H027" "H028" "H029" "H030" "H031"
## [31] "H032" "H033" "H035" "H036" "H037" "H038" "H039" "H040" "H041" "H042"
## [41] "H043" "H044" "H045" "H046" "H047" "H048" "H049" "H050" "H051" "H053"
## [51] "H054" "H055" "H056" "H057" "H058" "H059" "H060" "H062" "H063" "H064"
## [61] "H065" "H066" "H067" "H069" "H070" "H071" "H072" "H073" "H074" "H075"
## [71] "H076" "H077" "H078" "H079" "H080" "H081" "H082" "H083" "H084" "H085"
## [81] "H086" "H087" "H088" "H089" "H090" "H092" "H093" "H094" "H095" "H096"
## [91] "H097" "H098" "H099" "H100" "H101" "H102" "H103" "H104" "H105" "H106"
## [101] "H107" "H108" "H109" "H110" "H111" "H112" "H113" "H114" "H115" "H116"
## [111] "H117" "H118" "H119" "H120" "H121" "H122" "H126" "H127" "H128" "H133"
## [121] "H134" "H135" "H136" "H137" "H140" "H141" "H142" "H143" "H144" "H145"
## [131] "H146" "H147" "H148" "H149" "H150" "H151" "H152" "H153" "H154" "H155"
## [141] "H156" "H157" "H158" "H159" "H160" "H161" "H162" "H163" "H164" "H165"
## [151] "H166" "H167" "H168" "H169" "H170" "H171" "H172" "H173" "H174" "H175"
## [161] "H176" "H177" "H178" "H179" "H180" "H181" "H182" "H183" "H184" "H185"
## [171] "H186" "H187" "H188" "H189" "H190" "H191" "H192" "H193" "H194" "H195"
## [181] "H196" "H197" "H198" "H199" "H200" "H201" "H202" "H203" "H204" "H205"
## [191] "H206" "H207" "H208" "H209" "H210" "H211" "H212" "H213" "H214" "H215"
## [201] "H216" "H217" "H218" "H219" "H220" "H221" "H222" "H223" "H224" "H225"
## [211] "H226" "H227" "H228" "H229" "H230" "H231" "H232" "H233" "H234" "H235"
## [221] "H236" "H237" "H238" "H239" "H240" "H241" "H242" "H243" "H244" "H245"
## [231] "H246" "H247" "H248" "H249" "H250" "H251" "H252" "H253" "H254" "H255"
## [241] "H256" "H257" "H258" "H259" "H260" "H261" "H262" "H263" "H264" "H265"
## [251] "H266" "H267" "H268" "H269" "H270" "H271" "H272" "H273" "H274" "H275"
## [261] "H276" "H277" "H278" "H279" "H280" "H281" "H282" "H283" "MNC5" "MNC6"
## [271] "MNC7"Total number of samples.
## [1] 271A plot summarizing the presence of a given patient sample within each
data set.
The classification below stratifies data sets according to different types of experiments performed and included. Please refer to the manual for a more detailed information on the content of these data objects.
Patient metadata
Patient metadata is provided in the patmeta object.
##
## CLL T-PLL MCL MZL AML LPL B-PLL HCL
## 200 25 10 6 5 4 3 3
## hMNC HCL-V Sezary FL PTCL-NOS
## 3 2 2 1 1##
## FALSE TRUE
## 131 52##
## M U
## 104 84High-throughput drug screen data
The viability measurements from the high-throughput drug screen are
included in the drpar object. The metadata about the drugs
and drug concentrations used can be found in drugs and
conctab objects, respectively.
The drpar object includes multiple channels, each of
which consists of cells’ viability data for a single drug concentration
step. Channels viaraw.1_5 and viaraw.4_5
contain the mean viability score between multiple concentration steps as
indicated at the end of the channel name.
## [1] "viaraw.1" "viaraw.1_5" "viaraw.2" "viaraw.3" "viaraw.4"
## [6] "viaraw.4_5" "viaraw.5"# show viability data for the first 5 patients and 7 drugs in their lowest conc.
assayData(drpar)[["viaraw.1"]][1:7,1:5]## H001 H002 H003 H005 H009
## D_001 0.3283159 0.2632593 0.4969538 0.02410373 0.19080155
## D_002 0.8322242 1.0353523 0.8381608 0.16190086 0.41572819
## D_003 0.7832597 0.8428674 0.7449635 0.65899478 0.41898375
## D_004 0.6529834 0.5654266 0.3548642 0.02827099 0.04094826
## D_006 0.4779778 0.6871057 0.4894785 0.30327813 0.12296120
## D_007 0.5912538 0.7246273 0.5119393 0.10447502 0.15369103
## D_008 0.3728575 0.5261570 0.3161154 0.02266465 0.03423222Drug metadata.
## [1] 91## [1] "name" "main_targets" "target_category" "group"
## [5] "pathway" "distributor" "approved_042016" "devel_042016"Drug concentration steps (c1 - lowest, c5 - highest).
## c1 c2 c3 c4 c5
## D_001 1 0.25 0.063 0.016 0.004
## D_002 40 10.00 2.500 0.625 0.156
## D_003 40 10.00 2.500 0.625 0.156
## D_004 4 1.00 0.250 0.063 0.016
## D_006 40 10.00 2.500 0.625 0.156
## D_007 20 5.00 1.250 0.313 0.078The reproducibility of the screening platform was assessed by
screening 3 patient samples in two replicates. The viability
measurements are available for two time points: 48 h and 72 h after
adding the drug. The screen was performed for 67 drugs in 1-2 different
drug concentrations (16 in 1 and 51 in 2 drug concentrations). This data
is provided in day23rep.
## [1] "day2rep1" "day2rep2" "day3rep1" "day3rep2"# show viability data for 48 h time point for all patients marked as
# replicate 1 and 3 first drugs in all their conc.
drugs2Show = unique(fData(day23rep)$DrugID)[1:3]
assayData(day23rep)[["day2rep1"]][fData(day23rep)$DrugID %in% drugs2Show,]## H016 H022 H030
## D_001-0.05 0.6037813 0.3461988 0.7801167
## D_001-0.1 0.2637692 0.2190428 0.6309255
## D_002-1 0.9959188 0.8119468 0.9005653
## D_002-10 0.9381762 0.4948085 0.8112993
## D_003-1 0.9575617 0.6848139 0.8939423
## D_003-10 0.7988831 0.5007344 0.8348084The follow-up drug screen, which confirmed the targets and the signaling pathway dependence of the patient samples was performed for 128 samples and the following drugs: Cobimetinib, Ganetespib, Onalespib, SCH772984, Trametinib.
| Drug name | Target |
|---|---|
| Cobimetinib | MEK |
| Trametinib | MEK |
| SCH772984 | ERK1/2 |
| Ganetespib | Hsp90 |
| Onalespib | Hsp90 |
The data is included in the validateExp object.
## # A tibble: 6 × 4
## patientID Drug Concentration viab
## <chr> <chr> <dbl> <dbl>
## 1 H005 Cobimetinib 0.0032 0.749
## 2 H005 Cobimetinib 0.016 0.726
## 3 H005 Cobimetinib 0.08 0.723
## 4 H005 Cobimetinib 0.4 0.732
## 5 H005 Cobimetinib 2 0.744
## 6 H005 Ganetespib 0.0032 1.00Moreover, we also performed a small drug screen in order to check the
influence of the different cytokines/chemokines on the viability of the
samples. These data are included in cytokineViab
object.
## # A tibble: 6 × 11
## Patient Timepoint Recording_date Seeding_date Stimulation
## <chr> <chr> <date> <date> <chr>
## 1 H060 48h 2017-06-02 2017-05-31 IL-2
## 2 H060 48h 2017-06-02 2017-05-31 IL-2
## 3 H060 48h 2017-06-02 2017-05-31 IL-2
## 4 H060 48h 2017-06-02 2017-05-31 IL-4
## 5 H060 48h 2017-06-02 2017-05-31 IL-4
## 6 H060 48h 2017-06-02 2017-05-31 IL-4
## # ℹ 6 more variables: Cytokine_Concentration <fct>, Duplicate <dbl>,
## # Normalized_DMSO <dbl>, mtor <chr>, Edge <fct>,
## # Cytokine_Concentration2 <chr>Gene mutation data
The mutCOM object contains information on the presence
of gene mutations in the studied patient samples.
## [1] "binary"# the feature data includes detailed information about mutations in
# TP53 and BRAF genes, as well as clone size of
#del17p13, KRAS, UMODL1, CREBBP, PRPF8, trisomy12 mutations
colnames(fData(mutCOM))## [1] "TP53_CDS" "TP53_AA" "TP53_%" "TP53cs" "BRAF_CDS"
## [6] "BRAF_AA" "BRAF_%" "BRAFcs" "del17p13cs" "KRAScs"
## [11] "UMODL1cs" "CREBBPcs" "PRPF8cs" "trisomy12cs"Gene expression data
RNA-Seq data preprocessed with DESeq2 is
provided in the dds object.
## 1 2 3 4 6
## ENSG00000000003 0 0 9 0 2
## ENSG00000000005 0 0 0 0 0
## ENSG00000000419 1272 3443 1682 1574 3463
## ENSG00000000457 430 472 2285 1022 1030
## ENSG00000000460 270 1043 1882 760 830
## ENSG00000000938 9391 24963 40337 31327 10077
## ENSG00000000971 5 1 57 56 0# show the above with patient sample ids
assay(dds)[1:7,1:5] %>% `colnames<-` (colData(dds)$PatID[1:5])## H045 H109 H024 H056 H079
## ENSG00000000003 0 0 9 0 2
## ENSG00000000005 0 0 0 0 0
## ENSG00000000419 1272 3443 1682 1574 3463
## ENSG00000000457 430 472 2285 1022 1030
## ENSG00000000460 270 1043 1882 760 830
## ENSG00000000938 9391 24963 40337 31327 10077
## ENSG00000000971 5 1 57 56 0## [1] 63677## [1] 136Additionally, 12 patient samples underwent gene expression profiling
using Illumina microarrays before and 12 h after treatment with 5 drugs.
These data are included in the exprTreat data object.
## [1] "H112" "H108" "H238" "H194" "H173" "H234" "H169" "H233" "H094" "H109"
## [11] "H114" "H167"## [1] "ProbeID" "TargetID" "QC"
## [4] "Species" "Source" "Search_Key"
## [7] "Transcript" "ILMN_Gene" "Source_Reference_ID"
## [10] "RefSeq_ID" "Unigene_ID" "Entrez_Gene_ID"
## [13] "GI" "Accession" "Symbol"
## [16] "Protein_Product" "Probe_Id" "Array_Address_Id"
## [19] "Probe_Type" "Probe_Start" "Probe_Sequence"
## [22] "Chromosome" "Probe_Chr_Orientation" "Probe_Coordinates"
## [25] "Cytoband" "Definition" "Ontology_Component"
## [28] "Ontology_Process" "Ontology_Function" "Synonyms"
## [31] "Obsolete_Probe_Id" "Factor"# show expression level for the first patient and 3 first probes
Biobase::exprs(exprTreat)[1:3, pData(exprTreat)$PatientID==p[1]]## 200128470091_A 200128470091_B 200128470091_C 200128470091_D
## R00001 9.172930 9.086734 9.112330 9.202076
## R00002 4.931757 4.893040 4.439497 4.887295
## R00003 4.887619 4.668925 4.796266 5.298392
## 200128470091_E 200128470091_F
## R00001 9.073740 8.870751
## R00002 5.227140 5.379562
## R00003 5.038028 5.115141DNA methylation data
DNA methylation included in methData object contains
data for 196 patient samples and 5000 of the most variable CpG
sites.
# show the methylation for the first 7 CpGs and the first 5 patient samples
assay(methData)[1:7,1:5]## H005 H023 H006 H024 H010
## cg17479716 0.13512727 0.94295266 0.03052286 0.03086889 0.97261914
## cg00674365 0.97992788 0.02102404 0.98030338 0.97448056 0.06720939
## cg24299136 0.97637954 0.97179489 0.03779711 0.08808060 0.97364936
## cg18723409 0.03438240 0.57677823 0.96091989 0.96505311 0.07195807
## cg23844018 0.03451902 0.97407334 0.10804103 0.17808162 0.97309712
## cg08425796 0.04653624 0.95438826 0.05756599 0.11993044 0.94959487
## cg02956248 0.40450644 0.96655136 0.18370129 0.03276800 0.96370039## [1] "Strand" "Name"
## [3] "AddressA" "AddressB"
## [5] "ProbeSeqA" "ProbeSeqB"
## [7] "Type" "NextBase"
## [9] "Color" "Probe_rs"
## [11] "Probe_maf" "CpG_rs"
## [13] "CpG_maf" "SBE_rs"
## [15] "SBE_maf" "Islands_Name"
## [17] "Relation_to_Island" "Forward_Sequence"
## [19] "SourceSeq" "Random_Loci"
## [21] "Methyl27_Loci" "UCSC_RefGene_Name"
## [23] "UCSC_RefGene_Accession" "UCSC_RefGene_Group"
## [25] "Phantom" "DMR"
## [27] "Enhancer" "HMM_Island"
## [29] "Regulatory_Feature_Name" "Regulatory_Feature_Group"
## [31] "DHS"##
## 450k 850k
## 118 78Other
Object lpdAll is a convenient assembly of data contained
in the other data objects mentioned earlier in this vignette. For
details, please refer to the manual.
##
## gen IGHV Methylation_Cluster viab
## 89 1 1 448# show viability data for drug ibrutinib, idelalisib and dasatinib
# (in the mean of the two lowest concentration steps) and
# the first 5 patient samples
Biobase::exprs(lpdAll)[which(
with(fData(lpdAll),
name %in% c("ibrutinib", "idelalisib", "dasatinib") &
subtype=="4:5")), 1:5]## H005 H009 H010 H011 H012
## D_002_4:5 0.9915251 1.0966397 0.8994378 1.0815918 0.9402181
## D_003_4:5 1.0253976 0.9924037 0.8850690 1.0352433 0.9109271
## D_050_4:5 0.9857736 0.6161776 0.6717888 0.8889409 0.6151546Original data
The raw data from the whole exome sequencing, RNA-seq and DNA methylation arrays is stored in the European Genome-Phenome Archive (EGA) under accession number EGAS0000100174.
The preprocesed DNA methylation data, which include complete list of CpG sites (not only the 5000 with the highest variance) can be accessed through Bioconductor ExperimentHub platform.
Session info
## R version 4.6.1 (2026-06-24)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 26.04.1 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-r0.3.32.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=en_US.UTF-8
## [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] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] dplyr_1.2.1 ggplot2_4.0.3
## [3] reshape2_1.4.5 DESeq2_1.53.6
## [5] SummarizedExperiment_1.43.0 GenomicRanges_1.65.4
## [7] Seqinfo_1.3.2 IRanges_2.47.5
## [9] S4Vectors_0.51.10 MatrixGenerics_1.25.0
## [11] matrixStats_1.5.0 Biobase_2.73.2
## [13] BiocGenerics_0.59.12 generics_0.1.4
## [15] BloodCancerMultiOmics2017_1.33.0 BiocStyle_2.41.0
##
## loaded via a namespace (and not attached):
## [1] gtable_0.3.6 beeswarm_0.4.0 shape_1.4.6.1
## [4] xfun_0.61 bslib_0.12.0 devtools_2.5.2
## [7] lattice_0.23-1 vctrs_0.7.3 tools_4.6.1
## [10] parallel_4.6.1 tibble_3.3.1 pkgconfig_2.0.3
## [13] Matrix_1.7-6 RColorBrewer_1.1-3 S7_0.2.2
## [16] lifecycle_1.0.5 compiler_4.6.1 farver_2.1.2
## [19] stringr_1.6.0 codetools_0.2-20 htmltools_0.5.9
## [22] sys_3.4.3 usethis_3.2.2 buildtools_1.0.0
## [25] sass_0.4.10 yaml_2.3.12 glmnet_5.1
## [28] pillar_1.11.1 jquerylib_0.1.4 ipflasso_1.1
## [31] MASS_7.3-66 ellipsis_0.3.3 BiocParallel_1.47.0
## [34] DelayedArray_0.39.8 cachem_1.1.0 sessioninfo_1.2.4
## [37] iterators_1.0.14 abind_1.4-8 foreach_1.5.2
## [40] locfit_1.5-9.12 tidyselect_1.2.1 digest_0.6.39
## [43] stringi_1.8.9 purrr_1.2.2 maketools_1.3.2
## [46] splines_4.6.1 fastmap_1.2.0 grid_4.6.1
## [49] SparseArray_1.13.4 cli_3.6.6 magrittr_2.0.5
## [52] S4Arrays_1.13.2 utf8_1.2.6 survival_3.8-12
## [55] pkgbuild_1.4.8 withr_3.0.3 scales_1.4.0
## [58] XVector_0.53.0 rmarkdown_2.32 otel_0.2.0
## [61] memoise_2.0.1 evaluate_1.0.5 knitr_1.52
## [64] rlang_1.3.0 ggdendro_0.2.0 Rcpp_1.1.2
## [67] glue_1.8.1 BiocManager_1.30.27 pkgload_1.5.3
## [70] jsonlite_2.0.0 R6_2.6.1 plyr_1.8.9
## [73] fs_2.1.0