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Knit directory: BH3profiling/analysis/
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Genes will be considered in the analysis
BCL-2 MCL-1 BCL-xL BFL-1 BAX BAK BIM PUMA
"BCL2" "MCL1" "BCL2L1" "BCL2A1" "BAX" "BAK1" "BCL2L11" "BBC3"
NOXA COX5A COX5B SOD1 SOD2
"PMAIP1" "COX5A" "COX5B" "SOD1" "SOD2"
The first row shows the commonly used protein names and the second row shows the corresponding official gene symbols
BCL2 MCL1 BCL2L1 BCL2A1 BAX BAK1 BCL2L11 BBC3 PMAIP1 COX5A
TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
COX5B SOD1 SOD2
TRUE TRUE TRUE
[1] 13 71
BCL2 MCL1 BCL2L1 BCL2A1 BAX BAK1 BCL2L11 BBC3 PMAIP1 COX5A
TRUE FALSE FALSE FALSE TRUE FALSE TRUE FALSE FALSE TRUE
COX5B SOD1 SOD2
TRUE FALSE TRUE
[1] 6 91
Prepare patient genomic background
[1] "IGHV.status" "del11q" "del13q" "del17p" "trisomy12"
[6] "NOTCH1" "SF3B1" "TP53"

Only the associations between BAX and trisomy12 passed 10% FDR

Subset for DMSO and Ibr
Subset for interested genes
[1] 13 187

Prepare drug response profile
Prepare gene expression
Table of significant associations (10%)
In the treatment column:
- Ibrutinib_48h means the gene expression after
Ibrutinib treatment for 48 hours
- DMSO_48h means the gene expression after 48h
incubation with just DMSO
- FoldChange means the log fold change between
Ibrutinib and DMSO after 48 hourse
Note that in this experiment, we don’t have the real baseline (0 hour)

Prepare drug response profile
Table of significant associations (10%)
It’s interesting to see that the gene expression after 48h DMSO
incubation show stronger association to BH3 profile than the real
baseline gene expression (0h) we used in the manuscript
This could be because the BH3 profiling data we are using is
also the BH3 profile after 48h DMSO incubation
You can see from the below longitudinal gene expression profile,
some of those genes show strong changes during culturing in
DMSO

Subset for interested genes
[1] 6 10

Contrary to the gene expression after ex-vivo Ibr treatment, BCL2 protein level increased after in-vivo ibrutinib treatment
[1] 9 30
The trend is similar to the smaller cohort from
Heidelberg
[1] 12 70
It seems the trend on mRNA level is not consistent with the
trend on protein level..

R version 4.2.0 (2022-04-22)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur/Monterey 10.16
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.2/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] stats4 stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] latex2exp_0.9.4 forcats_0.5.1
[3] stringr_1.4.1 dplyr_1.1.4.9000
[5] purrr_0.3.4 readr_2.1.2
[7] tidyr_1.2.0 tibble_3.2.1
[9] ggplot2_3.4.1 tidyverse_1.3.2
[11] DESeq2_1.36.0 SummarizedExperiment_1.26.1
[13] Biobase_2.56.0 MatrixGenerics_1.8.1
[15] matrixStats_0.62.0 GenomicRanges_1.48.0
[17] GenomeInfoDb_1.32.2 IRanges_2.30.0
[19] S4Vectors_0.34.0 BiocGenerics_0.42.0
[21] jyluMisc_0.1.5
loaded via a namespace (and not attached):
[1] utf8_1.2.4 shinydashboard_0.7.2 tidyselect_1.2.1
[4] RSQLite_2.2.15 AnnotationDbi_1.58.0 htmlwidgets_1.5.4
[7] grid_4.2.0 BiocParallel_1.30.3 maxstat_0.7-25
[10] munsell_0.5.0 codetools_0.2-18 DT_0.23
[13] withr_3.0.0 colorspace_2.0-3 highr_0.9
[16] knitr_1.39 proDA_1.10.0 rstudioapi_0.13
[19] ggsignif_0.6.3 labeling_0.4.2 git2r_0.30.1
[22] slam_0.1-50 GenomeInfoDbData_1.2.8 KMsurv_0.1-5
[25] farver_2.1.1 bit64_4.0.5 rprojroot_2.0.3
[28] vctrs_0.6.5 generics_0.1.3 TH.data_1.1-1
[31] xfun_0.31 sets_1.0-21 R6_2.5.1
[34] ggbeeswarm_0.6.0 locfit_1.5-9.6 bitops_1.0-7
[37] cachem_1.0.6 fgsea_1.22.0 DelayedArray_0.22.0
[40] assertthat_0.2.1 promises_1.2.0.1 scales_1.2.0
[43] multcomp_1.4-19 googlesheets4_1.0.0 beeswarm_0.4.0
[46] gtable_0.3.0 extraDistr_1.9.1 sandwich_3.0-2
[49] workflowr_1.7.0 rlang_1.1.3 genefilter_1.78.0
[52] splines_4.2.0 rstatix_0.7.0 gargle_1.2.0
[55] broom_1.0.0 yaml_2.3.5 abind_1.4-5
[58] modelr_0.1.8 crosstalk_1.2.0 backports_1.4.1
[61] httpuv_1.6.6 tools_4.2.0 relations_0.6-12
[64] ellipsis_0.3.2 gplots_3.1.3 jquerylib_0.1.4
[67] RColorBrewer_1.1-3 Rcpp_1.0.9 visNetwork_2.1.0
[70] zlibbioc_1.42.0 RCurl_1.98-1.7 ggpubr_0.4.0
[73] cowplot_1.1.1 zoo_1.8-10 haven_2.5.0
[76] cluster_2.1.3 exactRankTests_0.8-35 fs_1.5.2
[79] magrittr_2.0.3 data.table_1.14.8 reprex_2.0.1
[82] survminer_0.4.9 googledrive_2.0.0 mvtnorm_1.1-3
[85] hms_1.1.1 shinyjs_2.1.0 mime_0.12
[88] evaluate_0.15 xtable_1.8-4 XML_3.99-0.10
[91] readxl_1.4.0 gridExtra_2.3 compiler_4.2.0
[94] KernSmooth_2.23-20 crayon_1.5.2 htmltools_0.5.4
[97] mgcv_1.8-40 later_1.3.0 tzdb_0.3.0
[100] geneplotter_1.74.0 lubridate_1.8.0 DBI_1.1.3
[103] dbplyr_2.2.1 MASS_7.3-58 Matrix_1.5-4
[106] car_3.1-0 cli_3.6.2 marray_1.74.0
[109] parallel_4.2.0 igraph_1.3.4 pkgconfig_2.0.3
[112] km.ci_0.5-6 piano_2.12.0 xml2_1.3.3
[115] annotate_1.74.0 vipor_0.4.5 bslib_0.4.1
[118] XVector_0.36.0 drc_3.0-1 rvest_1.0.2
[121] digest_0.6.30 Biostrings_2.64.0 rmarkdown_2.14
[124] cellranger_1.1.0 fastmatch_1.1-3 survMisc_0.5.6
[127] shiny_1.7.4 gtools_3.9.3 nlme_3.1-158
[130] lifecycle_1.0.4 jsonlite_1.8.3 carData_3.0-5
[133] limma_3.52.2 fansi_1.0.6 pillar_1.9.0
[136] lattice_0.20-45 KEGGREST_1.36.3 fastmap_1.1.0
[139] httr_1.4.3 plotrix_3.8-2 survival_3.4-0
[142] glue_1.7.0 png_0.1-7 bit_4.0.4
[145] stringi_1.7.8 sass_0.4.2 blob_1.2.3
[148] caTools_1.18.2 memoise_2.0.1