Explore the CDK response signatures in CLL
Junyan Lu
17 May 2022
Last updated: 2022-05-17
Checks: 5 2
Knit directory: EMBL2016/analysis/
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Load packages and datasets
library(jyluMisc)
library(tidyverse)
knitr::opts_chunk$set(warning = FALSE, message = FALSE)#processed screen data
load("../output/screenData.RData")
#patient annotation
load("../data/patMeta.RData")Summarise the trend of CDKi responses
Choose the drugs selected by Jarno
drugList <- c("Dinaciclib", "THZ1", "SNS-032", "Flavopiridol", "AT7519", "R547","PHA-767491")
drugList[1] "Dinaciclib" "THZ1" "SNS-032" "Flavopiridol" "AT7519"
[6] "R547" "PHA-767491" viabMat <- screenData %>% filter(diagnosis %in% "CLL", Drug %in% drugList) %>%
group_by(patientID, Drug) %>%
summarise(viab = mean(viab.auc)) %>%
pivot_wider(names_from = "patientID", values_from = "viab") %>%
column_to_rownames("Drug") %>% as.matrix()
patAnno <- patMeta %>% filter(Patient.ID %in% colnames(viabMat)) %>%
select(Patient.ID, IGHV.status, trisomy12, TP53, SF3B1, NOTCH1) %>%
dplyr::rename(patID = "Patient.ID")PCA
pcRes <- prcomp(t(viabMat), center = TRUE, scale. = TRUE)
pcTab <- pcRes$x[,1:2] %>% as_tibble(rownames = "patID") %>%
left_join(patAnno)
varExp <- pcRes$sdev^2
varExp <- varExp/sum(varExp)PCA plot
PCbiplot <- function(PC, x="PC1", y="PC2") {
# PC being a prcomp object
varExp = (pcRes$sdev^2)/sum(pcRes$sdev^2)
plotTab <- pcRes$x %>% data.frame() %>% rownames_to_column("patID") %>%
left_join(patAnno, by = "patID") %>%
filter(!is.na(IGHV.status),!is.na(trisomy12))
p <- ggplot(plotTab, aes(x=PC1, y=PC2)) +
geom_point(aes(color = IGHV.status, shape = trisomy12), size=3) +
theme_bw() + xlim(-5,5) + ylim(-5,5) +
xlab(sprintf("PC1 (%1.1f%%)", 100*varExp[1])) +
ylab(sprintf("PC2 (%1.1f%%)", 100*varExp[2])) +
theme(legend.position = "bottom")
datapc <- data.frame(varnames=rownames(PC$rotation), PC$rotation)
mult <- min(
(max(plotTab[,y]) - min(plotTab[,y])/(max(datapc[,y])-min(datapc[,y]))),
(max(plotTab[,x]) - min(plotTab[,x])/(max(datapc[,x])-min(datapc[,x])))
)
datapc <- transform(datapc,
v1 = .7 * mult * (get(x)),
v2 = .7 * mult * (get(y))
)
p <- p +
ggrepel::geom_text_repel(data=datapc, aes(x=v1, y=v2, label=varnames),
size = 5, vjust=1)
p <- p + geom_segment(data=datapc, aes(x=0, y=0, xend=v1, yend=v2), arrow=arrow(length=unit(0.2,"cm")), alpha=0.4)
p
}
PCbiplot(pcRes)
PC1 explains most of the variance, indicating those CDK inhibitors show similar trends. Perhaps except for Dinaciclib.
Heatmap of viabilities, ordered by PC1 value (not scaled)
library(pheatmap)
viabMat <- viabMat[,arrange(pcTab, PC1)$patID]
colAnno <- patAnno %>% mutate(PC1 = pcTab[match(patID, pcTab$patID),]$PC1) %>%
column_to_rownames("patID") %>% data.frame()
pheatmap(viabMat, cluster_cols = FALSE, cluster_rows = TRUE, annotation_col = colAnno, scale = "none")
Heatmap of viabilities, ordered by PC1 value (row-scaled)
library(pheatmap)
pheatmap(viabMat, cluster_cols = FALSE, cluster_rows = TRUE, annotation_col = colAnno, scale = "row")
Higher PC1 is associated with more resistant to CDK inhibitors
Correlation plot
library(corrplot)
corrplot(cor(t(viabMat)))
We can use PC1 to summarise the general gradient of the response to CDK inhibitors, as the response patterns of those inhibitors are quite similar.
Associations with genomics
Prepare genomic data
geneTab <- patMeta %>% select(Patient.ID, IGHV.status, del10p:U1) %>%
filter(Patient.ID %in% pcTab$patID) %>%
mutate(IGHV.status = as.factor(2-as.numeric(as.factor(IGHV.status)))) %>%
pivot_longer(-Patient.ID)
sumTab <- group_by(geneTab, name) %>%
summarise(fracNA = sum(is.na(value))/length(pcTab$patID),
numMut = sum(value %in% 1)) %>%
filter(numMut >=3, fracNA <= 0.2)
geneTab <- filter(geneTab, name %in% sumTab$name)Perform t-test
testTab <- pcTab %>% select(patID, PC1) %>%
full_join(geneTab, by = c(patID = "Patient.ID"))
resTab <- group_by(testTab, name) %>% nest() %>%
mutate(m=map(data, ~t.test(PC1 ~ value,., var.equal=TRUE))) %>%
mutate(res = map(m, broom::tidy)) %>%
unnest(res) %>%
select(name, p.value, estimate) %>%
arrange(p.value)
head(resTab)# A tibble: 6 × 3
# Groups: name [6]
name p.value estimate
<chr> <dbl> <dbl>
1 del17p 0.0452 1.20
2 del9q 0.0497 -1.77
3 BRAF 0.0557 1.59
4 FAT4 0.0710 -1.51
5 NOTCH1 0.0935 0.869
6 EGR2 0.114 -1.23 Boxplots
pList <- lapply(filter(resTab,p.value < 0.05)$name, function(x) {
plotTab <- filter(testTab, name == x)
ggplot(plotTab, aes(x=value, y=PC1, col=factor(value))) +
geom_boxplot() + ggbeeswarm::geom_quasirandom() +
theme(legend.position = "none") +
ggtitle(x)
})
cowplot::plot_grid(plotlist = pList, ncol=2)
del17p shows some weak association. # Association with mRNA expression
Pre-processing
Subsetting
load("~/CLLproject_jlu/var/ddsrna_180717.RData")
ddsSub <- dds[,dds$PatID %in% pcTab$patID]
ddsSub$PC1 <- pcTab[match(ddsSub$PatID, pcTab$patID),]$PC1
ddsSub$IGHV <- patMeta[match(ddsSub$PatID, patMeta$Patient.ID),]$IGHV.status
ddsSub$trisomy12 <- patMeta[match(ddsSub$PatID, patMeta$Patient.ID),]$trisomy12
ddsSub <- ddsSub[,!is.na(ddsSub$IGHV) & !is.na(ddsSub$trisomy12)]
#remove low abundance genes
ddsSub <- ddsSub[rowMedians(counts(ddsSub, normalized = TRUE),na.rm = TRUE)>10,]
#keep protein coding genes
ddsSub <- ddsSub[rowData(ddsSub)$biotype %in% "protein_coding" & !rowData(ddsSub)$symbol %in% c(NA,""),]Voom transformation
countMat <- counts(ddsSub)
exprMat <- limma::voom(counts = countMat, lib.size = ddsSub$sizeFactor)$ERemove invariant genes
sds <- genefilter::rowSds(exprMat)
exprMat <- exprMat[sds > genefilter::shorth(sds),]Correlation test using Limma
library(limma)
designMat <- model.matrix(~PC1+IGHV+trisomy12, colData(ddsSub))
fit <- lmFit(exprMat, designMat)
fit2 <- eBayes(fit)
resTab <- topTable(fit2, coef = "PC1", number =Inf) %>%
as_tibble(rownames ="id") %>%
mutate(symbol = rowData(ddsSub)[id,]$symbol)P-value histogram
hist(resTab$P.Value)
Associations are not strong.
Genes passed raw p value < 0.05 (none association passed 10% FDR)
resTab.sig <- filter(resTab, P.Value < 0.05)
resTab.sig %>% mutate_if(is.numeric, formatC, digits=2) %>%
DT::datatable()Boxplot of top9 genes
pList <- lapply(seq(9), function(i) {
rec <- resTab.sig[i,]
plotTab <- tibble(expr = exprMat[rec$id,],
PC1 = designMat[,"PC1"],
IGHV=factor(designMat[,"IGHVU"]))
ggplot(plotTab, aes(x=PC1, y=expr)) +
geom_point(aes(col = IGHV)) + geom_smooth(method = "lm") +
ggtitle(sprintf("%s (p=%s)",rec$symbol, formatC(rec$P.Value, digits=2)))
})
cowplot::plot_grid(plotlist= pList, ncol=3)
According the scatter plot, the associations are very moderate even though they passed 0.05 p-value
Pathway enrichment analysis
gmts <- list(H = "~/CLLproject_jlu/data/commonFiles/h.all.v6.2.symbols.gmt",
KEGG = "~/CLLproject_jlu/data/commonFiles/c2.cp.kegg.v6.2.symbols.gmt",
C6 = "~/CLLproject_jlu/data/commonFiles/c6.all.v6.2.symbols.gmt")Cancer hallmmarks
resEnrich <- runCamera(exprMat, designMat, gmts$H, id = rowData(ddsSub)$symbol, pCut = 0.1, ifFDR = TRUE)[1] "No sets passed the criteria"resEnrich$enrichPlotNULLKEGG
resEnrich <- runCamera(exprMat, designMat, gmts$KEGG, id = rowData(ddsSub)$symbol, pCut = 0.1, ifFDR = TRUE)[1] "No sets passed the criteria"resEnrich$enrichPlotNULLOncogenetic
resEnrich <- runCamera(exprMat, designMat, gmts$C6, id = rowData(ddsSub)$symbol, pCut = 0.1, ifFDR = TRUE)[1] "No sets passed the criteria"resEnrich$enrichPlotNULLAssociation with protein expression
Load datasets
library(proDA)
library(SummarizedExperiment)
#load datasets
load("~/CLLproject_jlu/var/proteomic_LUMOS_batch13.RData")Preprocessing
protCLL$PC1 <- pcTab[match(colnames(protCLL), pcTab$patID),]$PC1
protCLL <- protCLL[,!is.na(protCLL$IGHV.status) & !is.na(protCLL$trisomy12) & !is.na(protCLL$PC1)]
protMat <- assays(protCLL)[["count"]] #without imputation
protMatLog <- assays(protCLL)[["log2Norm"]]Sample size
dim(protCLL)[1] 3314 56Differential expression
colData <- data.frame(colData(protCLL))[,c("batch","IGHV.status","trisomy12","PC1")]
fit <- proDA(protMat, design = ~ . , col_data = colData)resTab <- test_diff(fit, "PC1") %>%
dplyr::rename(id = name, logFC = diff, t=t_statistic,
P.Value = pval, adj.P.Val = adj_pval) %>%
mutate(name = rowData(protCLL[id,])$hgnc_symbol) %>%
select(name, id, logFC, t, P.Value, adj.P.Val, n_obs) %>%
arrange(P.Value) %>%
as_tibble()hist(resTab$P.Value)
No clear associations
Table of proteins with raw p-values <0.05 (no results passed 10% FDR)
resTab.sig <- filter(resTab, P.Value < 0.05)
resTab.sig %>%
mutate_if(is.numeric, formatC, digits=2) %>%
DT::datatable()Boxplot of top9 associations
pList <- lapply(seq(9), function(i) {
rec <- resTab.sig[i,]
plotTab <- tibble(expr = protMat[rec$id,],
PC1 = colData[,"PC1"],
IGHV=factor(colData[,"IGHV.status"]))
ggplot(plotTab, aes(x=PC1, y=expr)) +
geom_point(aes(col = IGHV)) + geom_smooth(method = "lm") +
ggtitle(sprintf("%s (p=%s)",rec$name, formatC(rec$P.Value, digits=2)))
})
cowplot::plot_grid(plotlist= pList, ncol=3)
Pathway enrichment analysis
designMat <- model.matrix(~ batch + IGHV.status+trisomy12+PC1, colData)Cancer hallmmarks
protImp <- assays(protCLL)[["QRILC"]]
resEnrich <- runCamera(protImp, designMat, gmts$H, id = rowData(protCLL)$hgnc_symbol, pCut = 0.1, ifFDR = TRUE, contrast = "PC1")[1] "No sets passed the criteria"KEGG
resEnrich <- runCamera(protImp, designMat, gmts$KEGG, id = rowData(protCLL)$hgnc_symbol, pCut = 0.1, ifFDR = TRUE, contrast = "PC1")
resEnrich$enrichPlot
Oncogenetic
resEnrich <- runCamera(protImp, designMat, gmts$C6, id = rowData(protCLL)$hgnc_symbol, pCut = 0.1, ifFDR = TRUE,contrast = "PC1")[1] "No sets passed the criteria"resEnrich$enrichPlotNULLAssociations with BH3 profiling
BH3 profiling measures the cytochrome C release after treatment of BH3 peptides, to evaluate the sensitivity of cells to pro-apoptotic signals.
Preprocessing
load("../../BH3profiling/output/dynamicBH3.RData")
bh3Tab <- dynamicBH3 %>% filter(drug == "DMSO") %>%
group_by(patID, peptide) %>%
summarise(auc = mean(AUC))Association test
testTab <- pcTab %>% full_join(bh3Tab, by = "patID") %>%
filter(!is.na(auc),!is.na(PC1))
resTab <- group_by(testTab, peptide) %>% nest() %>%
mutate(m = map(data, ~cor.test(~PC1+auc,.))) %>%
mutate(res = map(m, broom::tidy)) %>%
unnest(res) %>% arrange(p.value) %>%
ungroup() %>%
select(peptide, estimate, p.value) %>%
mutate(p.adj = p.adjust(p.value, method= "BH"))Result table
resTab %>% mutate_if(is.numeric, formatC, digits=2) %>% DT::datatable()Plot significant associations (p<0.05)
pList <- lapply(filter(resTab,p.value < 0.05)$peptide, function(x) {
plotTab <- filter(testTab, peptide == x)
ggplot(plotTab, aes(x=auc, y=PC1)) +
geom_point(aes(col=IGHV.status)) + geom_smooth(method ="lm") +
ggtitle(x) +
xlab("BH3 priming")
})
cowplot::plot_grid(plotlist = pList, ncol=2)
Associations are pretty weak.
sessionInfo()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] proDA_1.10.0 limma_3.52.0
[3] DESeq2_1.36.0 SummarizedExperiment_1.26.1
[5] Biobase_2.56.0 MatrixGenerics_1.8.0
[7] matrixStats_0.62.0 GenomicRanges_1.48.0
[9] GenomeInfoDb_1.32.1 IRanges_2.30.0
[11] S4Vectors_0.34.0 BiocGenerics_0.42.0
[13] corrplot_0.92 pheatmap_1.0.12
[15] forcats_0.5.1 stringr_1.4.0
[17] dplyr_1.0.9 purrr_0.3.4
[19] readr_2.1.2 tidyr_1.2.0
[21] tibble_3.1.7 ggplot2_3.3.6
[23] tidyverse_1.3.1 jyluMisc_0.1.5
loaded via a namespace (and not attached):
[1] utf8_1.2.2 shinydashboard_0.7.2 tidyselect_1.1.2
[4] RSQLite_2.2.14 AnnotationDbi_1.58.0 htmlwidgets_1.5.4
[7] grid_4.2.0 BiocParallel_1.30.0 maxstat_0.7-25
[10] munsell_0.5.0 codetools_0.2-18 DT_0.23
[13] withr_2.5.0 colorspace_2.0-3 highr_0.9
[16] knitr_1.39 rstudioapi_0.13 ggsignif_0.6.3
[19] labeling_0.4.2 git2r_0.30.1 slam_0.1-50
[22] GenomeInfoDbData_1.2.8 KMsurv_0.1-5 bit64_4.0.5
[25] farver_2.1.0 rprojroot_2.0.3 vctrs_0.4.1
[28] generics_0.1.2 TH.data_1.1-1 xfun_0.31
[31] sets_1.0-21 R6_2.5.1 ggbeeswarm_0.6.0
[34] locfit_1.5-9.5 bitops_1.0-7 cachem_1.0.6
[37] fgsea_1.22.0 DelayedArray_0.22.0 assertthat_0.2.1
[40] promises_1.2.0.1 scales_1.2.0 multcomp_1.4-19
[43] beeswarm_0.4.0 gtable_0.3.0 sandwich_3.0-1
[46] workflowr_1.7.0 rlang_1.0.2 genefilter_1.78.0
[49] splines_4.2.0 rstatix_0.7.0 broom_0.8.0
[52] BiocManager_1.30.17 yaml_2.3.5 abind_1.4-5
[55] modelr_0.1.8 crosstalk_1.2.0 backports_1.4.1
[58] httpuv_1.6.5 tools_4.2.0 relations_0.6-12
[61] ellipsis_0.3.2 gplots_3.1.3 jquerylib_0.1.4
[64] RColorBrewer_1.1-3 Rcpp_1.0.8.3 visNetwork_2.1.0
[67] zlibbioc_1.42.0 RCurl_1.98-1.6 ggpubr_0.4.0
[70] cowplot_1.1.1 zoo_1.8-10 haven_2.5.0
[73] ggrepel_0.9.1 cluster_2.1.3 exactRankTests_0.8-35
[76] fs_1.5.2 magrittr_2.0.3 data.table_1.14.2
[79] reprex_2.0.1 survminer_0.4.9 mvtnorm_1.1-3
[82] hms_1.1.1 shinyjs_2.1.0 mime_0.12
[85] evaluate_0.15 xtable_1.8-4 XML_3.99-0.9
[88] readxl_1.4.0 gridExtra_2.3 compiler_4.2.0
[91] KernSmooth_2.23-20 crayon_1.5.1 htmltools_0.5.2
[94] mgcv_1.8-40 later_1.3.0 tzdb_0.3.0
[97] geneplotter_1.74.0 lubridate_1.8.0 DBI_1.1.2
[100] dbplyr_2.1.1 MASS_7.3-57 BiocStyle_2.24.0
[103] Matrix_1.4-1 car_3.0-13 cli_3.3.0
[106] marray_1.74.0 parallel_4.2.0 igraph_1.3.1
[109] pkgconfig_2.0.3 km.ci_0.5-6 piano_2.12.0
[112] xml2_1.3.3 annotate_1.74.0 vipor_0.4.5
[115] bslib_0.3.1 XVector_0.36.0 drc_3.0-1
[118] rvest_1.0.2 digest_0.6.29 Biostrings_2.64.0
[121] rmarkdown_2.14 cellranger_1.1.0 fastmatch_1.1-3
[124] survMisc_0.5.6 shiny_1.7.1 gtools_3.9.2
[127] nlme_3.1-157 lifecycle_1.0.1 jsonlite_1.8.0
[130] carData_3.0-5 fansi_1.0.3 pillar_1.7.0
[133] lattice_0.20-45 KEGGREST_1.36.0 fastmap_1.1.0
[136] httr_1.4.3 plotrix_3.8-2 survival_3.3-1
[139] glue_1.6.2 png_0.1-7 bit_4.0.4
[142] stringi_1.7.6 sass_0.4.1 blob_1.2.3
[145] caTools_1.18.2 memoise_2.0.1