In MalgorzataOles/BloodCancerMultiOmics2017: "Drug-perturbation-based stratification of blood cancer" by Dietrich S, Oles M, Lu J et al. - experimental data and complete analysis

library("BloodCancerMultiOmics2017")
library("Biobase")
library("ggbeeswarm")
library("ggplot2")
library("gridExtra")
library("dplyr")

plotDir = ifelse(exists(".standalone"), "", "part15/")
if(plotDir!="") if(!file.exists(plotDir)) dir.create(plotDir)

Association between HSP90 inhibitor response and IGHV status

We investigated additional HSP90 inhibitors (ganetespib, onalespib) in 120 patient samples from the original cohort (CLL), for whom IGHV status was available.

Load the additional drug response dataset.

data(list= c("validateExp","lpdAll"))

Preparing table for association test and plotting.

plotTab <- filter(validateExp, Drug %in% c("Ganetespib", "Onalespib")) %>%
  mutate(IGHV = Biobase::exprs(lpdAll)["IGHV Uppsala U/M", patientID]) %>%
  filter(!is.na(IGHV)) %>%
  mutate(IGHV = as.factor(ifelse(IGHV == 1, "M","U")),
         Concentration = as.factor(Concentration))

Association test using Student's t-test.

pTab <- group_by(plotTab, Drug, Concentration) %>%
  do(data.frame(p = t.test(viab ~ IGHV, .)$p.value)) %>%
  mutate(p = format(p, digits =2, scientific = TRUE))

Bee swarm plot.

pList <- group_by(plotTab, Drug) %>% 
  do(plots = ggplot(., aes(x=Concentration, y = viab)) + 
       stat_boxplot(geom = "errorbar", width = 0.3,
                    position = position_dodge(width=0.6), 
                    aes(dodge = IGHV)) +
       geom_boxplot(outlier.shape = NA, position = position_dodge(width=0.6), 
                    col="black", width=0.5, aes(dodge = IGHV)) + 
       geom_beeswarm(size=1,dodge.width=0.6, aes(col=IGHV)) +
       theme_classic() +
       scale_y_continuous(expand = c(0, 0),breaks=seq(0,1.2,0.20)) +
       coord_cartesian(ylim = c(0,1.30)) +
       xlab("Concentration (µM)") + ylab("Viability") + 
       ggtitle(unique(.$Drug)) +
       geom_text(data=filter(pTab, Drug == unique(.$Drug)), y = 1.25, 
                 aes(x=Concentration, label=sprintf("p=%s",p)),
                 size = 4.5) + 
      theme(axis.line.x = element_blank(),
            axis.ticks.x = element_blank(),
             axis.text  = element_text(size=15),
            axis.title = element_text(size =15),
             legend.text = element_text(size=13),
            legend.title = element_text(size=15),
             plot.title = element_text(face="bold", hjust=0.5, size=17),
             plot.margin = unit(c(0.5,0.5,0.5,0.5), "cm"))) 
grid.arrange(grobs = pList$plots, ncol =2)

The HSP90 inhibitors had higher activity in U-CLL, consistent with the result for AT13387. These data suggest that the finding of BCR (IGHV mutation) specific effects appears to be a compound class effect and further solidifies the results.

Association between MEK/ERK inhibitor response and trisomy12

To further investigate the association of trisomy 12 and MEK dependence, we investigated additional MEK and ERK inhibitors (cobimetinib, SCH772984 and trametinib) in 119 patients from the original cohort, for whom trisomy 12 status was available.

Preparing table for association test and plotting.

plotTab <- filter(validateExp, Drug %in%
                    c("Cobimetinib","SCH772984","Trametinib")) %>%
  mutate(Trisomy12 = Biobase::exprs(lpdAll)["trisomy12", patientID]) %>%
  filter(!is.na(Trisomy12)) %>%
  mutate(Trisomy12 = as.factor(ifelse(Trisomy12 == 1, "present","absent")),
         Concentration = as.factor(Concentration))

Association test using Student's t-test.

pTab <- group_by(plotTab, Drug, Concentration) %>% 
  do(data.frame(p = t.test(viab ~ Trisomy12, .)$p.value)) %>%
  mutate(p = format(p, digits =2, scientific = FALSE))

Bee swarm plot.

pList <- group_by(plotTab, Drug) %>% 
  do(plots = ggplot(., aes(x=Concentration, y = viab)) + 
       stat_boxplot(geom = "errorbar", width = 0.3,
                    position = position_dodge(width=0.6), 
                    aes(dodge = Trisomy12)) +
       geom_boxplot(outlier.shape = NA, position = position_dodge(width=0.6), 
                    col="black", width=0.5, aes(dodge = Trisomy12)) + 
       geom_beeswarm(size=1,dodge.width=0.6, aes(col=Trisomy12)) +
       theme_classic() +
       scale_y_continuous(expand = c(0, 0),breaks=seq(0,1.2,0.2)) +
       coord_cartesian(ylim = c(0,1.3)) +
       xlab("Concentration (µM)") + ylab("Viability") + 
       ggtitle(unique(.$Drug)) +
       geom_text(data=filter(pTab, Drug == unique(.$Drug)), y = 1.25, 
                 aes(x=Concentration, label=sprintf("p=%s",p)), size = 5) + 
       theme(axis.line.x = element_blank(),
             axis.ticks.x = element_blank(),
             axis.text  = element_text(size=15),
             axis.title = element_text(size =15),
             legend.text = element_text(size=13),
             legend.title = element_text(size=15),
             plot.title = element_text(face="bold", hjust=0.5, size=17),
             plot.margin = unit(c(0.5,0,0.5,0), "cm"))) 

grid.arrange(grobs = pList$plots, ncol =1)

Consistent with the data from the screen, samples with trisomy 12 showed higher sensitivity to MEK/ERK inhibitors.

sessionInfo()

rm(list=ls())

MalgorzataOles/BloodCancerMultiOmics2017 documentation built on March 29, 2024, 2:29 p.m.