In sgosline/mpnstXenoModeling: MPNST Xenograft Modeling

For each of the drugs and models of interest we would like to plot all of the dose response curves in the same cformat so that they can be combined.

knitr::opts_chunk$set(echo = TRUE)

require(remotes)
if(!require(mpnstXenoModeling)){
  remotes::install_github('sgosline/mpnstXenoModeling')
  library(mpnstXenoModeling)
}

if(!require('DT')){
  install.packages("DT")
  library(leapr)
}

library(dplyr)
library(tidyr)
library(ggrepel)

Load drug data and expression data

if(!exists("dataLoaded")){
  dataLoaded=TRUE
  loadPDXData()

  mtDrugData <- loadMicrotissueDrugData()
    res<-loadPDXDrugData()
  pdxDrugStats<-res$summary
  drugData=res$dose
}

Plot drugs

We now want to plot drug performance across samples of interest, so we can do a side by side of drugs and their combinations.

##these are the drugs we want to evaluate!!!
drug.list<-list(MirdaTrab=c('Mirdametinib','Mirdametinib;Trabectedin','Trabectedin'),
                OlaTrab=c('Olaparib','Olaparib;Trabectedin','Trabectedin'),
                ShpRibo=c('SHP099','Ribociclib;SHP099','Ribociclib'),
                ShpTram=c('SHP099','Trametinib','SHP099;Trametinib'))
drug.list.inc<-lapply(drug.list,tolower)
eval.drugs.pdx<-c('mirdametinib','mirdametinib+trabectedin','olaparib','trabectedin','ribociclib','trametinib','trametinib+ribociclib')

eval.samps<-c('MN-2','WU-225','JH-2-079c','JH-2-031','MN-1','WU-368')
eval.samps.inc <- c('MN-2', 'JH-2-079c') #other samples available: JHU 2-002, JHU 2-103

Microtissue Metrics

The first thing we need to do is establish good metrics for evaluating drug sensitivity. We experiment with a few here.

We are focusing on just 3 drugs and 2 combinations at this stage, across 4 PDX/MT/cell line combinations.

Microtissue Dose Response

library(cowplot)
mtd<-loadMicrotissueMetadata()
mtDrugData<-getMicroTissueDrugData(mtd)

##commented this out as it doesn't work. 
lapply(names(drug.list), function(drugGroup) {
  mt.subs <- subset(mtDrugData, CellLine %in% eval.samps) %>%
    subset(DrugCol %in% drug.list[[drugGroup]])

   samp.plots <- lapply(eval.samps, function(samp)
     plotMTTreatmentByDrug(mt.subs, samp))

   ggsave(
     paste0('SampleMT', drugGroup, 'plots.png'),
     cowplot::plot_grid(plotlist = samp.plots, ncol = 2),
     height = 12,
     width = 12
   )


  drug.plots<-lapply(intersect(drug.list[[drugGroup]],mtDrugData$DrugCol),
                     function(drug) plotMTTreatmentBySample(mt.subs,drug))

  ggsave(paste0('DrugMT',drugGroup,'plots.png'),
         cowplot::plot_grid(plotlist = drug.plots,ncol=2),height=12,width=12)
})

PDX Dose Response

res=loadPDXDrugData()

plots <- drugData %>%
  subset(Sample %in% eval.samps) %>%
  subset(drug %in% eval.drugs.pdx) %>%
  group_by(Sample, batch, .drop = F) %>%
  group_map( ~ plotPDXTreatmentBySample(.x), .keep = TRUE)

ggsave('SamplePDXplots.png',
       cowplot::plot_grid(plotlist = plots, ncol = 2))

Incucyte Dose Response

icyteData<-loadIncucyteData()
lapply(names(drug.list.inc),function(drugGroup){
  inc.subs<-subset(icyteData,model_system_name%in%eval.samps.inc)%>%
  subset(experimentalCondition%in%drug.list.inc[[drugGroup]])
  inc.subs
  samp.plots<-lapply(eval.samps.inc,function(samp) 
    plotIncTreatmentByDrug(inc.subs,samp))

  ggsave(paste0('SampleInc',drugGroup,'plots.png'),
         cowplot::plot_grid(plotlist = samp.plots,ncol=2),height=6,width=12)

#  drug.plots<-lapply(intersect(drug.list[[drugGroup]],mtDrugData$DrugCol),
  #                   function(drug) plotMTTreatmentBySample(mt.subs,drug))

 # ggsave(paste0('DrugMT',drugGroup,'plots.png'),
  #       cowplot::plot_grid(plotlist = drug.plots,ncol=2),height=12,width=12)
})