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immunotherapy_analysis.R
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# Load relevant libraries
library(data.table)
library(ggplot2)
library(grid)
library(gridExtra)
library(pROC)
library(RColorBrewer)
library(scales)
# Set working directory
setwd("YOUR DIRECTORY HERE") ## UPDATE THIS DIRECTORY
##### Data loading/pre-processing #####
# Load data
immunorx <- read.table('immunotherapy_data_table.tsv', sep='\t', header=T, row.names = NULL)
# Add up indels
immunorx$Total_indels <- immunorx$Inframe_insertions + immunorx$Inframe_deletions + immunorx$Frameshift_deletions + immunorx$Frameshift_insertions
immunorx$Frameshift_indels <- immunorx$Frameshift_deletions + immunorx$Frameshift_insertions
immunorx$Inframe_indels <- immunorx$Inframe_insertions + immunorx$Inframe_deletions
immunorx$Insertions <- immunorx$Inframe_insertions + immunorx$Frameshift_insertions
immunorx$Deletions <- immunorx$Inframe_deletions + immunorx$Frameshift_deletions
# Adjust mutational burdens by coverage
immunorx$Total_mutations_adj <- immunorx$Total_mutations/immunorx$Coverage
immunorx$SNVs_adj <- immunorx$SNVs/immunorx$Coverage
immunorx$Inframe_ins_adj <- immunorx$Inframe_insertions/immunorx$Coverage
immunorx$Inframe_del_adj <- immunorx$Inframe_deletions/immunorx$Coverage
immunorx$Frameshift_ins_adj <- immunorx$Frameshift_insertions/immunorx$Coverage
immunorx$Frameshift_del_adj <- immunorx$Frameshift_deletions/immunorx$Coverage
immunorx$Total_indels_adj <- immunorx$Total_indels/immunorx$Coverage
immunorx$Frameshift_indels_adj <- immunorx$Frameshift_indels/immunorx$Coverage
immunorx$Inframe_indels_adj <- immunorx$Inframe_indels/immunorx$Coverage
immunorx$Insertions_adj <- immunorx$Insertions/immunorx$Coverage
immunorx$Deletions_adj <- immunorx$Deletions/immunorx$Coverage
immunorx$Muse_variants_adj <- immunorx$Muse_variants/immunorx$Coverage
immunorx$Mutect_variants_adj <- immunorx$Mutect_variants/immunorx$Coverage
immunorx$Pindel_variants_adj <- immunorx$Pindel_variants/immunorx$Coverage
immunorx$Radia_variants_adj <- immunorx$Radia_variants/immunorx$Coverage
immunorx$Somaticsniper_variants_adj <- immunorx$Somaticsniper_variants/immunorx$Coverage
immunorx$Varscan_variants_adj <- immunorx$Varscan_variants/immunorx$Coverage
immunorx$AtoC_adj <- immunorx$AtoC/immunorx$Coverage
immunorx$AtoG_adj <- immunorx$AtoG/immunorx$Coverage
immunorx$AtoT_adj <- immunorx$AtoT/immunorx$Coverage
immunorx$CtoA_adj <- immunorx$CtoA/immunorx$Coverage
immunorx$CtoG_adj <- immunorx$CtoG/immunorx$Coverage
immunorx$CtoT_adj <- immunorx$CtoT/immunorx$Coverage
immunorx$Synonymous_SNVs_adj <- immunorx$Synonymous_SNVs/immunorx$Coverage
immunorx$Nonsynonymous_SNVs_adj <- immunorx$Nonsynonymous_SNVs/immunorx$Coverage
immunorx$Start_codon_mutations_adj <- immunorx$Start_codon_mutations/immunorx$Coverage
immunorx$Stop_codon_mutations_adj <- immunorx$Stop_codon_mutations/immunorx$Coverage
# Create data tables for each disease
melanoma <- immunorx[immunorx$Disease == 'melanoma',]
RCC <- immunorx[immunorx$Disease == 'RCC',]
NSCLC <- immunorx[immunorx$Disease == 'NSCLC',]
MMR <- subset(immunorx, immunorx$Disease == 'colon' | immunorx$Disease == 'endometrial' | immunorx$Disease == 'thyroid')
prostate <- immunorx[immunorx$Disease == 'prostate',]
##### Mutation/neoepitope burdens #####
# Find median mutation burdens
median(immunorx$Total_mutations)
median(immunorx$Total_mutations[immunorx$Disease == 'melanoma'])
median(immunorx$Total_mutations[immunorx$Disease == 'NSCLC'])
median(immunorx$Total_mutations[immunorx$Disease == 'RCC'])
median(immunorx$Total_mutations[immunorx$Disease == 'prostate'])
median(MMR$Total_mutations)
median(immunorx$Total_mutations_adj)
median(immunorx$Total_mutations_adj[immunorx$Disease == 'melanoma'])
median(immunorx$Total_mutations_adj[immunorx$Disease == 'NSCLC'])
median(immunorx$Total_mutations_adj[immunorx$Disease == 'RCC'])
median(immunorx$Total_mutations_adj[immunorx$Disease == 'prostate'])
median(MMR$Total_mutations_adj)
# Find average % of SNVs/indels
median(immunorx$SNVs/immunorx$Total_mutations)
median(immunorx$Total_indels[immunorx$Disease == 'melanoma']/immunorx$Total_mutations[immunorx$Disease == 'melanoma'])
median(immunorx$Total_indels[immunorx$Disease == 'NSCLC']/immunorx$Total_mutations[immunorx$Disease == 'NSCLC'])
median(immunorx$Total_indels[immunorx$Disease == 'RCC']/immunorx$Total_mutations[immunorx$Disease == 'RCC'])
median(immunorx$Total_indels[immunorx$Disease == 'prostate']/immunorx$Total_mutations[immunorx$Disease == 'prostate'])
median(MMR$Total_indels/MMR$Total_mutations) # 31.2
# Find median neoepitope burdens
median(immunorx$MHCnuggets_eps)
median(immunorx$MHCnuggets_ClassI_eps)
median(immunorx$MHCnuggets_ClassII_eps)
median(melanoma$MHCnuggets_eps)
median(melanoma$MHCnuggets_ClassI_eps)
median(melanoma$MHCnuggets_ClassII_eps)
median(MMR$MHCnuggets_eps)
median(MMR$MHCnuggets_ClassI_eps)
median(MMR$MHCnuggets_ClassII_eps)
median(NSCLC$MHCnuggets_eps)
median(NSCLC$MHCnuggets_ClassI_eps)
median(NSCLC$MHCnuggets_ClassII_eps)
median(RCC$MHCnuggets_eps)
median(RCC$MHCnuggets_ClassI_eps)
median(RCC$MHCnuggets_ClassII_eps)
median(prostate$MHCnuggets_eps)
median(prostate$MHCnuggets_ClassI_eps)
median(prostate$MHCnuggets_ClassII_eps)
##### Plotting burdens #####
# Function to plot raw mutational burden
plot_mutation_burden <- function(disease, start_pos, end_pos){
for (i in 1:length(disease$Total_mutations)){
if (disease$MSI_status[i] == 1){
color <- 'red'
} else {
color <- 'black'
}
points(i*1.75/length(disease$Total_mutations)+start_pos+0.1,log10(disease$Total_mutations[i]),pch=20, col=color)
}
med_val <- median(log10(disease$Total_mutations))
points(c(start_pos+0.2,end_pos-0.2),c(med_val,med_val),col="firebrick",type="l")
}
# Function to plot coverage-adjusted mutational burden
plot_adj_burden <- function(disease, start_pos, end_pos){
for (i in 1:length(disease$Total_mutations_adj)){
if (disease$MSI_status[i] == 1){
color <- 'red'
} else {
color <- 'black'
}
points(i*1.75/length(disease$Total_mutations_adj)+start_pos+0.1,log10(disease$Total_mutations_adj[i]),pch=20,col=color)
}
med_val <- median(log10(disease$Total_mutations_adj))
points(c(start_pos+0.2,end_pos-0.2),c(med_val,med_val),col="firebrick",type="l")
}
# Function to plot FS indel burden
plot_fs_indel_burden <- function(disease, start_pos, end_pos){
for (i in 1:length(disease$Frameshift_indels)){
if (disease$MSI_status[i] == 1){
color <- 'red'
} else {
color <- 'black'
}
points(i*1.75/length(disease$Frameshift_indels)+start_pos+0.1,log10(disease$Frameshift_indels[i]),
pch=20, col=color)
}
med_val <- median(log10(disease$Frameshift_indels))
points(c(start_pos+0.2,end_pos-0.2),c(med_val,med_val),col="firebrick",type="l")
}
# Function to plot deletion burden
plot_inframe_indel_burden <- function(disease, start_pos, end_pos){
for (i in 1:length(disease$Inframe_indels)){
if (disease$MSI_status[i] == 1){
color <- 'red'
} else {
color <- 'black'
}
points(i*1.75/length(disease$Inframe_indels)+start_pos+0.1,log10(disease$Inframe_indels[i]),
pch=20, col=color)
}
med_val <- median(log10(disease$Inframe_indels))
points(c(start_pos+0.2,end_pos-0.2),c(med_val,med_val),col="firebrick",type="l")
}
# Function to plot neoepitope burden
plot_binding_burden <- function(disease, start_pos, end_pos){
for (i in 1:length(disease$MHCnuggets_eps)){
if (disease$MSI_status[i] == 1){
color <- 'red'
} else {
color <- 'black'
}
points(i*1.75/length(disease$MHCnuggets_eps)+start_pos+0.1,log10(disease$MHCnuggets_eps[i]),
pch=20, col=color)
}
med_val <- median(log10(disease$MHCnuggets_eps))
points(c(start_pos+0.2,end_pos-0.2),c(med_val,med_val),col="firebrick",type="l")
}
# Layout combined adjusted mutation burden/neoepitope burden plot - Figure 1
layout(matrix(c(1,2), nrow = 1, byrow=TRUE))
par(mar=c(4,5,0.5,1))
# Coverage - adjusted mutations
MMR <- MMR[order(MMR$Total_mutations_adj),]
melanoma <- melanoma[order(melanoma$Total_mutations_adj),]
NSCLC <- NSCLC[order(NSCLC$Total_mutations_adj),]
RCC <- RCC[order(RCC$Total_mutations_adj),]
prostate <- prostate[order(prostate$Total_mutations_adj),]
plot(0,0,xlim=c(0.2,9.8),ylim=c(-1,3),type="n",axes=F,xlab="",
ylab="Mutation burden (# somatic variants/Mbp)", cex.lab=1.75)
axis(1, at = seq(1, 9, 2), labels = c("Prostate\nn=10", "NSCLC\nn=34", "RCC\nn=57",
"Melanoma\nn=285", "MMR\ndeficient\nn=28"),
cex.axis=0.7, tick=F, padj=0.5)
axis(2, at = seq(-1,3,1), labels = c(0.1,1,10,100,1000), cex.axis=1)
rect(xleft=2, xright=4, ytop=6, ybottom=-10, col="lightgray", border=NA)
rect(xleft=6, xright=8, ytop=6, ybottom=-10, col="lightgray", border=NA)
box(lty="solid", col="black")
mtext("A", side=3, line=-1.2, at=-3, cex=2)
plot_adj_burden(prostate,0,2)
plot_adj_burden(NSCLC,2,4)
plot_adj_burden(RCC,4,6)
plot_adj_burden(melanoma,6,8)
plot_adj_burden(MMR,8,10)
# Neoepitope
MMR <- MMR[order(MMR$MHCnuggets_eps),]
melanoma <- melanoma[order(melanoma$MHCnuggets_eps),]
NSCLC <- NSCLC[order(NSCLC$MHCnuggets_eps),]
RCC <- RCC[order(RCC$MHCnuggets_eps),]
prostate <- prostate[order(prostate$MHCnuggets_eps),]
plot(0,0,xlim=c(0.2,9.8),ylim=c(0,6.5),type="n",axes=F,xlab="",
ylab="Neoepitope burden (#)", cex.lab=1.75)
axis(1, at = seq(1, 9, 2), labels = c( "NSCLC\nn=34", "Prostate\nn=10",
"Melanoma\nn=285", "RCC\nn=57", "MMR\ndeficient\nn=28"),
cex.axis=0.7, tick=F, padj=0.5)
axis(2, at = seq(1,6,1), labels = c("10","100","1000","10000","100000","1000000"), cex.axis=1)
rect(xleft=2, xright=4, ytop=8, ybottom=-1, col="lightgray", border=NA)
rect(xleft=6, xright=8, ytop=8, ybottom=-1, col="lightgray", border=NA)
box(lty="solid", col="black")
mtext("B", side=3, line=-1.2, at=-3, cex=2)
plot_binding_burden(NSCLC,0,2)
plot_binding_burden(prostate,2,4)
plot_binding_burden(melanoma,4,6)
plot_binding_burden(RCC,6,8)
plot_binding_burden(MMR,8,10)
layout(matrix(c(1), nrow = 1, byrow=TRUE))
# Plot raw mutational burden - Supplementary Figure 2
MMR <- MMR[order(MMR$Total_mutations),]
melanoma <- melanoma[order(melanoma$Total_mutations),]
NSCLC <- NSCLC[order(NSCLC$Total_mutations),]
RCC <- RCC[order(RCC$Total_mutations),]
prostate <- prostate[order(prostate$Total_mutations),]
par(mar=c(5,7,2,5))
plot(0,0,xlim=c(0.2,9.8),ylim=c(1.7,4.9),type="n",axes=F,xlab="",ylab="Total mutation burden (raw #)",
cex.lab=2)
axis(1, at = seq(1, 9, 2), labels = c("Prostate\nn=10", "NSCLC\nn=34",
"Melanoma\nn=302", "RCC\nn=57", "MMR\ndeficient\nn=28"),
cex.axis=1.4, tick=F, padj=0.7)
axis(2, at = seq(0,5,1), labels = c("0","10","100","1000","10000","100000"), cex.axis=1.5)
rect(xleft=2, xright=4, ytop=6, ybottom=-1, col="lightgray", border=NA)
rect(xleft=6, xright=8, ytop=6, ybottom=-1, col="lightgray", border=NA)
box(lty="solid", col="black")
plot_mutation_burden(prostate,0,2)
plot_mutation_burden(NSCLC,2,4)
plot_mutation_burden(melanoma,4,6)
plot_mutation_burden(RCC,6,8)
plot_mutation_burden(MMR,8,10)
# Plot indel burdens - Supplementary Figure 3
# First FS
layout(matrix(c(1,2), nrow = 1, byrow=TRUE))
par(mar=c(4,4.5,0.5,1))
MMR <- MMR[order(MMR$Frameshift_indels),]
melanoma <- melanoma[order(melanoma$Frameshift_indels),]
NSCLC <- NSCLC[order(NSCLC$Frameshift_indels),]
RCC <- RCC[order(RCC$Frameshift_indels),]
prostate <- prostate[order(prostate$Frameshift_indels),]
plot(0,0,xlim=c(0.2,9.8),ylim=c(0,5),type="n",axes=F,xlab="",ylab="Total FS indel burden (raw #)",
cex.lab=1.75)
axis(1, at = seq(1, 9, 2), labels = c("Prostate\nn=10", "RCC\nn=57", "NSCLC\nn=34",
"Melanoma\nn=285", "MMR\ndeficient\nn=28"),
cex.axis=0.7, tick=F, padj=0.7)
axis(2, at = seq(0,5,1), labels = seq(0,5,1), cex.axis=1)
rect(xleft=2, xright=4, ytop=6, ybottom=-1, col="lightgray", border=NA)
rect(xleft=6, xright=8, ytop=6, ybottom=-1, col="lightgray", border=NA)
box(lty="solid", col="black")
mtext("A", side=3, line=-1.2, at=-2.5, cex=2)
plot_fs_indel_burden(NSCLC,0,2)
plot_fs_indel_burden(prostate,2,4)
plot_fs_indel_burden(RCC,4,6)
plot_fs_indel_burden(melanoma,6,8)
plot_fs_indel_burden(MMR,8,10)
# Then in-frame
MMR <- MMR[order(MMR$Inframe_indels),]
melanoma <- melanoma[order(melanoma$Inframe_indels),]
NSCLC <- NSCLC[order(NSCLC$Inframe_indels),]
RCC <- RCC[order(RCC$Inframe_indels),]
prostate <- prostate[order(prostate$Inframe_indels),]
plot(0,0,xlim=c(0.2,9.8),ylim=c(0,5),type="n",axes=F,xlab="",ylab="Total in-frame indel burden (raw #)",
cex.lab=1.75)
axis(1, at = seq(1, 9, 2), labels = c("Prostate\nn=10", "RCC\nn=57", "NSCLC\nn=34",
"Melanoma\nn=285", "MMR\ndeficient\nn=28"),
cex.axis=0.7, tick=F, padj=0.7)
axis(2, at = seq(0,5,1), labels = seq(0,5,1), cex.axis=1)
rect(xleft=2, xright=4, ytop=6, ybottom=-1, col="lightgray", border=NA)
rect(xleft=6, xright=8, ytop=6, ybottom=-1, col="lightgray", border=NA)
box(lty="solid", col="black")
mtext("B", side=3, line=-1.2, at=-2.5, cex=2)
plot_inframe_indel_burden(prostate,0,2)
plot_inframe_indel_burden(NSCLC,2,4)
plot_inframe_indel_burden(melanoma,4,6)
plot_inframe_indel_burden(RCC,6,8)
plot_inframe_indel_burden(MMR,8,10)
layout(matrix(c(1), nrow = 1, byrow=TRUE))
##### Comparing mutational burden and neoepitope burden #####
# Correlation between burdens - Supplementary Figure 4
x <- immunorx$Total_mutations
y <- immunorx$MHCnuggets_eps
summary(rlm(y~x)) # Get slope/intercept
par(mar=c(4.5,5,0.75,1))
plot(x, y, ylim=c(0, 2800000), xlim=c(0,73600),
xlab="Total mutation burden (raw #)", ylab="Neoepitope burden (raw #)",
cex.lab=2, cex.axis=1.75, pch=19, cex=0.5, axes=F)
axis(1, at = seq(0, 60000, 20000), labels = seq(0, 60000, 20000), cex.axis=1.75)
axis(2, at = seq(0,2500000,500000),
labels = c("0","500000","1000000","1500000", "2000000", "2500000"), cex.axis=1.75)
abline(rlm(y~x), col='red')
text(65000, -700, labels="y = 5.82x - 619.59",cex=1.5, col="black") # adjust for slope/intercept
box(lty="solid", col="black")
# Get Pearson correlation
cor.test(x,y)
# Set up panel.cor
panel.cor <- function(x, y, digits=2, prefix="")
{
usr <- par("usr"); on.exit(par(usr))
par(usr = c(0, 1, 0, 1))
r <- abs(cor(x, y))
txt <- format(c(r, 0.123456789), digits=digits)[1]
txt <- paste(prefix, txt, sep="")
test <- cor.test(x,y)
# borrowed from printCoefmat
Signif <- symnum(test$p.value, corr = FALSE, na = FALSE,
cutpoints = c(0, 0.001, 0.01, 0.05, 0.1, 1),
symbols = c("***", "**", "*", ".", " "))
text(0.5, 0.5, txt, cex=1.25)
text(.8, .8, Signif, cex=1.25, col=2)
}
# Corplot for mutational burden across callers - Supplementary Figure 15
pairs(cbind(immunorx$Total_mutations, immunorx$Muse_variants, immunorx$Mutect_variants, immunorx$Radia_variants,
immunorx$Somaticsniper_variants, immunorx$Varscan_variants, immunorx$Pindel_variants),
lower.panel=panel.smooth, upper.panel=panel.cor,
labels = c('Consensus\ncalls', 'MuSE', 'MuTect', 'RADIA', 'SomaticSniper', 'VarScan', 'Pindel'),
cex.labels = 1.25)
# Variance across callers
caller_variance <- rep(0, length(immunorx$Patient))
max_diff <- rep(0, length(immunorx$Patient))
for (i in 1:length(immunorx$Patient)){
counts <- c(immunorx$Muse_variants[i], immunorx$Mutect_variants[i], immunorx$Pindel_variants[i],
immunorx$Radia_variants[i], immunorx$Somaticsniper_variants[i], immunorx$Varscan_variants[i])
caller_variance[i] <- mad(counts)
max_diff[i] <- max(counts) - min(counts)
}
immunorx$Caller_variation <- caller_variance
immunorx$Max_TMB_diff <- max_diff
immunorx$Percent_TMB_diff <- immunorx$Max_TMB_diff/immunorx$Total_mutations
median(immunorx$Caller_variation)
median(immunorx$Max_TMB_diff)
median(immunorx$Percent_TMB_diff)
# Plot variance across callers - Supplementary Figure 16
x <- immunorx$Total_mutations
y <- immunorx$Caller_variation
summary(lm(y~x)) # Get slope/intercept
par(mar=c(4.5,5,0.75,1))
plot(x, y,
xlab="Somatic mutations (consensus calls, raw #)", ylab="Variation in mutation count (med. absolute deviation)",
cex.lab=1.7, cex.axis=1.25, pch=19, cex=0.5)
abline(lm(y~x), col='red')
text(66000, -300, labels="y = 0.15x + 108.1",cex=1.5, col="black") # adjust for slope/intercept
cor.test(x,y)
##### Identity of neoepitopes across callers #####
# Load upset plot data
df <- read.table('epitope_upset.tsv', sep='\t', header=T)
df$overlap <- factor(df$overlap, levels=c("Consensus", "RADIA", "MuTect", "Pindel", "SomaticSniper",
"VarScan", "MuSE", "Pindel_Consensus", "MuTect_SomaticSniper",
"MuSE_MuTect_Consensus", "SomaticSniper_VarScan_Consensus",
"MuSE_MuTect_RADIA_Consensus", "RADIA_SomaticSniper_VarScan_Consensus",
"MuSE_MuTect_RADIA_VarScan_Consensus",
"MuSE_MuTect_SomaticSniper_VarScan_Consensus",
"MuSE_MuTect_RADIA_SomaticSniper_VarScan_Consensus",
"MuSE_MuTect_Pindel_RADIA_VarScan_Consensus",
"MuSE_MuTect_Pindel_RADIA_SomaticSniper_VarScan_Consensus", "Other"
)
)
df$tool <- factor(df$tool, levels=c('MuSE', 'MuTect', 'Pindel', 'RADIA', 'SomaticSniper', 'VarScan', 'Consensus'))
df$classification <- factor(df$classification, levels=c('present', 'absent', 'other'))
df <- df[df$overlap != 'Other',]
df$overlap <- factor(df$overlap, levels=c("Consensus", "RADIA", "MuTect", "Pindel", "SomaticSniper",
"VarScan", "MuSE", "Pindel_Consensus", "MuTect_SomaticSniper",
"MuSE_MuTect_Consensus", "SomaticSniper_VarScan_Consensus",
"MuSE_MuTect_RADIA_Consensus", "RADIA_SomaticSniper_VarScan_Consensus",
"MuSE_MuTect_RADIA_VarScan_Consensus",
"MuSE_MuTect_SomaticSniper_VarScan_Consensus",
"MuSE_MuTect_RADIA_SomaticSniper_VarScan_Consensus",
"MuSE_MuTect_Pindel_RADIA_VarScan_Consensus",
"MuSE_MuTect_Pindel_RADIA_SomaticSniper_VarScan_Consensus", "Other"
)
)
# Set up data for pie chart
p <- ggplot(df, aes(x="", y=proportion, fill=classification))+geom_bar(width = 1, stat = 'identity')+theme(axis.text = element_blank(),
axis.ticks = element_blank(),
panel.grid = element_blank())
pie <- p + coord_polar('y',start = 0) + scale_fill_manual(values=c("black", "white", "white"))
# Facet with tools (rows) and columns (overlap in recognition)
final_graph <- pie+facet_grid(rows = vars(tool), labeller=label_wrap_gen(width=18),
cols = vars(overlap), switch='y')+theme(axis.text = element_blank(),
axis.ticks = element_blank(),
panel.grid = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank(),
strip.text.x = element_blank(),
strip.text.y = element_text(angle=180,
size = rel(1.5)),
strip.background = element_rect(fill = 'white'),
legend.position = "none",
panel.background = element_rect(fill="white",
colour = 'white'),
panel.border = element_rect(color = "black", fill = NA, size = 0.5),
panel.spacing=unit(0, "lines"),
plot.margin=unit(c(-5,1.2,0,0), 'cm'))
# Load count data for bar graph
df2 <- read.table('epitope_upset_counts.tsv', sep='\t', header=T)
df2$overlap <- factor(df2$overlap, levels=c("Consensus", "RADIA", "MuTect", "Pindel", "SomaticSniper",
"VarScan", "MuSE", "Pindel_Consensus", "MuTect_SomaticSniper",
"MuSE_MuTect_Consensus", "SomaticSniper_VarScan_Consensus",
"MuSE_MuTect_RADIA_Consensus", "RADIA_SomaticSniper_VarScan_Consensus",
"MuSE_MuTect_RADIA_VarScan_Consensus",
"MuSE_MuTect_SomaticSniper_VarScan_Consensus",
"MuSE_MuTect_RADIA_SomaticSniper_VarScan_Consensus",
"MuSE_MuTect_Pindel_RADIA_VarScan_Consensus",
"MuSE_MuTect_Pindel_RADIA_SomaticSniper_VarScan_Consensus", "Other"))
df2$count_name <- as.character(df2$count)
# Create bar plot color palette
color_pallette <- brewer.pal(7, "Set2")
full_color_pallette <- c(rep("gray", 7))
for (i in 1:5){
full_color_pallette <- append(full_color_pallette, rep(color_pallette[i], 2))
}
full_color_pallette <- append(full_color_pallette, c(color_pallette[6], color_pallette[7]))
# Create bar plot
p2 <- ggplot(df2,
aes(x=overlap,
y=count))+geom_bar(stat = 'identity',
fill = full_color_pallette,
color='black')+geom_text(aes(label=count_name),
hjust=0.5,
vjust=-0.2,
size = rel(2.5),
color="black")+theme(axis.text.x = element_blank(),
panel.grid = element_blank(),
axis.title.x = element_blank(),
axis.text = element_text(size = rel(1.5)),
axis.title.y = element_text(size = rel(1.5),
margin = margin(t = 0, r = 15, b = 0, l = 0)),
panel.background = element_rect(fill="white", colour = 'white'),
axis.line.y.left = element_line(colour = 'black'),
axis.ticks.x = element_blank(),
plot.margin=unit(c(2,0.2,0,1.1), 'cm'))+scale_y_continuous(trans = log10_trans(),
breaks = trans_breaks("log10", function(x) 10^x),
labels = trans_format("log10", math_format(10^.x)))+annotate("text",
label = "*",
x = 19,
y = 8e5,
size=rel(10))
# Arrange plots - Supplementary Figure 19
grid.arrange(p2+labs(y = "Neopeptide sequences (#)"), final_graph, layout_matrix=rbind(c(1), c(2)))
##### Driver mutation analysis #####
drivers <- read.table('drivers.tsv', sep='\t', header=T)
drivers$Percent_presented <- drivers$Presented_clinvar_variants/drivers$Total_clinvar_variants
drivers$Percent_eps_presented <- drivers$Presented_clinvar_epitopes/drivers$Total_clinvar_neopeptides
length(drivers$Total_clinvar_variants[drivers$Total_clinvar_variants > 0])
mean(drivers$Percent_presented[drivers$Total_clinvar_variants != 0])
mean(drivers$Percent_eps_presented[drivers$Total_clinvar_neopeptides != 0])
mean(drivers$Total_clinvar_variants)
mean(drivers$Total_clinvar_variants[drivers$Total_clinvar_variants > 0])
##### HLA presentation trends #####
ep_binders <- read.table('ep_binders.tsv', sep='\t', header=T)
mut_binders <- read.table('mut_binders.tsv', sep='\t', header=T)
ep_drivers <- read.table('driver_ep_binders.tsv', sep='\t', header=T)
mut_drivers <- read.table('driver_mut_binders.tsv', sep='\t', header=T)
immunorx$Eps_presented <- immunorx$MHCnuggets_eps/immunorx$Total_comprehensive_neoepitopes
median(immunorx$Eps_presented)
length(ep_binders$Epitope[ep_binders$Allele_count > 0])/length(ep_binders$Epitope) # 0.1135
# Find HLA heterozygosity
allele_count <- rep(NA, length(mut_binders$Patient))
for (i in 1:length(mut_binders$Patient)){
pat <- as.character(mut_binders$Patient[i])
hla1 <- immunorx$Tumor_HLA1_count[immunorx$Patient == pat]
hla2 <- immunorx$Tumor_HLA2_count[immunorx$Patient == pat]
allele_count[i] <- hla1 + hla2
}
mut_binders$HLA_count <- as.factor(allele_count)
allele_count <- rep(NA, length(mut_drivers$Patient))
for (i in 1:length(mut_drivers$Patient)){
pat <- as.character(mut_drivers$Patient[i])
hla1 <- immunorx$Tumor_HLA1_count[immunorx$Patient == pat]
hla2 <- immunorx$Tumor_HLA2_count[immunorx$Patient == pat]
allele_count[i] <- hla1 + hla2
}
mut_drivers$HLA_count <- as.factor(allele_count)
# Correlation plots
muts <- hist(mut_binders$Allele_count, breaks=0:9, plot=F)$counts/length(mut_binders$Allele_count)
drivers <- hist(mut_drivers$Allele_count, breaks=0:9, plot=F)$counts/length(mut_drivers$Allele_count)
counter1 <- c(0:8, 0:8)
full_muts <- c(muts, drivers)
n <- c(hist(mut_binders$Allele_count, breaks=0:9, plot=F)$counts, hist(mut_drivers$Allele_count, breaks=0:9, plot=F)$counts)
categories <- c(rep('All', length(muts)), rep('Driver', length(drivers)))
df1 <- as.data.frame(cbind(counter1, full_muts, categories, n))
colnames(df1) <- c('Count', 'Alleles', 'Category', 'N')
df1$Alleles <- as.numeric(as.character(df1$Alleles))
df1$Count <- as.numeric(as.character(df1$Count))
df1$N <- as.numeric(as.character(df1$N))
df1$Category <- factor(df1$Category, levels = rev(levels(df1$Category)))
eps <- hist(ep_binders$Allele_count[ep_binders$Allele_count > 0], breaks=1:8, plot=F)$counts/length(ep_binders$Allele_count[ep_binders$Allele_count > 0])
driver_eps <- hist(ep_drivers$Allele_count[ep_drivers$Allele_count > 0], breaks=1:8, plot=F)$counts/length(ep_drivers$Allele_count[ep_drivers$Allele_count > 0])
full_eps <- c(eps, driver_eps)
n2 <- c(hist(ep_binders$Allele_count[ep_binders$Allele_count > 0], breaks=1:8, plot=F)$counts, hist(ep_drivers$Allele_count[ep_drivers$Allele_count > 0], breaks=1:8, plot=F)$counts)
counter2 <- c(1:7, 1:7)
categories <- c(rep('All', length(eps)), rep('Driver', length(driver_eps)))
df2 <- as.data.frame(cbind(counter2, full_eps, categories, n2))
colnames(df2) <- c('Count', 'Alleles', 'Category', 'N')
df2$Alleles <- as.numeric(as.character(df2$Alleles))
df2$Count <- as.numeric(as.character(df2$Count))
df2$N <- as.numeric(as.character(df2$N))
df2$Category <- factor(df2$Category, levels = rev(levels(df2$Category)))
p0 <- ggplot(df1,
aes(x=Count,
y=Alleles, shape=Category, colour=Category, fill=Category))+geom_point(aes(size = N), shape=1)+geom_smooth(method='loess',
formula=y~x)+scale_x_continuous(breaks=c(0:8))+scale_color_manual(values=c('blue', 'red'))+scale_fill_manual(values=c('#00BFC4', '#F8766D'))
p1 <- p0+labs(title="",
x="HLA Alleles (#)",
y = "Mutations (% of total)")+scale_y_continuous(labels=percent_format(accuracy = .0001,
suffix = ''))+theme(panel.grid = element_blank(),
axis.title.x = element_text(size = rel(1.08)),
axis.text = element_text(size = rel(1.08)),
axis.title.y = element_text(size = rel(1.08)),
panel.background = element_rect(fill="white", colour = 'white'),
axis.line.y.left = element_line(colour = 'black'),
axis.line.x.bottom = element_line(colour = 'black'),
axis.line.x.top = element_line(colour = 'black'),
axis.line.y.right = element_line(colour = 'black'),
plot.tag = element_text(size = rel(1.5)),
legend.position = "bottom",
plot.margin=unit(c(0.1,0.1,0.2,0.1), 'cm'))+ labs(tag = "B")
p2 <- ggplot(df2,
aes(x=Count,
y=Alleles, shape=Category, colour=Category, fill=Category))+geom_point(aes(size = N), shape=1)+scale_size(trans="log10", breaks=c(1,100,10000,1000000), labels=c("1","100","10000","1000000"))+geom_smooth(method='lm',
formula=y~x,
fullrange=T)+scale_color_manual(values=c('blue', 'red'))+scale_fill_manual(values=c('#00BFC4', '#F8766D'))
p3 <- p2+scale_y_continuous(trans = log10_trans(),
labels=percent_format(accuracy = .0001,
suffix = ''))+scale_x_continuous(breaks=c(1:7))+labs(title="",
x="HLA Alleles (#)",
y = "Neoepitopes (% of total)")+theme(panel.grid = element_blank(),
axis.title.x = element_text(size = rel(1.08)),
axis.text = element_text(size = rel(1.08)),
axis.title.y = element_text(size = rel(1.08)),
panel.background = element_rect(fill="white",
colour = 'white'),
axis.line.y.left = element_line(colour = 'black'),
axis.line.x.bottom = element_line(colour = 'black'),
axis.line.x.top = element_line(colour = 'black'),
axis.line.y.right = element_line(colour = 'black'),
plot.tag = element_text(size = rel(1.5)),
plot.margin=unit(c(0.1,0.1,0.2,0.1), 'cm'))+ labs(tag = "A")
hla_levels <- levels(mut_binders$HLA_count)
patient_count <- rep(NA, length(hla_levels))
patient_count_drivers <- rep(NA, length(hla_levels))
med_alleles <- rep(NA, length(hla_levels))
med_alleles_driver <- rep(NA, length(hla_levels))
percent_presentable <- rep(NA, length(hla_levels))
percent_drivers <- rep(NA, length(hla_levels))
for (i in 1:length(hla_levels)){
mut_df <- mut_binders[mut_binders$HLA_count == hla_levels[i],]
med <- median(mut_df$Allele_count[mut_df$Allele_count > 0])
perc <- length(mut_df$Allele_count[mut_df$Allele_count > 0])/length(mut_df$Allele_count)
med_alleles[i] <- med
percent_presentable[i] <- perc
patient_count[i] <- length(unique(mut_df$Patient))
driver_df <- mut_drivers[mut_drivers$HLA_count == hla_levels[i],]
med <- median(driver_df$Allele_count[driver_df$Allele_count > 0])
perc <- length(driver_df$Allele_count[driver_df$Allele_count > 0])/length(driver_df$Allele_count)
med_alleles_driver[i] <- med
percent_drivers[i] <- perc
patient_count_drivers[i] <- length(unique(driver_df$Patient))
}
df3 <- as.data.frame(cbind(c(hla_levels, hla_levels), c(med_alleles, med_alleles_driver),
c(percent_presentable, percent_drivers),
c(rep('All', length(hla_levels)), rep('Driver', length(hla_levels))),
c(patient_count, patient_count_drivers)))
colnames(df3) <- c('HLA_count', 'Median_number_presenting', 'Percent_variants_presentable', 'Category', 'N')
df3$HLA_count <- as.numeric(as.character(df3$HLA_count ))
df3$N <- as.numeric(as.character(df3$N ))
df3$Median_number_presenting <- as.numeric(as.character(df3$Median_number_presenting ))
df3$Percent_variants_presentable <- as.numeric(as.character(df3$Percent_variants_presentable))
df3$Category <- factor(df3$Category, levels = rev(levels(df3$Category)))
p4 <- ggplot(df3,
aes(x=HLA_count,
y=Percent_variants_presentable, shape=Category, colour=Category, fill=Category))+geom_point(aes(size = N), shape=1)+geom_smooth(method='lm',
formula=y~x, fullrange=T)+scale_color_manual(values=c('blue', 'red'))+scale_fill_manual(values=c('#00BFC4', '#F8766D'))
p5 <- p4+scale_y_continuous(labels=percent_format(accuracy = 0.1,
suffix = ''))+scale_x_continuous(breaks=c(6:12))+labs(title="",
x="Unique patient HLA Alleles (#)",
y = "Presentable variants (% of total)")+theme(panel.grid = element_blank(),
axis.title.x = element_text(size = rel(1.08)),
axis.text = element_text(size = rel(1.08)),
axis.title.y = element_text(size = rel(1.08)),
panel.background = element_rect(fill="white",
colour = 'white'),
axis.line.y.left = element_line(colour = 'black'),
axis.line.x.bottom = element_line(colour = 'black'),
axis.line.x.top = element_line(colour = 'black'),
axis.line.y.right = element_line(colour = 'black'),
plot.tag = element_text(size = rel(1.5)),
plot.margin=unit(c(0.1,0.1,0.2,0.1), 'cm'))+ labs(tag = "C")
# Figure 3
grid.arrange(p3, p1, p5, layout_matrix=rbind(c(1), c(2), c(3)))
# Supplementary Figure 7
p6 <- ggplot(df3,
aes(x=HLA_count,
y=Median_number_presenting))+geom_point()+geom_smooth(method='lm',
formula=y~x,
color='red', fullrange=T)
p7 <- p6+scale_x_continuous(breaks=c(6:12))+labs(title="",
x="Unique patient HLA Alleles (#)",
y = "Median alleles presenting per variant (#)")+theme(panel.grid = element_blank(),
axis.title.x = element_text(size = rel(1.5)),
axis.text = element_text(size = rel(1.25)),
axis.title.y = element_text(size = rel(1.5)),
panel.background = element_rect(fill="white", colour = 'white'),
axis.line.y.left = element_line(colour = 'black'),
axis.line.x.bottom = element_line(colour = 'black'),
axis.line.x.top = element_line(colour = 'black'),
axis.line.y.right = element_line(colour = 'black'),
plot.tag = element_text(size = rel(1.5)),
legend.position="bottom",
legend.text = element_text(size = rel(1.25)),
legend.title = element_text(size = rel(1.25)),
plot.margin=unit(c(0.1,0.1,0.2,0.1), 'cm'))
p0 <- ggplot(mut_binders,
aes(x=Disease,
y=Allele_count))+geom_violin(fill='gray')+labs(title="",
x="",
y = "Alleles presenting per mutation (#)")+scale_x_discrete(limits=c("MMR-deficient",
"melanoma",
"prostate",
"NSCLC",
"RCC"))
p1 <- p0 + geom_boxplot(width=0.1, fill="white")+theme(panel.grid = element_blank(),
axis.title.x = element_blank(),
axis.text = element_text(size = rel(1.25)),
axis.title.y = element_text(size = rel(1.5)),
panel.background = element_rect(fill="white",
colour = 'white'),
axis.line.y.left = element_line(colour = 'black'),
axis.line.x.bottom = element_line(colour = 'black'),
axis.line.x.top = element_line(colour = 'black'),
axis.line.y.right = element_line(colour = 'black'),
legend.position = "none",
plot.tag = element_text(size = rel(1.5)),
plot.margin=unit(c(0.1,0.1,0.5,0.1), 'cm'))+ labs(tag = "B")
p2 <- ggplot(ep_binders[ep_binders$Allele_count > 0,],
aes(x=Disease,
y=Allele_count))+geom_violin(fill='gray')+labs(title="",
x="",
y = "Alleles presenting per epitope (#)")+scale_x_discrete(limits=c("MMR-deficient",
"melanoma",
"prostate",
"NSCLC",
"RCC"))
p3 <- p2 + geom_boxplot(width=0.1, fill="white")+theme(panel.grid = element_blank(),
axis.title.x = element_blank(),
axis.text = element_text(size = rel(1.25)),
axis.title.y = element_text(size = rel(1.5)),
panel.background = element_rect(fill="white",
colour = 'white'),
axis.line.y.left = element_line(colour = 'black'),
axis.line.x.bottom = element_line(colour = 'black'),
axis.line.x.top = element_line(colour = 'black'),
axis.line.y.right = element_line(colour = 'black'),
legend.position = "none",
plot.tag = element_text(size = rel(1.5)),
plot.margin=unit(c(0.1,0.1,0.5,0.1), 'cm'))+ labs(tag = "A")
# Supplementary Figure 6
grid.arrange(p3, p1, layout_matrix=rbind(c(1), c(2)))
##### Compare author-reported burdens with our burdens #####
# Mutational burden - Supplementary figure 17
x <- immunorx$Total_mutations
y <- immunorx$Original_total_mutations
summary(rlm(y~x)) # Get slope/intercept
par(mar=c(4.5,5,0.75,1))
plot(x, y, ylim=c(0, 26115), xlim=c(0,73160),
xlab="Total mutation burden (raw #)", ylab="Author-reported mutation burden (raw #)",
cex.lab=2, cex.axis=1.75, pch=19, cex=0.5)
abline(rlm(y~x), col='red')
text(65500, -500, labels="y = 0.04x + 473.8",cex=1.5, col="black") # adjust for slope/intercept
cor.test(x,y)
# Nonsynonymous mutational burden - supplementary figure 20
x <- immunorx$Nonsynonymous_SNVs
y <- immunorx$Original_nonsynonymous_mutations
summary(rlm(y~x)) # Get slope/intercept
par(mar=c(4.5,5,0.75,1))
plot(x, y,
xlab="Nonsynonymous mutation burden (raw #)", ylab="Author-reported nonsynonymous mutation burden (raw #)",
cex.lab=1.7, cex.axis=1.25, pch=19, cex=0.5)
abline(rlm(y~x), col='red')
text(12500, -100, labels="y = 0.90x - 3.40",cex=1.5, col="black") # adjust for slope/intercept
cor.test(x,y)
# Neoantigen burden - supplementary figure 21
x <- immunorx$Manuscript_binding_eps
y <- immunorx$Original_neoantigens
summary(rlm(y~x)) # Get slope/intercept
par(mar=c(4.5,5,0.75,1))
plot(x, y,
xlab="Neoepitope burden (# MHC-binding)", ylab="Author-reported neoepitope burden (# MHC-binding)",
cex.lab=1.7, cex.axis=1.25, pch=19, cex=0.5)
abline(rlm(y~x), col='red')
text(150000, 16500, labels="y = -0.0015x + 377.60",cex=1.5, col="black") # adjust for slope/intercept
cor.test(x,y)
# Disagreement in rank across all burdens - supplementary figure 19
par(mar=c(4.5,4.75,0.5,0.5))
plot(0,0,xlim=c(0,100),ylim=c(0,50),type="n",axes=T,xlab="Burden 'high' threshold (%ile)",
ylab="Disagreement (% of patients)", cex.lab=2, cex.axis=1.5)
box(lty="solid", col="black")
# plot nonsynonymous mutations
temp <- function(x) {
length(which(immunorx$Nonsynonymous_SNVs>=quantile(immunorx$Nonsynonymous_SNVs,x,na.rm=TRUE) & immunorx$Original_nonsynonymous_mutations < quantile(immunorx$Original_nonsynonymous_mutations,x,na.rm=TRUE))) + length(which(immunorx$Nonsynonymous_SNVs<quantile(immunorx$Nonsynonymous_SNVs,x,na.rm=TRUE) & immunorx$Original_nonsynonymous_mutations >=quantile(immunorx$Original_nonsynonymous_mutations,x,na.rm=TRUE)))
}
data <- cbind(1:99, 0)
for (i in 1:99) {
data[i,2] <- 100*temp(i/100)/length(which(!is.na(immunorx$Original_nonsynonymous_mutations)))
}
points(data[,1], data[,2], type="l", col='gray')
# plot all mutations
temp <- function(x) {
length(which(immunorx$Total_mutations>=quantile(immunorx$Total_mutations,x,na.rm=TRUE) & immunorx$Original_total_mutations < quantile(immunorx$Original_total_mutations,x,na.rm=TRUE))) + length(which(immunorx$Total_mutations<quantile(immunorx$Total_mutations,x,na.rm=TRUE) & immunorx$Original_total_mutations >=quantile(immunorx$Original_total_mutations,x,na.rm=TRUE)))
}
data <- cbind(1:99, 0)
for (i in 1:99) {
data[i,2] <- 100*temp(i/100)/length(which(!is.na(immunorx$Original_total_mutations)))
}
points(data[,1], data[,2], type="l", col='black')
# plot epitopes
temp <- function(x) {
length(which(immunorx$Manuscript_binding_eps>=quantile(immunorx$Manuscript_binding_eps,x,na.rm=TRUE) & immunorx$Original_neoantigens < quantile(immunorx$Original_neoantigens,x,na.rm=TRUE))) + length(which(immunorx$Manuscript_binding_eps<quantile(immunorx$Manuscript_binding_eps,x,na.rm=TRUE) & immunorx$Original_neoantigens >=quantile(immunorx$Original_neoantigens,x,na.rm=TRUE)))
}
data <- cbind(1:99, 0)
for (i in 1:99) {
data[i,2] <- 100*temp(i/100)/length(which(!is.na(immunorx$Original_neoantigens)))
}
points(data[,1], data[,2], type="l", col='blue')
rect(xleft=66, xright=103, ytop=50, ybottom=42, col="white", border="black")
rect(xleft=67, xright=72, ytop=49.5, ybottom=47.5, col="black", border="black")
rect(xleft=67, xright=72, ytop=47, ybottom=45, col="gray", border="black")
rect(xleft=67, xright=72, ytop=44.5, ybottom=42.5, col="blue", border="black")
text(81.5, 48.5, 'Total mutations')
text(88, 46, 'Nonsynonymous mutations')
text(80.5, 43.5, 'Neoantigens')
##### Extended neoepitope burden #####
extended <- read.table('summarized_epitope_burden.tsv', sep='\t', header=T)
colnames(extended)[2] <- "Tumor_ID"
merged_extended <- merge(immunorx, extended, by=c("Patient", "Tumor_ID"))
##### Incorporate neojunction burden #####
jx_data <- read.table('patient_jx_burdens.tsv', sep='\t', header=T)
merged_jx <- merge(immunorx, jx_data, by=c("Patient", "Tumor_ID"))
median(jx_data$Jx_burden)
median(merged_jx$Jx_burden[merged_jx$Disease == 'melanoma'])
median(merged_jx$Jx_burden[merged_jx$Disease == 'NSCLC'])
median(merged_jx$Jx_burden[merged_jx$Disease == 'prostate'])
median(merged_jx$Jx_burden[merged_jx$Disease == 'RCC'])
median(merged_jx$Jx_burden[merged_jx$Disease %in% c("colon", "endometrial", "thyroid")])
##### Incorporate retained introns #####
ri <- read.table('full_intron_retention_burden.tsv', sep='\t', header=T)
# Merge data
full_merged_rna <- merge(merged_extended, jx_data, by=c("Patient", "Tumor_ID"))
fuller_merged_rna <- merge(full_merged_rna, ri, by=c("Patient", "Tumor_ID"))
merged_rna <- fuller_merged_rna[!is.na(fuller_merged_rna$Jx_burden)]
median(merged_rna$Intron_burden)
median(merged_rna$Intron_burden[merged_rna$Intron_burden > 0])
median(merged_rna$Binding_intron_epitope_burden)
median(merged_rna$Binding_intron_epitope_burden[merged_rna$Binding_intron_epitope_burden > 0])
complete_merged_rna <- merge(merged_extended, jx_data, by=c("Patient", "Tumor_ID"), all=T)
final_complete_merged_rna <- merge(complete_merged_rna, ri, by=c("Patient", "Tumor_ID"), all=T)
write.table(final_complete_merged_rna, file="merged_rna_data.tsv", quote=F, col.names = T, row.names = F)
##### Investigating TVB #####
merged_rna$TVB <- merged_rna$Total_mutations + merged_rna$Jx_burden + merged_rna$Intron_burden
merged_rna$Delta_burden <- merged_rna$TVB - merged_rna$Total_mutations
merged_rna$RNA_variants <- merged_rna$Jx_burden + merged_rna$Intron_burden
# Proportion of RNA variation
mean(merged_rna$RNA_variants/merged_rna$TVB)
max(merged_rna$RNA_variants/merged_rna$TVB)
median(merged_rna$Total_mutations)
median(merged_rna$TVB)
median(merged_rna$Delta_burden)
# Load mutations for bar plotting
rna_enumerated_mutations <- read.table('rna_enumerated_mutations.tsv', sep='\t', header=T)
rna_enumerated_mutations$Class <- factor(rna_enumerated_mutations$Class, levels = rev(levels(rna_enumerated_mutations$Class)))
# Figure 2
ggplot(rna_enumerated_mutations,
aes(reorder(Patient,
-Mut_count)))+geom_bar(aes(fill=Class))+scale_fill_brewer(palette="Set1")+labs(title="",
x="Patients",
y = "Tumor Variant Burden (#)")+theme(axis.text.x = element_blank(),
panel.grid = element_blank(),
#axis.title.x = element_blank(),
axis.text = element_text(size = rel(1.5)),
axis.title.y = element_text(size = rel(1.5),
margin = margin(t = 0, r = 15, b = 0, l = 0)),
axis.title.x = element_text(size = rel(1.5)),
panel.background = element_rect(fill="white", colour = 'white'),
axis.line.y.left = element_line(colour = 'black'),
axis.line.x.bottom = element_line(colour = 'black'),
axis.ticks.x = element_blank(),
legend.text = element_text(size = rel(1.5)),
legend.title = element_text(size = rel(1.5)),
legend.position = c(0.95, 0.5),
plot.margin=unit(c(0,0.25,0,0.5), 'cm'))+scale_y_continuous(expand = c(0,0))+scale_x_discrete(expand = c(0.01,0))
# Load neoepitopes for plotting
rna_enumerated_neoepitopes <- read.table('rna_enumerated_neoepitopes.tsv', sep='\t', header=T)
rna_enumerated_neoepitopes$Class <- factor(rna_enumerated_neoepitopes$Class, levels = rev(levels(rna_enumerated_neoepitopes$Class)))
# Supplementary Figure 5 plot.margin=unit(c(0,0.25,0,0.5), 'cm'))
color_palette <- brewer.pal(3, "Dark2")
ggplot(rna_enumerated_neoepitopes,
aes(reorder(Patient,
-Mut_count)))+geom_bar(aes(fill=Class))+scale_fill_manual(values = c(color_palette[1], color_palette[3]))+labs(title="",
x="Patients",
y = "Tumor Neoepitope Burden (#)")+theme(axis.text.x = element_blank(),
panel.grid = element_blank(),
#axis.title.x = element_blank(),
axis.text = element_text(size = rel(1.5)),
axis.title.y = element_text(size = rel(1.5),
margin = margin(t = 0, r = 15, b = 0, l = 0)),
axis.title.x = element_text(size = rel(1.5)),
panel.background = element_rect(fill="white", colour = 'white'),
axis.line.y.left = element_line(colour = 'black'),
axis.line.x.bottom = element_line(colour = 'black'),
axis.ticks.x = element_blank(),
legend.text = element_text(size = rel(1.5)),
legend.title = element_text(size = rel(1.5)),
legend.position = c(0.95, 0.5),
plot.margin=unit(c(0,0.25,0,0.5), 'cm'))+scale_y_continuous(expand = c(0,0))+scale_x_discrete(expand = c(0.01,0))
##### NetCTLpan epitopes #####
netctlpan <- read.table('netctlpan_burdens.tsv', sep='\t', header=T, row.names = NULL)
immunorx_netctl <- merge(immunorx, netctlpan, by=c("Patient", "Tumor_ID"))
melanoma_ctl <- immunorx_netctl[immunorx_netctl$Disease == 'melanoma',]
RCC_ctl <- immunorx_netctl[immunorx_netctl$Disease == 'RCC',]
NSCLC_ctl <- immunorx_netctl[immunorx_netctl$Disease == 'NSCLC',]
MMR_ctl <- subset(immunorx_netctl, immunorx_netctl$Disease == 'colon' | immunorx_netctl$Disease == 'endometrial' | immunorx_netctl$Disease == 'thyroid')
prostate_ctl <- immunorx_netctl[immunorx_netctl$Disease == 'prostate',]
median(immunorx_netctl$NetCTLpan_epitopes)
median(melanoma_ctl$NetCTLpan_epitopes)
median(RCC_ctl$NetCTLpan_epitopes)
median(NSCLC_ctl$NetCTLpan_epitopes)
median(MMR_ctl$NetCTLpan_epitopes)
median(prostate_ctl$NetCTLpan_epitopes)
##### Predicting response to immunotherapy #####
# Set function for finding cells in tables
find_cell <- function(table, row, col, name="core-fg"){
l <- table$layout
which(l$t==row & l$l==col & l$name==name)}
### MHCnuggets 1 vs 2 - supplementary figure 9 ####
roc.all.mhcnuggets1 <- roc(Combined_response~MHCnuggets_ClassI_eps, data=immunorx)
roc.all.mhcnuggets2 <- roc(Combined_response~MHCnuggets_ClassI_eps, data=immunorx)
roc.mel.mhcnuggets1 <- roc(Combined_response~MHCnuggets_ClassI_eps, data=immunorx[immunorx$Disease == 'melanoma',])
roc.mel.mhcnuggets2 <- roc(Combined_response~MHCnuggets_ClassI_eps, data=immunorx[immunorx$Disease == 'melanoma',])
roc.rcc.mhcnuggets1 <- roc(Combined_response~MHCnuggets_ClassI_eps, data=immunorx[immunorx$Disease == 'RCC',])
roc.rcc.mhcnuggets2 <- roc(Combined_response~MHCnuggets_ClassI_eps, data=immunorx[immunorx$Disease == 'RCC',])
roc.nsclc.mhcnuggets1 <- roc(Combined_response~MHCnuggets_ClassI_eps, data=immunorx[immunorx$Disease == 'NSCLC',])
roc.nsclc.mhcnuggets2 <- roc(Combined_response~MHCnuggets_ClassI_eps, data=immunorx[immunorx$Disease == 'NSCLC',])
roc_list_all <- list(roc.all.mhcnuggets1, roc.all.mhcnuggets2)
mel_cancer_roc_list <- list(roc.mel.mhcnuggets1, roc.mel.mhcnuggets2)
rcc_cancer_roc_list <- list(roc.rcc.mhcnuggets1, roc.rcc.mhcnuggets2)
nsclc_cancer_roc_list <- list(roc.nsclc.mhcnuggets1, roc.nsclc.mhcnuggets2)
color_set <- c(brewer.pal(2, "Dark2"))
label_set <- c("MHCnuggets Class I", "MHCnuggets Class II")
# Create individual plots by disease
gg.all <- ggroc(roc_list_all,
linetype = 1, size=1, alpha=.9,
legacy.axes = F)+
geom_segment(aes(x = 1, xend = 0, y = 0, yend = 1), color="black", linetype="dashed")+
theme_classic()+
scale_color_manual(name = "",
labels = label_set,
values = color_set)+
ggtitle("All Cancers")+theme(legend.position = "bottom")+
xlab("Specificity")+
ylab("Sensitivity")+
theme(plot.title = element_text(face="bold", size = 16))
gg.mel <- ggroc(mel_cancer_roc_list,
linetype = 1, size=1, alpha=.9)+
geom_segment(aes(x = 1, xend = 0, y = 0, yend = 1), color="black", linetype="dashed")+
theme_classic()+
scale_color_manual(name = "",
labels = label_set,
values = color_set)+
ggtitle("Melanoma")+theme(legend.position = "bottom")+
xlab("Specificity")+
ylab("Sensitivity")+
theme(plot.title = element_text(face="bold", size = 16))
gg.rcc <- ggroc(rcc_cancer_roc_list,
linetype = 1, size=1, alpha=.9)+
geom_segment(aes(x = 1, xend = 0, y = 0, yend = 1), color="black", linetype="dashed")+
theme_classic()+
scale_color_manual(name = "",
labels = label_set,
values = color_set)+
ggtitle("RCC")+theme(legend.position = "bottom")+
xlab("Specificity")+