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METABRIC_E2_RMS_v2.Rmd
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METABRIC_E2_RMS_v2.Rmd
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---
title: "R Notebook"
output: html_notebook
---
```{r}
library(doParallel)
registerDoParallel(cores=(detectCores() - 1))
source("./ENDORSE_manuscript_scripts/Useful_functions.R")
```
```{r}
library(GSVA)
library(GSEABase)
library(survival)
library(survminer)
load(file="./../Output/metabric_er.RData")
metabric.os <- readRDS("./metabric.os.RDS")
metabric.os.event <- readRDS("./metabric.os.event.RDS")
metabric.s <- readRDS("./metabric.s.RDS")
ENDORSE <- readRDS("./ENDORSE.RDS")
H_ESTR_EARLY <- readRDS("./H_ESTR_EARLY.RDS")
```
#### Model comparison by splitting the training data 80/20
#### Re-train the classifier on training split
#### Predict only using the test set
#### Compare with the performance of independent predictors on the test set
```{r Split METABRIC into training and test splits for validatiobn}
set.seed(1)
nTrain <- floor(0.5*ncol(metabric.s))
TRAIN.id <- sample(c(1:ncol(metabric.s)), size = nTrain, replace = F)
test.id <- c(1:ncol(metabric.s))[-TRAIN.id]
##### Perform feature selection on the training split for ENDORSE ######
# Survival objects
TRAIN.surv = Surv(metabric.os[TRAIN.id], as.numeric(metabric.os.event[TRAIN.id]))
TRAIN.age <- metabric.annotV2$AGE_AT_DIAGNOSIS[match(colnames(metabric.s)[TRAIN.id], metabric.annotV2$PATIENT_ID)]
# Make the signatures
library(glmnet)
library(doParallel)
TRAIN.cv.exp <- cv.glmnet(x=cbind(t(metabric.s[, TRAIN.id]), TRAIN.age),
y=TRAIN.surv, family = 'cox', alpha=1, parallel = T)
TRAIN_gseed <- names(which(getCoefs(TRAIN.cv.exp, option='1se') > 0))
TRAIN_geneset <- names(GetCorGenes(TRAIN_gseed, t(metabric.s[, TRAIN.id]), cof=0.75))
#####
MB.TRAIN <- gsva(metabric.s[, TRAIN.id], gset.idx.list = list("ENDORSE"=TRAIN_geneset,
"HALLMARK_ESTROGEN_RESPONSE_EARLY" = H_ESTR_EARLY), verbose=TRUE, kcdf="Gaussian", method='ssgsea', ssgsea.norm=T)
MB.test <- gsva(metabric.s[, test.id], gset.idx.list = list("ENDORSE"=TRAIN_geneset,
"HALLMARK_ESTROGEN_RESPONSE_EARLY" = H_ESTR_EARLY), verbose=TRUE, kcdf="Gaussian", method='ssgsea', ssgsea.norm=T)
df.MB.TRAIN <- data.frame('Time'=metabric.os[TRAIN.id],
'Event'=as.numeric(metabric.os.event)[TRAIN.id],
t(MB.TRAIN))
cp.6.TRAIN <- coxph(Surv(Time, Event) ~ ENDORSE + HALLMARK_ESTROGEN_RESPONSE_EARLY, data=df.MB.TRAIN, x=T)
MB.TRAIN.pred <- predict(cp.6.TRAIN, data.frame(t(MB.TRAIN)), type="risk")
MB.TRAIN.pred.grp <- MakeRiskGrp(MB.TRAIN.pred)
df.MB.TRAIN$Risk <- MB.TRAIN.pred
df.MB.TRAIN$Group <- MB.TRAIN.pred.grp
TRAIN.fit <- survfit(Surv(Time, Event) ~ Group, data=df.MB.TRAIN)
Plt.TRAIN <- ggsurvplot(TRAIN.fit, data=df.MB.TRAIN, palette="jco",
censor.shape="|", censor.size=2,
break.time.by=60,
legend.title="ENDORSE", legend.labs=c("Low risk", "Medium risk", "High risk"),
#surv.median.line = "hv",
risk.table=F,
ggtheme=theme_classic2(base_size = 14))
summary(coxph(Surv(Time, Event) ~ Group, data=df.MB.TRAIN)) #p=3e-09
summary(coxph(Surv(Time, Event) ~ Risk, data=df.MB.TRAIN)) #p=3e-10
# predict
MB.test.pred <- predict(cp.6.TRAIN, data.frame(t(MB.test)), type="risk")
MB.test.pred.grp <- MakeRiskGrp(MB.test.pred)
#print(paste("ENDORSE score = ", round(MB.test.pred, 2)))
table(MB.test.pred.grp)
df.MB.test <- data.frame('Time'=metabric.os[test.id],
'Event'=as.numeric(metabric.os.event)[test.id],
'Risk'=MB.test.pred,
'Group'=MB.test.pred.grp,
t(MB.test))
test.fit <- survfit(Surv(Time, Event) ~ Group, data=df.MB.test)
Plt.test <- ggsurvplot(test.fit, data=df.MB.test, palette="jco",
censor.shape="|", censor.size=2,
break.time.by=60,
legend.title="ENDORSE", legend.labs=c("Low risk", "Medium risk", "High risk"),
#surv.median.line = "hv",
risk.table=F,
ggtheme=theme_classic2(base_size = 14))
summary(coxph(Surv(Time, Event) ~ Group, data=df.MB.test)) #p=2e-08
summary(coxph(Surv(Time, Event) ~ Risk, data=df.MB.test)) #p=3e-09
pdf("./../Figures/METABRIC.TRAIN.test.Survplot.pdf", height=5, width=5)
print(Plt.TRAIN)
print(Plt.test)
dev.off()
```
```{r External Signatures}
library(rms)
############# Apply TransCONFIRM signature #########################
MB.test.TransCONFIRM <- TransCONFIRM(t(metabric.s[, test.id]))
############# Apply SET signature #########################
MB.test.SET <- SET_Score(t(metabric.s[, test.id]))
############# Apply ODX scores #########################
MB.test.ODX <- ODX_RS(scale(t(metabric.s[, test.id])))
MB.test.ODX.cat <- array(data=0, dim=length(MB.test.ODX))
MB.test.ODX.cat[which(MB.test.ODX > quantile(MB.test.ODX, 0.25))] <- 1
df.MB.test <- cbind(df.MB.test, "TransCONFIRM"=MB.test.TransCONFIRM, "SET"=MB.test.SET,
"ODX"=MB.test.ODX,"ODX_Cat"=MB.test.ODX.cat)
cp.test.risk <- coxph(Surv(Time, Event) ~ Risk, data=df.MB.test, x=T)
cp.test.group <- coxph(Surv(Time, Event) ~ Group, data=df.MB.test, x=T)
cp.test.TC <- coxph(Surv(Time, Event) ~ TransCONFIRM, data=df.MB.test, x=T)
cp.test.SET <- coxph(Surv(Time, Event) ~ SET, data=df.MB.test, x=T)
cp.test.ODX <- coxph(Surv(Time, Event) ~ ODX, data=df.MB.test, x=T)
cp.test.ODXCat <- coxph(Surv(Time, Event) ~ as.factor(ODX_Cat), data=df.MB.test, x=T)
cph.test.risk <- cph(Surv(Time, Event) ~ Risk, data=df.MB.test, x=T, y=T)
cph.test.group <- cph(Surv(Time, Event) ~ Group, data=df.MB.test, x=T, y=T)
cph.test.TC <- cph(Surv(Time, Event) ~ TransCONFIRM, data=df.MB.test, x=T, y=T)
cph.test.SET <- cph(Surv(Time, Event) ~ SET, data=df.MB.test, x=T, y=T)
cph.test.ODX <- cph(Surv(Time, Event) ~ ODX, data=df.MB.test, x=T, y=T)
cph.test.ODXCat <- cph(Surv(Time, Event) ~ ODX_Cat, data=df.MB.test, x=T, y=T)
set.seed(1)
validate(cph.test.risk, method='boot', B=150)
validate(cph.test.group, method='boot', B=150)
validate(cph.test.TC, method='boot', B=150)
validate(cph.test.SET, method='boot', B=150)
validate(cph.test.ODX, method='boot', B=150)
validate(cph.test.ODXCat, method='boot', B=150)
```
```{r METABRIC covariates}
#Other classifiers
MB.annot.extra <- read.delim("./../Data/METABRIC/brca_metabric_clinical_data.tsv", header=T)
x <- match(colnames(metabric.s[, test.id]), MB.annot.extra$Patient.ID)
df.MB.test <- data.frame(df.MB.test,
'TumorGrade'=MB.annot.extra$Neoplasm.Histologic.Grade[x],
'MutationCount'=MB.annot.extra$Mutation.Count[x],
'PAM50'=MB.annot.extra$Pam50...Claudin.low.subtype[x],
'IntClust'=MB.annot.extra$Integrative.Cluster[x])
cp.test.grade <- coxph(Surv(Time, Event) ~ TumorGrade, data=df.MB.test, x=T)
cp.test.mutcount <- coxph(Surv(Time, Event) ~ MutationCount, data=df.MB.test, x=T)
cp.test.PAM50 <- coxph(Surv(Time, Event) ~ PAM50, data=df.MB.test, x=T)
cp.test.IntClust <- coxph(Surv(Time, Event) ~ IntClust, data=df.MB.test, x=T)
cph.test.grade <- cph(Surv(Time, Event) ~ TumorGrade, data=df.MB.test, x=T, y=T)
cph.test.mutcount <- cph(Surv(Time, Event) ~ MutationCount, data=df.MB.test, x=T, y=T)
cph.test.PAM50 <- cph(Surv(Time, Event) ~ PAM50, data=df.MB.test, x=T, y=T)
cph.test.IntClust <- cph(Surv(Time, Event) ~ IntClust, data=df.MB.test, x=T, y=T)
set.seed(1)
validate(cph.test.grade, method='boot', B=150)
validate(cph.test.mutcount, method='boot', B=150)
validate(cph.test.PAM50, method='boot', B=150)
validate(cph.test.IntClust, method='boot', B=150)
```
```{r Proliferation index}
######### Compare meta-PCNA proliferation signature -----------
library(ProliferativeIndex)
temp <- readDataForPI(data.frame(metabric.s[,test.id]), modelIDs = ENDORSE)
test.PI <- calculatePI(temp)
df.MB.test$PI <- test.PI
cp.test.PI <- coxph(Surv(Time, Event) ~ PI, data=df.MB.test, x=T)
cph.test.PI <- cph(Surv(Time, Event) ~ PI, data=df.MB.test, x=T, y=T)
set.seed(1)
validate(cph.test.PI, method='boot', B=150)
```
```{r Partial likelihood ratio tests}
##### Cox models using Survival package ######
cp.test.0 <- coxph(Surv(Time, Event) ~ ENDORSE, data=df.MB.test, x=T)
cp.test.5 <- coxph(Surv(Time, Event) ~ HALLMARK_ESTROGEN_RESPONSE_EARLY, data=df.MB.test, x=T)
cp.test.6 <- coxph(Surv(Time, Event) ~ ENDORSE + HALLMARK_ESTROGEN_RESPONSE_EARLY, data=df.MB.test, x=T) ### Chosen one
library(nonnestcox)
sink("./../Output/METABRIC_test_plrtest.txt")
z1 <- which(!is.na(df.MB.test$TumorGrade))
z2 <- which(!is.na(df.MB.test$MutationCount))
cp.6_temp1 <- coxph(Surv(Time, Event) ~ ENDORSE + HALLMARK_ESTROGEN_RESPONSE_EARLY, data=df.MB.test[z1, ], x=T)
cp.6_temp2 <- coxph(Surv(Time, Event) ~ ENDORSE + HALLMARK_ESTROGEN_RESPONSE_EARLY, data=df.MB.test[z2, ], x=T)
cp.test.grade <- coxph(Surv(Time, Event) ~ TumorGrade, data=df.MB.test[z1, ], x=T)
cp.test.mutcount <- coxph(Surv(Time, Event) ~ MutationCount, data=df.MB.test[z2, ], x=T)
print("Grade"); plrtest(cp.6_temp1, cp.test.grade, nested=F)
print("Mutation count"); plrtest(cp.6_temp2, cp.test.mutcount, nested=F)
print("PAM50"); plrtest(cp.test.6, cp.test.PAM50, nested=F)
print("IntClust"); plrtest(cp.test.6, cp.test.IntClust, nested=F)
print("Proliferation index"); plrtest(cp.test.6, cp.test.PI, nested=F)
print("TransConfirm"); plrtest(cp.test.6, cp.test.TC, nested=F)
print("SET"); plrtest(cp.test.6, cp.test.SET, nested=F)
print("ODX"); plrtest(cp.test.6, cp.test.ODX, nested=F)
print("ODXCat"); plrtest(cp.test.6, cp.test.ODXCat, nested=F)
sink()
```
```{r Plot corrected Dxy from validation}
model.0 <- cph(Surv(Time, Event) ~ ENDORSE, data=df.MB.test, x=T, y=T, surv = T)
model.5 <- cph(Surv(Time, Event) ~ HALLMARK_ESTROGEN_RESPONSE_EARLY, data=df.MB.test,
x=T, y=T, surv = T)
model.6 <- cph(Surv(Time, Event) ~ ENDORSE + HALLMARK_ESTROGEN_RESPONSE_EARLY, data=df.MB.test,
x=T, y=T, surv = T)
v.list <- list()
set.seed(1)
v.list[[1]] <- validate(model.6, method='boot', B=150)
v.list[[2]] <- validate(cph.test.grade, method='boot', B=150)
v.list[[3]] <- validate(cph.test.mutcount, method='boot', B=150)
v.list[[4]] <- validate(cph.test.PAM50, method='boot', B=150)
v.list[[5]] <- validate(cph.test.IntClust, method='boot', B=150)
v.list[[6]] <- validate(cph.test.PI, method='boot', B=150)
v.list[[7]] <- validate(cph.test.TC, method='boot', B=150)
v.list[[8]] <- validate(cph.test.SET, method='boot', B=150)
v.list[[9]] <- validate(cph.test.ODX, method='boot', B=150)
v.list[[10]] <- validate(cph.test.ODXCat, method='boot', B=150)
# Create dataframe
Labs <- c("ENDORSE", "Tumor grade", "Mutation count", "PAM50", "IntClust", "Proliferation index",
"TransCONFIRM", "SET", "ODX", "ODX25")
df.val <- data.frame("Dxy" = unlist(lapply(v.list, "[", 1,5)),
"Labs" = factor(Labs, levels=rev(Labs)))
# Horizontal version
Pdxy <- ggplot(df.val, aes(x=Labs, y=Dxy)) +
geom_segment( aes(x=Labs, xend=Labs, y=0, yend=Dxy), color="skyblue", alpha=0.5) +
geom_point(color="orange", size=4) +
theme_bw(base_size = 12) +
coord_flip() +
theme(panel.grid.major.y = element_blank(),
panel.grid.minor.x = element_blank(),
panel.border = element_blank(),
axis.ticks.y = element_blank()) +
labs(x="Prediction model", y="Corrected Dxy")
pdf("./../Figures/METABRIC.test.Validate.Dxy.B150.pdf", height = 4, width=8)
print(Pdxy)
dev.off()
```
```{r TRAIN test annotation}
SPLITS <- array(data=NA, dim=ncol(metabric.s))
SPLITS[match(colnames(metabric.s)[TRAIN.id], MB.annot.extraV2$Patient.ID)] <- "Train"
SPLITS[match(colnames(metabric.s)[test.id], MB.annot.extraV2$Patient.ID)] <- "Test"
MB.annot.extraV2$SPLITS <- SPLITS
t.test(MB.annot.extraV2$Tumor.Stage ~ MB.annot.extraV2$SPLITS)
t.test(MB.annot.extraV2$Neoplasm.Histologic.Grade ~ MB.annot.extraV2$SPLITS)
t.test(MB.annot.extraV2$Tumor.Size ~ MB.annot.extraV2$SPLITS)
t.test(MB.annot.extraV2$Mutation.Count ~ MB.annot.extraV2$SPLITS)
p1 <- ggplot(MB.annot.extraV2, aes(x=SPLITS, y=Tumor.Stage, fill=SPLITS)) +
geom_boxplot(width=0.5) +
#geom_jitter(width=0.1, alpha=0.15) +
theme_classic(base_size = 14) +
theme(legend.position = 'none') +
scale_fill_brewer(palette = "Accent") +
labs(y="Stage", x="")
p2 <- ggplot(MB.annot.extraV2, aes(x=SPLITS, y=Neoplasm.Histologic.Grade, fill=SPLITS)) +
geom_boxplot(width=0.5) +
#geom_jitter(width=0.1, alpha=0.15) +
theme_classic(base_size = 14) +
theme(legend.position = 'none') +
scale_fill_brewer(palette = "Accent") +
labs(y="Grade", x="")
p3 <- ggplot(MB.annot.extraV2, aes(x=SPLITS, y=Tumor.Size, fill=SPLITS)) +
geom_boxplot(width=0.5) +
#geom_jitter(width=0.1, alpha=0.15) +
theme_classic(base_size = 14) +
theme(legend.position = 'none') +
scale_fill_brewer(palette = "Accent") +
labs(y="Size", x="")
p4 <- ggplot(MB.annot.extraV2, aes(x=SPLITS, y=Mutation.Count, fill=SPLITS)) +
geom_boxplot(width=0.5) +
#geom_jitter(width=0.1, alpha=0.15) +
theme_classic(base_size = 14) +
theme(legend.position = 'none') +
scale_fill_brewer(palette = "Accent") +
labs(y="Mutation count", x="")
pdf("./../Figures/METABRIC.TRAIN_test_annot.pdf", width=6.5, height=2.5)
gridExtra::grid.arrange(p1, p2, p3, p4, nrow=1)
dev.off()
```
```{r Key pathways in METABRIC patients with ENDORSE > 1 and OS <5 or >5 years}
metabric.hall <- readRDS("./../Data/METABRIC.HALLMARK.RDS")
metabric.c2 <- readRDS("./../Data/METABRIC.C2.RDS")
metabric.c6 <- readRDS("./../Data/METABRIC.C6.RDS")
Ev1 <- na.omit(which(df.E2MB$Time < 60 & E2.risk >= 1))
Ev2 <- na.omit(which(df.E2MB$Time >= 60 & E2.risk >= 1))
Ev.labs <- array(dim=length(df.E2MB$Time))
Ev.labs[Ev1] <- "E"
Ev.labs[Ev2] <- "L"
t.res <- function(X, labs) {
T.tests.p <- array(dim=ncol(X))
T.tests.fc <- array(dim=ncol(X))
for (i in 1:ncol(X)) {
T.tests <- t.test(X[, i] ~ labs)
T.tests.p[i] <- T.tests[[3]]
T.tests.fc[i] <- T.tests[[5]][2]/T.tests[[5]][1]
}
T.tests.fdr <- p.adjust(T.tests.p, method = 'fdr')
return(data.frame("Name"=colnames(X), "p"=T.tests.p, "fdr"=T.tests.fdr, "fc"=T.tests.fc))
}
Ev.hall.ts <- t.res(t(metabric.hall), Ev.labs)
Ev.c2.ts <- t.res(t(metabric.c2), Ev.labs)
Ev.c6.ts <- t.res(t(metabric.c6), Ev.labs)
Ev.hall.ts[which(Ev.hall.ts$p < 0.05), ]
Ev.c2.ts[which(Ev.c2.ts$p < 0.05), ]
Ev.c6.ts[which(Ev.c6.ts$p < 0.05), ]
write.csv(Ev.hall.ts, "./../Output/Ev.hall.ts.csv")
write.csv(Ev.c2.ts, "./../Output/Ev.c2.ts.csv")
write.csv(Ev.c6.ts, "./../Output/Ev.c6.ts.csv")
```