In rpiskol/CMString: CMS classification using a selected set of genes that are profiled using the NanoStrin echnology

knitr::opts_chunk$set(
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Four consensus molecular subtypes (CMS1-4) of colorectal cancer (CRC) defined by gene expression in mostly primary, early-stage tumors were identified and found to be associated with distinctive biological features and clinical outcomes. Given that distant metastasis largely accounts for CRC-related mortality, we sought to examine the molecular and clinical attributes of CMS in metastatic CRC (mCRC).

We have developed a custom disease-focused NanoString based CMS classifier that is ideally suited to interrogate archival tissues. Here we show how to apply this classifier to a set of early stage patient samples from the CIT data set as well as primary and metastatic samples from a procured set of patients with late stage disease.

This vignette is available by typing:

vignette(package = "CMString", topic = "vignette")

Installation

Please follow the following instructions to install the package:

install.packages("devtools")
devtools::install_github("rpiskol/CMString")

Load the package once it is installed:

library(CMString)
library(Biobase)

Available data sets and their retrieval

Available data sets can be shown using the showAvailableExpressionDataSets function:

showAvailableExpressionDataSets()

Each of them can be loaded using the getExpressionDataSet function, which returns an ExpressionSet that contains the expression data as well as pheno data

CIT.eset <- getExpressionDataSet(dataset = "CIT")

CIT.eset

head(pData(CIT.eset), n=2)

CMS classification of CIT data

CMS classification can be performed using the classifyCMString function. The thresh parameter specifies the minimum confidence level to assign a CMS to a sample (e.g. the higher the thresh parameter, the higher the confidence has to be for assignment):

classification <- classifyCMString(CIT.eset, thresh = .4)

The resulting classification contains the predicted CMS subtypes and their probabilities.

table(classification$clu.pred)

head(classification$pred)

We can add the predicted CMS subtypes back to the pheno data:

CMS.glmnet <- classification$clu.pred[sampleNames(CIT.eset)]
CIT.eset$CMS.glmnet <- ifelse(is.na(CMS.glmnet), "UNK", 
                                                 paste0("CMS",CMS.glmnet))

The distribution of CMS subtypes can be plotted as a pie chart:

pieChart(pData(CIT.eset), digits = 2)

The predicted CMS types can be compared to the "Gold Standard" Network classification by Guinney et al (see here) using the plotContingencyGeneral function:

plotContingencyGeneral(CIT.eset$CMS.glmnet,
                       CIT.eset$CMS_network,
                       xlab="CMString Classification",
                       ylab="Gold Standard Classification",
                       main = "CIT")

The classification and expression data can also be plotted as a heat map with CMS annotation and prediction probabilties using the figClassifyCMS function:

figClassifyCMS(x=classification)

Gene and sample names can be toggled on/off:

figClassifyCMS(x=classification,gene.labs = FALSE, samp.labs = FALSE)

Instead of providing the result of the clustering procedure, also each element of the clustering result can be provided separately. This allows to modify the ordering of genes (determined by the hierarchical clustering in gclus.f) or samples (determined by nam.ord)

figClassifyCMS(pred = classification$pred, 
               clu.pred = classification$clu.pred, 
               sdat.sig = classification$sdat.sig,
               gclu.f = rev(classification$gclu.f), 
               nam.ord = rev(classification$nam.ord), 
               missing.genes = classification$missing.genes,
               cex.axis.labs = 0.5, gene.labs = FALSE,samp.labs = FALSE)

CMS classification of Procured samples

Our procured data set from late stage CRC patients consist of 312 samples. These include 182 primary samples and 130 metastases. The samples have been profiled using a custom probe set that allows to determine the expression of ~800 disease related genes.

Procured.eset <- getExpressionDataSet(dataset = "Procured")

table(Procured.eset$Type)

We can use either gene symbols or Entrez gene IDs for classification. In addition, the classification function accepts not only ExpressionSets but also gene expression matrices and data frames as input.

# classification using Entrez IDs
Procured.exprs.entrez <- exprs(Procured.eset)
rownames(Procured.exprs.entrez) <- fData(Procured.eset)$Entrez
Procured.classification.entrez <- classifyCMString(Procured.exprs.entrez, thresh=.5)

# classification using Symbols
Procured.exprs.symbol <- exprs(Procured.eset)
rownames(Procured.exprs.symbol) <- fData(Procured.eset)$Symbol
Procured.classification.symbol <- classifyCMString(Procured.exprs.symbol, thresh=.5)

xtabs(~Procured.classification.symbol$clu.pred + Procured.classification.entrez$clu.pred)

As previously, a pie chart of CMS distributions and heatmap can be generated:

CMS.glmnet <- Procured.classification.entrez$clu.pred[sampleNames(Procured.eset)]
Procured.eset$CMS.glmnet <- ifelse(is.na(CMS.glmnet), "UNK", paste0("CMS",CMS.glmnet))

# plot pie chart
pieChart(pData(Procured.eset), digits = 0)
# plot heatmap for all samples
figClassifyCMS(x=Procured.classification.entrez,gene.labs = FALSE, samp.labs = FALSE)

We can discard low-confidence predictions using the subsetSamplesBySignificance function:

Procured.classification.entrez.highConf <- subsetSamplesBySignificance(Procured.classification.entrez, thresh=.5)

Given that the Procured data set contains primary samples and metastases it might be desirable to separate predictions into these two. This can be achieved using the subsetSamplesByName function:

sampids.primary <- Procured.eset$Sample[which(Procured.eset$Type == "Primary")]
sampids.met <- Procured.eset$Sample[which(Procured.eset$Type == "Met")]

Procured.classification.entrez.primary <- subsetSamplesByName(Procured.classification.entrez, 
                                                              sampids.primary)
Procured.classification.entrez.met <- subsetSamplesByName(Procured.classification.entrez, 
                                                          sampids.met)

# plot heatmap for primary samples
figClassifyCMS(x=Procured.classification.entrez.primary, gene.labs = FALSE)
# plot heatmap for metastases
figClassifyCMS(x=Procured.classification.entrez.met, gene.labs = FALSE)

Retrieval of classifier features

Classifier features can be retrieved individually for each sybtype using the getSignatureGenesPerCMS function. If desired, they can then be combined into one list. Furthermore, features can be retrieved either in form of Entrez gene IDs or gene symbols.

# as Entrez IDs
ids.entrez <- lapply(1:4,function(i){getSignatureGenesPerCMS(i, "Entrez" ,"lambda.min")})

# as Symbols
ids.symbol <- lapply(1:4,function(i){getSignatureGenesPerCMS(i, "Symbol" ,"lambda.min")})

sapply(ids.entrez, length)
sapply(ids.symbol, length)

length(unique(unlist(ids.symbol)))
sort(unique(unlist(ids.symbol)))

Session Info

sessionInfo()

rpiskol/CMString documentation built on May 24, 2019, 8:52 a.m.