Assembling Clinical Information for the Bonome Ovarian Data

by Keith A. Baggerly

1 Executive Summary

1.1 Introduction

We want to produce an RData file with the clinical information for the ovarian cancer samples profiled by Bonome et al. on U133A arrays.

1.2 Methods

We acquired clinical annotation from the Gene Expression Omnibus (GEO) pages descending from the main page GSE26712, http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE26712, on Sep 12, 2012. This includes the GEO GSM id for each sample, the sample ID, Surgery Outcome (DOD, AWD, NED), and Survival in Years.

A csv file of this annotation is stored in RawData as bonomeClinical.csv.

We load the clinical information into a data frame, and construct an R “Surv”“ object for overall survival.

1.3 Results

We save bonomeClinical and bonomeOSYrs to the RData file "bonomeClinical.RData”.

2 Libraries

We first load the libraries we will use in this report.

library(survival)

## Loading required package: splines

3 Loading the Data

Here we simply load the table of clinical information.

bonomeClinical <- read.table(file.path("RawData", "Bonome", "Clinical", "bonomeClinical.csv"), 
    header = TRUE, sep = ",")
dim(bonomeClinical)

## [1] 195   5

bonomeClinical[1:3, ]

##      GEO.ID SampleID SurgeryOutcome Status SurvivalYears
## 1 GSM657519 HOSE2237                                  NA
## 2 GSM657520 HOSE2008                                  NA
## 3 GSM657521 HOSE2061                                  NA

rownames(bonomeClinical) <- as.character(bonomeClinical[, "SampleID"])

4 Defining Overall Survival

Next, we define an R “Surv”“ object for overall survival (OS) We begin by looking at the recorded values for patient status.

table(bonomeClinical[, "Status"])

## 
##     AWD DOD NED 
##  10  24 129  32

Here, AWD = Alive with Disease, DOD = Dead of Disease, and NED = Alive with no Evidence of Disease.

Next, we define an indicator vectors for OS.

bonomeOSStatus <- rep(NA, nrow(bonomeClinical))
bonomeOSStatus[bonomeClinical[, "Status"] == "AWD"] <- "Censored"
bonomeOSStatus[bonomeClinical[, "Status"] == "DOD"] <- "Uncensored"
bonomeOSStatus[bonomeClinical[, "Status"] == "NED"] <- "Censored"
table(bonomeOSStatus)

## bonomeOSStatus
##   Censored Uncensored 
##         56        129

Now we create the Surv object.

bonomeOSYrs <- Surv(bonomeClinical[, "SurvivalYears"], bonomeOSStatus == "Uncensored")
rownames(bonomeOSYrs) <- rownames(bonomeClinical)

5 Saving RData

Now we save the relevant information to an RData object.

save(bonomeClinical, bonomeOSYrs, file = file.path("RDataObjects", "bonomeClinical.RData"))

6 Appendix

6.1 File Location

getwd()

## [1] "\\\\mdadqsfs02/workspace/kabagg/RDPaper/Webpage/ResidualDisease"

6.2 SessionInfo

sessionInfo()

## R version 2.15.3 (2013-03-01)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## 
## locale:
## [1] LC_COLLATE=English_United States.1252 
## [2] LC_CTYPE=English_United States.1252   
## [3] LC_MONETARY=English_United States.1252
## [4] LC_NUMERIC=C                          
## [5] LC_TIME=English_United States.1252    
## 
## attached base packages:
## [1] splines   stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
## [1] survival_2.37-4 knitr_1.2      
## 
## loaded via a namespace (and not attached):
## [1] digest_0.6.3   evaluate_0.4.3 formatR_0.7    stringr_0.6.2 
## [5] tools_2.15.3

7 References

[1] Bonome T, Levine DA, Shih J, Randonovich M, Pise-Masison CA, Bogomolniy F, Ozbun L, Brady J, Barrett JC, Boyd J, Birrer MJ. A gene signature predicting for survival in suboptimally debulked patients with ovarian cancer. Cancer Res, 68(13):5478-86, 2008.