Assembling Clinical Information for the Tothill 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 [Tothill et al.](#tothill08)
### 1.2 Methods
We acquired clinical annotation from two sources on Sep 13, 2012:
"clinical\_anns.csv" from the Gene Expression Omnibus (GEO) page
for GSE9891,
[http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE9891](http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE9891),
and p.27-30
of the supplementary data pdf for [Tothill et al.](#tothill08),
[http://clincancerres.aacrjournals.org/content/14/16/5198/suppl/DC1](http://clincancerres.aacrjournals.org/content/14/16/5198/suppl/DC1).
A csv file of this annotation, together with an extra column
specifying the GEO GSM ID for each sample, is stored in RawData
as tothillClinical.csv.
We load the clinical information into a data frame, and
construct R "Surv" objects for overall and progression
(relapse) free survival.
We also construct a binary indicator vector for the
presence or absence of residual disease (RD).
### 1.3 Results
We save tothillClinical,
tothillOSMos,
tothillPFSMos, and
tothillRD
to the RData file "tothillClinical.RData".
## 2 Options and Libraries
We first load the options and libraries we will use
in this report.
```r
library(survival)
```
## 3 Loading the Data
Here we simply load the table of clinical information.
```r
tothillClinical <- read.table(file.path("RawData", "Tothill", "Clinical", "tothillClinical.csv"),
header = TRUE, sep = ",")
dim(tothillClinical)
```
```
## [1] 285 17
```
```r
tothillClinical[1:3, ]
```
```
## GEO.ID SampleID KMeansGroup ClinicalType HistologicSubtype
## 1 GSM249839 49 5 MAL Ser
## 2 GSM250001 129 1 MAL Ser
## 3 GSM250000 146 NC MAL Ser
## PrimarySite Stage Grade Age Status Pltx Tax Neo MosToRelapse MosToDeath
## 1 OV III 3 56 D Y N N 7 8
## 2 OV III 3 65 D Y N N 7 15
## 3 OV III 3 56 PF Y N N 166 166
## ResidDisease ArraySite
## 1 <1 OV
## 2 >1 PE
## 3 >1 OV
```
```r
rownames(tothillClinical) <- paste("X", tothillClinical[, "SampleID"], sep = "")
```
## 4 Defining Overall and Progression-Free Survival
Next, we define R "Surv" objects for overall survival (OS) and
progression-free survival (PFS). We begin by looking at
the recorded values for patient status.
```r
table(tothillClinical[, "Status"])
```
```
##
## D D* PF R
## 3 111 2 92 77
```
According to the supplementary information table,
D = Dead, D* = Dead of Other Causes, PF = Alive
Progression-Free, and R = Alive and Relapsed.
Next, we define indicator vectors for OS and PFS.
We begin with OS.
```r
tothillOSStatus <- rep(NA, nrow(tothillClinical))
tothillOSStatus[tothillClinical$Status == "D"] <- "Uncensored"
tothillOSStatus[tothillClinical$Status == "D*"] <- "Uncensored"
tothillOSStatus[tothillClinical$Status == "PF"] <- "Censored"
tothillOSStatus[tothillClinical$Status == "R"] <- "Censored"
table(tothillOSStatus)
```
```
## tothillOSStatus
## Censored Uncensored
## 169 113
```
Next we deal with PFS.
```r
tothillPFStatus <- rep(NA, nrow(tothillClinical))
tothillPFStatus[tothillClinical$Status == "D"] <- "Uncensored"
tothillPFStatus[tothillClinical$Status == "D*"] <- "Uncensored"
tothillPFStatus[tothillClinical$Status == "PF"] <- "Censored"
tothillPFStatus[tothillClinical$Status == "R"] <- "Uncensored"
table(tothillPFStatus)
```
```
## tothillPFStatus
## Censored Uncensored
## 92 190
```
Now we create the Surv objects.
```r
tothillOSMos <- Surv(tothillClinical[, "MosToDeath"], tothillOSStatus == "Uncensored")
rownames(tothillOSMos) <- rownames(tothillClinical)
tothillPFSMos <- Surv(tothillClinical[, "MosToRelapse"], tothillPFStatus ==
"Uncensored")
rownames(tothillPFSMos) <- rownames(tothillClinical)
```
## 5 Defining a Residual Disease Indicator
Now we summarize the Residual Disease (RD) information
into a single indicator vector specifying if there
is any RD ("RD") or no RD ("No RD"). We begin
by tabulating the information we have.
```r
table(tothillClinical[, "ResidDisease"])
```
```
##
## <1 >1 macro size NK nil NK
## 76 70 18 84 37
```
According to the supplementary information from
[Tothill et al.](#tothill08),
"macro size NK" = macroscopic disease size unknown
(but there is some), and "NK" = residual disease
unknown.
We now define the indicator.
```r
tothillRD <- rep(NA, nrow(tothillClinical))
tothillRD[tothillClinical[, "ResidDisease"] == "<1"] <- "RD"
tothillRD[tothillClinical[, "ResidDisease"] == ">1"] <- "RD"
tothillRD[tothillClinical[, "ResidDisease"] == "macro size NK"] <- "RD"
tothillRD[tothillClinical[, "ResidDisease"] == "nil"] <- "No RD"
table(tothillRD)
```
```
## tothillRD
## No RD RD
## 84 164
```
```r
names(tothillRD) <- rownames(tothillClinical)
```
## 6 Saving RData
Now we save the relevant information to an RData object.
```r
save(tothillClinical, tothillOSMos, tothillPFSMos, tothillRD, file = file.path("RDataObjects",
"tothillClinical.RData"))
```
## 7 Appendix
### 7.1 File Location
```r
getwd()
```
```
## [1] "\\\\mdadqsfs02/workspace/kabagg/RDPaper/Webpage/ResidualDisease"
```
### 7.2 SessionInfo
```r
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
```
## 8 References
>
[1] Tothill RW, Tinker AV, George J, Brown R, Fox SB, Lade S,
Johnson DS, Trivett MK, Etemadmoghadam D, Locandro B, Traficante N,
Fereday S, Hung JA, Chiew YE, Haviv I;
Australian Ovarian Cancer Study Group, Gertig D, DeFazio A, Bowtell DD.
Novel molecular subtypes of serous and endometrioid ovarian cancer
linked to clinical outcome.
Clin Cancer Res, 14(16):5198-208, 2008.