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 libraries, message=FALSE}

library(survival)

```

## 3 Loading the Data

Here we simply load the table of clinical information.

```{r loadTothillClinical}

tothillClinical <-
    read.table(file.path("RawData","Tothill","Clinical","tothillClinical.csv"),
               header=TRUE, sep=",")
dim(tothillClinical)
tothillClinical[1:3,]

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 examineStatus}

table(tothillClinical[,"Status"])

```

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 defineOS}

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)

```

Next we deal with PFS.

```{r definePFS}

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)

```

Now we create the Surv objects.

```{r createSurvs}

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 tableRDStatus}

table(tothillClinical[,"ResidDisease"])

```

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 specifyRDIndicator}

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)
names(tothillRD) <- rownames(tothillClinical)

```

## 6 Saving RData

Now we save the relevant information to an RData object.

```{r saveTothillClinical}

save(tothillClinical,
     tothillOSMos,
     tothillPFSMos,
     tothillRD,
     file=file.path("RDataObjects","tothillClinical.RData"))

```


## 7 Appendix

### 7.1 File Location

```{r getLocation}

getwd()

```

### 7.2 SessionInfo

```{r sessionInfo}

sessionInfo();

```

## 8 References

> <p id="tothill08">
  [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.
  <em>Clin Cancer Res</em>, <b>14(16)<b>:5198-208, 2008. <p>
