Filtering Samples from the Tothill Data to Focus on RD
======================================================
by Keith A. Baggerly
## 1 Executive Summary
### 1.1 Introduction
[Tothill et al.](#tothill08) profiled 285 ovarian
tumor samples, but not all of the patients had the
same type of disease, or had residual disease (RD) information
recorded. We want to identify the high-grade serous
ovarian tumors with RD information to focus the question
more precisely.
### 1.2 Methods
Starting with the previously assembled table of clinical
information, we examine the various columns and see which
clinical features would justify exclusion from the set
being examined.
We consider
- RD status, excluding samples with no RD information.
- Clinical Type, excluding low malignant potential (LMP) samples.
- Histologic Subtype, excluding non-serous (Adeno and Endo) samples.
- Array Site, excluding samples not coming from the
ovary (OV) or peritoneum (PE).
- Neoadjuvant Treatment, excluding samples from patients
who received chemotherapy before sample acquisition.
- Grade, excluding Grade 1 samples.
We use these rules to build up a data frame with
two columns: sampleUse (Used or Unused), and whyExcluded.
### 1.3 Results
We exclude 96 of the 285 samples for various reasons.
Of the 189 that remain, 139 are RD and 50 are No RD.
We save tothillFilteredSamples
to the RData file "tothillFilteredSamples.RData".
## 2 Libraries
We first load the libraries we will use
in this report.
## 3 Loading the Data
Here we simply load the previously assembled clinical information.
```r
load(file.path("RDataObjects", "tothillClinical.RData"))
tothillClinical[1:3, ]
```
```
## GEO.ID SampleID KMeansGroup ClinicalType HistologicSubtype
## X49 GSM249839 49 5 MAL Ser
## X129 GSM250001 129 1 MAL Ser
## X146 GSM250000 146 NC MAL Ser
## PrimarySite Stage Grade Age Status Pltx Tax Neo MosToRelapse
## X49 OV III 3 56 D Y N N 7
## X129 OV III 3 65 D Y N N 7
## X146 OV III 3 56 PF Y N N 166
## MosToDeath ResidDisease ArraySite
## X49 8 <1 OV
## X129 15 >1 PE
## X146 166 >1 OV
```
## 4 Filtering Samples Used
We now walk through the various criteria, and seeing
what these imply for inclusion of the various samples.
Our default assumption is that all samples are used.
```r
sampleUse <- rep("Used", nrow(tothillClinical))
names(sampleUse) <- rownames(tothillClinical)
whyExcluded <- rep("", nrow(tothillClinical))
names(whyExcluded) <- rownames(tothillClinical)
```
### 4.1 Residual Disease
First, we check residual disease status, and
exclude patients with no information.
```r
table(tothillClinical[, "ResidDisease"])
```
```
##
## <1 >1 macro size NK nil NK
## 76 70 18 84 37
```
```r
sampleUse[tothillClinical[, "ResidDisease"] == "NK"] <- "Unused"
whyExcluded[tothillClinical[, "ResidDisease"] == "NK"] <- paste(whyExcluded[tothillClinical[,
"ResidDisease"] == "NK"], "-No RD Info-", sep = "")
table(sampleUse)
```
```
## sampleUse
## Unused Used
## 37 248
```
### 4.2 Clinical Type
Next, we look at clinical type.
Some of the samples are known to be of low malignant
potential (LMP), and we don't want to use them.
```r
table(tothillClinical[, "ClinicalType"])
```
```
##
## LMP MAL
## 18 267
```
```r
sampleUse[tothillClinical[, "ClinicalType"] == "LMP"] <- "Unused"
whyExcluded[tothillClinical[, "ClinicalType"] == "LMP"] <- paste(whyExcluded[tothillClinical[,
"ClinicalType"] == "LMP"], "-LMP-", sep = "")
table(sampleUse)
```
```
## sampleUse
## Unused Used
## 53 232
```
### 4.3 Histologic Subtype
Next, we look at histologic subtype.
We only want to keep serous (Ser) tumor samples.
```r
table(tothillClinical[, "HistologicSubtype"])
```
```
##
## Adeno Endo Ser
## 1 20 264
```
```r
sampleUse[tothillClinical[, "HistologicSubtype"] == "Adeno"] <- "Unused"
sampleUse[tothillClinical[, "HistologicSubtype"] == "Endo"] <- "Unused"
whyExcluded[tothillClinical[, "HistologicSubtype"] == "Adeno"] <- paste(whyExcluded[tothillClinical[,
"HistologicSubtype"] == "Adeno"], "-Adeno Subtype-", sep = "")
whyExcluded[tothillClinical[, "HistologicSubtype"] == "Endo"] <- paste(whyExcluded[tothillClinical[,
"HistologicSubtype"] == "Endo"], "-Endo Subtype-", sep = "")
table(sampleUse)
```
```
## sampleUse
## Unused Used
## 68 217
```
### 4.4 Array Site
Next, we look at the site the sample was taken from
(the "array site""). We want tumors from the ovary
or the peritoneum.
```r
table(tothillClinical[, "ArraySite"])
```
```
##
## BN CO FT OM Other OV OV or OM PE
## 1 4 2 2 3 200 1 71
## UT
## 1
```
```r
sampleUse[!is.element(tothillClinical[, "ArraySite"], c("OV", "PE"))] <- "Unused"
whyExcluded[!is.element(tothillClinical[, "ArraySite"], c("OV", "PE"))] <- paste(whyExcluded[!is.element(tothillClinical[,
"ArraySite"], c("OV", "PE"))], "-Not OV or PE-", sep = "")
table(sampleUse)
```
```
## sampleUse
## Unused Used
## 77 208
```
### 4.5 Neoadjuvant Chemo
Next, we look at whether the patients received
neoadjuvant chemotherapy. We want to focus on
chemo-naive tumors.
```r
table(tothillClinical[, "Neo"])
```
```
##
## N Y
## 3 264 18
```
```r
sampleUse[tothillClinical[, "Neo"] == ""] <- "Unused"
sampleUse[tothillClinical[, "Neo"] == "Y"] <- "Unused"
whyExcluded[tothillClinical[, "Neo"] == ""] <- paste(whyExcluded[tothillClinical[,
"Neo"] == ""], "-NeoAdj Unk-", sep = "")
whyExcluded[tothillClinical[, "Neo"] == "Y"] <- paste(whyExcluded[tothillClinical[,
"Neo"] == ""], "-NeoAdj Chemo-", sep = "")
table(sampleUse)
```
```
## sampleUse
## Unused Used
## 91 194
```
With respect to neoadjuvant chemo, we exlude patients who
either received therapy or for whom this info is unavailable.
This mostly reduces the number of RD samples.
### 4.6 Grade
Next, we look at grade. We want only Grade 2 or 3
samples.
```r
table(tothillClinical[, "Grade"])
```
```
##
## 1 2 3
## 19 97 164
```
```r
sampleUse[is.na(tothillClinical[, "Grade"])] <- "Unused"
sampleUse[tothillClinical[, "Grade"] == 1] <- "Unused"
whyExcluded[is.na(tothillClinical[, "Grade"])] <- paste(whyExcluded[is.na(tothillClinical[,
"Grade"])], "-Grade NA-", sep = "")
whyExcluded[which(tothillClinical[, "Grade"] == 1)] <- paste(whyExcluded[which(tothillClinical[,
"Grade"] == 1)], "-Grade 1-", sep = "")
table(sampleUse)
```
```
## sampleUse
## Unused Used
## 96 189
```
### 4.7 Final Tally
Now we see how many RD and No RD samples remain.
```r
table(sampleUse, tothillRD)
```
```
## tothillRD
## sampleUse No RD RD
## Unused 34 25
## Used 50 139
```
## 5 Building the Data Frame
Now we bundle the assembled information into a data
frame for later use.
```r
tothillFilteredSamples <- data.frame(sampleUse = sampleUse, whyExcluded = whyExcluded,
row.names = rownames(tothillClinical))
```
## 6 Saving RData
Now we save the relevant information to an RData object.
```r
save(tothillFilteredSamples, file = file.path("RDataObjects", "tothillFilteredSamples.RData"))
```
## 7 Appendix
### 7.1 File Location
```r
getwd()
```
```
## [1] "/Users/slt/SLT WORKSPACE/EXEMPT/OVARIAN/Ovarian residual disease study 2012/RD manuscript/Web page for paper/Webpage"
```
### 7.2 SessionInfo
```r
sessionInfo()
```
```
## R version 3.0.2 (2013-09-25)
## Platform: x86_64-apple-darwin10.8.0 (64-bit)
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] knitr_1.5
##
## loaded via a namespace (and not attached):
## [1] evaluate_0.5.1 formatR_0.9 stringr_0.6.2 tools_3.0.2
```
## 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.