Introduction

The RadioGx package implements a standardized data structure for storing highly curated results from Radiogenomic experiments. Such experiments investigate the relationship between different cancer cell lines and their response to various doses and types of ionizing radiation. The package is intended for use in conjunction with the PharmacoGx package which provides a similar data structure and API for storing highly curated Pharmacogenomic experiments.

On top of the S4 RadioSet class, this package also provides a standard API to access functions related to fitting dose response curves, calculating survival fraction and fitting linear-quadratic models. Additional functions for calculating the correlation between radiation dose and radiation response allow for characterization of the radiation sensitivity of myriad cancer cell lines representing a diverse set of cancer phenotypes.

It is our hope that this package can aid clinicians and fellow researchers in treatment planning and radiation sensitivity discovery in existing cancer types as well as prospectively in new in vitro and in vivo models of cancer.

Creating a RadioSet

Documentation for creating a RadioSet object will be added to this package in the coming months. In the mean time consult the ‘Creating A PharmacoSet’ vignette from the PharmacoGx Bioconductor package for an example of creating a related data structure.

Basic Functionalities of RadioGx

Installing RadioGx

To install the RadioGx package, run:

BiocManager::install('RadioGx', version='devel')

library(RadioGx)

RadioSet

The RadioSet has a structure similar to the PharmacoSet and also inherits from the CoreSet^[exported by CoreGx] class . The radiation slot is implemented in RadioGx to hold relevant metadata about the type(s) of radiation used in the dose-response experiment, and is analogous to the drug slot in a PharmacoSet. The remainder of the slots mirror the PharmacoSet.

Downloading an RSet

RadioGx provides an interface similar to PharmacoGx and Xeva for downloading our curated versions of published datasets.

To get a list of available RadioSets, use:

RSets <- availableRSets()

As the RadioGx package was only recently released, there is currently only one dataset available. Let’s download the ‘Cleveland’ RSet, which contains a highly curated version of the data from Yang et al., 2016.

Cleveland <- downloadRSet('Cleveland', saveDir='.')

data(clevelandSmall)
clevelandSmall

## <RadioSet>
## Name:  Cleveland 
## Date Created:  Thu Mar  2 20:14:42 2023 
## Number of samples:  5 
## Molecular profiles:
## RNA :
##   Dim: 20049, 5 
## RNAseq :
##   Dim: 61958, 5 
## mutation :
##   Dim: 19285, 0 
## CNV :
##   Dim: 24960, 2 
## Treatment response:
## Drug pertubation:
##    Please look at pertNumber(cSet) to determine number of experiments  for each drug-sample combination.
## Drug sensitivity:
##    Number of Experiments:  5 
##    Please look at sensNumber(cSet) to determine number of  experiments for each drug-sample combination.

Similar to PharmacoGx and Xeva, a summary of the contents of the RadioSet is printed when calling a RadioSet in the console. We can see that the clevelandSmall RSet contains sensitivity information for 5 cell-lines treated with a single type of radiation. The RSet also contains rna^[microarray], rna-seq and cnv molecular data for a subset of available cell-lines.

Accessing Data

RadioGx stores three major categories of data: metadata/annotations, molecular data and radiation response data. These are demarcated in Fig. @ref(fig:radioset) using green, blue and red, respectively. Accessor methods are available to retrieve all three kinds of data from an RSet; the accessor methods for each component are listed in the bottom most cell of each object in the RadioGx class diagram. We will discuss a subset of these methods now.

Accessing metadata

Metadata in an RSet is stored in the same slots as in a PharmacoSet, and can be accessed using the same generic accessor functions as in PharmacoGx. A unique slot, radiation has additional accessor methods to retrieve the radiation types used in a given sensitivity experiment.

# Get the radiation info data.frame from an RSet
radInf <- radiationInfo(clevelandSmall)

knitr::kable(radInf)

	treatmentid
radiation	radiation

Currently, only one type of radiation has been used in an RSet. However, we hope to add new RSets covering a wider range of radiation sensitivity and perturbation experiments in the near future. The following method is also available to retrieve the radiation types as a character vector instead of a data.frame.

radTypes <- radiationTypes(clevelandSmall)
radTypes

## [1] "radiation"

Accessing molecular data

Molecular data in an RSet is contained in the molecularProfiles slot and can be accessed the same way it is for a PSet.

# Get the list (equivalent to @molecularProfiles, except that it is robust to changes in RSet structure
str(molecularProfilesSlot(clevelandSmall), max.level=2)

## List of 4
##  $ rna     :Formal class 'SummarizedExperiment' [package "SummarizedExperiment"] with 5 slots
##  $ rnaseq  :Formal class 'SummarizedExperiment' [package "SummarizedExperiment"] with 5 slots
##  $ mutation:Formal class 'SummarizedExperiment' [package "SummarizedExperiment"] with 5 slots
##  $ cnv     :Formal class 'SummarizedExperiment' [package "SummarizedExperiment"] with 5 slots

# Get the names from the list
mDataNames(clevelandSmall)

## [1] "rna"      "rnaseq"   "mutation" "cnv"

All molecular data in an RSet (any class inheriting from CoreSet, actually) is contained in a SummarizedExperiment object. While SummarizedExperiment comes with it’s own set of accessors, we recommend using available RadioGx accessor methods as it allows your scripts to be robust to future changes in the structure of a RadioSet object.

# Get sample metadata
phenoInf <- phenoInfo(clevelandSmall, 'cnv')

To keep the document formatted nicely, the following tables have been subset to the first three rows and columns.

Sample_title	Sample_geo_accession	Sample_status	Sample_submission_date	Sample_last_update_date
MHH-NB-11	GSM888395	Public on Mar 20 2012	Mar 06 2012	Dec 21 2012
CHP-212	GSM887996	Public on Mar 20 2012	Mar 06 2012	Dec 21 2012

# Get feature metadata
featInfo <- featureInfo(clevelandSmall, 'rna')

Probe	EnsemblGeneId	EntrezGeneId	Symbol	GeneBioType
ENSG00000000003_at	ENSG00000000003	7105	TSPAN6	protein_coding
ENSG00000000005_at	ENSG00000000005	64102	TNMD	protein_coding
ENSG00000000419_at	ENSG00000000419	8813	DPM1	protein_coding

# Access the moleclar feature data
mProf <- molecularProfiles(clevelandSmall, 'rnaseq')

	G20472.CHP-212.2	G28026.KP-N-SI9s.1	G28552.MHH-NB-11.1	G28900.IMR-32.3	G41744.NB-1.5
ENSG00000000003	4.206	3.455	2.281	4.242	4.453
ENSG00000000005	0.000	0.138	0.000	0.803	0.000
ENSG00000000419	5.347	5.921	4.228	5.233	6.292

Accessing response data

Data from radiation sensitivity and/or perturbation experiments is also retrieved the same way it is for a PSet. Currently, only sensitivity experiments have been included in a RadioSet.

# Get sensitivity slot
sens <- treatmentResponse(clevelandSmall)

# Get sensitivity raw data
sensRaw <- sensitivityRaw(clevelandSmall)

# Get sensitivity profiles
sensProf <- sensitivityProfiles(clevelandSmall)

# Get sensitivity info
sensInfo <- sensitivityInfo(clevelandSmall)

Fitting Linear Quadratic (LQ) Models

RadioGx provides a number of functions for analyzing dose response experiments. To use these functions, we must first fit a statistical model to the dose response data. This package exports a function for fitting linear-quadratic models to dose response data. The function can be used with data contained in a RadioSet or with raw dose-response data.

# Extract raw sensitvity data from the RadioSet
sensRaw <- sensitivityRaw(clevelandSmall)

str(sensRaw)

##  num [1:5, 1:9, 1:2] 1 1 1 1 1 1 1 1 1 1 ...
##  - attr(*, "dimnames")=List of 3
##   ..$ : chr [1:5] "IMR-32_radiation_2" "CHP-212_radiation_4" "KP-N-S19s_radiation_5" "MHH-NB-11_radiation_6" ...
##   ..$ : chr [1:9] "doses1" "doses2" "doses3" "doses4" ...
##   ..$ : chr [1:2] "Dose" "Viability"

The data returned by sensitivityRaw(RSet) is a three dimensional array, but it can also be thought of as a set of experiment by treatment matrices. We can see by the dimnames of the third dimensions that the first matrix holds the radiation dose (in Gy) for each experiment, while the second matrix holds the viability measurements for the cell-line after each dose in the experimental series.

# Find a cancer cell-line of interest
head(sensitivityInfo(clevelandSmall)$sampleid)

## [1] "IMR-32"    "CHP-212"   "KP-N-S19s" "MHH-NB-11" "NB1"

cancerCellLine <- sensitivityInfo(clevelandSmall)$sampleid[1]
print(cancerCellLine)

## [1] "IMR-32"

# Get the radiation doses and associated survival data from clevelandSmall
radiationDoses <- sensRaw[1, , 'Dose']
survivalFractions <- sensRaw[1, , 'Viability']

LQmodel <- linearQuadraticModel(D=radiationDoses,
                                SF=survivalFractions)
LQmodel

##     alpha      beta 
## 0.8098218 0.0000000 
## attr(,"Rsquare")
## [1] 0.9199636

Above we see that LQmodel contains the alpha and beta coefficients for the dose response curve fit to the dose and viability data for the IMR-32 cancer cell-line. Based on the \(R^2\) attribute we can see that the model fit for this data is good, with 92% of observed variance explained by the model.

Calculating Dose-Response Metrics

RadioGx provides a number of functions for calculating common dose response metrics such as surviving fraction (SF), area under the curve (AUC) and dose at which only 10% of cancer cells survive (D10).

Some of these functions require the alpha and beta coefficients, as calculated above using the linearQuadraticModel function.

survFracAfter2Units <- computeSF2(pars=LQmodel)
print(survFracAfter2Units)

## [1] 0.1979692

dose10PercentSurv <- computeD10(pars=LQmodel)
print(dose10PercentSurv)

## [1] 2.843323

We see from the above code cell that after two units of radiation, 19.797% of cancer cells remain relative to the initial population. Conversely, using computeD10 we see that on average 2.843 units of radiation need to be administered to result in 10% cell-line survival (i.e., 90% of cancer cells are killed).

Other dose-response metrics can be computed directly using radiation dose and cancer cell viability data.

areaUnderDoseRespCurve <- RadioGx::computeAUC(D=radiationDoses, pars=LQmodel, lower=0,
                                     upper=1)
print(areaUnderDoseRespCurve)

## [1] 0.6854133

In the above code block we compute the AUC for a dose-response curve between a dose of 0 to 1 Gy. This area can be interpreted as the total proportion of cells killed during the administration of 1 Gy of radiation.

Dose-Response Curves

The doseResponseCurve function can be used to generate plots of surviving fraction vs dose for radiation sensitivity experiments. In this example we provide raw data values to create the plot. When the plot.type is set to “Both”, a linear-quadratic model will also be fit to the supplied dose-response values.

doseResponseCurve(
  Ds=list("Experiment 1" = c(0, 2, 4, 6)),
  SFs=list("Experiment 1" = c(1,.6,.4,.2)),
  plot.type="Both"
)

Additionally, doseResponseCurve can be used to create dose response curves directly from a curated RadioSet object. When utilizing this feature, a cell-line must be selected from the RadioSet. This can be done by name if you know which cell-line you are looking for. If you don’t know which cell-line you want to visualize, the available cell-lines can be explored using the cellInfo function.

doseResponseCurve(
  rSets=list(clevelandSmall),
  cellline=cellInfo(clevelandSmall)$sampleid[5]
)

Summarizing Sensitivity

To retrieve a radiation type by cell-line summary of a sensitivity experiment, we use the summarizeSensitivityProfiles function. This will return a matrix where rows are radiation type^[Note there is currently only one radiation type available, but in the future there will be more], columns are cell-line and values are viability measurements summarized using summary.stat^[Available options are ‘mean’, ‘median’, ‘first’, ‘last’]. The sensitivity measure to summarize can be specified using sensitivity.measure^[See sensitivityMeasures(RSet) for available options].

sensSummary <- summarizeSensitivityProfiles(clevelandSmall)

sensSummary[, 1:3]

##   CHP-212    IMR-32 KP-N-S19s 
## 1.3440925 0.5490508 2.3251731

Summarizing Molecular Data

mprofSummary <- summarizeMolecularProfiles(clevelandSmall, mDataType='rna', summary.stat='median', fill.missing=FALSE)

mprofSummary

## class: SummarizedExperiment 
## dim: 20049 5 
## metadata(3): experimentData annotation protocolData
## assays(2): exprs se.exprs
## rownames(20049): ENSG00000000003 ENSG00000000005 ... ENSG00000280439
##   ENSG00000280448
## rowData names(7): Probe EnsemblGeneId ... BEST rownames
## colnames(5): CHP-212 IMR-32 KP-N-S19s MHH-NB-11 NB1
## colData names(25): samplename filename ... rownames tissueid

Due to a lack of replicates in the clevelandSmall RSet, the returned SummarizedExperiment object contains the same information as the original. For other experiments with replicates, however, the result should contain one column per unique cell-line id. For ease of interoperability with the response data contained in an RSet, if fill.missing is FALSE empty columns for the cell-lines in the sensitivity experiment, but not in the molecular profile will be added to the SummarizedExperiment such that the dimensions are equal.

Molecular Signatures for Biomarker Discovery

The true usefulness of the RadioGx packages comes from the ability to determine gene signatures for a cell-lines from a sensitivity experiment. Cell-lines of interest to a given researcher can be selected and a molecular signature computed which correlates specific molecular features with a given sensitivity profile. Using this method one could identify signatures associated with either radio-sensitivity or radio-resistance. Combining this signature with drug response gene signatures from PharmacoGx, as will be done in the subsequent section, one can identify drugs which could augment the effectiveness of a given radiation signature. Perhaps more powerfully, drugs which target features associated with radio-resistance can be found, potentially synergistically increasing the overall effectiveness of the combined treatment.

radSensSig <- radSensitivitySig(clevelandSmall, mDataType='rna', features=fNames(clevelandSmall, 'rna')[2:5], nthread=1)

## Computing radiation sensitivity signatures...

radSensSig@.Data

## , , estimate
## 
##                  radiation
## ENSG00000000005 -0.3794080
## ENSG00000000419  0.7569346
## ENSG00000000457  0.9303156
## ENSG00000000460  0.4064764
## 
## , , se
## 
##                 radiation
## ENSG00000000005 0.5341815
## ENSG00000000419 0.3772930
## ENSG00000000457 0.2117490
## ENSG00000000460 0.5275026
## 
## , , n
## 
##                 radiation
## ENSG00000000005         5
## ENSG00000000419         5
## ENSG00000000457         5
## ENSG00000000460         5
## 
## , , tstat
## 
##                  radiation
## ENSG00000000005 -0.7102606
## ENSG00000000419  2.0062252
## ENSG00000000457  4.3934837
## ENSG00000000460  0.7705675
## 
## , , fstat
## 
##                  radiation
## ENSG00000000005  0.5044701
## ENSG00000000419  4.0249395
## ENSG00000000457 19.3026988
## ENSG00000000460  0.5937743
## 
## , , pvalue
## 
##                  radiation
## ENSG00000000005 0.52877664
## ENSG00000000419 0.13848843
## ENSG00000000457 0.02184966
## ENSG00000000460 0.49708576
## 
## , , df
## 
##                 radiation
## ENSG00000000005         3
## ENSG00000000419         3
## ENSG00000000457         3
## ENSG00000000460         3
## 
## , , fdr
## 
##                  radiation
## ENSG00000000005 0.52877664
## ENSG00000000419 0.27697686
## ENSG00000000457 0.08739866
## ENSG00000000460 0.52877664