Drug combinations are an effective strategy to overcome incomplete response or acquired resistance to monotherapies in clinical oncology and beyond. As such, detecting molecular signatures of drug synergy or antagonism in pre-clinical model systems is a major priority to accelerate the adoption of novel treatment strategies in the clinic. The release of PharmacoGx 3.0 introduces support for storing, analyzing and visualizing the results of drug combination experiments using the PharmacoSet class, and will enable reseachers across a range of disciplines to more easily mine t he published drug combination literature for promising molecular signatures of synergy or antagonism which can be validated in retrospective patient data and prioritized for prospective clinical trails.
TREDataMapperTreatmentResponseExperimentFor a comprehensive introduction to detecting biomarkers of drug synergy or antagonism with CoreGx and PharmacoGx, please see our workshop from BioC2022.
The Mathews Griner dataset was generated in a high-throughput drug combination screening study conducted to explore potential drug synergies and antagonisms in a panel of 459 compounds combined with the Tyrosine Kinase inhibitor ibrutinib in cancer cell lines from the activated B-cell like subtype of diffuse large B-cell lymphoma (Mathews Griner et al. 2014). The study uses a 6 x 6 combination matrix design, where each respective drug in a combination undergoes a 4-fold dilution series over 4 steps, with the last well left untreated. This can be conceptualized as a drug combination matrix where the last row and column represent monotherapy experiments for each compound in the pair. A total of 36 viability measurements were taken via the CellTitreGlo assay for each drug pair, being comprised of pair-wise combinations of each dose level in the respective dilution series matrix.
We selected this dataset for our vignette to simplify comparison with the
SynergyFinder package, which was previously used to analyze this dataset
(Yadav et al. 2015).
library(PharmacoGx)
library(CoreGx)
library(data.table)
library(ggplot2)We have included a compressed CSV version of the Mathews Griner dataset which we curated to have informative compound names. You can read the file in with your CSV reader of choice.
input_file <- system.file("extdata/mathews_griner.csv.tar.gz",
package="PharmacoGx")
mathews_griner <- fread(input_file)Before modelling dose-response or treatment synergy/antagonism with PharmacoGx we
must first build a TreatmentResponseExperiment from our high-throughput
drug combination data. This S4 class was designed to store and analyze high
dimensional treatment response data, such as the dose-response screens in cancer
cell lines which are the subject of this analysis. For a detailed explanation
of the class design, please refer to the
TreatmentResponseExperiment vignette in CoreGx.
Due to the highly diverse set of experimental designs used in drug combination
studies, the first step in any drug combination analysis is to generate a
hypothesis about the study design. For the TreatmentResponseExperiment,
we must identify which columns in our raw data are required to uniquely
identify every treatment (our rows), sample (our columns) and observation
(our assays). As a starting point we recommend trying treatment identifiers and
their respective doses for rows, sample identifiers for columns, and the union
of these for observations.1 Giving names to columns in your group list will
automatically rename them when the TreatmentResponseExperiment gets created!
groups <- list(
rowDataMap=c(
treatment1id="RowName", treatment2id="ColName",
treatment1dose="RowConcs", treatment2dose="ColConcs"
),
colDataMap=c("sampleid")
)
groups[["assayMap"]] <- c(groups$rowDataMap, groups$colDataMap)
(groups)## $rowDataMap
## treatment1id treatment2id treatment1dose treatment2dose
## "RowName" "ColName" "RowConcs" "ColConcs"
##
## $colDataMap
## [1] "sampleid"
##
## $assayMap
## treatment1id treatment2id treatment1dose treatment2dose
## "RowName" "ColName" "RowConcs" "ColConcs" "sampleid"These initial guesses can be insufficient to uniquely identify our treatments or samples if there are technical or biological replicates in the data. While some publications are explicit about the presence of such measurements, others are not and require us to explore the data to identify them. In general, we recommend undocumented replicates be treated as technical and used to quantify noise in the assay unless there is a good reason to believe they are from distinct biological entities.
We can identify undocumented replicates using a “group by” operation. This
operation uses the split-apply-combine strategy (also called Map-Reduce) to
compute some aggregation over subsets of a data.frame. A grouping has
undocumented replicates if more than one row is assigned to any group which we
hypothesized to uniquely identify the observations in our data. We check this
below using the .N special variable from the data.table package, which
counts the number of instances in each group defined by the columns in by.
This operation could also be accomplished with dplyr or even base R, though
they may be slower and have less concise syntax.
# The := operator modifies a data.table by reference (i.e., without making a copy)
mathews_griner[, tech_rep := seq_len(.N), by=c(groups[["assayMap"]])]
if (max(mathews_griner[["tech_rep"]]) > 1) {
groups[["colDataMap"]] <- c(groups[["colDataMap"]], "tech_rep")
groups[["assayMap"]] <- c(groups[["assayMap"]], "tech_rep")
} else {
# delete the additional column if not needed
message("No technical replicates in this dataset!")
mathews_griner[["tech_reps"]] <- NULL
}For the Mathews Griner dataset, we do indeed have undocumented replicates! These will be treated a technical replicates.
TREDataMapperOnce we are confident we know which columns are needed to uniquely
identify our treatments and samples, we can create a TREDataMapper using
our raw data and mapping hypothesis. The TREDataMapper is a helper class
designed to make creating a TreatmentResponseExperiment from diverse drug
combination experimental designs easier for users.2 See the
TreatmentResponseExperiment vignette in CoreGx to learn more about the
TREDataMapper class.
The guessMapping method does the necessary internal work to map additional
columns in your dataset to the appopriate category—treatment metadata,
sample metadata or assay data—and returns the column names for each group
in the “mapped_columns” list item.
(treMapper <- TREDataMapper(rawdata=mathews_griner))## <TREDataMapper>
## rawdata: dim(16776, 21)
## BlockId Col Row viability AssayId Size RowSid RowName
## <int> <int> <int> <num> <int> <int> <char> <char>
## 1: 1 1 1 14.47499 241 6 NCGC00181170-01 Bendamustine
## 2: 1 2 1 28.67605 241 6 NCGC00181170-01 Bendamustine
## 3: 1 3 1 48.73285 241 6 NCGC00181170-01 Bendamustine
## 13 variable(s) not shown: [RowTarget <char>, ColSid <char>, ColName
## <char>, ColTarget <char>, RowIC50 <int>, ColIC50 <int>, RowConcs
## <num>, ColConcs <num>, RowConcUnit <char>, ColConcUnit <char>, ...]
##
rowDataMap:
## rowIDs:
## rowMeta:
## colDataMap:
## colIDs:
## colMeta:
## assayMap:
## metadataMap: NAWe will know we have successfully mapped all of our data if the “unmapped” list
item in our guess has no column names in it. If this is not the case, you
may have to refine your hypothesis to include additional information needed
to uniquely identify each observation in your dataset.3 The metadata item in
the list returned from guessMapping captures all columns in our dataset which
only have one unique value. These will be assigned to the metadata slot of
the TreatmentResponseExperiment to save memory.
(guess <- guessMapping(treMapper, groups, subset=TRUE))## [CoreGx::guessMapping,LongTableDataMapper-method]
## Mapping for group rowDataMap: RowName, ColName, RowConcs, ColConcs## [CoreGx::guessMapping,LongTableDataMapper-method]
## Mapping for group colDataMap: sampleid, tech_rep## [CoreGx::guessMapping,LongTableDataMapper-method]
## Mapping for group assayMap: RowName, ColName, RowConcs, ColConcs, sampleid, tech_rep## $metadata
## $metadata$id_columns
## [1] NA
##
## $metadata$mapped_columns
## [1] "AssayId" "Size" "ColSid" "ColTarget" "RowIC50"
## [6] "ColIC50" "RowConcUnit" "ColConcUnit" "tissueid"
##
##
## $rowDataMap
## $rowDataMap$id_columns
## treatment1id treatment2id treatment1dose treatment2dose
## "RowName" "ColName" "RowConcs" "ColConcs"
##
## $rowDataMap$mapped_columns
## [1] "Col" "Row" "RowTarget"
##
##
## $colDataMap
## $colDataMap$id_columns
## [1] "sampleid" "tech_rep"
##
## $colDataMap$mapped_columns
## character(0)
##
##
## $assayMap
## $assayMap$id_columns
## treatment1id treatment2id treatment1dose treatment2dose
## "RowName" "ColName" "RowConcs" "ColConcs" "sampleid"
##
## "tech_rep"
##
## $assayMap$mapped_columns
## [1] "BlockId" "viability" "RowSid"
##
##
## $unmapped
## character(0)Once we have mapped all our columns, we can assign their names to the
TREDataMapper object and use it to construct the TreatmentResponseExperiment
we will use in downstream dose-response and synergy-antagonism modelling.4 We
need to give names to the metadataMap and assayMap in the TREDataMapper,
since there can be more than one item in each of these slots of a
TreatmentResponseExperiment.
metadataMap(treMapper) <- list(experiment_metadata=guess$metadata$mapped_columns)
rowDataMap(treMapper) <- guess$rowDataMap
colDataMap(treMapper) <- guess$colDataMap
assayMap(treMapper) <- list(raw=guess$assayMap)
treMapper## <TREDataMapper>
## rawdata: dim(16776, 21)
## BlockId Col Row viability AssayId Size RowSid RowName
## <int> <int> <int> <num> <int> <int> <char> <char>
## 1: 1 1 1 14.47499 241 6 NCGC00181170-01 Bendamustine
## 2: 1 2 1 28.67605 241 6 NCGC00181170-01 Bendamustine
## 3: 1 3 1 48.73285 241 6 NCGC00181170-01 Bendamustine
## 13 variable(s) not shown: [RowTarget <char>, ColSid <char>, ColName
## <char>, ColTarget <char>, RowIC50 <int>, ColIC50 <int>, RowConcs
## <num>, ColConcs <num>, RowConcUnit <char>, ColConcUnit <char>, ...]
##
rowDataMap:
## rowIDs: RowName, ColName, RowConcs, ColConcs
## rowMeta: Col, Row, RowTarget
## colDataMap:
## colIDs: sampleid, tech_rep
## colMeta:
## assayMap:
## raw:
## keys: RowName, ColName, RowConcs, ColConcs, sampleid, tech_rep
## values: BlockId, viability, RowSid
## metadataMap:
## experiment_metadata: AssayId, Size, ColSid, ColTarget, RowIC50, ColIC50, RowConcUnit, ColConcUnit, tissueidTreatmentResponseExperimentPharmacoGx includes the metaConstruct method to simplify creation of a
TreatmentResponseExperiment object. Simply call it on the TREDataMapper you
created previously to instantiate the object.
(tre <- metaConstruct(treMapper))## 2024-04-14 18:41:43 Building assay index...
## 2024-04-14 18:41:43 Joining rowData to assayIndex...
## 2024-04-14 18:41:44 Joining colData to assayIndex...
## 2024-04-14 18:41:45 Joining assays to assayIndex...
## 2024-04-14 18:41:46 Setting assayIndex key...
## 2024-04-14 18:41:47 Building LongTable...## <TreatmentResponseExperiment>
## dim: 16668 2
## assays(1): raw
## rownames(16668): (-)-Gossypol:Ibrutinib (PCI-32765):0:0 (-)-Gossypol:Ibrutinib (PCI-32765):0:0.1954 ... methyl jasmonate:Ibrutinib (PCI-32765):2500:12.5 methyl jasmonate:Ibrutinib (PCI-32765):2500:50
## rowData(7): treatment1id treatment2id treatment1dose ... Col Row RowTarget
## colnames(2): TMD8:1 TMD8:2
## colData(2): sampleid tech_rep
## metadata(1): experiment_metadataThe viability measurements in the Mathews Griner data have already been normalized relative to the time zero control. However, PharmacoGx recommends further normalizing against the untreated control to limit the range of your viability measurements to be close to [0, 1]. The untreated control is the well which has only been treated with the drug delivery vehicle (usually the solvent DMSO) and has thus been allowed to grow over the treatment exposure time.
To accomplish this for our current dataset, we can use a sub-query where we
select the viability at index (6, 6) of our drug combination matrix. This
well has not been treated with either drug. Dividing by it normalizes the
observed viability in our treatment wells relative to any growth that may
have occured during treatment. We further truncate our viability values at zero,
since any values below this are likely a result of technical
noise in our assay.5 The .SD special variable, short for “subset data”, is a
data.table feature that allows you to implement complex sub-queries. It
contains a reference to the table being queried.
raw <- tre[["raw"]]
raw[,
viability := viability / .SD[treatment1dose == 0 & treatment2dose == 0, viability],
by=c("treatment1id", "treatment2id", "sampleid", "tech_rep")
]
raw[, viability := pmax(0, viability)] # truncate min viability at 0
tre[["raw"]] <- rawAs a sanity check that our normalization was effective, we will look at the range of our viabiltiy values. In most cases, this should be very close to [0, 1], since we do not expect treatment with one or more compound to increase the growth of our cell lines relative to the untreated control.
tre[["raw"]][, range(viability)]## [1] 0.00000 60.96139Some of our treated viabilties are >60x higher than our control! This is very unlikely to be a real signal and probably indicates there was an issue with the viability measurement for our dose 0 x 0 well. To quality control our results, we will find the treatment combination with this observation and remove it from downstream analysis. It is essential to perform regular sanity checks to ensure your data is plausible given the experimental setup.
(bad_treatments <- tre[["raw"]][viability > 2, unique(treatment1id)])## [1] "IKK-16"Only a single treatment has a viability measurement higher than twice our control. We will remove this and leave the remaining values in place, since we can simply truncate them at viability of 1 to include as many combinations in our analyses as possible.
(tre <- subset(tre, !(treatment1id %in% bad_treatments)))## <TreatmentResponseExperiment>
## dim: 16632 2
## assays(1): raw
## rownames(16632): (-)-Gossypol:Ibrutinib (PCI-32765):0:0 (-)-Gossypol:Ibrutinib (PCI-32765):0:0.1954 ... methyl jasmonate:Ibrutinib (PCI-32765):2500:12.5 methyl jasmonate:Ibrutinib (PCI-32765):2500:50
## rowData(7): treatment1id treatment2id treatment1dose ... Col Row RowTarget
## colnames(2): TMD8:1 TMD8:2
## colData(2): sampleid tech_rep
## metadata(1): experiment_metadataWe will inspect the viability range again to ensure the bad data has been removed.
tre[["raw"]][, range(viability)]## [1] 0.000000 1.858926The range for viabilities is now much more reasonable, and we can move on to fitting dose-response curves to our monotherapy measurements.
The endoaggregate method allows us to extract an assay from our
TreatmentResponseExperiment, compute a group by (aggregation) over it,
then assign it back to our TreatmentResponseExperiment via a join, all in a
single function call. Since we currently only want to fit curves to the
monotherapy experiments in our drug matrix we will use the subset argument to
filter the assay to only monotherapy rows before applying the aggregation.
This method is useful to update existing assays, or to create
new ones. The assay argument specifies the assay to aggregate over and the
target argument specifies the name of the assay to assign the results to.
If the target does not already exist, a new assay will be created otherwise
the specified target will be updated. If you exclude this argument, the
assay you select automatically becomes the target. The endoaggregate
method is endomorphic, a class of methods that always return
the same type they are called on. This means that endoaggregate always
returns a new TreatmentResponseExperiment.
While subsetting out our monotherapy viabilities, we can also summarize
viabilities over our techinical replicates by excluding that column from our
by argument. Any additional arguments to endoaggregate via ... are
assumed to be aggregation calls and will be computed for each group
identified in by and assigned to target. The name given to any argument
in ... will be the column name for that computation in the resulting
TreatmentResponseExperiment.
tre_qc <- tre |>
endoaggregate(
subset=treatment2dose == 0, # filter to only monotherapy rows
assay="raw",
target="mono_viability", # create a new assay named mono_viability
mean_viability=pmin(1, mean(viability)),
by=c("treatment1id", "treatment1dose", "sampleid")
)Once we have isolated our monotherapy viabilities, we can once again use
endoaggregate to fit our dose-response curves. This time we will use the
enlist=FALSE option which allows us to assign intermediate variables
during our aggregation. Pass in an entire code block to endoggregate
to use this feature and only the returned list will be assigned to the target
assay, with each list item as a column with the corresponding name. To prevent
repeating our curve fit parameters, we will create a new assay for them since
we are now summarizing over dose. This helps ensure we use only as much memory
as is necessary to store the analysis results in our
TreatmentResponseExperiment.
tre_fit <- tre_qc |>
endoaggregate(
{ # the entire code block is evaluated for each group in our group by
# 1. fit a log logistic curve over the dose range
fit <- PharmacoGx::logLogisticRegression(treatment1dose, mean_viability,
viability_as_pct=FALSE)
# 2. compute curve summary metrics
ic50 <- PharmacoGx::computeIC50(treatment1dose, Hill_fit=fit)
aac <- PharmacoGx::computeAUC(treatment1dose, Hill_fit=fit)
# 3. assemble the results into a list, each item will become a
# column in the target assay.
list(
HS=fit[["HS"]],
E_inf = fit[["E_inf"]],
EC50 = fit[["EC50"]],
Rsq=as.numeric(unlist(attributes(fit))),
aac_recomputed=aac,
ic50_recomputed=ic50
)
},
assay="mono_viability",
target="mono_profiles",
enlist=FALSE, # this option enables the use of a code block for aggregation
by=c("treatment1id", "sampleid"),
nthread=2 # parallelize over multiple cores to speed up the computation
)Since we require the monotherapy curve parameters to calculate dose-response
curve dependent synergy metrics such as Loewe and ZIP, we will add these to a
new drug combination assay to make computing synergy metrics possible within
a single endoaggregate call.6 If we don’t name an aggregation in
endoaggregate a default name will be inferred from the function name and its
first argument. In this case, the column will be called “mean_viability”.
tre_combo <- tre_fit |>
endoaggregate(
assay="raw",
target="combo_viability",
mean(viability),
by=c("treatment1id", "treatment2id", "treatment1dose", "treatment2dose",
"sampleid")
)The mergeAssays method is a convenient way to perform joins between the
assays of a TreatmentResponseExperiment endomorphically. It is equivalent
to extracting the assays from the object, performing a join using the merge
command, and assigning back to the assay specified as the x argument. This
method accepts all the same parameters as the data.table::merge function,
but requires the assays in x and y be specified as assay names instead of
actual assay tables.7 The endomorphic nature of this operation allows the
result to be piped into additional calls, as demonstrated below.
tre_combo <- tre_combo |>
mergeAssays(
x="combo_viability",
y="mono_profiles",
by=c("treatment1id", "sampleid")
) |>
mergeAssays(
x="combo_viability",
y="mono_profiles",
by.x=c("treatment2id", "sampleid"),
by.y=c("treatment1id", "sampleid"),
suffixes=c("_1", "_2") # add sufixes to duplicate column names
)The endoaggregate method is compatible with standard data.table syntax,
since assays are implemented internally using this package. As such, we can
use a sub-query (via .SD) to pull out the viability measurements for each
individual drug in our combination. This value is needed to compute the
Highest Single Agent and Bliss synergy metrics. We will add these values to
our “combo_viability” assay so all the synergy metrics can be calculated in
a single step.8 The endoaggregate method can also be
used to apply arbitrary functions to the data in an assay. Just specify the
entire assay key to the by argument to compute the function for every row
(i.e., to perform no aggregation or summary of the data).
tre_combo <- tre_combo |>
endoaggregate(
viability_1=.SD[treatment2dose == 0, mean_viability],
assay="combo_viability",
by=c("treatment1id", "treatment1dose", "sampleid")
) |>
endoaggregate(
viability_2=.SD[treatment1dose == 0, mean_viability],
assay="combo_viability",
by=c("treatment1id", "treatment2dose", "sampleid")
)Now that we have assembled all the requisite information into the “combo_viability” assay, we are ready to compute our synergy scores!
Drug synergy or antagonism is detected as the deviation in the observerd treatment response above or below the expected response. Determining the expected response, however, is non-trivial and several different reference models have been proposed in the literature for the expected response to a drug combination. PharmacoGx has implemented functions to calculate the expected response under the Highest Single Agent (HSA), Loewe Additivity, Zero Interaction Potency (ZIP) and Bliss independence null models of drug synergy.9 The formula for each of these models is different when using proportion of response vs proportion of viability. In PharmacoGx all synergy computations assume we are working with proportion viability. If you instead have response values you can convert them to viabilities by subtracting the normalized responses from 1 (or 100 if using percentages).
These models are discussed in more detail in our workshop from the 2022 Bioconductor conference, linked at the top of this vignette. Addition resources to learn about drug synergy models include (Yadav et al. 2015) for mathematical definitions of each reference model and (Vlot et al. 2019) for exploring the assumptions of each model and how they can affect resulting drug synergy predicitons.
Below we use PharmacoGx to compute the expected response under all four null reference models of drug synergy.
tre_synergy <- tre_combo |>
endoaggregate(
assay="combo_viability",
HSA_ref=PharmacoGx::computeHSA(viability_1, viability_2),
Bliss_ref=PharmacoGx::computeBliss(viability_1, viability_2),
Loewe_ref=PharmacoGx::computeLoewe(
treatment1dose, HS_1=HS_1, EC50_1=EC50_1, E_inf_1=E_inf_1,
treatment2dose, HS_2=HS_2, EC50_2=EC50_2, E_inf_2=E_inf_2
),
ZIP_ref=computeZIP(
treatment1dose, HS_1=HS_1, EC50_1=EC50_1, E_inf_1=E_inf_1,
treatment2dose, HS_2=HS_2, EC50_2=EC50_2, E_inf_2=E_inf_2
),
by=assayKeys(tre_combo, "combo_viability"),
nthread=2
)Once we have our null hypotheis for no drug synergy, computing a synergy score is as simple as taking the difference or the ratio between the observed drug combination viability and each of our reference models. We prefer taking the difference, since the resulting value can be interpreted as the proportion of cell death above or below the expected value for each model.
tre_synergy <- tre_synergy |>
endoaggregate(
assay="combo_viability",
HSA_score=HSA_ref - mean_viability,
Bliss_score=Bliss_ref - mean_viability,
Loewe_score=Loewe_ref - mean_viability,
ZIP_score=ZIP_ref - mean_viability,
by=assayKeys(tre_synergy, "combo_viability")
)In addition to standard synergy scores, the ZIP model also provides a more subtle metric of drug synergy that attempts to quantify shifts in the relative potency and efficacy of drugs in a combination. This metrics is referred to as ZIP delta, and is computed from a two-way curve fit for the expected effect of adding drug 1 to drug 2 and drug 2 to drug 1 for a pair in combinaton. (Yadav et al. 2015).
Below we demonstrate how to compute ZIP delta using PharmacoGx. We first use
the estimateProjParams method to compute the dose-response curve parameters
for the two-way curve fits.
tre_zip <- tre_synergy |>
endoaggregate(
assay="combo_viability",
subset=treatment2dose != 0,
{
zip_fit <- estimateProjParams(
dose_to=treatment1dose,
combo_viability=mean_viability,
dose_add=unique(treatment2dose),
EC50_add=unique(EC50_2),
HS_add=unique(HS_2),
E_inf_add=unique(E_inf_2)
)
setNames(zip_fit, paste0(names(zip_fit), "_2_to_1"))
},
enlist=FALSE,
by=c("treatment1id", "treatment2id", "treatment2dose", "sampleid"),
nthread=2
)
tre_zip <- tre_zip |>
endoaggregate(
assay="combo_viability",
subset=treatment1dose != 0,
{
zip_fit <- estimateProjParams(
dose_to=treatment2dose,
combo_viability=mean_viability,
dose_add=unique(treatment1dose),
EC50_add=unique(EC50_1),
HS_add=unique(HS_1),
E_inf_add=unique(E_inf_1)
)
setNames(zip_fit, paste0(names(zip_fit), "_1_to_2"))
},
enlist=FALSE,
by=c("treatment1id", "treatment2id", "treatment1dose", "sampleid"),
nthread=2
)Then we use the .deltaScore function to compute the final delta score
using the two-way fit curve parameters from the previous step.
tre_zip <- tre_zip |>
endoaggregate(
assay="combo_viability",
ZIP_delta=.deltaScore(
EC50_1_to_2=EC50_proj_1_to_2, EC50_2_to_1=EC50_proj_2_to_1,
EC50_1=EC50_1, EC50_2=EC50_2,
HS_1_to_2=HS_proj_1_to_2, HS_2_to_1=HS_proj_2_to_1,
HS_1=HS_1, HS_2=HS_2,
E_inf_1_to_2=E_inf_proj_1_to_2, E_inf_2_to_1=E_inf_proj_2_to_1,
E_inf_1=E_inf_1, E_inf_2=E_inf_2,
treatment1dose=treatment1dose, treatment2dose=treatment2dose,
ZIP=ZIP_ref
),
by=assayKeys(tre_zip, "combo_viability")
)Now that we have computed synergy scores for all of our drug pairs, we can visualize our results before moving on to downstream analyses looking for biomarkers of synergy and antagonism.
Since the Loewe and ZIP synergy metrics require fitting dose-response curves, it is possible that bad fits could yield misleading synergy scores. Given this fact, we recommend applying a curve fit quality filter before continuing with downstream analysis. Below we require curves to obtain an R-squared greater than 0.5 (that is, >50% variance of the data explained by the fit) before ranking treatment pairs by their synergy score. If you are ranking using HSA or Bliss, such a filter is not required, as these two metrics use only the observed monotherapy viabilities to calculate the expected viability.
The following code filters to only high quality curve fits and then finds the 15 most synergistic treatment pairs based on their ZIP delta score.
combo_viab <- tre_zip[["combo_viability"]]
(top_15_combo <- combo_viab[
Rsq_1 > 0.5 & Rsqr_1_to_2 > 0.5 & Rsqr_2_to_1 > 0.5,
.(
max_delta=max(ZIP_delta, na.rm=TRUE),
mean_delta=mean(ZIP_delta, na.rm=TRUE),
max_bliss=max(Bliss_score, na.rm=TRUE),
mean_bliss=mean(Bliss_score, na.rm=TRUE)
),
by=.(treatment1id, treatment2id, sampleid)
][
order(-max_delta),
unique(.SD)
][1:15])Before visualizing drug synergy, we recommend filling in any NA synergy scores
to avoid gaps in the produced plots. This can be easily accomplished using
the data.table::setnafill function with the last observation carried forward
strategy.
top_15_combo_df <- combo_viab[top_15_combo, on=c('treatment1id', 'treatment2id', 'sampleid')]
# Last observation carried forward for NA/NaN delta scores, to make plot look nicer
setnafill(top_15_combo_df, type="locf", cols="ZIP_delta")The ggplot2 package can be used to visualize the synergy scores for our
top 15 most synergistic treatment pairs. Below we provide example code to
produce both a contour plot (synergy surface) as well as a heat map to gain
a more inuitive understanding of how synergy or antagonism changes over the
full dose range of each drug combination matrix.
top_15_combo_df |>
ggplot(aes(x=treatment1dose, y=treatment2dose, z=ZIP_delta * 100)) +
scale_x_log10(oob=scales::squish_infinite) +
scale_y_log10(oob=scales::squish_infinite) +
geom_contour_filled(
breaks=c(-100, -80, -40, -20, -10, -1, 1, 10, 20, 40, 80, 100)
) +
facet_wrap(~ treatment1id, nrow=3, ncol=5) +
scale_fill_brewer(palette="RdBu", direction=-1, drop=FALSE)top_15_combo_df |>
ggplot(aes(x=factor(treatment1dose), y=factor(treatment2dose))) +
geom_tile(aes(fill=ZIP_delta * 100)) +
facet_wrap(~treatment1id, nrow=3, ncol=5) +
scale_fill_gradient2(low="blue", mid="white", high="red", midpoint=0)To learn how to use synergy scores for biomarker discovery please review our workshop from Bioconductor 2022, linked in the first section of this document.
## R Under development (unstable) (2024-03-18 r86148)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 22.04.4 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.19-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_GB LC_COLLATE=C
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## time zone: America/New_York
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
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## other attached packages:
## [1] ggplot2_3.5.0 data.table_1.15.4
## [3] PharmacoGx_3.7.5 CoreGx_2.7.3
## [5] SummarizedExperiment_1.33.3 Biobase_2.63.1
## [7] GenomicRanges_1.55.4 GenomeInfoDb_1.39.13
## [9] IRanges_2.37.1 S4Vectors_0.41.6
## [11] MatrixGenerics_1.15.0 matrixStats_1.3.0
## [13] BiocGenerics_0.49.1 knitcitations_1.0.12
## [15] knitr_1.46 BiocStyle_2.31.0
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## loaded via a namespace (and not attached):
## [1] bitops_1.0-7 rlang_1.1.3
## [3] magrittr_2.0.3 shinydashboard_0.7.2
## [5] compiler_4.4.0 reshape2_1.4.4
## [7] vctrs_0.6.5 relations_0.6-13
## [9] stringr_1.5.1 pkgconfig_2.0.3
## [11] crayon_1.5.2 fastmap_1.1.1
## [13] backports_1.4.1 XVector_0.43.1
## [15] caTools_1.18.2 utf8_1.2.4
## [17] promises_1.3.0 rmarkdown_2.26
## [19] UCSC.utils_0.99.6 coop_0.6-3
## [21] xfun_0.43 MultiAssayExperiment_1.29.1
## [23] zlibbioc_1.49.3 cachem_1.0.8
## [25] jsonlite_1.8.8 RefManageR_1.4.0
## [27] SnowballC_0.7.1 later_1.3.2
## [29] DelayedArray_0.29.9 BiocParallel_1.37.1
## [31] parallel_4.4.0 sets_1.0-25
## [33] cluster_2.1.6 R6_2.5.1
## [35] RColorBrewer_1.1-3 bslib_0.7.0
## [37] stringi_1.8.3 limma_3.59.8
## [39] boot_1.3-30 lubridate_1.9.3
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## [47] Matrix_1.7-0 igraph_2.0.3
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## [53] gplots_3.1.3.1 codetools_0.2-20
## [55] lattice_0.22-6 tibble_3.2.1
## [57] plyr_1.8.9 withr_3.0.0
## [59] shiny_1.8.1.1 BumpyMatrix_1.11.0
## [61] evaluate_0.23 bench_1.1.3
## [63] xml2_1.3.6 pillar_1.9.0
## [65] lsa_0.73.3 BiocManager_1.30.22
## [67] KernSmooth_2.23-22 DT_0.33
## [69] checkmate_2.3.1 shinyjs_2.1.0
## [71] piano_2.19.0 generics_0.1.3
## [73] munsell_0.5.1 scales_1.3.0
## [75] gtools_3.9.5 xtable_1.8-4
## [77] marray_1.81.0 glue_1.7.0
## [79] slam_0.1-50 tools_4.4.0
## [81] fgsea_1.29.0 visNetwork_2.1.2
## [83] fastmatch_1.1-4 cowplot_1.1.3
## [85] grid_4.4.0 bibtex_0.5.1
## [87] colorspace_2.1-0 GenomeInfoDbData_1.2.12
## [89] cli_3.6.2 fansi_1.0.6
## [91] S4Arrays_1.3.7 dplyr_1.1.4
## [93] gtable_0.3.4 R.methodsS3_1.8.2
## [95] sass_0.4.9 digest_0.6.35
## [97] SparseArray_1.3.5 htmlwidgets_1.6.4
## [99] R.oo_1.26.0 htmltools_0.5.8.1
## [101] lifecycle_1.0.4 httr_1.4.7
## [103] statmod_1.5.0 mime_0.12Mathews Griner, Lesley A., Rajarshi Guha, Paul Shinn, Ryan M. Young, Jonathan M. Keller, Dongbo Liu, Ian S. Goldlust, et al. 2014. “High-Throughput Combinatorial Screening Identifies Drugs That Cooperate with Ibrutinib to Kill Activated B-celllike Diffuse Large B-cell Lymphoma Cells.” Proceedings of the National Academy of Sciences 111 (6): 2349–54. https://doi.org/10.1073/pnas.1311846111.
Vlot, Anna H. C., Natália Aniceto, Michael P. Menden, Gudrun Ulrich-Merzenich, and Andreas Bender. 2019. “Applying Synergy Metrics to Combination Screening Data: Agreements, Disagreements and Pitfalls.” Drug Discovery Today 24 (12): 2286–98. https://doi.org/10.1016/j.drudis.2019.09.002.
Yadav, Bhagwan, Krister Wennerberg, Tero Aittokallio, and Jing Tang. 2015. “Searching for Drug Synergy in Complex DoseResponse Landscapes Using an Interaction Potency Model.” Computational and Structural Biotechnology Journal 13 (January): 504–13. https://doi.org/10.1016/j.csbj.2015.09.001.