Approximately 10,000 intracellular measurements (fluorescence signals proportional to the concentrations of phosphorylated proteins) and extracellular measurements (concentrations of cytokines released in response to cell stimulation) were acquired in human normal hepatocytes and the hepatocellular carcinoma cell line HepG2 cells. The datasets consist of measurements of 17 phospho-proteins (at 0, 30min, and 3 hrs) and 20 cytokines (at 0, 3hrs, and 24hrs) in two cell types (normal and cancer) after perturbations to the pathway induced by the combinatorial treatment of 7 stimuli and 7 selective inhibitors.
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The Two Challenges
The goal of this signaling response challenge is to predict the response to perturbations of a signaling pathway in normal and cancer human hepatocytes. We have implemented two sub-challenges:
The phospho-proteomics challenge. This challenge consists of predicting a subset of data points that have been measured but removed from the normal and cancer hepatocytes datasets. Specifically, we ask the participants to predict the concentration of the 17 phospho-proteins at two time points (30 minutes and 3 hours) in each one of 7 combinations of ligands and inhibitors for both the normal and cancer hepatocytes. As data, we provide the concentrations of all those 17 phospho-proteins for all the other combinations of ligands and inhibitors for both the normal and cancer hepatocytes. The t=0 time point does not need to be predicted as it corresponds to the unstimulated condition (no stimulus was applied; only inhibitor). Therefore, for each inhibitor, the un-stimulated t=0 value for each phospho-protein is the same across data panels corresponding to different stimuli.
The cytokine-release challenge. This challenge consists of predicting a subset of data points that have been measured but removed from the normal and cancer hepatocytes datasets. Specifically, we ask the participants to predict the concentration of the 20 cytokines at two time points (3 and 24 hours) in each one of 7 combinations of ligands and inhibitors for both the normal and cancer hepatocytes. As data, we provide the concentrations of all those 20 cytokines for all the other combinations of ligands and inhibitors.for both the normal and cancer hepatocytes. The t=0 time point does not need to be predicted as it corresponds to the unstimulated condition (no stimulus was applied ; only inhibitor) Therefore, for each inhibitor, the un-stimulated t=0 value for each cytokine is the same across data panels corresponding to different stimuli.
The Datasets
Human normal and cancer hepatocytes (cell line HepG2s) were treated with 7 stimuli (Table 1a) that are relevant to hepatocyte physiology. For each applied stimulus, 7 selective inhibitors (Table 1b) that block the activity of specific molecules have been applied independently (i.e., only one inhibitor at a time). For each combination of stimulus-inhibitor, the concentration of 17 intracellular phospho-protein molecules (Table 1c) were measured at three time points (0, 30min, 3hours) after stimulation. Also for each combination of stimulus-inhibitor the extra-cellular concentration of 20 cytokines (Table 1d) released by the cells were measured at 3 time points (0, 3hrs, 24hrs) after stimulation. The experimental design is shown schematically in Figure 1, where the data for either a phospho-protein or a cytokine data is exemplified.
The data is contained in two spreadsheets, one for the phosphorylation data (PhosphoproteinChallenge_DREAM3.csv) and one for the cytokine release data (CytokineChallenge_DREAM3.csv). The data is structured according to the following format: in both files the first column contains the cell type (Normal or Cancer), the second column specifies the stimulus, the third column lists the inhibitor, and the fourth column contains the time of data acquisition in minutes. From column 5 to 21, the file PhosphoproteinChallenge_DREAM3.csv contains the abundance of the 17 phospho-proteins in arbitrary fluorescence units and in the order given in Table 1c. From column 5 to 24, the file PhosphoproteinChallenge_DREAM3.csv contains the abundance of the 20 measured extracellular cytokines in arbitrary fluorescence units and in the order given in Table 1d. The values that have to be predicted have been replaced in the data files by the text: “PREDICT”.
Useful Information regarding measurements
(a) Data integrity / linearity. Significant effort was dedicated to data integrity. The data are reported as arbitrary (fluorescence) units in the range between 0 and ~29000. The upper limit (~29000) corresponds to the saturation limit of the detector. Experiments were performed in such a way that measurements are as much as possible within the linear range of the detector. In general, data can be considered linear but there are a few cases that measurements are closer to the upper detection limit of ~29000 (e.g. some cJUN and IL8 measurements) where linearity might have been lost.
(b) Detection limits/Repeatability. The coefficient of variation for repeated measurements was found to be ~8% (mostly due to biological error). With our current experimental design the instrument detector can report data with accuracy as low as ~300. For example, changes from 55 fluorescence units (FU) to 110 FU cannot be considered “2 fold increase” because values lie within the noise error of the detector. On the contrary, data from 1000 to 2000 are significant.
(c) Inhibitor effects. There are cases in which our inhibitors (i.e. MEKi, p38i, and JNKi) target molecules whose phosphorylation we measure (i.e. MEK12, p38, and JNK). In the case where the inhibitor is present, the phosphorylation state of the corresponding molecule (i.e. phospho-MEK, phospho-p38, and phospho-JNK) should be assumed "absent" and the phosphorylation value should not be used. This known inhibitor effect is more pronounced on the allosteric inhibitors (i.e. the effect of MEK inhibitor on the MEK phosphorylation). The effects of the inhibitors are indirectly corroborated from the phosphorylation state of their downstream targets (i.e. MEK -> ERK, p38 -> HSP27, JNK -> cJUN).
Additional data
Any additional prior data already present in the literature can be used. This could be especially useful if a model of the network is needed as part of a method to predict the excluded data.
Submission Information
The participants to this challenge should submit predictions for either the phospho-protein concentration subchallenge or the cytokine release subchallenge, or both. The submission format of the predictions should be as follows:
For the phospho-proteomics sub-challenge, predictors should make a copy of the DREAM3_PhosphoproteinChallenge_Predictions.csv file, and rename it
TeamName__PhosphoproteinChallenge_Predictions.csv,
where TeamName is the name of the team with which you registered for the challenge. Fill in the boxes replacing the text “PREDICT” with the best prediction for the phospho-protein indicated in the header row, for the Simulus/Inhibitor/Time of data acquisition indicated in each row. If you do not add the predicted values for any stimulus-inhibitor-time-phosphoprotein combination, we will consider that your prediction was random. Save your file in the comma separated values (csv) format.
For the Cytokine release prediction challenge, predictors should make a copy of the DREAM3_CytokineChallenge_Predictions.csv file, and rename it
TeamName_CytokineChallenge_Predictions.csv,
where TeamName is the name of the team with which you registered for the challenge. Fill in the boxes replacing the text “PREDICT” with the best prediction for the cytokine indicated in the header row, for the Simulus/Inhibitor/Time of data acquisition indicated in each line. If you do not add the predicted values for any stimulus-inhibitor-time-phosphoprotein combination, we will consider that your prediction was random. Save your file in the comma separated values (csv) format.
Scoring Metrics
For the N predictions to be made in each of the challenges, we will compute the score
A p-value will be assigned to each of the submissions both in the phosphoprotein concentration and the cytokine release predictions.