HPN-DREAM Breast Cancer Network Inference Challenge

Understanding signaling networks might bring more insights on cancer treatment because cells respond to their environment by activating these networks and phosphorylation reactions play important roles in these networks.

The goal of this challenge is to advance our ability and knowledge on signaling networks inference and protein phosphorylation dynamics prediction. Also, we are asked to develop a visualization method for the data.

The dataset provided is extensive and a result of RPPA (reverse-phase protein array) experiments. It has four (breast cancer) cell lines, each has proteomics data obtained under 3 different inhibitors and one control (DMSO) and 8 different stimuli over 7 time points. And each contains levels of about 45 phosphoproteins. There is also additional dataset with all proteins measured (phosphorylated forms and total proteins) later time points. Moreover, there is an in silico data with similar characteristics (See Data Description on Synapse).

RPPA is a method to quantitate protein levels in lysates from cells or tissues. A video about this technique can be watched on this link.

Using this data, we are asked to complete three sub-challenges.

(1) Network Inference: Modeling causal signaling networks from training data(2) Time-course Prediction: Prediction of trajectories of protein levels following inhibitor perturbation(s) not seen in the training data(3) Visualization: Designing a visualization strategy for high-dimensional molecular time-course data sets such as the ones used in this challenge

More information can be found their official website.

And more about the sub-challenges and how we approach to solve them are coming soon.

• phosphorylation
• proteomics
• lysates
• reverse-phase protein array
• bioinformatics
• rppa
• network inference
• breast cancer
• dream challenge
• in silico
• dmso