2. Breast cancer is a complex and heterogeneous disease Tumor samples Protein expression Clinical features Mutational status Adapted from TCGA, Nature 2012 Transcriptional Subtype

3. Breast cancer is a complex and heterogeneous disease Tumor samples Protein expression Clinical features Mutational status Adapted from TCGA, Nature 2012 Transcriptional Subtype

4. Genomic and epigenomic aberrations (mutations, copy number changes, etc) influence cancer development Collection of aberrations in an individual sample create a unique “biological context” that influences cell signaling Improved understanding of network function will lead to the development of more effective therapies HPN-DREAM Challenge: How are signaling pathways deregulated across breast cancers? Patient Tumor Cell Line

5. High-throughput screen of protein signaling dynamics Inhibitors DMSO FGFR1/3i AKTi AKTi+MEKi. Inhibitor N Stimuli Serum PBS EGF Insulin FGF1 HGF NRG1 IGF1 … … ~200 Proteins 8 Stimuli 5 Treatments MCF7 … … … … … ~200 Proteins 8 Stimuli 5 Treatments UACC812 … … … … … ~200 Proteins 8 Stimuli 5 Treatments BT20 … … … … ~45 Proteins 8 Stimuli N Inhibitors BT549 … … … … Time 4 Cell Lines Inhibitor Stimulus … 05154 7 Timepoints 4 cell lines x 8 stimuli = 32 biological contexts for network prediction

6. Hold out a subset of inhibitor data for assessment of network inference and timecourse predictions Training Data (4) treatments) Test Data ( N -4) treatments) FGFR1/3i AKTi AKTi+MEKi DMSO All Data ( N treatments) Test1 Test2 …. TestN-4 Creating a “Gold Standard” for assessment of predictions 45

7. Mimics key aspects and characteristics of the experimental data Generated from a dynamical signaling network model Inferred networks can be assessed against against a true gold standard with known network structure Companion in silico challenge

8. Task: Create a network where nodes represent phosphoproteins and directed edges represent causal relationships between the nodes Assessment: Score against held-out test data Predict Training Data 1A Experimental data: predict 32 context-specific networks 1B In silico data: predict 1 network Complete submission requires both A and B parts

9. Using inhibitor data to infer network structure Causal edges: 1. predict that perturbing (ie, inhibiting) parent node A will induce change in child node B Time Node B abundance With A inhibitor AB Cell Line 1, Stimulus 1 Time AB Cell Line 2, Stimulus 1 Node B abundance 2. are context-specific, and vary with cell line and stimulus Control

10. Training Data Predict Task: Build a dynamical model to predict phospho-protein trajectories following inhibition of test nodes Assessment: Score against held-out test data Time Protein Abundance 2A Experimental data 2B In silico data

11. Task: Devise novel approaches to represent high-dimensional timecourse datasets Assessment: Crowd-based peer-review Submit Training Data

12. HPN-DREAM Challenge: Participation  237+ registered participants  Complete final submissions:  SC1 Network Inference: 59  SC2 Timecourse Pred: 10  SC3 Visualization: 14  Collaborative Bonus Round to foster exchange of ideas and development of hybrid models  Some details of assessment and test data will not be released until after the close of the collaborative round

13. Analysis and scoring Steven Hill Thomas Cokelaer *Sach Mukherjee In silico data generation Michael Unger *Heinz Koeppl Experimental data generation Nicole Nesser Katie Johnson-Camacho Gordon Mills Joe Gray *Paul Spellman Challenge organizers Laura Heiser Julio Saez-Rodriguez Thea Norman *Gustavo Stolovitzky Synapse development Jay Hodgson Bruce Hoff Mike Kellen *Steven Friend Heritage Provider Network Jonathan Gluck Poster: DREAM03 synapse.org/#!Challenges:DREAM8

15. Serum PBS EGF Insulin FGF1 HGF NRG1 IGF1 Serum PBS EGF Insulin FGF1 HGF NRG1 IGF1 Serum PBS EGF Insulin FGF1 HGF NRG1 IGF1 Serum PBS EGF Insulin FGF1 HGF NRG1 IGF1 Serum PBS EGF Insulin FGF1 HGF NRG1 IGF1 DMSO Inhib 1Inhib 2Inhib 3Inhib 4 Sustained response Transient response Proteins An information rich timecourse