breast cancer is a complex and heterogeneous disease tumor samples protein expression clinical...
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HPN-DREAM Breast Cancer Network Inference Challenge
Breast cancer is a complex and heterogeneous disease
Tumor samples
Protein expression
Clinical features
Mutational status
Adapted from TCGA, Nature 2012
Transcriptional Subtype
Breast cancer is a complex and heterogeneous disease
Tumor samples
Protein expression
Clinical features
Mutational status
Adapted from TCGA, Nature 2012
Transcriptional Subtype
• 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
High-throughput screen of protein signaling dynamics
InhibitorsDMSO
FGFR1/3iAKTi
AKTi+MEKi..
Inhibitor N
Stimuli
Serum
PBSEGF
Insulin
FGF1HGF
NRG1IGF1
……
~200
Pro
tein
s
8 Stimuli 5
Treatments
MCF7…
……
……
~200
Pro
tein
s
8 Stimuli 5
Treatments
UACC812
……
…
……
~200
Pro
tein
s
8 Stimuli 5
Treatments
BT20
……
…
…~45 P
rote
ins 8
Stimuli
N Inhibitors
BT549
……
……
Time
4 C
ell
Lines
Inhibitor Stimulus
…0 5 1
54
7 Timepoints
Data generated by Reverse Phase Protein Array (RPPA)
4 cell lines x 8 stimuli = 32 biological contexts for network prediction
Hold out a subset of inhibitor data for assessment of network inference and timecourse predictions
Training Data (4) treatments)
Test Data (N-4) treatments)
FGFR1/3iAKTi
AKTi+MEKiDMSOAll Data (N
treatments)
Test1Test2
….TestN-4
Creating a “Gold Standard” for assessment of predictions
4545
Mimics key aspects and characteristics of the experimental dataGenerated from a dynamical signaling network modelInferred networks can be assessed against against a true gold standard with known network structure
Companion in silico challenge
Subchallenge 1: Network Inference
Task: Create a network where nodes represent phosphoproteins and directed edges represent causal relationships between the nodesAssessment: Score against held-out test data
Predict
Training Data
1A Experimental data: predict 32 context-specific networks1B In silico data: predict 1 networkComplete submission requires both A and B parts
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
ab
undance
With A inhibitor
A B
Cell Line 1, Stimulus 1
Time
A B
Cell Line 2,
Stimulus 1
Node B
ab
undance
2. are context-specific, and vary with cell line and stimulus
Control
Subchallenge 2: Timecourse prediction
Training Data
Predict
Task: Build a dynamical model to predict phospho-protein trajectories following inhibition of test nodesAssessment: Score against held-out test data
TimePro
tein
A
bun
dance
2A Experimental data2B In silico data
Subchallenge 3: Visualization
Task: Devise novel approaches to represent high-dimensional timecourse datasetsAssessment: Crowd-based peer-review
Submit
Training Data
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
HPN-DREAM ContributorsAnalysis and scoring
Steven HillThomas Cokelaer*Sach Mukherjee
In silico data generationMichael Unger*Heinz Koeppl
Experimental data generation
Nicole NesserKatie Johnson-Camacho
Gordon MillsJoe Gray
*Paul Spellman
Challenge organizersLaura Heiser
Julio Saez-RodriguezThea Norman
*Gustavo Stolovitzky
Synapse developmentJay HodgsonBruce HoffMike Kellen
*Steven Friend
Heritage Provider Network
Jonathan Gluck
Poster: DREAM03synapse.org/#!Challenges:DREAM8
Seru m
PB
SEG
FIn
sul
inFG
F 1H
GF
NR
G 1IG
F1
Seru m
PB
SEG
FIn
sul
inFG
F 1H
GF
NR
G 1IG
F1
Seru m
PB
SEG
FIn
sul
inFG
F 1H
GF
NR
G 1IG
F1
Seru m
PB
SEG
FIn
sul
inFG
F 1H
GF
NR
G 1IG
F1
Seru m
PB
SEG
FIn
sul
inFG
F 1H
GF
NR
G 1IG
F1
DMSO Inhib 1 Inhib 2 Inhib 3 Inhib 4
Sustained responseTransient response
Pro
tein
sAn information rich timecourse