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Modeling molecular diversity in cancer
Integrating “omics”, mathematical models and functional cancer biology
Lawrence Berkeley National Laboratory
University of California, San Francisco
University of California, Berkeley
SRI International
Netherlands Cancer Institute
MD Anderson Cancer Center
EXPERIMENT
MODEL TEST
EXPERIMENT
MODEL TEST
Modeling molecular diversity in cancer
• A collection of cell lines as a model of molecular and biological diversity
• Three integrative biology examplesAssociating pathways and markers with responseModeling MEK signaling diversity using pathway logic Bayesian network models of AKT signaling
Identifying and understanding “omic” determinants of therapeutic response in breast cancer
Modeling molecular diversity in cancer
• A collection of cell lines as a model of molecular and biological diversity
• Three integrative biology examplesAssociating markers with responseModeling MEK signaling diversity using pathway logic Bayesian network models of AKT signaling
Identifying and understanding “omic” determinants of therapeutic response in breast cancer
Model requirements
• The molecular abnormalities that influence drug response in primary tumors must be functioning in the model
• The panel must have sufficient molecular diversity so that statistical analyses will have the power to identify molecular features associated with response
Identifying and understanding “omic” determinants of therapeutic response
Neve et al, Cancer Cell 2006Chin et al, Cancer Cell, 2006
Freq
uenc
y
Genome location
Expression Copy number
Cell lines
Tumors
Cell lines as models of primary breast tumors
A collection of 50 cell lines retain important transcriptional and genomic features of primary tumors
Cell lines
Tumors
Modeling molecular diversity in cancer
• A collection of cell lines as a model of molecular and biological diversity
• Three integrative biology examplesAssociating markers with responseModeling MEK signaling diversity using pathway logic Bayesian network models of AKT signaling
Integrating “omics”, mathematical models and functional cancer biology
Associating molecular markers with response to lapatinib
Debo Das, 2007
Training TestAdaptive splines
Prediction: Molecular markers and networks associated with sensitivity and resistance will
predict clinical response
Test: Cell line markers predict response in HER2 positive patients
EGF30001: A randomized, Phase III study of Paclitaxel + Lapatinib vs. Paclitaxel + PlaceboHER2, GRB7, CRK, ACOT9, LJ31079, DDX5
GSK-LBNL collaboration
Modeling molecular diversity in cancer
• A collection of cell lines as a model of molecular and biological diversity
• Three integrative biology examplesAssociating markers with responseModeling MEK signaling diversity using pathway logic Bayesian network models of AKT signaling
Identifying and understanding “omic” determinants of therapeutic response in breast cancer
Protein abundances
Transcript levels
+
Curated network model
Hierarchical analysis of Pathway Logic states and rules
Heiser, Spellman, Talcott, Knapp, Lauderote
Baseline levels populate PL model statesRules define predicted pathway activity
Example network of one cell line
Hierarchical analysis of network features
Prediction: PAK1 is required for network activation of MEK/ERK
cascade in luminal cell lines
0
1
2
3
4
5
6
7
8
9
1 2 3
-log1
0 G
I50
pak1+pak1-
Test: PAK1+ luminal cell lines are more sensitive to MEK inhibitors
CI1040 GSK-MEKi U0126
Modeling molecular diversity in cancer
• A collection of cell lines as a model of molecular and biological diversity
• Three integrative biology examplesAssociating pathways and markers with responseModeling MEK signaling diversity using pathway logic Bayesian network models of AKT signaling
Identifying and understanding “omic” determinants of therapeutic response in breast cancer
Therapeutic agents show strong luminal subtype specificity
Kuo, Guan, Hu, Bayani 2007
Sen
sitiv
ity (-
log1
0GI 50
)
4.0
5.0
6.0
7.0
8.0
9.0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
Lapatinib
4.0
5.0
6.0
7.0
8.0
9.0
Sen
sitiv
ity (-
log1
0GI 50
)
4.0
5.0
6.0
7.0
8.0
9.0CI1040 (MEK) Paclitaxel
‐0.5
0.0
0.5
1.0
1.5
2.0
2.5
Sub
type
resp
onse
met
riclo
g 10la
GI 50
-lo
g 10b G
I 50
Basal Luminal
AKT pathway inhibitors show strong luminal subtype specificity
Bayesian network analysis reveals AKT dependent signaling in luminal lines
Mukherjee , Speed, Neve, et al., 2007
Prediction: PI3-kinase pathway mutations will occur preferentially in luminal subtype cell lines
4.0
5.0
6.0
7.0
8.0
9.0
Sens
itivi
ty (-
log1
0GI 50
)
LU BaA BaB HMEC Unknown
Test: AKT-inhibitor responsive cell lines carry PI3-kinase pathway mutations
Kuo, Neve, Spellman et al., 2007
AKT pathway mutations12/13 AKT pathway mutations in primary tumors are in the luminal
subtype
Modeling molecular diversity in cancer
• A collection of cell lines as a model of molecular and biological diversity
• Three integrative biology examplesAssociating pathways and markers with responseModeling MEK signaling diversity using pathway logic Bayesian network models of AKT signaling
Integrating “omics”, mathematical models and functional cancer biology
Cell /Genome BiologyRich NeveMina BissellPhilippe GascardFrank McCormickMary Helen Barcellos HoffRene BernardsGordon Mills
Comp. BiolPaul SpellmanLaura HeiserKeith LauderoteMerrill KnappCarolyn TalcottSach MukherjeeTerry SpeedJane FridlyandBahram ParvinLisa WilliamsSteve Ashton
Surgery/PathologyBritt Marie LjungFred WaldmanShanaz DairkeeLaura Esserman
EngineeringEarl CorrellBob NordmeyerJian JinDamir Sudar
Exp. TherapeuticsMaria KoehlerMike PressMichael ArbushitesTona GilmerBarbara WeberRichard Wooster
ICBP, SPORE, GSK, Affymetrix, Genentech, Panomics,Cellgate, Cell Biosciences, Komen, Avon, EGF30001 Trial Investigators
Collaborating Laboratories & Support
