Modeling molecular diversity in cancer Integrating “omics”, mathematical models and functional cancer biology Lawrence Berkeley National Laboratory University

Modeling molecular diversity in cancerModeling molecular diversity in cancer

Integrating “omics”, mathematical models Integrating “omics”, mathematical models and functional cancer biologyand 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

RR


• 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 Identifying and understanding “omic” determinants of therapeutic response in breast cancerof therapeutic response in breast cancer



• Three integrative biology examplesAssociating markers with responseModeling MEK signaling diversity using pathway logic Bayesian network models of AKT signaling


Model requirementsModel 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 Identifying and understanding “omic” determinants of therapeutic responsetherapeutic response

Luminal

Basal

Neve et al, Cancer Cell 2006Chin et al, Cancer Cell, 2006

Fre

quen

cy

Genome location

ExpressionCopy number

Cell lines

Tumors

Cell lines as models of primary Cell lines as models of primary breast tumorsbreast tumors

A collection of 50 cell lines retain important A collection of 50 cell lines retain important transcriptional and genomic features of primary tumorstranscriptional and genomic features of primary tumors

Cell lines

Tumors





Associating molecular markers with Associating molecular markers with response to lapatinibresponse 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 Test: Cell line markers predict response in HER2 positive patientsresponse in HER2 positive patients

EGF30001: A randomized, Phase III study of Paclitaxel + Lapatinib vs. Paclitaxel + PlaceboHER2, GRB7, CRK, ACOT9, LJ31079, DDX5

GSK-LBNL collaboration





Protein abundances

Transcript levels

+

Curated network model

Hierarchical analysis of Pathway Hierarchical analysis of Pathway Logic states and rulesLogic states and rules

Heiser, Spellman, Talcott, Knapp, Lauderote

Baseline levels populate PL model statesRules define predicted pathway activity

Example network of Example network of one cell lineone cell line

Hierarchical analysis of network featuresHierarchical analysis of network features

Prediction: PAK1 is required for Prediction: PAK1 is required for network activation of MEK/ERK network activation of MEK/ERK

cascade in luminal cell linescascade in luminal cell lines

0

1

2

3

4

5

6

7

8

9

1 2 3

-log1

0 G

I50

pak1+

pak1-

Test: PAK1Test: PAK1++ luminal cell lines are more luminal cell lines are more sensitive to MEK inhibitorssensitive to MEK inhibitors

CI1040 GSK-MEKi U0126





Therapeutic agents show strong Therapeutic agents show strong luminal subtype specificityluminal subtype specificity

Kuo, Guan, Hu, Bayani 2007

Se

nsi

tivity

(-lo

g1

0G

I 50)

Lapatinib

Se

nsi

tivity

(-lo

g1

0G

I 50)

Paclitaxel

Sub

type

res

pons

e m

etric

log 1

0laG

I 50 -

log 1

0b GI 5

0

Basal Luminal

AKT pathway inhibitors show AKT pathway inhibitors show strong luminal subtype specificitystrong luminal subtype specificity

Bayesian network analysis reveals AKT Bayesian network analysis reveals AKT dependent signaling in luminal linesdependent signaling in luminal lines

Mukherjee , Speed, Neve, et al., 2007

Prediction: PI3-kinase pathway mutations will occur preferentially in luminal subtype cell lines

Sen

siti

vity

(-l

og

10G

I 50)

LU BaA BaB HMEC Unknown

-2

-1

0

1

2

3

Test: AKT-inhibitor responsive cell lines carry Test: AKT-inhibitor responsive cell lines carry PI3-kinase pathway mutationsPI3-kinase pathway mutations

Kuo, Neve, Spellman et al., 2007

AKT pathway mutations12/13 AKT pathway mutations in primary tumors are in the luminal

subtype

12/13 AKT pathway mutations in primary tumors are in the luminal

subtype





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

Documents

Modeling molecular diversity in cancer Integrating “omics”, mathematical models and functional cancer biology Lawrence Berkeley National Laboratory University