modeling molecular diversity in cancer integrating “omics”, mathematical models and functional...
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![Page 1: Modeling molecular diversity in cancer Integrating “omics”, mathematical models and functional cancer biology Lawrence Berkeley National Laboratory University](https://reader030.vdocuments.mx/reader030/viewer/2022032703/56649d2b5503460f94a00f5e/html5/thumbnails/1.jpg)
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
![Page 2: Modeling molecular diversity in cancer Integrating “omics”, mathematical models and functional cancer biology Lawrence Berkeley National Laboratory University](https://reader030.vdocuments.mx/reader030/viewer/2022032703/56649d2b5503460f94a00f5e/html5/thumbnails/2.jpg)
Modeling molecular diversity in cancerModeling 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 Identifying and understanding “omic” determinants of therapeutic response in breast cancerof therapeutic response in breast cancer
![Page 3: Modeling molecular diversity in cancer Integrating “omics”, mathematical models and functional cancer biology Lawrence Berkeley National Laboratory University](https://reader030.vdocuments.mx/reader030/viewer/2022032703/56649d2b5503460f94a00f5e/html5/thumbnails/3.jpg)
Modeling molecular diversity in cancerModeling 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 Identifying and understanding “omic” determinants of therapeutic response in breast cancerof therapeutic response in breast cancer
![Page 4: Modeling molecular diversity in cancer Integrating “omics”, mathematical models and functional cancer biology Lawrence Berkeley National Laboratory University](https://reader030.vdocuments.mx/reader030/viewer/2022032703/56649d2b5503460f94a00f5e/html5/thumbnails/4.jpg)
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
![Page 5: Modeling molecular diversity in cancer Integrating “omics”, mathematical models and functional cancer biology Lawrence Berkeley National Laboratory University](https://reader030.vdocuments.mx/reader030/viewer/2022032703/56649d2b5503460f94a00f5e/html5/thumbnails/5.jpg)
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
![Page 6: Modeling molecular diversity in cancer Integrating “omics”, mathematical models and functional cancer biology Lawrence Berkeley National Laboratory University](https://reader030.vdocuments.mx/reader030/viewer/2022032703/56649d2b5503460f94a00f5e/html5/thumbnails/6.jpg)
Modeling molecular diversity in cancerModeling 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 Integrating “omics”, mathematical models and functional cancer biologyand functional cancer biology
![Page 7: Modeling molecular diversity in cancer Integrating “omics”, mathematical models and functional cancer biology Lawrence Berkeley National Laboratory University](https://reader030.vdocuments.mx/reader030/viewer/2022032703/56649d2b5503460f94a00f5e/html5/thumbnails/7.jpg)
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
![Page 8: Modeling molecular diversity in cancer Integrating “omics”, mathematical models and functional cancer biology Lawrence Berkeley National Laboratory University](https://reader030.vdocuments.mx/reader030/viewer/2022032703/56649d2b5503460f94a00f5e/html5/thumbnails/8.jpg)
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
![Page 9: Modeling molecular diversity in cancer Integrating “omics”, mathematical models and functional cancer biology Lawrence Berkeley National Laboratory University](https://reader030.vdocuments.mx/reader030/viewer/2022032703/56649d2b5503460f94a00f5e/html5/thumbnails/9.jpg)
Modeling molecular diversity in cancerModeling 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 Identifying and understanding “omic” determinants of therapeutic response in breast cancerof therapeutic response in breast cancer
![Page 10: Modeling molecular diversity in cancer Integrating “omics”, mathematical models and functional cancer biology Lawrence Berkeley National Laboratory University](https://reader030.vdocuments.mx/reader030/viewer/2022032703/56649d2b5503460f94a00f5e/html5/thumbnails/10.jpg)
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
![Page 11: Modeling molecular diversity in cancer Integrating “omics”, mathematical models and functional cancer biology Lawrence Berkeley National Laboratory University](https://reader030.vdocuments.mx/reader030/viewer/2022032703/56649d2b5503460f94a00f5e/html5/thumbnails/11.jpg)
Example network of Example network of one cell lineone cell line
![Page 12: Modeling molecular diversity in cancer Integrating “omics”, mathematical models and functional cancer biology Lawrence Berkeley National Laboratory University](https://reader030.vdocuments.mx/reader030/viewer/2022032703/56649d2b5503460f94a00f5e/html5/thumbnails/12.jpg)
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
![Page 13: Modeling molecular diversity in cancer Integrating “omics”, mathematical models and functional cancer biology Lawrence Berkeley National Laboratory University](https://reader030.vdocuments.mx/reader030/viewer/2022032703/56649d2b5503460f94a00f5e/html5/thumbnails/13.jpg)
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
![Page 14: Modeling molecular diversity in cancer Integrating “omics”, mathematical models and functional cancer biology Lawrence Berkeley National Laboratory University](https://reader030.vdocuments.mx/reader030/viewer/2022032703/56649d2b5503460f94a00f5e/html5/thumbnails/14.jpg)
Modeling molecular diversity in cancerModeling 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 Identifying and understanding “omic” determinants of therapeutic response in breast cancerof therapeutic response in breast cancer
![Page 15: Modeling molecular diversity in cancer Integrating “omics”, mathematical models and functional cancer biology Lawrence Berkeley National Laboratory University](https://reader030.vdocuments.mx/reader030/viewer/2022032703/56649d2b5503460f94a00f5e/html5/thumbnails/15.jpg)
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
![Page 16: Modeling molecular diversity in cancer Integrating “omics”, mathematical models and functional cancer biology Lawrence Berkeley National Laboratory University](https://reader030.vdocuments.mx/reader030/viewer/2022032703/56649d2b5503460f94a00f5e/html5/thumbnails/16.jpg)
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
![Page 17: Modeling molecular diversity in cancer Integrating “omics”, mathematical models and functional cancer biology Lawrence Berkeley National Laboratory University](https://reader030.vdocuments.mx/reader030/viewer/2022032703/56649d2b5503460f94a00f5e/html5/thumbnails/17.jpg)
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
![Page 18: Modeling molecular diversity in cancer Integrating “omics”, mathematical models and functional cancer biology Lawrence Berkeley National Laboratory University](https://reader030.vdocuments.mx/reader030/viewer/2022032703/56649d2b5503460f94a00f5e/html5/thumbnails/18.jpg)
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
![Page 19: Modeling molecular diversity in cancer Integrating “omics”, mathematical models and functional cancer biology Lawrence Berkeley National Laboratory University](https://reader030.vdocuments.mx/reader030/viewer/2022032703/56649d2b5503460f94a00f5e/html5/thumbnails/19.jpg)
Modeling molecular diversity in cancerModeling 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 Integrating “omics”, mathematical models and functional cancer biologyand functional cancer biology
![Page 20: Modeling molecular diversity in cancer Integrating “omics”, mathematical models and functional cancer biology Lawrence Berkeley National Laboratory University](https://reader030.vdocuments.mx/reader030/viewer/2022032703/56649d2b5503460f94a00f5e/html5/thumbnails/20.jpg)
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