Oncogene and Tumor Suppressor gene networks in cell cycle checkpoints, apoptosis & cell survival

Baltazar D. Aguda

Seminar given at Indiana University-Purdue University at Indianapolis, 6 Oct 2006

Mathematical Biosciences InstituteOhio State Universitybdaguda@mbi.osu.edu

Oncogene Tumor Suppressor Gene

x

x

x

x x

xx

DOMINANT(oncogene)

RECESSIVE(TSG)

INTERMEDIATE

Normal Cell Abnormal Cell Transformed Cell

Ref: R Hesketh, 1997

cancer

Oncogenes & TSGs associated with some human cancers

cervical carcinoma : P53, RB1, MYC, MYCN, HRAS

colorectal carcinoma : APC, MCC, DCC, P53, TGFBR2, KRAS2

breast carcinoma : MYB, MYC, cyclin D1, cyclin D3, EGFR, HER2, HRAS, HSTF1, INT2, YES1, P53, RB1, BRCA1

CML : BCR-ABL, MYC, NRAS, RB1, P53, ERG, TLS,HOXA9, NUP98, AML1, EAP, EVI1, MN1, TEL,MDS1

Cellular processes involved?

Molecular pathways & networks

The positions of these oncogenes and TSGs in these networks represent key nodes whose perturbations are crucial to the stability & evolution of the cancer-relevant cellular processes.

{ set of oncogenes & TSGs }

Network stability analysis & control

Cellular processes involved?

Molecular pathways & networks

{ set of oncogenes & TSGs }

Network stability analysis & control

Cell cycleApoptosisSignalingAngiogenesisMetastasisSenescenceEtc.

Cellular processes involved?

Molecular pathways & networks

{ set of oncogenes & TSGs }

Network stability analysis & control

Pathway ontology (BIOPAX)DBs/KBs (e.g. BIND, Biocarta, GenMAPP, Reactome)

Proposal: Library of Pathway Modules (functional modules according to specific cellular processes; e.g. Biocarta)

Repository of Models: Biomodels Database: http://www.ebi.ac.uk/biomodels/ CellML model repository: http://www.cellml.org/examples/repository/index.html

Cellular processes involved?

Molecular pathways & networks

{ set of oncogenes & TSGs }

Network stability analysis & control

Topological analysis of qNETs (qualitative networks)

Network modularization schemes [e.g. Aguda & Algar (2003) Cell Cycle 2: 538 ]

Model extraction algorithms

Network perturbation and control protocols

Oncogenes & TSGs in G1-S Network

The G1-S transition & Restriction Point

R

G1

G2

M

S

Fig 7 of reference above

R

Ekholm SV, Zickert P, Reed SI, Zetterberg A. (2001) Mol Cell Biol 21: 3256.

G1

pRB

ORC

Cdc6

MCMs

pre-RCCdc7

E2F

DP

Cdk2/Cyclin-E

Myc

Max

pRb

Cyclin-D/cdk4 Cyclin-A/cdk2

Cdk2/Cyclin-E

TK, DHFR

Cdc25Ap27

G1-S regulatory network

pRB

ORC

Cdc6

MCMs

pre-RCCdc7

E2F

DP

Cdk2/Cyclin-E

Myc

Max

pRb

Cyclin-D/cdk4 Cyclin-A/cdk2

Cdk2/Cyclin-E

TK,DHFR

Cdc25Ap27

RB1

defective in all retinoblastomas & in some other cancers:

small cell-lung carcinomas non-small cell lung cancers bladder & pancreatic carcinomas human breast carcinoma human prostate carcinoma

pRB

ORC

Cdc6

MCMs

pre-RCCdc7

E2F

DP

Cdk2/Cyclin-E

Myc

Max

pRb

Cyclin-D/cdk4 Cyclin-A/cdk2

Cdk2/Cyclin-E

TK,DHFR

Cdc25Ap27

E2F1Lung carcinomasEtc.

ORC

Cdc6

MCMs

pre-RCCdc7

E2F

DP

Cdk2/Cyclin-E

Myc

Max

pRb

Cyclin-D/cdk4 Cyclin-A/cdk2

Cdk2/Cyclin-E

TK,DHFR

Cdc25Ap27

KIP1

Leukemia, variety of solid tumors

ORC

Cdc6

MCMs

pre-RCCdc7

E2F

DP

Cdk2/Cyclin-E

Myc

Max

pRb

Cyclin-D/CDK4 Cyclin-A/cdk2

Cdk2/Cyclin-E

TK,DHFR

Cdc25Ap27

D-cyclin genes CCND1, CCND2, CCND3

D1 over-expressed in some gastric, breast, and aesophageal cancers. Sarcomas, uterine and colorectal carcinomas, malignant melanoma.

ORC

Cdc6

MCMs

pre-RCCdc7

E2F

DP

Cdk2/Cyclin-E

Myc

Max

pRb

Cyclin-D/CDK4 Cyclin-A/cdk2

Cdk2/Cyclin-E

TK,DHFR

Cdc25Ap27

CDK4

Dominant oncogene: CDK4 mutation isresistant to inhibition by INK4A.

CDK4 and CDK6 over-expressed in some tumorcell lines and CDK4 is amplified in some tumors(50% glioblastomas).

ORC

Cdc6

MCMs

pre-RCCdc7

E2F

DP

Cdk2/Cyclin-E

Myc

Max

pRb

Cyclin-D/CDK4 Cyclin-A/cdk2

Cdk2/Cyclin-E

TK,DHFR

Cdc25Ap27

Myc

Small cell lung carcinoma, breast and cervical carcinomas, Ewing’s sarcoma, Burkitt’s lymphoma, neuroblastoma,retinoblastoma

pRB

ORC

Cdc6

MCMs

pre-RCCdc7

E2F

DP

Cdk2/Cyclin-E

Myc

Max

pRb

Cyclin-D/cdk4 Cyclin-A/cdk2

Cdk2/Cyclin-E

TK, DHFR

Cdc25Ap27

Switching associated with R point?

Network Structure & Instability

qNET analysis

mij 0 Xj activates Xi ( Xj Xi ) mij 0 Xj inhibits Xi ( Xj Xi )

qNET graphs from M

mij= [xi/ xj]o •

1-cycle mii

Cycle strength graph

2-cycle mijmji

3-cycle mijmjkmki

Xi

Xi Xj

•

••

Xi Xj

Xk

•

••

det(λI-M) = n + 1n-1 + 2 n-2 + … + n-1 + n = 0

where 1 = i [-C1(i)] 2 = i,j [-C1(i)][-C1(j)] + jk [-C2(jk)]3 = i,j,k [-C1(i)][-C1(j)][-C1(k)] + i,jk [-C1(i)][-C2(jk)] + ijk [-C3(ijk)]

... where C1(i) = mii (1-cycles) C2(jk) = mjkmkj (2-cycles) C3(ijk) = mijmjkmki (3-cycles)

… ...

eigenvalues are functions of cycles only

dx/dt = f(x)

d(x)/dt = M(x)

Hurwitz Determinants, = functions of Ck ‘s

X1 X2

X3

sufficient instability conditions

[1] S > 0

[2] T < 0

[3] SD < T when T > 0

1-cycle S = m33

2-cycle D = m12 m21

3-cycle T = m21 m13 m32

X Y X X X X X Y

X Y X Y X Y

X Y X X X Y

X Y X Y X Y

X Y X Y

YY

X X XYYY

Y X Y Y X Y Y Y

X YX Y

X Y X Y

X Y X Y

X Y X YX Y X Y

U

S

MS

U UMS MS

U U U U U US S

U U U U U U

U

UUU U U US S

? ? ?

X Y

X Y

X Y

X Y

X Y

Topologies

U = unstable

S = stable

MS = marginally stable

? = undecided

qNET

(2006)

Modeling the G1 Checkpoint

pRB

ORC

Cdc6

MCMs

pre-RCCdc7

E2F

DP

Cdk2/Cyclin-E

Myc

Max

pRb

Cyclin-D/cdk4 Cyclin-A/cdk2

Cdk2/Cyclin-E

TK,DHFR

Cdc25Ap27

Model Subnetwork for the Initiation of S phase

p27/CycE/CDK2

iCycE/CDK2aCycE/CDK2.

.

p27

.

....

aCdc25AiCdc25A

CDK2p27 Cdc25A

A SHARP SWITCH

Unstable couplings between cycles

BD Aguda (1999) Oncogene 18: 2846.

CDK - Cdc25 couple

X1Y1

1f

1r

Y2X2

2r

2f

[Y2]ss

E2

s

s

u

[ ]ss

E1

Y2

Y1

[Y2]ss

E1

0

0

0

mass-action kinetics in graphs shown;similar for Michaelis-Menten kinetics

ss

ss

Y1 & Y2 turned ‘on’ only if

E1*E2 > (k1r/k1f)*(k2r/k2f)

Transcritical Bifurcation in Positively Coupled Cycles

BD Aguda & Y Tang (1999) Cell Prolif. 32: 321.

1 sustained2 t_off = 803 t_off = 504 t_off = 305 t_off = 296 t_off = 28

time

Cyclin D/CDK4

GFs

Simulation of CDK2 activation

Fig 7 of reference above

R

Ekholm SV, Zickert P, Reed SI, Zetterberg A. (2001) Mol Cell Biol 21: 3256.

G1 G2S M

p27 E/cdk2

cdc25A

R G2 check

target of checkpoint signaling: unstable network motifs

Wee1 B/cdk1

cdc25C

Aguda & Tang (1999) Cell Proliferation

Aguda (1999) PNASAguda (1999) Oncogene

J. Bartek & J. Lukas (2001) Curr. Opin. Cell Bio. 13 : 738

Pre-RC

CDK2

Cyc E

DNA damage

DNA replication

Cdc25A

ATM ATR

Chk1,2Mdm2

ARF

p53

p21

Cdc25A degradation

DNA damage signaling in G1

Signaling, Cell cycle & Apoptosis

Oncogenes in G1 signaling

Aguda BD (2001) Chaos 11 : 269-276.

die cycle

signal

die cycle

signal

die cycle

signal

die cycle

signal

I II III IV

CLASSIFICATION OF SIGNALING PATHWAYS

Modularization

p27Cdc25A cycE/cdk2

E2F

Myc

Rb cycD/cdk4

Apoptosome

DISC

Exec. Caspases Apoptosis

ARFMdm2p53

Bax Bcl-2

S phase

Bad

details of Case I

die cycle

signal

a

b

c

b’

modeling Case I

Aguda & Algar (2003) Cell Cycle 2: 538

module-module interactions

apoptosis

cycling

quiescent

Craciun, Aguda & Friedman (2005) Mathematical Biosciences and Engineering 2: 473-485

ks

ksd2Strength of negative feedback

signal intensity

p53 vs Akt

with K.B. (Dave) Wee, PhD student

Cell Survival and Death

Apoptosome

DISC

Exec. Caspases

DNA damage

p53

APOPTOSIS MACHINERY

FasL, Bax, PUMA

Akt

Growth factors

Bad, Caspase9, FKHR

p53 vs Akt

p53 Akt

Bhuvanesh et al. Genes & Dev (2002) 16

Experimental indications of antagonism between p53 & Akt

Feedback loops between p53 & Akt

p53

AktPTEN

Mdm2

PIP3

Mayo, L. D. & Donner, D. B. (2002) Trends Biochem. Sci. 27, 462-467.

p53

AktPTEN

Mdm2

PIP3

TP53

Mutated in about half of known human cancers.

p53

AktPTEN

Mdm2

PIP3

PTEN

Cancers of the breast, prostate, liver,stomach; gliomas; etc.

p53

AktPTEN

Mdm2

PIP3

Akt1

Cancers of the breast,pancreas, prostate;carious carcinomas

p53

AktPTEN

Mdm2

PIP3

Mdm2

Overexpressed in morethan 40 different typesof malignancies, incl.solid tumors, sarcomas,and leukemias

11

20

*][

]53[

vvdt

Aktd

vvdt

pd

*][][][

]53[])53[(

]53*][[

*])[(

*][

])53[1])([(

][

2

22

1

11

1

11

00

AktAktAkt

pkpj

pAktkv

Aktj

Aktkv

pAktj

Aktkv

kv

tot

d

where

p53 AKT

Model Q1

Akt*

Bistable switch

ActiveAkt

Total Akt Protein Level

Activep53

Steady State Level

Model Q2

Model Q3

p53

AKT

Mdm2

p53

AKT

PTEN

PIP3

Mdm2

Wee & Aguda (2006) Biophys J

Robustness of bistable switch

Robustness

k2

k0

Tot Akt

Tot Akt

3ss

3ss

1ss

1ss

Wee & Aguda (2006) Biophys J

p*

k* k*

p*

k*

p*

k*p*

k*

p*

Activep53

Total Akt Protein Level

Apoptotic thresholds

Apoptotic thresholds (predictions)

Active

p53

Total Akt Protein Level

sets of initial conditions that lead to apoptosis

Summary & Conclusions

1. Networks of oncogenes & TSGs in the G1-S cell cycle transitionand p53-Akt interaction were analyzed.

2. There are unstable network motifs associated with G1 and G2checkpoints which are targeted by signaling pathways to arrest or slow down the cell cycle. Oncogenes & TSGs are involved in these

motifs and signaling.

3. The positive feedback loop between Akt and p53 gives rise to a bistable switch that may govern a cell survival-death switch. Model

predictions were given on apoptotic thresholds.

4. Modularization of networks coordinating the cell cycle and apoptosiswere presented and analyzed.

5. qNET analysis is useful for network stability stability analysis & formodel building.

ACKNOWLEDGEMENTS

Collaborators: Keng Boon (Dave) Wee PhD Student, National University of Singapore

Avner Friedman Mathematical Biosciences Institute Ohio State University, USA

Gheorghe Craciun University of Wisconsin-Madison, USA

END