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

Baltazar D. Aguda. Oncogene and Tumor Suppressor gene networks in cell cycle checkpoints, apoptosis & cell survival. Mathematical Biosciences Institute Ohio State University bdaguda@mbi.osu.edu.

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

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  1. Baltazar D. Aguda Oncogene and Tumor Suppressor gene networks in cell cycle checkpoints, apoptosis & cell survival Mathematical Biosciences Institute Ohio State University bdaguda@mbi.osu.edu Seminar given at Indiana University-Purdue University at Indianapolis, 6 Oct 2006

  2. Oncogene Tumor Suppressor Gene cancer Normal Cell Abnormal Cell Transformed Cell DOMINANT (oncogene) x RECESSIVE (TSG) x x x x x x INTERMEDIATE Ref: R Hesketh, 1997

  3. Oncogenes & TSGs associated with some human cancers breast carcinoma : MYB, MYC, cyclin D1, cyclin D3, EGFR, HER2, HRAS, HSTF1, INT2, YES1, P53, RB1, BRCA1 cervical carcinoma : P53, RB1, MYC, MYCN, HRAS colorectal carcinoma : APC, MCC, DCC, P53, TGFBR2, KRAS2 CML : BCR-ABL,MYC, NRAS,RB1, P53, ERG, TLS, HOXA9, NUP98, AML1, EAP, EVI1, MN1, TEL, MDS1

  4. { set of oncogenes & TSGs } Cellular processes involved? Molecular pathways & networks Network stability analysis & control 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.

  5. Cell cycle Apoptosis Signaling Angiogenesis Metastasis Senescence Etc. { set of oncogenes & TSGs } Cellular processes involved? Molecular pathways & networks Network stability analysis & control

  6. 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 { set of oncogenes & TSGs } Cellular processes involved? Molecular pathways & networks Network stability analysis & control

  7. 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 { set of oncogenes & TSGs } Cellular processes involved? Molecular pathways & networks Network stability analysis & control

  8. Oncogenes & TSGs in G1-S Network

  9. M G1 G2 R S The G1-S transition & Restriction Point

  10. Ekholm SV, Zickert P, Reed SI, Zetterberg A. (2001) Mol Cell Biol 21: 3256. R G1 Fig 7 of reference above

  11. Cyclin-D/cdk4 Cyclin-A/cdk2 TK, DHFR pRb pRB Cdc6 E2F ORC DP MCMs pre-RC Cdc7 Cdk2/Cyclin-E Cdk2/Cyclin-E p27 Cdc25A Myc Max G1-S regulatory network

  12. 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 Cyclin-D/cdk4 Cyclin-A/cdk2 TK, DHFR pRb pRB Cdc6 E2F ORC DP MCMs pre-RC Cdc7 Cdk2/Cyclin-E Cdk2/Cyclin-E p27 Cdc25A Myc Max

  13. E2F1 Lung carcinomas Etc. Cyclin-D/cdk4 Cyclin-A/cdk2 TK, DHFR pRb pRB Cdc6 E2F ORC DP MCMs pre-RC Cdc7 Cdk2/Cyclin-E Cdk2/Cyclin-E p27 Cdc25A Myc Max

  14. Cyclin-D/cdk4 Cyclin-A/cdk2 KIP1 Leukemia, variety of solid tumors TK, DHFR pRb Cdc6 E2F ORC DP MCMs pre-RC Cdc7 Cdk2/Cyclin-E Cdk2/Cyclin-E p27 Cdc25A Myc Max

  15. Cyclin-D/CDK4 Cyclin-A/cdk2 D-cyclin genes CCND1, CCND2, CCND3 D1 over-expressed in some gastric, breast, and aesophageal cancers. Sarcomas, uterine and colorectal carcinomas, malignant melanoma. TK, DHFR pRb Cdc6 E2F ORC DP MCMs pre-RC Cdc7 Cdk2/Cyclin-E Cdk2/Cyclin-E p27 Cdc25A Myc Max

  16. Cyclin-D/CDK4 Cyclin-A/cdk2 CDK4 Dominant oncogene: CDK4 mutation is resistant to inhibition by INK4A. CDK4 and CDK6 over-expressed in some tumor cell lines and CDK4 is amplified in some tumors (50% glioblastomas). TK, DHFR pRb Cdc6 E2F ORC DP MCMs pre-RC Cdc7 Cdk2/Cyclin-E Cdk2/Cyclin-E p27 Cdc25A Myc Max

  17. Cyclin-D/CDK4 Cyclin-A/cdk2 Myc Small cell lung carcinoma, breast and cervical carcinomas, Ewing’s sarcoma, Burkitt’s lymphoma, neuroblastoma, retinoblastoma TK, DHFR pRb Cdc6 E2F ORC DP MCMs pre-RC Cdc7 Cdk2/Cyclin-E Cdk2/Cyclin-E p27 Cdc25A Myc Max

  18. Switching associated with R point? Cyclin-D/cdk4 Cyclin-A/cdk2 TK, DHFR pRb pRB Cdc6 E2F ORC DP MCMs pre-RC Cdc7 Cdk2/Cyclin-E Cdk2/Cyclin-E p27 Cdc25A Myc Max

  19. Network Structure & Instability qNET analysis

  20. • mij= [xi/ xj]o Cycle strength graph • Xi 1-cycle mii mij 0 Xjactivates Xi ( Xj Xi ) mij 0 Xj inhibits Xi ( Xj Xi ) • Xi Xj • 2-cycle mijmji • Xi Xj 3-cycle mijmjkmki • • Xk qNET graphs from M

  21. eigenvalues are functions of cycles only dx/dt = f(x) d(Dx)/dt = M(Dx) det(λI-M) = ln + a1ln-1 + a2ln-2 + … + an-1l + an = 0 where a1 = i [-C1(i)] a2 = i,j [-C1(i)][-C1(j)] + jk [-C2(jk)] a3 = 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) … ... Hurwitz Determinants, D = functions of Ck ‘s

  22. X1 X2 1-cycle S = m33 2-cycleD = m12 m21 X3 3-cycleT = m21 m13 m32 sufficient instability conditions [1] S > 0 [2] T < 0 [3] SD < T when T > 0

  23. X Y U MS MS U    Y Y X X X Y X Y X Y     X X X X X X X Y Y Y Y Y Y Y Y Y Y Y X Y U U U S U U U S X Y X Y X Y ? ? ? U U U S U U U X Y X X X X X Y X Y X Y X X Y X X X Y X Y X Y U S U U S U U U Y Y Y X Y X Y X Y U MS U Topologies qNET U = unstable S = stable  Y X MS = marginally stable  ? = undecided   Y X    Y X

  24. (2006)

  25. Modeling the G1 Checkpoint

  26. Model Subnetwork for the Initiation of S phase Cyclin-D/cdk4 Cyclin-A/cdk2 TK, DHFR pRb pRB Cdc6 E2F ORC DP MCMs pre-RC Cdc7 Cdk2/Cyclin-E Cdk2/Cyclin-E p27 Cdc25A Myc Max

  27. A SHARP SWITCH p27 CDK2 Cdc25A p27/CycE/CDK2 . . . iCycE/CDK2 p27 aCycE/CDK2 . . . . iCdc25A aCdc25A

  28. CDK - Cdc25 couple Unstable couplings between cycles BD Aguda (1999) Oncogene 18: 2846.

  29. [Y2]ss s 1r u s E2 Y1 X1 1f [Y2]ss 2f X2 Y2 E1 2r Y1 [ ]ss Y2 E1 Transcritical Bifurcation in Positively Coupled Cycles 0 0 ss Y1 & Y2 turned ‘on’ only if E1*E2 > (k1r/k1f)*(k2r/k2f) ss mass-action kinetics in graphs shown; similar for Michaelis-Menten kinetics 0

  30. Simulation of CDK2 activation 1 sustained 2 t_off = 80 3 t_off = 50 4 t_off = 30 5 t_off = 29 6 t_off = 28 time GFs Cyclin D/CDK4 BD Aguda & Y Tang (1999) Cell Prolif. 32: 321.

  31. Ekholm SV, Zickert P, Reed SI, Zetterberg A. (2001) Mol Cell Biol 21: 3256. R Fig 7 of reference above

  32. Aguda (1999) PNAS Aguda (1999) Oncogene p27 Wee1 E/cdk2 B/cdk1 cdc25A cdc25C R G2 check G1 S G2 M target of checkpoint signaling: unstable network motifs Aguda & Tang (1999) Cell Proliferation

  33. DNA damage signaling in G1 DNA damage ATM ATR ARF Chk1,2 Mdm2 Cdc25A degradation p53 Cdc25A Cyc E p21 CDK2 Pre-RC DNA replication J. Bartek & J. Lukas (2001) Curr. Opin. Cell Bio. 13 : 738

  34. Signaling, Cell cycle & Apoptosis

  35. Oncogenes in G1 signaling Aguda BD (2001) Chaos 11 : 269-276.

  36. CLASSIFICATION OF SIGNALING PATHWAYS signal signal signal signal die cycle die cycle die cycle die cycle I II III IV Modularization

  37. details of Case I Myc Rb cycD/cdk4 E2F p53 Mdm2 ARF Bad Bax Bcl-2 Cdc25A cycE/cdk2 p27 DISC S phase Apoptosome Apoptosis Exec. Caspases

  38. module-module interactions modeling Case I signal b b’ c a die cycle Aguda & Algar (2003) Cell Cycle 2: 538

  39. apoptosis cycling quiescent ks signal intensity ksd2 Strength of negative feedback Craciun, Aguda & Friedman (2005) Mathematical Biosciences and Engineering 2: 473-485

  40. p53 vs Akt Cell Survival and Death with K.B. (Dave) Wee, PhD student

  41. p53 vs Akt Growth factors DNA damage p53 Akt FasL, Bax, PUMA Bad, Caspase9, FKHR APOPTOSIS MACHINERY DISC Apoptosome Exec. Caspases

  42. Experimental indications of antagonism between p53 & Akt Bhuvanesh et al. Genes & Dev (2002) 16 p53 Akt

  43. Feedback loops between p53 & Akt PTEN PIP3 Akt p53 Mdm2 Mayo, L. D. & Donner, D. B. (2002) Trends Biochem. Sci. 27, 462-467.

  44. TP53 Mutated in about half of known human cancers. PTEN PIP3 Akt p53 Mdm2

  45. PTEN PIP3 Akt PTEN Cancers of the breast, prostate, liver, stomach; gliomas; etc. p53 Mdm2

  46. PTEN PIP3 Akt Akt1 Cancers of the breast, pancreas, prostate; carious carcinomas p53 Mdm2

  47. Mdm2 Overexpressed in more than 40 different types of malignancies, incl. solid tumors, sarcomas, and leukemias PTEN PIP3 Akt p53 Mdm2

  48. Akt* Model Q1 p53 AKT where

  49. Steady State Level Active Akt Active p53 Total Akt Protein Level Bistable switch

  50. p53 PTEN PIP3 Mdm2 AKT p53 AKT Mdm2 Robustness of bistable switch Wee & Aguda (2006) Biophys J

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