1. - ( ) mRNA : - - Proteins - ( ) -

2. : , , , Genes mRNA Proteins , , , ,

3. - Proteomic image Microarray image

4. : () , Proteins - Ovarian pattern http://www.cancer.gov/cancertopics/screening

5. () ( , , , ..) (targeted therapy) , -,

6.

7. : : - -

8. : , , , ! 1 2 3 4

9. , , , ...

10. : (Herceptin, Pertuzumab)

11. , ErbB - - / ErbB1 (EGFR) Gefitinib AstraZeneca Small molecule, ATP competitor Erlotinib Genentech, Roche Small molecule, ATP competitor Cetuximab Merck mAbErbB2 (HER2) Trastuzumab Genentech mAb (Herceptin) Pertuzumab Genentech mAbErbB3 (HER3) MM-121 Merrimack mAb Pharmaceuticals

12. : - (.) suppression Sensistivity Resistance Signal in vitro

13. : -

14. PTEN anti-ErbB2 :- Faratian et al, Cancer Res, 2009, 69(16):6713-20

15. PTEN anti-ErbB2 : - ()Faratian et al, Cancer Res, 2009, 69(16):6713-20

16. - Inhibitor 2 Inhibitor 1

17. /

18. Backup slides

19. ~ 300 : : , , , , : , , , , 490 > 2,6 2008 .. , .. ,.. , 2010

20. 23,718.5 ( 476,000 ) 16.5 100000 ( 7.7) 288,047.7 199.9 100000

21. : 57,9%, 63% 43%! ! ,

22. 20

23. -: ( ), Pap-test ( ) : , PSA (prostate specific antigen) - , - -

24. , (, , ..)

25. ,

26.

27. - : ... : Lee Hood, Institute for Systems Biology

28.

29. No cancer Mass spectrometry Proteins Blood sample Proteins Blood Best fit molecular fingerprint sample Cancer Normal marker proteins marker proteins Mass spectrometryCancer

30. anti-ErbB2 , : Herceptin ( ) 96% vs 95% , 1 (Piccart-Gebhart et al., 2005) 91% vs 87%, 4 (Romond et al., 2005). , - rbB2+

31. : - , -ErbB2 - ( )

32. : - (.) suppression Sensistivity Resistance Signal in vitro

33. : - (.) suppression Sensistivity Resistance Signal in vitro

34. : - (.) suppression Sensistivity Resistance Signal in vitro

35. ErbB3/2 -ErbB2

36. : - (.) suppression Sensistivity Resistance Signal in vitro

37. -Per +Per ErbB2 , A.Sorokin

38. : - suppression Sensistivity Resistance Signal in vitro

39. 2008 .. , .. ,.. , 2010

40. 2008 .. , .. ,.. , 2010

41. 2008 .. , .. ,.. , 2010

42. Best fit molecular fingerprint Normal Cancer

43. Diagnostics can stratify populations by drug response Diagnostic test positive Likely to benefit from Dx+ therapy Tx+ Toxicity test positive Likely to have toxic Dx- response Diagnostic test negative Not likely to benefit from therapySeptember 2010 Confidential 43

44. SPECIFIED ABSTRACTED Differential equations Markov chains Bayesian networks mechanisms Boolean/fuzzy logic models Statistical mining(includingmolecularstructure-based influencescomputation) relationships Doug Lauffenburger, MIT

45. : 70% 15 ; 59,000 (40.0 / 100,000 )

46. Lack of response has enormous costsAspect Numeric2008 Rx spend, US 292 B$ (of 800 B$ WW)Percent Rx not effective 20 90% (average 50%)Adverse events, US ~2,000,000Fatal adverse events, US ~100,000Cost adverse drug events, US Cost 45-135 B$Percent events avoidable 20 35%Percent of care decisions informed by 70%diagnosticsDiagnostics as percent of healthcare 3-4%market http://www.dnapolicy.org/images/issuebriefpdfs/PGx%20IB.pdf Jerel Davis, McKinsey & CoSeptember 2010 Confidential 46