1. 1 From Mice to Men Cancer is not inevitable at Old Age Talk to OEHHA Oakland April 12th 2002 Richard Wilson Mallinckrodt Research Professor of Physics Harvard University Work by F Pompei and R Wilson

2. 2 Age Specific Cancer Incidence for Two Major Historical Models, Compared to the Beta Model and SEER Data I(t)=(  t) k-1 (1-  t  I(t)=at k-1 I(t)   1  2  N(s)exp[(  2 -  2 )(t -s)]ds

3. 3 Sir Richard DOLL distrusted data above age 65 SEER data on Cancer incidence now believed to be reliable WE ASSUME that they are reliable

4. 4 Multistage or Clonal Expansion suggest continuous increase Moolgavkar suggested a flattening as the pool of cells for later stages is depleted

5. 5 Beta Fit to SEER Data Age-specific incidence per 100,000

6. 6 We fit to a formula I(t) = (  t) k-1 (1-  t) (Beta formula) We will later make it a model

7. 7 Beta Fit to SEER Data Age-specific incidence per 100,000

8. 8

9. 9

10. 10 Beta Fit to SEER Data Age-specific incidence per 100,000

11. 11 Try a fit to data in another country (Holland)

12. 12 Beta Fit to SEER Data Age-specific incidence per 100,000 For the 6 gender-specific sites the fits are performed with t = (age-15)  0, as suggested by Armitage and Doll (1954).

13. 13 Beta Fit to SEER Data Age-specific incidence per 100,000 (Ries et al 2000)

14. 14 Beta Fit to California Data Age-specific incidence per 100,000 (Saltzstein et al 1998)

15. 15 Beta Fit to Dutch Data Age-specific incidence per 100,000 (de Rijke et al 2000), error bars ±2 SEM

16. 16 Beta Fit to Dutch Data Age-specific incidence per 100,000 (de Rijke et al 2000), error bars ±2 SEM

17. 17 Now combine all data (normalized in vertical axis) on one plot....

18. 18 Age-Specific Incidence Normalized to the Peak Value for Each Cancer. All Male Sites Except Childhood Cancers (Hodgkins, Thyroid, Testes).

19. 19 If it is a general phenomenon it must work for mice. The bias in data recording that troubles Doll is not there Although other biases may be

20. 20 Liver Tumor Rates Vs. Age for NTP (TDMS) Mice Controls Removed for Natural Death or Morbidity Error bars = ±1 SEM

21. 21 ED01 Control Mice Age-Specific Mortality With Beta Function Fit. Error bars = ±1 SEM

22. 22 ED01 Age-specific Mortality for All Neoplasms Causes of Death vs. Dose of 2-AAF, With Beta Function Fit. Error bars = ±1 SEM

23. 23 ED01 Age-specific Mortality for All Neoplasms Causes of Death vs. Dose of 2-AAF, With Beta Function Fit. Error bars = ±1 SEM

24. 24 ED01 Age-specific Mortality for All Neoplasms Causes of Death vs. Dose of 2-AAF, With Beta Function Fit. Error bars = ±1 SEM

25. 25 ED01 Age-specific Mortality for All Neoplasms Causes of Death vs. Dose of 2-AAF, With Beta Function Fit. Error bars = ±1 SEM

26. 26 What are the possible causes? Doll’s argument about unreliability of data Moolgavkar’s argument We suggest: SENESCENCE

27. 27 Cell Replicative Senescence As Biological Cause of the Turnover Widely accepted characteristics of replicative senescence: 1.That cellular replicative capacity is limited has been known for 40 years. 2.Has been observed in vitro and in vivo for many cell types, both animal and human. 3.Is closely related to the ageing process. 4.Is a dominant phenotype when fused with immortal tumor-derived cells. 5.Considered to be an important anti-tumor mechanism. 6.Cells senesce by fraction of population, rather than all at the same time. 7.Senescent cells function normally, but are unable to repair or renew themselves.

28. 28 Cell Replicative Senescence: Cells Retaining Proliferative Ability Decrease With Number of Cell Divisions.

29. 29 Cell Replicative Senescence: Increase in Age Decreases the Number of Cells With Replicative Capacity.

30. 30 Cell Replicative Senescence: Beta Model Cells In Vitro Age Non-senescent cells Cells In Vivo Age Remaining pool of cells able to cause cancer Cells in “Cancer Pool” = N o (1-  t)  = (lifespan) -1 I(t) = (  t) k-1 (1-  t)

31. 31 Influence of Senescence Rate on Age-Specific Cancer Incidence in Mice.

32. 32 We can change senescence We suggest: That is the role of P53 and the main role of melatonin

33. 33 Probability of Tumors in p53 Altered Mice Compared to Beta and MVK-s Model Predictions.

34. 34 Age-Specific Cancer Mortality for Female CBA Mice Dosed with Melatonin vs. Controls. Data from Anisimov et al 2001.

35. 35 Influence of Senescence on Cancer Mortality and Lifetime.

36. 36 Senescence and Dietary Restriction

37. 37 Senescence and Dietary Restriction

38. 38 Suggestion that time stretches This is easily modeled by changing t to t’ in the equation Weight restriction seems good on all counts. But where do we stop?

39. 39 Important future work more data sets Doll’s doctor’s study More mice and rat data Combined cancer bioassay and senescence study Model senescence and all other cell depleting mechanisms

40. 40 Conclusions 1.Cancer incidence 2.turnover may be caused by cellular senescence 3.Reducing senescence 4.prolongs life 5.but cancer is increased. 6.This may be an attractive intervention because 7.one may be able to cure cancer.

41. 41 This will be the thesis work of: Mr Frank Pompei assisted by Mr Michael Polkanov Age Distribution of Cancer at old age Human and Environmental Risk Assessment November 2001 Age distribution of cancer in mice Toxicology and Industrial Health (2001) The role of Senescence in age distribution of cancer in preparation