1. From Mice to Men, Cancers Are Not Certain At Old Age Francesco Pompei, Ph.D. and Richard Wilson, D.Phil. Harvard University Presented at the Belle Non-Linear Dose-Response Relationships in Biology, Toxicology and Medicine International Conference University of Massachusetts, Amherst, MA June 11, 2002

2. 2 Age Specific Cancer Incidence for Two Major Historical Models, Compared to SEER Data and Beta-Senescence Model

3. 3 Beta Fit to SEER Data Age-specific incidence per 100,000 (Ries et al 2000)

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7. 7 For the 6 gender-specific sites the fits are performed with t = (age-15)  0, as suggested by Armitage and Doll (1954).

8. 8 Age-Specific Incidence Normalized to the Peak Value for Each Cancer. All Male Sites Except Childhood Cancers (Hodgkins, Thyroid, Testes).

9. 9 Estimated Lifespan Probability of Cancer: Area Under the Curve Males Any specific cancer: Hodgkins Disease = 0.003 -» Prostate = 0.37 At least one cancer of any type = 0.70 Females Any specific cancer : Hodgkins Disease = 0.002 -» Breast = 0.21 At least one cancer of any type = 0.53 Result is contrary to the common understanding: "if a person lives long enough he or she will get cancer," The data suggest that "if a person lives long enough, he or she may avoid cancer entirely," with about a one in three chance for men and an even chance for women.

10. 10 Is the Turnover Present in Mice? Mice Data Sources Need undosed controls data for full natural lifetime (~3 years). Need sufficient numbers for statistical significance in cancer incidence trends. NTP data is limited due to 2-year “lifetime”, except rare dietary restricted studies to 1100 days. ED01 data (courtesy R. Kodell) of 2-AAF included 24,000 single strain female mice, uniform conditions, and allowed to live to 1001 days. Age-specific mortality is appropriate measure for comparison to human incidence results

11. 11 ED01 Control Mice Age-Specific Mortality With Beta Function Fit. Error bars = ±1 SEM

12. 12 ED01 Control Mice Age-Specific Mortality With Beta Function Fit.

13. 13 Cell Replicative Senescence As Possible 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.

14. 14 Cell Replicative Senescence: Cells Retaining Proliferative Ability Decrease With Number of Cell Divisions.

15. 15 Cell Replicative Senescence: Increase in Age Decreases the Number of Cells With Replicative Capacity.

16. 16 Cell Replicative Senescence: Beta-Senescence Model Cells In Vitro Age (doublings) Non-senescent cells Cells In Vivo Age (t) Remaining pool of cells able to cause cancer Cell fraction retaining proliferative ability = (1-  t) Cancer: I(t) = (  t) k-1 (1-  t) Longevity  = t o -1 toto

17. 17 1.Mice with altered p53 (gene which is well known to influence senescence): set lifetime t o =    then calculate cancer. 2.Mice with long-term dosing of melatonin (known anti-oxidant): set lifetime t o =    then calculate cancer. 3.Mice with dietary restriction (80-90% of 300 biomarkers tested show evidence of slower ageing): set t = ct’, where c is proportional to caloric intake. Also equivalent to replacing  and  with c  ’ and c  ’ respectively. Search for Senescence Interventions to Test the Model: Altered longevity, modeled as t o =  -1 Altered cancer, modeled as I(t) = (  t) k-1 (1-  t)

18. 18 Influence of Senescence Rate on Age-Specific Cancer Incidence in Mice. Beta model fit to ED01 undosed controls is I(t) = (  t) k-1 (1-  t), where  = 0.00115, k-1 = 5,  =0.00108 (Pompei et al 2001). Equivalent MVK-s model fits shown. Senescence rate is the value of parameter . Senescence rate increase by 21% is calculated from Tyner et al (2002) results of 21% reduction in median lifespan for p53 +/m mice compared to normal p53 +/+ mice. Senescence rate of 50% is an assumption for p53 +/- mice of Tyner et al.

19. 19 Probability of Tumors in p53 Altered Mice Compared to Beta-s and MVK-s Model Predictions. Effect of Senescence on Tumor Probability in Mice 0 10 20 30 40 50 60 70 80 90 100 p53+/+ (Tyner et al 2002) Beta-sMVK-sp53+/m (Tyner et al 2002) Beta-sMVK-sp53+/- (Tyner et al 2002) Beta-sMVK-s Percent of mice with tumors Normal senescence Enhanced senescence Reduced senescence Modeled lifetime probability of cancer is calculated as Prob = 1- exp[  M(t) dt], where M(t) is age specific mortality. Tyner et al results for p53 +/+, p53 +/m, and p53 +/- are interpreted as normal senescence, 21% enhanced senescence, and 50% reduced senescence respectively. Arrow indicate Tyner data reported as >80% tumor rate.

20. 20 Age-Specific Cancer Mortality for Female CBA Mice Dosed with Melatonin vs. Controls. Data from Anisimov et al 2001.

21. 21 Influence of Senescence on Cancer Mortality and Lifetime Data from Tyner et al (2002) for mice with p53 +/+, p53 +/m, and p53 +/- ; compared to Beta model predictions. Beta model predictions for cancer mortality are Prob = 1-exp[  M(t) dt]. Beta model predictions for lifetime are calculated as the lesser of: age at which senescence reaches 100% (t = 1/  ), or age at which age-specific cancer mortality reaches 80% [M(t) = 0.8]. Human cancer mortality computed from SEER data.

22. 22 Senescence and Dietary Restriction Liver tumor incidence vs. weight for two studies of control female B6C3F1 mice. Seilkop data based on body weight measured at 12 months, Haseman data based on maximum weekly average weight. The Beta-senescence-time model fit was developed by varying t in proportion to weight.

23. 23 Senescence and Dietary Restriction Results of five rodent studies of the effect of DR on mean lifespan. The Beta-senescence-time model comparison line is computed by varying t in proportion to caloric intake.

24. 24 Conclusions 1.Cancer incidence turnover likely caused by cellular senescence. 2.Reducing senescence might be an attractive intervention to prolong life, even if cancer is increased. 3.Dietary restriction might be an example of interventions that both reduce senescence and reduce carcinogenesis. There may be others. For those interested in this work, we have two published papers and one draft available. Please leave a business card to receive copies.

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26. 26 Is the Turnover Present in Rats? 1.“Two tons of Rats” study of nitrosamine compounds dosed to Colworth rats. 2.4080 total rats in study, 540 in lowest six doses (0 to 0.5 ppm), where there was little measurable effect due to dose. 3.Results published by Peto et al 1991. 4.Age-specific mortality (includes morbidity) from liver tumors extracted from published Kaplan Meier cumulative mortality curves.

27. 27 Rats Liver Tumor Age-Specific Mortality for Nitrosamine Study at Low Doses, Compared to ED01 Mice