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
Age Specific Cancer Incidence for Two
Major Historical Models, Compared to
SEER Data and Beta-Senescence
Model
Age-Specific Incidence (per 100,000)
I(t)  m1m2 N(s)exp[(a2 - b2 )(t -s)]ds
5000
A-D power law
4500
MVK clonal expansion
Beta-senescence model
4000
I(t)=at k-1
SEER (all sites M, F)
3500
3000
2500
I(t)=(at) k-1(1-bt)
2000
1500
1000
500
0
0
20
40
60
80
100
120
Age
2
Beta Fit to SEER Data
Age-specific incidence per 100,000 (Ries et al 2000)
Lung and bronchus
a
Male
a = 0.00755
b = 0.0105
k-1 = 6.6
Fit = 0.99
700
600
500
Female
0.007
0.0108
6.5
0.98
b
Colon rectum
700
800
Male
a = 0.00732
b = 0.01003
k-1 = 7
Fit = 1.00
600
500
Female
0.00717
0.00995
7.3
1.00
400
400
300
300
200
200
100
100
0
0
0
20
40
60
80
c
Urinary bladder
350
Male
a = 0.00688
b = 0.01007
k-1 = 7.2
Fit = 1.00
300
250
100
Female
0.00525
0.0098
6.7
1.00
0
20
140
150
60
100
40
50
80
100
d
Male
Female
0.00481
a = 0.00509
0.0101
b = 0.00997
k-1 = 5.7
5.7
Fit = 0.99
1.00
100
80
60
Non-Hodgkins lymphoma
120
200
40
20
0
0
20
40
60
80
100
0
0
20
40
60
80
100
3
Beta Fit to SEER Data
Age-specific incidence per 100,000 (Ries et al 2000)
e
Leukemias
140
Male
a = 0.0048
b = 0.00925
k-1 = 5.9
Fit = 0.99
120
100
Female
0.0043
0.009
5.9
0.99
f
Melanomas
120
Male
a = 0.0023
b = 0.0089
k-1 = 3.5
Fit = 1.00
100
80
Female
0.00034
0.007
2
0.98
80
60
60
40
40
20
20
0
0
0
20
40
60
80
100
Stomach
140
Male
a = 0.00542
b = 0.00952
k-1 = 6.7
Fit = 1.00
120
100
0
120
20
Female
0.00475
0.00925
6.7
1.00
60
80
Oral cavity and pharynx
120
g
40
Male
a = 0.0038
b = 0.01015
k-1 = 4.6
Fit = 0.99
100
80
100
h
Female
0.00305
0.00985
4.6
0.99
80
60
60
40
40
20
20
0
0
0
20
40
60
80
100
0
20
40
60
80
100
4
Beta Fit to SEER Data
Age-specific incidence per 100,000 (Ries et al 2000)
i
Pancreas
120
Male
a = 0.00545
b = 0.00995
k-1 = 6.6
Fit = 1.00
100
80
Kidney and renal pelvis
100
90
Female
0.00515
0.0095
6.6
1.00
Male
a = 0.00435
b = 0.0102
k-1 = 5.2
Fit = 0.99
80
70
60
j
Female
0.0038
0.0102
5.2
1.00
50
60
40
40
30
20
20
10
0
0
0
20
40
60
80
k
Multiple myelomas
80
Male
a = 0.00493
b = 0.00998
k-1 = 6.5
Fit = 1.00
70
60
50
100
a
0
40
60
80
100
Esophagus
50
Male
a = 0.00464
b = 0.01035
k-1 = 6
Fit = 0.98
45
Female
0.00463
0.01015
6.5
1.00
20
40
35
l
Female
0.00363
0.0097
6
0.98
30
40
25
30
20
20
15
10
10
5
0
0
20
40
60
80
100
0
0
20
40
60
80
100
5
Beta Fit to SEER Data
Age-specific incidence per 100,000 (Ries et al 2000)
60
m
Liver and bile duct
Male
a = 0.00439
b = 0.01025
k-1 = 5.8
Fit = 0.99
50
40
Female
0.00411
0.01
6.3
1.0
50
Larynx
45
Male
a = 0.0047
b = 0.0108
k-1 = 5.9
Fit = 0.96
40
35
30
30
n
Female
0.0031
0.0108
5.4
0.93
25
20
20
15
10
10
5
0
0
0
20
40
60
80
Brain and other nervous
40
Male
a = 0.00295
b = 0.0102
k-1 = 4.5
Fit = 0.94
35
30
100
o
Female
0.002655
0.0102
4.5
0.94
0
40
60
80
Thyroid
25
Male
a = 0.0002
b = 0.009
k-1 = 2
Fit = 0.96
20
25
20
100
p
Female
0.00025
0.0102
1.9
0.71
15
20
10
15
10
5
5
0
0
0
20
40
60
80
100
0
3000
2500
2000
1500
20
40
60
80
r
Total non-sex sites
Age-specific cancer
incidences for all 17 non-sex
sites summed for each age
interval, for both SEER data
and Beta fits.
100
6
Beta Fit to SEER Data
Age-specific incidence per 100,000 (Ries et al 2000)
For the 6 gender-specific sites the fits are performed with
t = (age-15)  0, as suggested by Armitage and Doll (1954).
600
a
Prostate
1400
a = 0.00375
b = 0.0115
k-1 = 2.8
Fit = 1.00
500
a = 0.0085
b = 0.0122
k-1 = 4.8
Fit = 0.96
1200
1000
800
b
Breast (F)
400
300
600
200
400
100
200
0
0
0
20
140
Corpus Uteri
120
a = 0.0038
b = 0.0124
k-1 = 3.7
Fit = 0.98
100
80
40
60
80
100
c
0
20
80
Ovary
70
40
60
80
100
d
a = 0.00142
b = 0.0108
k-1 = 2.6
Fit = 1.00
60
50
40
60
30
40
20
20
10
0
0
0
20
40
60
80
100
0
20
40
60
80
7
100
Age-Specific Incidence
Normalized to the Peak Value
for Each Cancer.
All Male Sites Except Childhood Cancers (Hodgkins, Thyroid, Testes).
Brain (M)
Colo-rectal (M)
Esophagus (M)
Kidney (M)
Larynx (M)
Beta parameters
a = 0.01655
k-1 = 5.1
b = 0.0098
1
0.9
Leukemias (M)
Liver (M)
Lung (M)
Melanomas (M)
Age -Specific Cancer Incidence Normalized to Peak
Myelomas (M)
Lymphoma (M)
0.8
Oral (M)
Pancreas (M)
0.7
Stomach (M)
Bladder (M)
Prostate
0.6
Mean (SEER-M)
Beta model of SEER
Colorectal (Dutch)
0.5
Lung (Dutch)
Prostate (Dutch)
Stomach (Dutch)
0.4
Lymphoma (Dutch)
Bladder (Dutch)
0.3
Esophagus (HK)
Stomach (HK)
Colorectal (HK)
0.2
Lung (HK)
Prostate (HK)
Bladder (HK)
0.1
Colorectal (Calif)
Lung (Calif)
0
Prostate (Calif)
0
20
40
60
80
100
Age
8
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.
9
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
10
ED01 Control Mice Age-Specific
Mortality With Beta Function Fit.
RCSTY_B: Reticulum Cell Sarcomas
% per 100 animal-days at risk
8
Age-specific mortality M(t)
7
Beta model fit to M(t)
6
Age-specific incidence I(t) (from Sheldon)
5
M(400-600) > M(200-400); p=5E-8
M(600-800) > M(400-600); p<1E-10
M(800-1001) < M(600-800); p<1E-10
4
3
2
1
0
0
200
400
600
800
1000
Age (days)
Lymphomas
4.0
% per 100 animal-days at risk
3.5
Age-specific mortality M(t)
Beta model fit to M(t)
Age-specific incidence I(t) (from Sheldon)
3.0
2.5
M(400-600) > M(200-400); p=0.01
M(600-800) > M(400-600); p=0.0001
M(800-1001) < M(600-800); p<1E-10
2.0
1.5
1.0
0.5
0.0
0
200
400
600
800
1000
Age (days)
Error bars = ±1 SEM
11
ED01 Control Mice Age-Specific
Mortality With Beta Function Fit.
Lung Alveoli Tumors
Age-specific mortality M(t)
Beta model fit to M(t)
Age-specific incidence I(t) (from Sheldon)
% per 100 animal-days at risk
5.0
4.5
4.0
3.5
M(400-600) > M(200-400); p=0.0004
M(600-800) > M(400-600); p=2E-10
M(800-1001) < M(600-800); p=0.0004
3.0
2.5
2.0
1.5
1.0
0.5
0.0
0
200
400
600
800
1000
Age (days)
12
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.
13
Cell Replicative Senescence:
Cells Retaining Proliferative Ability
Decrease With Number of Cell
Divisions.
N ormal f ibrob lasts (H art et al
1976)
Percent of cells able to proli ferate
100
90
U V irradiated f ibro blasts (H art et
al 1976)
80
N ormal fibr oblasts (W ynfordT ho mas 1999)
70
AG O708 6A (Thomas e t al 1997)
60
D D 1 (Thomas et al 199 7)
50
40
30
20
10
0
0
10
20
30
40
50
60
70
80
In v itro populat ion doublings
14
Replicative capacity (norm alized to highest value
measured )
Cell Replicative Senescence:
Increase in Age Decreases
the Number of Cells
With Replicative Capacity.
1 .0
V ascular smoo th muscle
cells (Ruiz-Torres et al
1999)
0 .9
0 .8
Adreno cortical cells (Y ang
et al 2001)
0 .7
0 .6
0 .5
0 .4
0 .3
0 .2
0 .1
0 .0
0
20
40
60
80
10 0
Donor age (years)
15
Cell Replicative Senescence:
Beta-Senescence Model
Cells In Vitro
Age
(doublings)
Cells In Vivo
Age (t)
to
Cell fraction retaining
proliferative ability = (1-bt)
Cancer: I(t) = (at)k-1(1-bt)
Longevity: b = to -1
16
Search for Senescence
Interventions to Test the Model:
Altered longevity, modeled as to = b-1
Altered cancer, modeled as I(t) = (at)k-1(1-bt)
1.
Mice with altered p53 (gene which is
well known to influence senescence):
set lifetime to = b-1, then calculate
cancer.
2.
Mice with long-term dosing of
melatonin (known anti-oxidant): set
lifetime to = b-1, 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 a and b with ca’
and c b’ respectively.
17
Influence of Senescence
Rate on Age-Specific Cancer
Incidence in Mice.
Age-Specific Cancer Mortality: Beta and MVK/s Models of
Senescence Effects
Age specific mortality (percent of population at risk
per 100 days)
25
ED01 mice controls (Pompei et al 2001)
Beta-senescence model
MVK/s model
20
Normal senescence x 0.5
15
10
Normal senescence
5
Normal senescence x 1.21
0
0
200
400
600
800
1000
Age (days)
Beta model fit to ED01 undosed controls is I(t) = (at)k-1(1-bt),
where a = 0.00115, k-1 = 5, b =0.00108 (Pompei et al 2001).
Equivalent MVK-s model fits shown. Senescence rate is the value of
parameter b. 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.
18
Probability of Tumors in p53
Altered Mice Compared to Beta-s
and MVK-s Model Predictions.
Effect of Senescence on Tumor Probability in Mice
100
Percent of mice with tumors
90
Normal senescence
Enhanced senescence
Reduced senescence
80
70
60
50
40
30
20
10
0
p53+/+ Beta-s
(Tyner
et al
2002)
MVK-s
p53+/m Beta-s MVK-s
(Tyner
et al
2002)
p53+/(Tyner
et al
2002)
Beta-s MVK-s
Modeled lifetime probability of cancer is calculated as Prob = 1exp[- 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.
19
Age-Specific Cancer Mortality for
Female CBA Mice Dosed with
Melatonin vs. Controls.
Effect of Melatonin Dose on Cancer Mortality
Age-specific mortality (per 90 animaldays at risk)
1.0
0.9
Controls
Melatonin Dosed
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0.0
0
200
400
600
800
1000
Average age at death (days)
Data from Anisimov et al 2001.
20
Influence of Senescence on
Cancer Mortality and Lifetime
Mice Cancer Mortality and Lifetime vs. Senescence
p53+/+ mice cancer mortality
p53+/m mice cancer mortality
1.4
Cancer moratlity or relative lifetime
p53+/- mice cancer mortality
p53-/- mice cancer mortality
1.2
p53+/+ mice lifetime
1
p53+/m mice lifetime
p53+/- mice lifetime
0.8
p53-/- mice lifetime
Melatonin controls cancer mortality
0.6
Melatonin dosed cancer mortality
Melatonin controls lifetime
0.4
Melatonin dosed lifetime
ED01 mice cancer mortality
0.2
Human cancer mortality
Beta-s model of cancer mortality
0
0
0.2
0.4
0.6
0.8
1
Normalized senescence
1.2
1.4
1.6
Beta-s model of lifetime
------- Curve fit for lifetime data
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/b), or age at which age-specific cancer mortality reaches
80% [M(t) = 0.8]. Human cancer mortality computed from SEER data.
21
Senescence
and Dietary Restriction
Liver Tumors vs. Weight for
Female Control B6C3F1 Mice
1
Haseman 1991
Seilkop 1995
Beta-senescence-time model fit
0.9
0.8
Liver tumor rate
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0
10
20
30
40
Weight (g)
50
60
70
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.
22
Senescence
and Dietary Restriction
Rodent Longevity vs. Deitary Restriction
Weindruch et al 1986
Weindruch et al 1982
Masoro et al 1982
Fernandes et al 1976
Sheldon et al 1995
Ad libitum
Beta-senescence-time model
2
Relative Longevity
1.8
1.6
1.4
1.2
1
0.8
0.6
0.4
0.6
0.8
1
1.2
Caloric intake relative to ad libitum
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.
23
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.
24
25
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.
26
Rats Liver Tumor Age-Specific Mortality
for Nitrosamine Study at Low Doses,
Compared to ED01 Mice
0.18
Rats-low dose nitrosomine (Peto et al 1991)
Possible Beta fit for Peto rats data
Age-specific mortality (per 100 animal-days)
0.16
ED01 mice: controls (Pompei et al 2001)
0.14
0.12
Beta fit for ED01 mice data
Error bars = +/- 1 SE
0.1
0.08
0.06
0.04
0.02
0
-100
100
300
500
700
900
1100
1300
1500
Age - Days
27