Title: Natalia Komarova
1Somatic evolution and cancer
- Natalia Komarova
- (University of California - Irvine)
2Plan
- Introduction The concept of somatic evolution
- Methodology Stochastic processes on
selection-mutation networks - Two particular problems
- Stem cells, initiation of cancer and optimal
tissue architecture (with L.Wang and P.Cheng) - Drug therapy and generation of resistance
neutral evolution inside a tumor (with D.Wodarz)
3Darwinian evolution (of species)
- Time-scale hundreds of millions of years
- Organisms reproduce and die in an environment
with shared resources
4Darwinian evolution (of species)
- Time-scale hundreds of millions of years
- Organisms reproduce and die in an environment
with shared resources - Inheritable germline mutations (variability)
- Selection
- (survival of the fittest)
5Somatic evolution
- Cells reproduce and die inside an organ of one
organism - Time-scale tens of years
6Somatic evolution
- Cells reproduce and die inside an organ of one
organism - Time-scale tens of years
- Inheritable mutations in cells genomes
(variability) - Selection
- (survival of the fittest)
7Cancer as somatic evolution
- Cells in a multicellular organism have evolved to
co-operate and perform their respective functions
for the good of the whole organism
8Cancer as somatic evolution
- Cells in a multicellular organism have evolved to
co-operate and perform their respective functions
for the good of the whole organism - A mutant cell that refuses to co-operate may
have a selective advantage
9Cancer as somatic evolution
- Cells in a multicellular organism have evolved to
co-operate and perform their respective functions
for the good of the whole organism - A mutant cell that refuses to co-operate may
have a selective advantage - The offspring of such a cell may spread
10Cancer as somatic evolution
- Cells in a multicellular organism have evolved to
co-operate and perform their respective functions
for the good of the whole organism - A mutant cell that refuses to co-operate may
have a selective advantage - The offspring of such a cell may spread
- This is a beginning of cancer
11Progression to cancer
12Progression to cancer
Constant population
13Progression to cancer
Advantageous mutant
14Progression to cancer
Clonal expansion
15Progression to cancer
Saturation
16Progression to cancer
Advantageous mutant
17Progression to cancer
Wave of clonal expansion
18Genetic pathways to colon cancer (Bert
Vogelstein)
Multi-stage carcinogenesis
19Methodology modeling a colony of cells
- Cells can divide, mutate and die
20Methodology modeling a colony of cells
- Cells can divide, mutate and die
- Mutations happen according to a
mutation-selection diagram, e.g.
u1
u4
u2
u3
(r3)
(r4)
(r2)
(1)
(r1)
21Mutation-selection network
u8
(r3)
u8
(r2)
(r6)
u8
u5
(1)
(r4)
(r1)
(r6)
u2
u2
u5
u8
(r1)
(r5)
(r7)
22Stochastic dynamics on a selection-mutation
network
23A birth-death process with mutations
Selection-mutation diagram
Number of is i
u
(1)
(r )
Number of is jN-i
Fitness 1
Fitness r gt1
24Evolutionary selection dynamics
Fitness 1
Fitness r gt1
25Evolutionary selection dynamics
Fitness 1
Fitness r gt1
26Evolutionary selection dynamics
Fitness 1
Fitness r gt1
27Evolutionary selection dynamics
Fitness 1
Fitness r gt1
28Evolutionary selection dynamics
Fitness 1
Fitness r gt1
29Evolutionary selection dynamics
Start from only one cell of the second
type. Suppress further mutations. What is the
chance that it will take over?
Fitness 1
Fitness r gt1
30Evolutionary selection dynamics
Start from only one cell of the second type. What
is the chance that it will take over?
If r1 then 1/N If rlt1 then lt
1/N If rgt1 then gt 1/N If r
then 1
Fitness 1
Fitness r gt1
31Evolutionary selection dynamics
Start from zero cell of the second type. What is
the expected time until the second type takes
over?
Fitness 1
Fitness r gt1
32Evolutionary selection dynamics
Start from zero cell of the second type. What is
the expected time until the second type takes
over?
In the case of rare mutations,
we can show that
Fitness 1
Fitness r gt1
33Two-hit process (Alfred Knudson 1971)
34A two-step process
35A two-step process
36A two step process
37A two-step process
Scenario 1 gets fixated first, and then
a mutant of is created
Number of cells
time
38Stochastic tunneling
39Two-hit process
Scenario 2 A mutant of is created before
reaches fixation
Number of cells
time
40The coarse-grained description
Long-lived states x0 all green x1 all
blue x2 at least one red
41Stochastic tunneling
Neutral intermediate mutant
Disadvantageous intermediate mutant
Assume that and
42Stem cells, initiation of cancer and optimal
tissue architecture
43Colon tissue architecture
44Colon tissue architecture
Crypts of a colon
45Colon tissue architecture
Crypts of a colon
46Cancer of epithelial tissues
Gut
Cells in a crypt of a colon
47Cancer of epithelial tissues
Cells in a crypt of a colon
Gut
Stem cells replenish the tissue asymmetric
divisions
48Cancer of epithelial tissues
Cells in a crypt of a colon
Gut
Proliferating cells divide symmetrically and
differentiate
Stem cells replenish the tissue asymmetric
divisions
49Cancer of epithelial tissues
Cells in a crypt of a colon
Gut
Differentiated cells get shed off into the lumen
Proliferating cells divide symmetrically and
differentiate
Stem cells replenish the tissue asymmetric
divisions
50Finite branching process
51What is known
- Normal cells undergo apoptosis at the top of the
crypt, the tissue is renewed and cell number is
constant
52What is known
- Normal cells undergo apoptosis at the top of the
crypt, the tissue is renewed and cell number is
constant - One of the earliest events in colon cancer is
inactivation of the APC gene
53What is known
- Normal cells undergo apoptosis at the top of the
crypt, the tissue is renewed and cell number is
constant - One of the earliest events in colon cancer is
inactivation of the APC gene - APC-/- cells do not undergo apoptosis at the top
of the crypt
54What is NOT known
- What is the cellular origin of cancer?
- Which cells harbor the first dangerous mutaton?
- Are the stem cells the ones in danger?
- Which compartment must be targeted by drugs?
?
?
?
55Colon cancer initiation
- Both copies of the APC gene must be mutated
before a phenotypic change is observed (tumor
suppressor gene)
X
X
X
APC-/-
APC/-
APC/
56Cellular origins of cancer
Gut
If a stem cell tem cell acquires a mutation,
the whole crypt is transformed
57Cellular origins of cancer
Gut
If a daughter cell acquires a mutation, it will
probably get washed out before a second mutation
can hit
58What is the cellular origin of cancer?
59Colon cancer initiation
60Colon cancer initiation
61Colon cancer initiation
62Colon cancer initiation
63Colon cancer initiation
64Colon cancer initiation
65First mutation in a daughter cell
66First mutation in a daughter cell
67First mutation in a daughter cell
68First mutation in a daughter cell
69First mutation in a daughter cell
70First mutation in a daughter cell
71Cellular origins of cancer
- The prevailing theory is that the mutations
leading to cancer initiation occur is stem cells
72Cellular origins of cancer
- The prevailing theory is that the mutations
leading to cancer initiation occur is stem cells - Therefore, all prevention and treatment
strategies must target the stem cells
73Cellular origins of cancer
- The prevailing theory is that the mutations
leading to cancer initiation occur is stem cells - Therefore, all prevention and treatment
strategies must target the stem cells - Differentiated cells (most cells!) do not count
74Mathematical approach
- Formulate a model which distinguishes between
stem and differentiated cells - Calculate the relative probability of various
mutation patterns
75First mutation in a daughter cell
76First mutation in a daughter cell
77First mutation in a daughter cell
78First mutation in a daughter cell
79First mutation in a daughter cell
80First mutation in a daughter cell
81Stochastic tunneling in a heterogeneous population
- At least one mutation happens in a stem cell (cf.
the two-step process) - 2) Both mutations happen in a daughter cell no
fixation of an intermediate mutant (cf tunneling)
82Stochastic tunneling in a heterogeneous population
Lower rate
- At least one mutation happens in a stem cell (cf.
the two-step process) - 2) Both mutations happen in a daughter cell no
fixation of an intermediate mutant (cf tunneling)
83Cellular origins of cancer
- If the tissue is organized into compartments with
stem cells and daughter cells, the risk of
mutations is lower than in homogeneous populations
84Cellular origins of cancer
- If the tissue is organized into compartments with
stem cells and daughter cells, the risk of
mutations is lower than in a homogeneous
population - Cellular origin of cancer is not necessarily the
stem cell. Under some circumstances, daughter
cells are the ones at risk.
85Cellular origins of cancer
- If the tissue is organized into compartments with
stem cells and daughter cells, the risk of
mutations is lower than in a homogeneous
populations - Cellular origin of cancer is not necessarily the
stem cell. Under some circumstances, daughter
cells are the ones at risk. - Stem cells are not the entire story!!!
86Optimal tissue architecture
- How does tissue architecture help protect against
cancer? - What are parameters of the architecture that
minimize the risk of cancer? - How does protection against cancer change with
the individuals age?
87Optimal number of stem cells
m1
m2
Crypt size is n16
m4
m8
88Probability to develop dysplasia
One stem cell
Probability to develop dysplasia
Many stem cells
Time (individuals age)
89The optimal solution is time-dependent!
Optimum many stem cells
One stem cell
Probability to develop dysplasia
Many stem cells
Optimum one stem cell
Time (individuals age)
90Optimization problem
- The optimum number of stem cells is high in young
age, and low in old age - Assume that tissue architecture cannot change
with time must choose a time-independent
solution - Selection mostly acts upon reproductive ages, so
the preferred evolutionary strategy is to keep
the risk of cancer low while the organism is
young
91Evolutionary compromise
Many stem cells
Probability to develop dysplasia
One stem cell
Time (individuals age)
92Evolutionary compromise
Many stem cells
While keeping the risk of cancer low at the
young age, the preferred evolutionary strategy
works against the
older age, actually
increasing
the
likelihood of cancer!
Probability to develop dysplasia
One stem cell
Time (individuals age)
93Cancer vs aging
- Cancer and aging are two sides of the same coin..
94Drug therapy and generation of resistance
95Leukemia
- Most common blood cancer
- Four major types
-
- Acute Myeloid Leukemia (AML),
- Chronic Lymphocytic Leukemia (CLL),
- Chronic Myeloid Leukemia (CML),
- Acute Lymphocytic Leukemia (ALL)
96Leukemia
- Most common blood cancer
- Four major types
-
- Acute Myeloid Leukemia (AML),
- Chronic Lymphocytic Leukemia (CLL),
- Chronic Myeloid Leukemia (CML),
- Acute Lymphocytic Leukemia (ALL)
97CML
- Chronic phase (2-5 years)
- Accelerated phase (6-18 months)
- Blast crisis (survival 3-6 months)
98Targeted cancer drugs
- Traditional drugs very toxic agents that kill
dividing cells
99Targeted cancer drugs
- Traditional drugs very toxic agents that kill
dividing cells - New drugs small molecule inhibitors
- Target the pathways which make cancerous cells
cancerous (Gleevec)
100Gleevec a new generation drug
Bcr-Abl
101Gleevec a new generation drug
Bcr-Abl
Bcr-Abl
102Small molecule inhibitors
103Targeted cancer drugs
104Targeted cancer drugs
- Very effective
- Not toxic
- Resistance poses a
- problem
Gleevec
Bcr-Abl protein
105Targeted cancer drugs
- Very effective
- Not toxic
- Resistance poses a
- problem
Mutation
Gleevec
Bcr-Abl protein
106Treatment without resistance
treatment
time
107Development of resistance
treatment
108How can one prevent resistance?
- In HIV treat with multiple drugs
- It takes one mutation to develop resistance of
one drug. It takes n mutations to develop
resistance to n drugs. - Goal describe the generation of resistance
before and after therapy.
109Mutation network for developing resistance
against n3 drugs
110During a short time-interval, Dt, a cell of type
Ai can
- Reproduce faithfully with probability
- Li(1-Suj) Dt
111During a short time-interval, Dt, a cell of type
Ai can
- Reproduce faithfully with probability
- Li(1-Suj) Dt
- Produce one cell identical to itself, and a
mutant cell of type Aj with probability Liuj Dt
112During a short time-interval, Dt, a cell of type
Ai can
- Reproduce faithfully with probability
- Li(1-Suj) Dt
- Produce one cell identical to itself, and a
mutant cell of type Aj with probability Liuj Dt - Die with probability Di Dt
113The method
Assume just one drug. xij(t) is the probability
to have i susceptible and j resistantcells at
time t.
F(x,yt)Sxij(t)xjyi is the probability
generating function.
114The method
xij(t) is the probability to have i susceptible
and j resistant cells at time t.
F(x,yt)Sxij(t)xjyi is the probability
generating function.
115For multiple drugs
xi0, i1, , im(t) is the probability to have is
cells of type As at time t.
F(x0,x1,,xmt) S xi0, i1, , im(t) x0im
xmi0 is the probability generating function.
F(0,1,,1t) is the probability that at time t
there are no cells of type Am
F(0,0,,0t) is the probability that at time t
the colony is extinct
116The method
The probability that at time t the colony is
extinct is F(0,0,,0t)
xnM(t), where M is the initial of cells and
xn is the solution of
The probability of treatment failure is
117The questions
- Does resistance mostly arise before or after the
start of treatment? - How does generation of resistance depend on the
properties of cancer growth (high turnover DL vs
low turnover DltltL) - How does the number of drugs influence the
success of treatment?
1181. How important is pre-existence of mutants?
119Single drug therapy
120Single drug therapy
Generation during treatment
Pre-existance
121Single drug therapy
Unrealistic!
Generation during treatment
Pre-existance
122Single drug therapy
Pre-existance gtgt Generation during
treatment
123Multiple drug therapies
Fully susceptible
Partially susceptible
Fully resistant
124Development of resistance
Fully susceptible
Partially susceptible
Fully resistant
1251. How important is pre-existence of resistant
mutants?
- For both single- and multiple-drug therapies,
- resistant mutants are likely to be produced
before start of treatment, and not in the
course of treatment
1262. How does generation of resistance depend on
the turnover rate of cancer?
- Low turnover (growth rategtgtdeath rate)
- Fewer cell divisions needed to reach a certain
size - High turnover (growth ratedeath rate)
- Many cell divisions needed to reach a certain
size
127Single drug therapy
Low turnover cancer, DltltL
128Single drug therapy
High turnover cancer, DL
More mutant colonies are produced, but
the probability of colony survival is
proportionally smaller
1292. How does generation of resistance depend on
the turnover rate of cancer?
- Single drug therapies the production of mutants
is independent of the turnover
1302. How does generation of resistance depend on
the turnover rate of cancer?
- Single drug therapies the production of mutants
is independent of the turnover - Multiple drug therapies the production of
mutants is much larger for cancers with a high
turnover
1313. The size of failure
- Suppose we start treatment at size N
- Calculate the probability of treatment failure
- Find the size at which the probability of failure
is d0.01
1323. The size of failure
- Suppose we start treatment at size N
- Calculate the probability of treatment failure
- Find the size at which the probability of failure
is d0.01 - The size of failure increases with of drugs and
decreases with mutation rate
133Minimum of drugs for different parameter values
1013 cells
u10-8-10-9 is the basic point mutation rate,
u10-4 is associated with genetic instabilities
134Minimum of drugs for different parameter values
1013 cells
u10-8-10-9 is the basic point mutation rate,
u10-4 is associated with genetic instabilities
135Minimum of drugs for different parameter values
1013 cells
u10-8-10-9 is the basic point mutation rate,
u10-4 is associated with genetic instabilities
136Minimum of drugs for different parameter values
1013 cells
u10-8-10-9 is the basic point mutation rate,
u10-4 is associated with genetic instabilities
137Minimum of drugs for different parameter values
1013 cells
u10-8-10-9 is the basic point mutation rate,
u10-4 is associated with genetic instabilities
138CML leukemia
- Gleevec
- u10-8-10-9
- D/L between 0.1 and 0.5 (low turnover)
- Size of advanced cancers is 1013 cells
139Log size of treatment failure
u10-8
u10-6
140Application for CML
- The model suggests that 3 drugs are needed to
push the size of failure (1 failure) up to 1013
cells
141Conclusions
- Main concept cancer is a highly structured
evolutionary process - Main tool stochastic processes on
selection-mutation networks - We addressed questions of cellular origins of
cancer and generation of drug resistance - There are many more questions in cancer research
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145Multiple drug treatments
- For fast turnover cancers, adding more drugs will
not prevent generation of resistance
146Size of failure for different turnover rates