natalia komarova (university of california - irvine) somatic evolution and cancer
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Natalia Komarova
(University of California - Irvine)
Somatic evolution and cancer
Plan• Introduction: The concept of somatic evolution• Methodology: Stochastic processes on
selection-mutation networks
Two particular problems:
1. Stem cells, initiation of cancer and optimal tissue architecture (with L.Wang and P.Cheng)
2. Drug therapy and generation of resistance: neutral evolution inside a tumor (with D.Wodarz)
Darwinian evolution (of species)
• Time-scale: hundreds of millions of years
• Organisms reproduce and die in an environment with shared resources
Darwinian 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)
Somatic evolution
• Cells reproduce and die inside an organ of one organism
• Time-scale: tens of years
Somatic 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)
Cancer 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
Cancer 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
Cancer 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
Cancer 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
Progression to cancer
Progression to cancer
Constant population
Progression to cancer
Advantageous mutant
Progression to cancer
Clonal expansion
Progression to cancer
Saturation
Progression to cancer
Advantageous mutant
Progression to cancer
Wave of clonal expansion
Genetic pathways to colon cancer (Bert Vogelstein)
“Multi-stage carcinogenesis”
Methodology: modeling a colony of cells
• Cells can divide, mutate and die
Methodology: modeling a colony of cells
• Cells can divide, mutate and die
• Mutations happen according to a “mutation-selection diagram”, e.g.
(1) (r1) (r2) (r3) (r4)
u1 u2 u3u4
Mutation-selection network
1u1u
4u
1u
(1) (r1) 3uu2
u5
(r2)(r3)
(r4)
(r5)
(r6)
u8
(r7)u8(r1)
u5
u8
u8
(r6)3u
u2
Stochastic dynamics on a selection-mutation network
Number of is i
A birth-death process with mutations
Fitness = 1
Fitness = r >1
u
Selection-mutation diagram:
(1) (r ) Number of is j=N-i
Evolutionary selection dynamics
Fitness = 1
Fitness = r >1
Evolutionary selection dynamics
Fitness = 1
Fitness = r >1
Evolutionary selection dynamics
Fitness = 1
Fitness = r >1
Evolutionary selection dynamics
Fitness = 1
Fitness = r >1
Evolutionary selection dynamics
Fitness = 1
Fitness = r >1
Evolutionary selection dynamics
Fitness = 1
Fitness = r >1
Start from only one cell of the second type.Suppress further mutations.What is the chance that it will take over?
Evolutionary selection dynamics
Fitness = 1
Fitness = r >1
Start from only one cell of the second type.What is the chance that it will take over?
1/1
1/1)(
Nr
rr
If r=1 then = 1/NIf r<1 then < 1/NIf r>1 then > 1/NIf r then = 1
Evolutionary selection dynamics
Fitness = 1
Fitness = r >1
Start from zero cell of the second type.What is the expected time until the second type takes over?
Evolutionary selection dynamics
Fitness = 1
Fitness = r >1
Start from zero cell of the second type.What is the expected time until the second type takes over?
)(1 rNuT
In the case of rare mutations,
Nu /1we can show that
Two-hit process (Alfred Knudson 1971)
1uu
(1) (r) (a)
1r
What is the probability that by time t a mutant of has been created?
Assume that and 1a
A two-step process1uu
A two-step process1uu
A two step process
…
…
1uu
A two-step process1uu
(1) (r) (a)
Scenario 1: gets fixated first, and then a mutant of is created;
time
Num
ber
of c
ells
Stochastic tunneling
…
1uu
Two-hit process
time
Num
ber
of c
ells
Scenario 2:A mutant of is created before reaches fixation
1uu
(1) (r) (a)
The coarse-grained description
1210102
1210101
0200100
xRxRx
xRxRx
xRxRx
20R
10R21R Long-lived states:
x0 …“all green”x1 …“all blue”x2 …“at least one red”
Stochastic tunneling
1NuNu
Assume that and 1r 1a
120 uNuR
r
rNuuR
1
120
1|1| ur
1|1| ur
20RNeutral intermediate mutant
Disadvantageous intermediate mutant
Stem cells, initiation of cancer and optimal tissue architecture
Colon tissue architecture
Colon tissue architecture
Crypts of a colon
Colon tissue architecture
Crypts of a colon
Cancer of epithelial tissues
Cells in a crypt of a colon
Gut
Cancer of epithelial tissues
Stem cells replenish thetissue; asymmetric divisions
Cells in a crypt of a colonGut
Cancer of epithelial tissues
Stem cells replenish thetissue; asymmetric divisions
Gut
Proliferating cells dividesymmetrically and differentiate
Cells in a crypt of a colon
Cancer of epithelial tissues
Stem cells replenish thetissue; asymmetric divisions
Gut
Proliferating cells dividesymmetrically and differentiate
Differentiated cells get shed off into the lumen
Cells in a crypt of a colon
Finite branching process
What is known:• Normal cells undergo apoptosis at the top of the
crypt, the tissue is renewed and cell number is constant
What 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
What 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
What 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?
?
?
?
Colon cancer initiation
• Both copies of the APC gene must be mutated before a phenotypic change is observed (tumor suppressor gene)
APC+/+ APC+/- APC-/-
X XX
Cellular origins of cancer
If a stem cell tem cell acquires a mutation, the whole crypt is transformed
Gut
Cellular origins of cancer
If a daughter cell acquiresa mutation, it will probablyget washed out beforea second mutation can hit
Gut
What is the cellular origin of cancer?
Colon cancer initiation
Colon cancer initiation
Colon cancer initiation
Colon cancer initiation
Colon cancer initiation
Colon cancer initiation
First mutation in a daughter cell
First mutation in a daughter cell
First mutation in a daughter cell
First mutation in a daughter cell
First mutation in a daughter cell
First mutation in a daughter cell
Cellular origins of cancer
• The prevailing theory is that the mutations leading to cancer initiation occur is stem cells
Cellular 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
Cellular 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
Mathematical approach:
• Formulate a model which distinguishes between stem and differentiated cells
• Calculate the relative probability of various mutation patterns
First mutation in a daughter cell
First mutation in a daughter cell
First mutation in a daughter cell
First mutation in a daughter cell
First mutation in a daughter cell
First mutation in a daughter cell
Stochastic tunneling in a heterogeneous population
1Nuu
1) 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)
20R 1120 log uNuuR
) .( 1uNuRcf
Stochastic tunneling in a heterogeneous population
1Nuu
1) 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)
20R 1120 log uNuuR
) .( 1uNuRcf Lower rate
Cellular 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
Cellular 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.
Nuu
1log 11
Cellular 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!!!
Optimal 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 individual’s age?
Optimal number of stem cells
m=1m=2
m=4m=8
Crypt size isn=16
Probability to develop dysplasia
Time (individual’s age)
Pro
babi
lity
to d
evel
op d
yspl
asia
One stem cell
Many stem cells
The optimal solution is time-dependent!
Time (individual’s age)
Pro
babi
lity
to d
evel
op d
yspl
asia
Optimum:one stemcell
Optimum:many stem cells
Many stem cells
One stem cell
Optimization 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
Evolutionary compromiseP
roba
bili
ty to
dev
elop
dys
plas
ia
Time (individual’s age)
One stem cell
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!
Evolutionary compromiseP
roba
bili
ty to
dev
elop
dys
plas
ia
Time (individual’s age)
One stem cell
Many stem cells
Cancer vs aging
• Cancer and aging are two sides of the same coin…..
Drug therapy and generation of resistance
Leukemia
• Most common blood cancer
• Four major types:
Acute Myeloid Leukemia (AML),
Chronic Lymphocytic Leukemia (CLL),
Chronic Myeloid Leukemia (CML),
Acute Lymphocytic Leukemia (ALL)
Leukemia
• Most common blood cancer
• Four major types:
Acute Myeloid Leukemia (AML),
Chronic Lymphocytic Leukemia (CLL),
Chronic Myeloid Leukemia (CML),
Acute Lymphocytic Leukemia (ALL)
CML• Chronic phase (2-5 years)
• Accelerated phase (6-18 months)
• Blast crisis (survival 3-6 months)
Targeted cancer drugs
• Traditional drugs: very toxic agents that kill dividing cells
Targeted 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)
Gleevec: a new generation drug
Bcr-Abl
Gleevec: a new generation drug
Bcr-Abl Bcr-Abl
Small molecule inhibitors
Targeted cancer drugs
• Very effective
• Not toxic
Targeted cancer drugs
• Very effective
• Not toxic
• Resistance poses a
problem
Bcr-Abl protein
Gleevec
Targeted cancer drugs
• Very effective
• Not toxic
• Resistance poses a
problem
Bcr-Abl protein
Gleevec
Mutation
Treatment without resistance
time
treatment
Development of resistance
treatment
How 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.
Mutation network for developing resistance against n=3 drugs
During a short time-interval, t, a cell of type Ai can:
• Reproduce faithfully with probability
Li(1-uj) t
During a short time-interval, t, a cell of type Ai can:
• Reproduce faithfully with probability
Li(1-uj) t
• Produce one cell identical to itself, and a mutant cell of type Aj with probability Liuj t
During a short time-interval, t, a cell of type Ai can:
• Reproduce faithfully with probability
Li(1-uj) t
• Produce one cell identical to itself, and a mutant cell of type Aj with probability Liuj t
• Die with probability Di t
The method
]))((1)[()1()1(
])1)[(()1()1)(()(
ij1ji,j1,i
1-ji,j1,-iij
tjiDLttDjtDi
tiLuLjttuLittt
DyDLLuxyuLy
DxDLLxxt
)]([)1()( 22
Assume just one drug. ij(t) is the probability to have i susceptible and j resistantcells at time t.
x,y;tij(t)xjyi is the probability generating function.
))()(()1()1(
])1)[(()1()1)((
ij1ji,j1,i
1-ji,j1,-iij
jiDLtDjDi
iLuLjtuLit
The method
]))((1)[()1()1(
])1)[(()1()1)(()(
ij1ji,j1,i
1-ji,j1,-iij
tjiDLttDjtDi
tiLuLjttuLittt
))()(()1()1(
])1)[(()1()1)((
ij1ji,j1,i
1-ji,j1,-iij
jiDLtDjDi
iLuLjtuLit
ij(t) is the probability to have i susceptible and j resistantcells at time t.
x,y;tij(t)xjyi is the probability generating function.
.)]([)1(
;)(2
2
DyDLLuxyuLy
DxDLLxx
For multiple drugs:
niDxDLLiuxxiuLx
DxDLLxx
iiii
0 ,)]([)1(
;)(
12
02
00
i0, i1, …, im(t) is the probability to have is cells of type As at time t.
x0,x1,…,xm;ti0, i1, …, im(t) x0im …xm
i0
is the probability generating function.
0,1,…,1;tis the probability that at time t there are no cells of type Am
0,0,…,0;tis the probability that at time t the colony is extinct
The method
.0)0(
,0 ,)]([)1(
;)(
12
02
00
i
iiii
x
niDxDLLiuxxiuLx
DxDLLxx
he probability that at time t the colony is extinct is (0,0,…,0;t) =xn
M(t),
where M is the initial # of cells and xn is the solution of
The probability of treatment failure is
)(lim1 txP Mntfail
The questions:
1. Does resistance mostly arise before or after the start of treatment?
2. How does generation of resistance depend on the properties of cancer growth (high turnover D~L vs low turnover D<<L)
3. How does the number of drugs influence the success of treatment?
1. How important is pre-existence of mutants?
Single drug therapy
Single drug therapy
Pre-existance = Generation during treatment
Single drug therapy
Pre-existance = Generation during treatment
Unrealistic!
Single drug therapy
Pre-existance >> Generation during treatment
Multiple drug therapies
Fully susceptible
Fully resistant
Partially susceptible
Development of resistance
Fully susceptible
Partially susceptible
Fully resistant
1. 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
2. How does generation of resistance depend on the turnover
rate of cancer?
• Low turnover (growth rate>>death rate)
Fewer cell divisions needed to reach a certain size
• High turnover (growth rate~death rate)
Many cell divisions needed to reach a certain size
Single drug therapy
Low turnover cancer, D<<L
Single drug therapy
High turnover cancer, D~L
More mutant colonies are produced, but theprobability of colony survival is proportionally smaller…
2. How does generation of resistance depend on the turnover
rate of cancer?
• Single drug therapies: the production of mutants is independent of the turnover
2. 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
3. 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=0.01
3. 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=0.01
• The size of failure increases with # of drugs and decreases with mutation rate
Minimum # of drugs for different parameter values
1013 cells
u=10-8-10-9 is the basic point mutation rate, u=10-4 is associated with genetic instabilities
Minimum # of drugs for different parameter values
1013 cells
u=10-8-10-9 is the basic point mutation rate, u=10-4 is associated with genetic instabilities
Minimum # of drugs for different parameter values
1013 cells
u=10-8-10-9 is the basic point mutation rate, u=10-4 is associated with genetic instabilities
Minimum # of drugs for different parameter values
1013 cells
u=10-8-10-9 is the basic point mutation rate, u=10-4 is associated with genetic instabilities
Minimum # of drugs for different parameter values
1013 cells
u=10-8-10-9 is the basic point mutation rate, u=10-4 is associated with genetic instabilities
CML leukemia
• Gleevec
• u=10-8-10-9
• D/L between 0.1 and 0.5 (low turnover)
• Size of advanced cancers is 1013 cells
Log size of treatment failure
(a) 1 drug 2 drugs 3 drugs 4 drugs 5 drugs D/L=0.1 5.95 12.34 18.45 24.38 30.19 D/L=0.5 5.95 12.13 17.99 23.69 29.26 D/L=0.9 5.95 11.48 16.70 21.74 26.66 (b) 1 drug 2 drugs 3 drugs 4 drugs 5 drugs D/L=0.1 4.00 8.55 12.80 16.89 20.86 D/L=0.5 4.00 8.31 12.37 16.20 19.93 D/L=0.9 4.00 7.68 11.07 14.40 17.40
u=10-8
u=10-6
Application for CML
• The model suggests that 3 drugs are needed to push the size of failure (1% failure) up to 1013 cells
Conclusions
• 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…
Multiple drug treatments
• For fast turnover cancers, adding more drugs will not prevent generation of resistance
Size of failure for different turnover rates