1. Natalia Komarova (University of California - Irvine) Somatic evolution and cancer

2. 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)

3. Darwinian evolution (of species) Time-scale: hundreds of millions of years Organisms reproduce and die in an environment with shared resources

4. 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)

5. Somatic evolution Cells reproduce and die inside an organ of one organism Time-scale: tens of years

6. 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)

7. 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

8. 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

9. 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

10. 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

11. Progression to cancer

12. Constant population

13. Progression to cancer Advantageous mutant

14. Progression to cancer Clonal expansion

15. Progression to cancer Saturation

16. Progression to cancer Advantageous mutant

17. Progression to cancer Wave of clonal expansion

18. Genetic pathways to colon cancer (Bert Vogelstein) “Multi-stage carcinogenesis”

19. Methodology: modeling a colony of cells Cells can divide, mutate and die

20. Methodology: modeling a colony of cells Cells can divide, mutate and die Mutations happen according to a “mutation-selection diagram”, e.g. (1)(r 1 ) (r 2 ) (r 3 )(r 4 ) u1u1 u2u2 u3u3 u4u4

21. Mutation-selection network (1) (r 1 ) u2u2 u5u5 (r 2 ) (r 3 ) (r 4 ) (r 5 ) (r 6 ) u8u8 (r 7 ) u8u8 (r 1 ) u5u5 u8u8 u8u8 (r 6 ) u2u2

22. Stochastic dynamics on a selection-mutation network

23. 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

24. Evolutionary selection dynamics Fitness = 1 Fitness = r >1

25. Evolutionary selection dynamics Fitness = 1 Fitness = r >1

26. Evolutionary selection dynamics Fitness = 1 Fitness = r >1

27. Evolutionary selection dynamics Fitness = 1 Fitness = r >1

28. Evolutionary selection dynamics Fitness = 1 Fitness = r >1

29. 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?

30. 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? If r=1 then = 1/N If r<1 then < 1/N If r>1 then > 1/N If r then = 1

31. 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?

32. 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? In the case of rare mutations, we can show that

33. Two-hit process (Alfred Knudson 1971) (1)(r) (a) What is the probability that by time t a mutant of has been created? Assume that and

34. A two-step process

36. A two step process … …

37. A two-step process (1) (r) (a) Scenario 1: gets fixated first, and then a mutant of is created; time Number of cells

38. Stochastic tunneling …

39. Two-hit process time Number of cells Scenario 2: A mutant of is created before reaches fixation (1) (r) (a)

40. The coarse-grained description Long-lived states: x 0 …“all green” x 1 …“all blue” x 2 …“at least one red”

41. Stochastic tunneling Assume that and Neutral intermediate mutant Disadvantageous intermediate mutant

42. Stem cells, initiation of cancer and optimal tissue architecture

43. Colon tissue architecture

44. Crypts of a colon

45. Colon tissue architecture Crypts of a colon

46. Cancer of epithelial tissues Cells in a crypt of a colon Gut

47. Cancer of epithelial tissues Stem cells replenish the tissue; asymmetric divisions Cells in a crypt of a colon Gut

48. Cancer of epithelial tissues Stem cells replenish the tissue; asymmetric divisions Gut Proliferating cells divide symmetrically and differentiate Cells in a crypt of a colon

49. Cancer of epithelial tissues Stem cells replenish the tissue; asymmetric divisions Gut Proliferating cells divide symmetrically and differentiate Differentiated cells get shed off into the lumen Cells in a crypt of a colon

50. Finite branching process

51. What is known: Normal cells undergo apoptosis at the top of the crypt, the tissue is renewed and cell number is constant

52. 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

53. 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

54. 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? ? ? ?

55. 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 X X

56. Cellular origins of cancer If a stem cell tem cell acquires a mutation, the whole crypt is transformed Gut

57. Cellular origins of cancer If a daughter cell acquires a mutation, it will probably get washed out before a second mutation can hit Gut

58. What is the cellular origin of cancer?

59. Colon cancer initiation

65. First mutation in a daughter cell

71. Cellular origins of cancer The prevailing theory is that the mutations leading to cancer initiation occur is stem cells

72. 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

73. 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

74. Mathematical approach: Formulate a model which distinguishes between stem and differentiated cells Calculate the relative probability of various mutation patterns

75. First mutation in a daughter cell

81. Stochastic tunneling in a heterogeneous population 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)

82. Stochastic tunneling in a heterogeneous population 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) Lower rate

83. 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

84. 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.

85. 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!!!

86. 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?

87. Optimal number of stem cells m=1 m=2 m=4 m=8 Crypt size is n=16

88. Probability to develop dysplasia Time (individual’s age) Probability to develop dysplasia One stem cell Many stem cells

89. The optimal solution is time- dependent! Time (individual’s age) Probability to develop dysplasia Optimum: one stem cell Optimum: many stem cells Many stem cells One stem cell

90. 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

91. Evolutionary compromise Probability to develop dysplasia Time (individual’s age) One stem cell Many stem cells

92. 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 compromise Probability to develop dysplasia Time (individual’s age) One stem cell Many stem cells

93. Cancer vs aging Cancer and aging are two sides of the same coin…..

94. Drug therapy and generation of resistance

95. Leukemia Most common blood cancer Four major types: Acute Myeloid Leukemia (AML), Chronic Lymphocytic Leukemia (CLL), Chronic Myeloid Leukemia (CML), Acute Lymphocytic Leukemia (ALL)

96. Leukemia Most common blood cancer Four major types: Acute Myeloid Leukemia (AML), Chronic Lymphocytic Leukemia (CLL), Chronic Myeloid Leukemia (CML), Acute Lymphocytic Leukemia (ALL)

97. CML Chronic phase (2-5 years) Accelerated phase (6-18 months) Blast crisis (survival 3-6 months)

98. Targeted cancer drugs Traditional drugs: very toxic agents that kill dividing cells

99. 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)

100. Gleevec: a new generation drug Bcr-Abl

101. Gleevec: a new generation drug Bcr-Abl

102. Small molecule inhibitors

103. Targeted cancer drugs Very effective Not toxic

104. Targeted cancer drugs Very effective Not toxic Resistance poses a problem Bcr-Abl protein Gleevec

105. Targeted cancer drugs Very effective Not toxic Resistance poses a problem Bcr-Abl protein Gleevec Mutation

106. Treatment without resistance time treatment

107. Development of resistance treatment

108. 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.

109. Mutation network for developing resistance against n=3 drugs

110. During a short time-interval,  t, a cell of type A i can: Reproduce faithfully with probability L i (1-  u j )  t

111. During a short time-interval,  t, a cell of type A i can: Reproduce faithfully with probability L i (1-  u j )  t Produce one cell identical to itself, and a mutant cell of type A j with probability L i u j  t

112. During a short time-interval,  t, a cell of type A i can: Reproduce faithfully with probability L i (1-  u j )  t Produce one cell identical to itself, and a mutant cell of type A j with probability L i u j  t Die with probability D i  t

113. The method Assume just one drug.  ij (t) is the probability to have i susceptible and j resistantcells at time t.  x,y;t  ij (t)x j y i is the probability generating function.

114. The method  ij (t) is the probability to have i susceptible and j resistant cells at time t.  x,y;t  ij (t)x j y i is the probability generating function.

115. For multiple drugs:  i0, i1, …, im (t) is the probability to have i s cells of type A s at time t.  x 0,x 1,…,x m ;t  i0, i1, …, im (t) x 0 im …x m i0 is the probability generating function.  0,1,…,1;t  is the probability that at time t there are no cells of type A m  0,0,…,0;t  is the probability that at time t the colony is extinct

116. The method  he probability that at time t the colony is extinct is  (0,0,…,0;t) =x n M (t), where M is the initial # of cells and x n is the solution of The probability of treatment failure is

117. 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?

118. 1. How important is pre-existence of mutants?

119. Single drug therapy

120. Pre-existance = Generation during treatment

121. Single drug therapy Pre-existance = Generation during treatment Unrealistic!

122. Single drug therapy Pre-existance >> Generation during treatment

123. Multiple drug therapies Fully susceptible Fully resistant Partially susceptible

124. Development of resistance Fully susceptible Partially susceptible Fully resistant

125. 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

126. 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

127. Single drug therapy Low turnover cancer, D<<L

128. Single drug therapy High turnover cancer, D~L More mutant colonies are produced, but the probability of colony survival is proportionally smaller…

129. 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

130. 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

131. 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

132. 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

133. Minimum # of drugs for different parameter values 10 13 cells u=10 -8 -10 -9 is the basic point mutation rate, u=10 -4 is associated with genetic instabilities

134. Minimum # of drugs for different parameter values 10 13 cells u=10 -8 -10 -9 is the basic point mutation rate, u=10 -4 is associated with genetic instabilities

135. Minimum # of drugs for different parameter values 10 13 cells u=10 -8 -10 -9 is the basic point mutation rate, u=10 -4 is associated with genetic instabilities

136. Minimum # of drugs for different parameter values 10 13 cells u=10 -8 -10 -9 is the basic point mutation rate, u=10 -4 is associated with genetic instabilities

137. Minimum # of drugs for different parameter values 10 13 cells u=10 -8 -10 -9 is the basic point mutation rate, u=10 -4 is associated with genetic instabilities

138. CML leukemia Gleevec u=10 -8 -10 -9 D/L between 0.1 and 0.5 (low turnover) Size of advanced cancers is 10 13 cells

139. Log size of treatment failure u=10 -8 u=10 -6

140. Application for CML The model suggests that 3 drugs are needed to push the size of failure (1% failure) up to 10 13 cells

141. 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…

145. Multiple drug treatments For fast turnover cancers, adding more drugs will not prevent generation of resistance

146. Size of failure for different turnover rates