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76 II.9. APPENDIX

II.9. APPENDIX 77 the class of strictly increasing, continuous mapping of[0, T]onto itself. Ifλ∈Λ, thenλ(0) =0 and λ(T) =T. The Skorokhod topology on D([0, T],(MF(X ),∥.∥0)) is generated by the distance

d(µ, ν) =inf

λΛ

⎧⎪⎪⎨⎪⎪

⎩ max⎧⎪⎪

⎨⎪⎪⎩ sup

t∈[0,T]∣λ(t) −t∣, sup

t∈[0,T]∥µt−νλt0⎫⎪⎪

⎬⎪⎪⎭

⎫⎪⎪⎬⎪⎪

, (II.9.3)

onD([0, T],(MF(X ),∥.∥0)), see e.g. [12], Chap. 3. Since the identity lies inΛit is clear that d(µ, ν) ≤supt∈[0,T]∥µt−νt0. Therefore, if a sequence of random elements with state space D([0, T],MF(X ))equipped with the metric induced by the normsupt∈[0,T]∥µt0convergences in probability toµ, it also convergences in probability to µif D([0, T],MF(X ))is equipped with the metric d.

Proposition II.9.2. Fix >0 and let σK a sequence in K with K1/2+α ≪σK ≪1. LetZn be a Markov chain with state spaceN0 and with the following transition probabilities

P[Zn+1=j∣Zn=i] =p(i, j) =⎧⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎩

1, for i=0 andj=1,

1

2 −C1iK1+C2σK, for i≥1 andj=i+1,

1

2 +C1iK1−C2σK, for i≥1 andj=i−1,

(II.9.4)

for some constantsC1>0andC2≥0. Let τi be the first hitting time of leveliby Z and let Pa

denote the law of Z conditioned on Z0 =a. Then, for all M ≥ 8CC12 and for all a≤ 13M σKK

Klim→∞eKPaM σ

KK0] =0. (II.9.5) Remark 9. The proposition can be seen as a moderate deviation result for this particular Markov chain. More precisely, we can prove that there exist two constantsM >0 andC3>0 which depend only onC1 andC2 such that for a< 13M σKK

PaM σKK0] ≤exp(−C3K1((13M σKK)2−a2)), (II.9.6) for all K large enough.

Proof. We calculate this probability with some standard potential theory arguments (cf. [17]).

LethM σKK,0(a) be the solution of the Dirichlet problem with λ=0, i.e.,

LhM σKK,0(x) =0, for 0<x< ⌈M σKK⌉ (II.9.7) hM σKK,0(x) =1, for x≥ ⌈M σKK⌉

hM σKK,0(x) =0, for x=0.

Therefore, we obtain for 0<a< ⌈M σKK⌉

PaM σKK0] =hM σKK,0(a) = ∑ai=1 1 π(i) 1

p(i,i1)

i=M σ1 KK 1 π(i) 1

p(i,i1)

, (II.9.8)

whereπ= (π(0), π(1), π(2), . . .)is an invariant measure of the one-dimensional Markov chain Zn. In our case any invariant measure π has to satisfy, for alli≥1,

π(0) =p(1,0)π(1) and π(i) =p(i−1, i)π(i−1) +p(i+1, i)π(i+1). (II.9.9)

78 II.9. APPENDIX Therefore,π withπ(0) =1, π(1) = p(1,01 ) andπ(i) = ∏ij=11p(j,j+1)

p(j,j1) 1

p(i,i1) is the unique invariant measure for the Markov chainZn. Thus we get from (II.9.8) that

hM σKK,0(a) = ∑ai=1ij=11 p(j,j1) p(j,j+1)

iM σ=1 KKij=11 p(j,j1) p(j,j+1)

(II.9.10)

= ∑ai=1exp(∑ij=11ln(11+2C2C11KK−1−1jj+2C2C22σσKK))

iM σ=1 KKexp(∑ij=11ln(11+2C2C11KK−1−1jj+2C2C22σσKK)

´¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¸¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¶

=∶f(j)

).

For all j≤M σKK we can approximatef(j) as follows

f(j) =ln(1+14C2C1K1K−1−1jj4C+2C2σ2σKK) (II.9.11)

= 14C2C1K1K−1−1jj+4C2C2σ2σKK −O((14C2C1K1K−1−1jj+4C2C2σ2σKK)2)

=4C1Kj −4C2σK+O((Kj)2KKj +2σ2K)

=4C1Kj −4C2σK+O((M σK)2) Therefore,

hM σKK,0(a) ≤ ∑ai=1exp(∑ij=114C1Kj +O((M σK)2)

iM σ=1 KKexp(∑ij=114C1 j

K −4C2σK−O((M σK)2) (II.9.12)

≤ aexp(2C1a2K1+O(a(M σK)2)

iM σ=1 KKexp(2C1K1(i2−i) −4C2σKi−O((i−1)(M σK)2))

≤ aexp(2C1a2K1+O(a(M σK)2)

iM σ=1 KK

2M σKKexp(2C1K1i2−(2C1K1+4C2σK)i−O(i(M σK)2)). ChoosingM ≥8CC12, if a< M σ3KK, then

hM σKK,0(a) ≤ aexp(2C1a2K1+O(a(M σK)2)

1

2⌈M σKK⌉exp((12C1M−2C2)M 2σ2KK−O((σM)3K+σKM))

≤2a(⌈M σKK⌉)1 exp(C1K1(2a214(⌈M σKK⌉)2))

≤exp(−C3K1((13⌈M σKK⌉)2−a2)). (II.9.13) SinceK1/2+α≪σK whenK tends to infinity, (II.9.5) follows.

Proposition II.9.3. Let (Zt)t0 be a branching process with birth rate per individual b and death rate per individual d. Let τi be the first hitting time of level i by Z and let Pj denote the law of Z conditioned on Z0=j, andEj the corresponding expectation. Then

Pjk0] = (d/b)j−1

(d/b)k−1 for all 1≤j≤k−1, (II.9.14)

∣P1k0] − [b−d]+

b ∣ ≤ k1 and (II.9.15)

E1k∧τ0] ≤ 1+ln(k)

b , (II.9.16)

II.9. APPENDIX 79 where [b−d]+≡max{b−d ,0}. Moreover, if Zt is slightly super-critical, i.e., b=d+, then

maxnk

Enk∧τ0]

Pnk0] ≤ 1+ln(k)

(II.9.17)

Proof. Letpj≡Pjk0]. Thenp0=0,pk=1, andpj = b+bdpj+1+b+ddpj1 for all 1≤j≤k−1 by the Markov property. From this recursion, we obtain the characteristic polynomial

P(x) =bx2− (b+d)x+d. (II.9.18)

With its roots1 andd/b, we obtain the following general solution for the recursion pn0⋅1n1(d

b)n, (II.9.19)

where κ0 and κ1 are constants. From the initial condition p0 = 0 and pk = 1, we obtain κ0 = −((db)k−1)1 and κ1= ((db)k−1)1. Therefore,

pn= (db)n−1

(db)k−1 and p1=

d b −1

(db)k−1 = 1

1+db +. . .+ (db)k1. (II.9.20) Ifd≥b, this computation implies thatp1≡P1k0] ≤1/k and[b−d]+=0. Ifd<b,

P1k0] −b−d b =

d b −1

(db)k−1 − (1−db)(db)k−1

(db)k−1 = (db −1)(db)k (db)k−1 =

d b −1

1− (bd)k (II.9.21)

=

d

b(1−db)

1− (db)k = 1

b

d(1+db +. . .+ (bd)k1) = 1

b

d+. . .+ (db)k

≤ 1 k.

Similarly, ifen≡Enk∧τ0], thenenis the solution of the following non-homogeneous Dirichlet problem:

L en= −1, for n∈ {1, .., k−1} (II.9.22) en=0, for n∈N0∖ {1, .., k−1},

where(Lf)(x) =x(b[f(x+1) −f(x)] +d[f(x−1) −f(x)])is the generator of the branching process Z. Therefore, we have to solve the following non-homogeneous recurrence

en+2b+bden+1+dben= b(n+11) and e0=ek=0 (II.9.23) We solve this by variation of parameters. Thus, we first solve the associated linear homoge-neous recurrence relation:

hn+2b+bdhn+1+dbhn=0 (II.9.24) As we have seen beforehn21+κ3(db)j for anyκ2, κ3∈Rsolves the equation. Obverse that this functions are the harmonic functions ofL. Second, we have to find a particular solution.

Let(x1j, x2j) the solution of the system of linear equations

x1j+ (db)j+1x2j =0 (II.9.25) x1j+ (dd)j+2x2j = −b(j1+1), (II.9.26)

80 II.9. APPENDIX then

epn= ∑nj=01x1j1n+ ∑nj=01x2j(db)n = b1dnj=11

j +b1dnj=1 1

j(bd)j(db)n (II.9.27)

= b1dnj=11

j((db)nj−1)

is a particular solution. Now, we obtain we obtain the following general solution for the recurrence:

en=hn+epn23(db)n+b1dnj=1 1

j((db)nj−1). (II.9.28) We have the boundary conditione0=ek=0, thereforeκ2 and κ3 are given by the solution of the following system of linear equations

κ23(db)0+b1d0j=11

j((db)0j−1) =0, (II.9.29) κ23(db)k+b1dkj=11

j((db)kj−1) =0, (II.9.30) and we obtain that

en= b1dk

j=1 1 j

(db)kj−1

(db)k−1 −b1dk

j=1 1 j

(db)kj−1

(db)k−1 (db)n+b1dn

j=1 1

j((db)nj−1) (II.9.31)

= b1dk

j=1 1 j

((db)kj−1)(1− (db)n)

(db)k−1 +b1dn

j=1 1

j((db)nj−1).

With this formula we can easily prove the second inequality of the proposition, e1= b1dk

n=1 1 n

(db)kn−1

(db)k−1 (1−db) +0≤1bk

n=1 1

n ≤1+ln(k)

b . (II.9.32)

Finally, we obtain for slightly super-criticalZt, i.e., withb=d+, Enk∧τ0]

Pnk0] = en

pn = b1dk

j=1 1

j( (db)kj−1)(−1)

´¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¸¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¶

1

+b1dn

j=1 1 j

((db)nj−1)(1− (db)k) 1− (db)n

´¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¸¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¶

0

(II.9.33)

1k

j=1 1

j ≤ 1+ln(k)

,

which proves (II.9.17).

Proposition II.9.4. Let(ZtK)t0 be a sequence branching process with birth rate per individ-ual b≥0 and death rate per individual d≥0 and ∣b−d∣ =O(σK), where K1/2+α ≪σK ≪1.

Let τi be the first hitting time of level i by Z and let Pj denote the law of Z conditioned on Z0=j.

(a) The invasion probability can be approximated up to an error of order exp(−Kα):

Klim→∞exp(Kα) ∣P1σKK0] −[b−d]+

b ∣ =0. (II.9.34)

II.9. APPENDIX 81 (b) If b>d(super-critical case), we have exponential tails, i.e.,

Klim→∞exp(σKα/3)P1σKK>ln(K)σK1α/2∣τσKK0] =0 (II.9.35) and

Klim→∞exp(Kα)PσKKK0] =0 (II.9.36) Proof. (a) Compare with (II.9.14) that

P1σKK0] = (d/b) −1

(d/b)σKK−1. (II.9.37) If b > d (sub-critical case), there exist two constants Csub > 0 and C¯sub > 0 such that 1+ CsubσK≤d/b≤1+C¯subσK. Therefore, the left hand side of (II.9.37) does not exceed

subσK

(1+CsubσK)σKK−1 ≤ C¯subσK

exp(CsubσK⌈σKK⌉−O(σK3 K))−1 =o(eKα). (II.9.38) The last equality holds, since K≪σK2 K. Ifb>d(super-critical case), we obtain similarly

∣P1k0] − b−d b ∣ =RRRRR

RRRRR R

d b −1 1− (db)kRRRRR

RRRRR

R=o(exp(−Kα)). (II.9.39) (b) Compare with [2] page 41, that

P1σKK>ln(K)σK1α/2∣τσKK0] (II.9.40)

≤exp⎛

⎝−⎢⎢

⎢⎢⎢⎣

ln(K)σK1α/2

emaxn≤⌈σKKEnσKK∣τσKK0]

⎥⎥⎥⎥

⎥⎦

⎠≤exp(−σKα/3), where the last inequality holds, because we can apply Proposition II.9.3

n≤⌈maxσKKEnσKK∣τσKK0] = max

n≤⌈σKK

EnσKK∧τ01τ0>τσK K]

Pn0σKK] (II.9.41)

≤O(ln(K)σK1). On the other hand, we have

PσKKK0] =1−(d/b)σKK−1

(d/b)K−1 ≤exp(−K) (II.9.42) since d/b=1−O(σK)and K≪σKK.

Proposition II.9.5. Let (ZnK)n0 a sequence of discrete time Markov chain with state space Z and with transition probabilities

P[ZnK+1=j∣ZnK =i] =p(i, j) =⎧⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎩

1

2 +CσK, ifj=i+1,

1

2 −CσK, ifj=i−1,

0, else,

(II.9.43)

for some constantC≠0. Letτi be the first hitting time of leveliby ZK and letPj denote the law of ZK conditioned on Z0K=j and letσK a zero sequence such thatK12+α≪σK≪1.

82 II.9. APPENDIX (a) If ZK is slightly supercritical, i.e., C>0, then, for all i≥1

Klim→∞exp(Kα)Pi⌈(/2)σKK(i1)⌈(/2)σKK(i+1)⌈(/2)σKK] =0. (II.9.44) (b) If ZK is slightly subcritical, i.e., C<0, then, for all constants C1, C2, C3>0

Klim→∞exp(Kα)P(C1+C2)⌈σKK(C1+C2+C3)⌈σKKC1σKK] =0. (II.9.45) Proof. Since the transition probabilities of ZK do not depend on the state of ZK, we have that

Pi⌈(/2)σKK(i1)⌈(/2)σKK(i+1)⌈(/2)σKK]=P⌈(/2)σKK02⌈(/2)σKK] (II.9.46) By (II.9.14) the left side of (II.9.46) is equal

1− (1−2CσK+O(σK2 ))⌈(/2)σKK

1− (1−2CσK+O(σK2))2⌈(/2)σKK ≥1−exp(−K2α), (II.9.47) since σK2K≫K. With the same arguments, we obtain also (II.9.45).

83

Chapter III

A stochastic model for

immunotherapy of cancer and the polymorphic evolution sequence for

populations with phenotypic plasticity

In this chapter we propose an extension of the individual-based model in population dynamics introduced in Section I.2, which broadens the range of biological applications. The primary motivation is modeling of immunotherapy of malignant tumors. The main expansions are that we have three different actors in this context (T-cells, cytokines, and cancer cells), that we distinguish cancer cells by phenotype and genotype, that we include environment-dependent phenotypic plasticity, and that we take into account the therapy effects. We illustrate the new setup by using it to model various phenomena arising in immunotherapy and we argue why stochastic models may help to understand the resistance of tumors to therapeutic approaches and thus may have non-trivial consequences on tumor treatment protocols. Furthermore, we show that the interplay of genetic mutations and phenotypic switches on different time scales as well as the occurrence of metastability phenomena raise new mathematical challenges. In the present thesis we focuses more on these theoretical aspects which arise by including pheno-typic plasticity in the standard individual-based model describing the evolution of an asexual reproducing, competitive population. More precisely, we study the behavior of this process on a large (evolutionary) time scale and in the simultaneous limits of large population size (K → ∞) and rare mutations (uK → 0), proving convergence to a Markov pure jump pro-cess, which can be seen as a generalization of the polymorphic evolution sequence (cf. [25, 30]).

Parts of the presented results were previously published in Scientific Reports [9] as a joint work with L. Coquille, H. Mayer, M. Hölzel, M. Rogava, T. Tüting, and A. Bovier (cf. Section I.3 for details).

III.1 Introduction

The treatment of various cancers with immunotherapies received a lot of attention in the medical as well as the mathematical modeling communities during the last decades [108, 88, 50, 72, 65, 76]. Many different therapeutic approaches were developed and tested experimentally.

As for the classical therapies such as chemo- and radiotherapy,resistanceis an important issue also for immunotherapy: although a therapy leads to an initial phase of remission, very often

84 III.1. INTRODUCTION a relapse occurs. The main driving forces for resistance are considered to be the genotypic and phenotypic heterogeneity of tumors, which may be enhanced during therapy, see [76, 96, 65]

and references therein. A tumor is a complex tissue which evolves in mutual influence with its environment [32]. In this chapter, we consider the example of melanoma (tumors associated to skin cancer) under T-cell therapy. Our work is motivated by the experiments of Landsberg et al. [92], which investigate melanoma in mice underadoptive cell transfer (ACT) therapy. This therapeutic approach involves the injection of T-cells which recognize a melanocyte-specific antigen and are able to kill differentiated types of melanoma cells. The therapy induces an inflammation and the melanoma cells react to this environmental change by switching their phenotype, i.e. by passing from a differentiated phenotype to a dedifferentiated one (special markers on the cell surface disappear). The T-cells recognize the cancerous cells through the markers which are down regulated in the dedifferentiated types. Thus, they are not capable of killing the dedifferentiated cancer cells anymore and a relapse is often observed. The phenotype switch is enhanced, if pro-inflammatory cytokines, called TNF-α(Tumor Necrosis Factor), are present. A second reason for the appearance of a relapse is that the T-cells become exhausted and are not working efficiently anymore. This problem was addressed by re-stimulation of the T-cells, but this led only to a delay in the occurrence of the relapse.

Of course, other immune cells and cytokines are also present. However, according to the careful control experiments, their influence can be neglected in the context of the phenomena considered here. Cell division is not required for switching, and switching is reversible. This means that the melanoma cells can recover their initial (differentiated) phenotype [92]. The switch is thus a purely phenotypic change which is not induced by a mutation. Figure III.1 is a graphical representation of the relevant underlying mechanisms, reported in [92].

Figure III.1: Dynamics of the experiments described in [92].

In this chapter, we propose a quantitative mathematical model that can reproduce the phenomena observed in the experiments of [92], and which allows to simulate different therapy protocols, including some where several types of T-cells are used. It is an extension of the individual-based stochastic models of adaptive dynamics, introduced in Metz et al. [104] and developed and analyzed by many authors in recent years (see e.g. [15, 16, 43, 27, 25, 28, 19, 29, 33]), to the setting of tumor growth under immunotherapy. More precisely, the main expansions are:

(i) Three different classes of actors are included: T-cells, cytokines, and cancer cells.

III.1. INTRODUCTION 85 (ii) For cancer cells two types of transitions are allowed: genotypic mutations and phenotypic

switches.

(iii) Phenotypic changes can be affected by the environment which is not modeled determin-istically as in [29] but as particles undergoing the random dynamics as well.

(iv) For modeling the therapy effect, a predator-prey mechanism (between cancer cells and immune cells) is included.

(v) A birth-reducing competition term is included which takes into account that competition may also affect the reproduction behavior.

In general, these class of stochastic models describe the evolution of interacting cell popula-tions, in which the relevant events for each individual (e.g. birth and death) occur randomly.

It is well known that in the limit of large cell-populations, these models are approximated by deterministic kinetic rate models (cf. Theorem III.2.1), which are widely used in the modeling of cell populations. However, these approximations are inaccurate and fail to account for important phenomena if the numbers of individuals in some sub-populations become small.

In such situations, random fluctuation may become highly significant and completely alter the long-term behavior of the system. For example, in a phase of remission during therapy, the cancer and the T-cell populations drop to a low level and may die out due to fluctuations. A number of (mostly deterministic) models have been proposed that describe the development of a tumor under treatment, focusing on different aspects. For example, a deterministic model for ACT therapy is presented in [50]. Stochastic approaches were used to understand certain aspects of tumor development, for example rate models [69] or multi-type branching processes;

see the book by Durrett [49] or [20, 3, 48]. To our knowledge, however, it is a novel feature of our models to describe the coevolution of immune- and tumor cells taking into account both interactions and phenotypic plasticity. Our models can help to understanding the interplay of therapy and resistance, in particular in the case of immunotherapy, and may be used to predict successful therapy protocols.

Besides being able to describe the experiments and making predictions about therapy pro-tocols, we are also interested in more theoretical aspects which arise by including switching rates in the standard model, more precisely, in the interplay between the fast phenotypical changes by switching and the slow genotypical changes by mutation. In this context, the typical questions of adaptive dynamics arise again. E.g., can we describe the evolution of the system by successive mutant invasions, or rather, under which conditions does the microscopic process which incorporates fast phenotypical switches converge in the limit of a large popula-tion size in combinapopula-tion with only rare mutapopula-tional events to a Markov jump process and how does this jump process look like. In fact, we prove by expanding the techniques of [30] that the microscopic process converges in this limit on the evolutionary time scale to a generalization of the Polymorphic Evolution Sequences (PES) introduced in [30] (cf. Theorem III.4.3). The main difference in the proof is that we have to couple the process with multi-type branching processes instead of normal branching processes, which leads also to a different definition of invasion fitness in this setting.

The remainder of this chapter is structured as follows. In Section III.2 we define the model and state the convergence towards a quadratic system of ODEs in the large population limit. In Section III.3, we present an example which qualitatively models the therapy carried out in Landsberg et al. [92]. We point out a phenomenon of relapse caused by random fluctuations. In Section III.4 we consider the case of rare mutations. We start with giving a pathwise definition of the individual-based model which is only extended by phenotypic plasticity (cf. Subsection III.4.1). In Subsection III.4.2 we state and prove the convergence of

86 III.2. THE MICROSCOPIC MODEL