The development of cancer from a population of precancerous cells is a rapid evolutionary process. During progression, cells evolve several new traits for survive and proliferation via a few key `driver' mutations. However, these few driver alterations reside in a cancer genome alongside tens of thousands of additional `passenger' mutations. Passengers are widely believed to have no role in cancer, yet many passengers fall within functional genomic elements that may have potentially deleterious effects on the cancer cells. Here we investigate the potential of moderately deleterious passengers to accumulate and alter neoplastic progression. Evolutionary simulations suggest that moderately-deleterious passengers accumulate during progression and largely evade natural selection. Accumulation is possible because of cancer's unique evolutionary constraints: an initially small population size, an elevated mutation rate, and a need to acquire several driver mutations within a short evolutionary timeframe. Cancer dynamics can be theoretically understood as a tug-of-war between rare, strongly-beneficial drives and frequent mildly-deleterious passengers. In this formalism, passengers present a barrier to cancer progression describable by a critical population size, below which most lesions fail to progress, and a critical mutation rate, above which cancers collapse. In essence, cancer progression can be subverted by its own unique evolutionary constraints. The collective burden of passengers explain several oncological phenomena that are difficult to explain otherwise. Genomics data confirms that many passengers are likely damaging and have largely evaded negative selection, while age-incidence curves and the distribution of mutation totals suggests that drivers and passengers exhibit competing effects. These data also provide estimates of the strength of drivers and passengers. Finally, we use our model to explore cancer treatments. We identify two broad regimes of adaptive evolutionary dynamics and use these regimes to understand outcomes from various treatment strategies. Our theory explains previously paradoxical treatment outcomes and suggest that passengers could serve as a biomarker of response to mutagenic therapies. Deleterious passengers are targetable by either (i) increasing the mutation rate or (ii) exacerbating their deleterious effects. Our results suggest a unique framework for understanding cancer progression as a balance between driver and passenger mutations.

Several scientific and technological advancements in radiation oncology have resulted in dramatic improvements in dose conformity and delivery to the target volumes using external beam radiation therapy (EBRT). However, radiation therapy acts as a double-edged sword leading to drastic side-effects, one of them being secondary malignant neoplasms in cancer survivors. The latency time for the occurrence of second cancers is around $10$-$20$ years. Therefore, it is very important to evaluate the risks associated with various types of clinically relevant radiation treatment protocols, to minimize the second cancer risks to critical structures without impairing treatment to the primary tumor volume. A widely used biologically motivated model (known as the initiation-inactivation-proliferation model) with heterogeneous dose volume distributions of Hodgkin's lymphoma survivors is used to evaluate the excess relative risks (ERR). There has been a paradigm shift in radiation therapy from purely photon therapy to other particle therapies in cancer treatments. The extension of the model to include the dependence of linear energy transfer (LET) on the radio-biological parameters and mutation rate for charged particle therapy is discussed. Due to the increase in the use of combined modality regimens to treat several cancers, it is extremely important to evaluate the second cancer risks associated with these anti-cancer therapies. The extension of the model to include chemotherapy induced effects is also discussed. There have been several clinical studies on early and late relapses of cancerous tumors. A tumor control probability (TCP) model with recurrence dynamics in conjunction with the second cancer model is developed in order to enable design of efficient radiation regimens to increase the tumor control probability and relapse time, and at the same time decrease secondary cancer risks. Evolutionary dynamics has played an important role in modeling cancer progression of primary cancers. Spatial models of evolutionary dynamics are considered to be more appropriate to understand cancer progression for obvious reasons. In this context, a spatial evolutionary framework on lattices and unstructured meshes is developed to investigate the effect of cellular motility on the fixation probability. In the later part of this work, this model is extended to incorporate random fitness distributions into the lattices to explore the dynamics of invasion probability in the presence and absence of migration.

The division of resources between a group of people may cause con- flicts: Individuals with varying roles and responsibilities will claim different shares of the surplus to be divided. In this dissertation, we analyze how the decision to form a group will influence the bargaining behaviour of the members of that group. People will act collectively as certain tasks may require the participation of a specific number of individuals before it can be completed. We examine whether certain mechanisms can efficiently promote group formation for the sake of surplus production, and then, what will be the effect of these mechanisms on the behaviour of the group members. For these reasons, we constructed a novel surplus production and distribution interaction which we call the Anticipation Game (AG). The AG can be played between only two players (pairwise interaction) or among more then two players (group interaction). In our study we will analyze both the pairwise AG and the group version of AG, first by obtaining our own empirical data and then by performing a stochastic evolutionary analysis. We aim to provide answers on: i) how will a reputation based partner approval mechanism influence the surplus distribution in both the pairwise and the group AG, ii) will then limitations in obtaining the reputation of a potential partner alter the results of the pairwise AG? iii) will we notice any effect on the behaviour of players when they can repeatedly cooperate with the same partners in group interactions, iv) how natural selection may have shaped the behaviour of players in group formation interactions (both pairwise and group AG evolutionary analysis). / Doctorat en Sciences / info:eu-repo/semantics/nonPublished