abstract: Understanding intratumor heterogeneity and their driver genes is critical to designing personalized treatments and improving clinical outcomes of cancers. Such investigations require accurate delineation of the subclonal composition of a tumor, which to date can only be reliably inferred from deep-sequencing data (>300x depth). The resulting algorithm from the work presented here, incorporates an adaptive error model into statistical decomposition of mixed populations, which corrects the mean-variance dependency of sequencing data at the subclonal level and enables accurate subclonal discovery in tumors sequenced at standard depths (30-50x). Tested on extensive computer simulations and real-world data, this new method, named model-based adaptive grouping of subclones (MAGOS), consistently outperforms existing methods on minimum sequencing depth, decomposition accuracy and computation efficiency. MAGOS supports subclone analysis using single nucleotide variants and copy number variants from one or more samples of an individual tumor. GUST algorithm, on the other hand is a novel method in detecting the cancer type specific driver genes. Combination of MAGOS and GUST results can provide insights into cancer progression. Applications of MAGOS and GUST to whole-exome sequencing data of 33 different cancer types’ samples discovered a significant association between subclonal diversity and their drivers and patient overall survival. / Dissertation/Thesis / Doctoral Dissertation Biomedical Informatics 2019

Evolution is an ubiquitous feature of living systems. The genetic composition of a population changes in response to the primary evolutionary forces: mutation, selection and genetic drift. Organisms undergoing rapid adaptation acquire multiple mutations that are physically linked in the genome, so their fates are mutually dependent and selection only acts on these loci in their entirety. This aspect has been largely overlooked in the study of asexual or somatic evolution and plays a major role in the evolution of bacterial and viral infections and cancer. In this thesis, we put forward a theoretical description for a minimal model of evolutionary dynamics to identify driver mutations, which carry a large positive fitness effect, among passenger mutations that hitchhike on successful genomes. We examine the effect this mode of selection has on genomic patterns of variation to infer the location of driver mutations and estimate their selection coefficient from time series of mutation frequencies. We then present a probabilistic model to reconstruct genotypically distinct lineages in mixed cell populations from DNA sequencing. This method uses Hidden Markov Models for the deconvolution of genetically diverse populations and can be applied to clonal admixtures of genomes in any asexual population, from evolving pathogens to the somatic evolution of cancer. To understand the effects of selection on rapidly adapting populations, we constructed sequence ensembles in a recombinant library of budding yeast (S. cerevisiae). Using DNA sequencing, we characterised the directed evolution of these populations under selective inhibition of rate-limiting steps of the cell cycle. We observed recurrent patterns of adaptive mutations and characterised common mutational processes, but the spectrum of mutations at the molecular level remained stochastic. Finally, we investigated the effect of genetic variation on the fate of new mutations, which gives rise to complex evolutionary dynamics. We demonstrate that the fitness variance of the population can set a selective threshold on new mutations, setting a limit to the efficiency of selection. In summary, we combined statistical analyses of genomic sequences, mathematical models of evolutionary dynamics and experiments in molecular evolution to advance our understanding of rapid adaptation. Our results open new avenues in our understanding of population dynamics that can be translated to a range of biological systems.

abstract: The immune system plays a dual role during neoplastic progression. It can suppress tumor growth by eliminating cancer cells, and also promote neoplastic expansion by either selecting for tumor cells that are fitter to survive in an immunocompetent host or by establishing the right conditions within the tumor microenvironment. First, I present a model to study the dynamics of subclonal evolution of cancer. I model selection through time as an epistatic process. That is, the fitness change in a given cell is not simply additive, but depends on previous mutations. Simulation studies indicate that tumors are composed of myriads of small subclones at the time of diagnosis. Because some of these rare subclones harbor pre-existing treatment-resistant mutations, they present a major challenge to precision medicine. Second, I study the question of self and non-self discrimination by the immune system, which is fundamental in the field in cancer immunology. By performing a quantitative analysis of the biochemical properties of thousands of MHC class I peptides, I find that hydrophobicity of T cell receptors contact residues is a hallmark of immunogenic epitopes. Based on these findings, I further develop a computational model to predict immunogenic epitopes which facilitate the development of T cell vaccines against pathogen and tumor antigens. Lastly, I study the effect of early detection in the context of Ebola. I develope a simple mathematical model calibrated to the transmission dynamics of Ebola virus in West Africa. My findings suggest that a strategy that focuses on early diagnosis of high-risk individuals, caregivers, and health-care workers at the pre-symptomatic stage, when combined with public health measures to improve the speed and efficacy of isolation of infectious individuals, can lead to rapid reductions in Ebola transmission. / Dissertation/Thesis / Doctoral Dissertation Applied Mathematics 2016

No description available.

Cancer as a disease causes about 8.8 million deaths worldwide per year, a number that will largely increase in the next decades. Although the cellular processes involved in tumor emergence are more and more understood, the implications of specific changes at the cellular scale on tumor emergence at the tissue scale remain elusive. Main reasons for this lack of understanding are that the cellular processes are often hardly observable especially in the early phase of tumor development and that the interplay between cellular and tissue scale is difficult to deduce. Cell-based mathematical models provide a valuable tool to investigate in which way observable phenomena on the tissue scale develop by cellular processes. The implications of these models can elucidate underlying mechanisms and generate quantitative predictions that can be experimentally validated. In this thesis, we infer the role of genetic and phenotypic cell changes on tumor development with the help of cell-based Markov chain models which are calibrated by tissue-scale data. In the first part, we utilize data on the diagnosed fractions of benign and malignant tumor subtypes to unravel the consequences of genetic cell changes on tumor development. We introduce extensions of Moran models to investigate two specific biological questions. First, we evaluate the tumor regression behavior of pilocytic astrocytoma which represents the most common brain tumor in children and young adults. We formulate a Moran model with two absorbing states representing different subtypes of this tumor, derive the absorption probabilities in these states and calculate the tumor regression probability within the model. This analysis allows to predict the chance for tumor regression in dependency of the remaining tumor size and implies a different clinical resection strategy for pilocytic astrocytoma compared to other brain tumors. Second, we shed light on the hardly observable early cellular dynamics of tumor development and its consequences on the emergence of different tumor subtypes on the tissue scale. For this purpose, we utilize spatial and non-spatial Moran models with two absorbing states which describe both benign and malignant tumor subtypes and estimate lower and upper bounds for the range of cellular competition in different tissues. Our results suggest the existence of small and tissue-specific tumor-originating niches in which the fate of tumor development is decided long before a tumor manifests. These findings might help to identify the tumor-originating cell types for different cancer types. From a theoretical point of view, the novel analytical results regarding the absorption behavior of our extended Moran models contribute to a better understanding of this model class and have several applications also beyond the scope of this thesis. The second part is devoted to the investigation of the role of phenotypic plasticity of cancer cells in tumor development. In order to understand how phenotypic heterogeneity in tumors arises we describe cell state changes by a Markov chain model. This model allows to quantify the cell state transitions leading to the observed heterogeneity from experimental tissue-scale data on the evolution of cell state proportions. In order to bridge the gap between mathematical modeling and the analysis of such data, we developed an R package called CellTrans which is freely available. This package automatizes the whole process of mathematical modeling and can be utilized to (i) infer the transition probabilities between different cell states, (ii) predict cell line compositions at a certain time, (iii) predict equilibrium cell state compositions and (iv) estimate the time needed to reach this equilibrium. We utilize publicly available data on the evolution of cell compositions to demonstrate the applicability of CellTrans. Moreover, we apply CellTrans to investigate the observed cellular phenotypic heterogeneity in glioblastoma. For this purpose, we use data on the evolution of glioblastoma cell line compositions to infer to which extent the heterogeneity in these tumors can be explained by hierarchical phenotypic transitions. We also demonstrate in which way our newly developed R package can be utilized to analyze the influence of different micro-environmental conditions on cell state proportions. Summarized, this thesis contributes to gain a better understanding of the consequences of both genetic and phenotypic cell changes on tumor development with the help of Markov chain models which are motivated by the specific underlying biological questions. Moreover, the analysis of the novel Moran models provides new theoretical results, in particular regarding the absorption behavior of the underlying stochastic processes.