Inferring cellular mechanisms of tumor development from tissue-scale data: A Markov chain approach

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

EMA recommendation to suspend HES is hazardous

Peer reviewe

Patterns of Tumor Progression Predict Small and Tissue-Specific Tumor-Originating Niches

The development of cancer is a multistep process in which cells increase in malignancy through progressive alterations. Such altered cells compete with wild-type cells and have to establish within a tissue in order to induce tumor formation. The range of this competition and the tumor-originating cell type which acquires the first alteration is unknown for most human tissues, mainly because the involved processes are hardly observable, aggravating an understanding of early tumor development. On the tissue scale, one observes different progression types, namely with and without detectable benign precursor stages. Human epidemiological data on the ratios of the two progression types exhibit large differences between cancers. The idea of this study is to utilize data of the ratios of progression types in human cancers to estimate the homeostatic range of competition in human tissues. This homeostatic competition range can be interpreted as necessary numbers of altered cells to induce tumor formation on the tissue scale. For this purpose, we develop a cell-based stochastic model which is calibrated with newly-interpreted human epidemiological data. We find that the number of tumor cells which inevitably leads to later tumor formation is surprisingly small compared to the overall tumor and largely depends on the human tissue type. This result points toward the existence of a tissue-specific tumor-originating niche in which the fate of tumor development is decided early and long before a tumor becomes detectable. Moreover, our results suggest that the fixation of tumor cells in the tumor-originating niche triggers new processes which accelerate tumor growth after normal tissue homeostasis is voided. Our estimate for the human colon agrees well with the size of the stem cell niche in colonic crypts. For other tissues, our results might aid to identify the tumor-originating cell type. For instance, data on primary and secondary glioblastoma suggest that the tumors originate from a cell type competing in a range of 300 – 1,900 cells

A survey on the availability, usage and perception of neuromuscular monitors in Europe

Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. This research was funded by the Flanders Innovation and Entrepreneurship Fund (VLAIO), the Willy Gepts Fund for Scientific Research, the Society for Anesthesia and Resuscitation of Belgium (BeSARPP), and the Vrije Universiteit Brussel (VUB).Peer reviewedPostprin

Physical Attacks on the Railway System

Recent attacks encouraged public interest in physical security for railways. Knowing about and learning from previous attacks is necessary to secure against them. This paper presents a structured data set of physical attacks against railways. We analyze the data regarding the used means, the railway system's target component, the attacker type, and the geographical distribution of attacks. The results indicate a growing heterogeneity of observed attacks in the recent decade compared to the previous decades and centuries, making protecting railways more complex

Epidemiology of SARS‑CoV‑2

Purpose SARS-CoV-2 is a recently emerged ß-coronavirus. Here we present the current knowledge on its epidemiologic features. Methods Non-systematic review. Results SARS-CoV-2 replicates in the upper and lower respiratory tract. It is mainly transmitted by droplets and aerosols from asymptomatic and symptomatic infected subjects. The consensus estimate for the basis reproduction number (R₀) is between 2 and 3, and the median incubation period is 5.7 (range 2–14) days. Similar to SARS and MERS, superspreading events have been reported, the dispersion parameter (kappa) is estimated at 0.1. Most infections are uncomplicated, and 5–10% of patients are hospitalized, mainly due to pneumonia with severe inflammation. Complications are respiratory and multiorgan failure; risk factors for complicated disease are higher age, hypertension, diabetes, chronic cardiovascular, chronic pulmonary disease and immunodeficiency. Nosocomial and infections in medical personnel have been reported. Drastic reductions of social contacts have been implemented in many countries with outbreaks of SARS-CoV-2, leading to rapid reductions. Most interventions have used bundles, but which of the measures have been more or less effective is still unknown. The current estimate for the infection’s fatality rate is 0.5–1%. Using current models of age-dependent infection fatality rates, upper and lower limits for the attack rate in Germany can be estimated between 0.4 and 1.6%, lower than in most European countries. Conclusions Despite a rapid worldwide spread, attack rates have been low in most regions, demonstrating the efficacy of control measures

The K2-HERMES Survey: Age and Metallicity of the Thick Disc

Asteroseismology is a promising tool to study Galactic structure and evolution because it can probe the ages of stars. Earlier attempts comparing seismic data from the {\it Kepler} satellite with predictions from Galaxy models found that the models predicted more low-mass stars compared to the observed distribution of masses. It was unclear if the mismatch was due to inaccuracies in the Galactic models, or the unknown aspects of the selection function of the stars. Using new data from the K2 mission, which has a well-defined selection function, we find that an old metal-poor thick disc, as used in previous Galactic models, is incompatible with the asteroseismic information. We show that spectroscopic measurements of [Fe/H] and [$\alpha$/Fe] elemental abundances from the GALAH survey indicate a mean metallicity of $\log (Z/Z_{\odot})=-0.16$ for the thick disc. Here $Z$ is the effective solar-scaled metallicity, which is a function of [Fe/H] and [$\alpha$/Fe]. With the revised disc metallicities, for the first time, the theoretically predicted distribution of seismic masses show excellent agreement with the observed distribution of masses. This provides an indirect verification of the asteroseismic mass scaling relation is good to within five percent. Using an importance-sampling framework that takes the selection function into account, we fit a population synthesis model of the Galaxy to the observed seismic and spectroscopic data. Assuming the asteroseismic scaling relations are correct, we estimate the mean age of the thick disc to be about 10 Gyr, in agreement with the traditional idea of an old $\alpha$-enhanced thick disc.Comment: 21 pages, submitted to MNRA

The GALAH survey and Gaia DR2: (non-)existence of five sparse high-latitude open clusters

Sparse open clusters can be found at high galactic latitudes where loosely populated clusters are more easily detected against the lower stellar background. Because most star formation takes place in the thin disc, the observed population of clusters far from the Galactic plane is hard to explain. We combined spectral parameters from the GALAH survey with the Gaia DR2 catalogue to study the dynamics and chemistry of five old sparse high-latitude clusters in more detail. We find that four of them (NGC 1252, NGC 6994, NGC 7772, NGC 7826) – originally classified in 1888 – are not clusters but are instead chance projections on the sky. Member stars quoted in the literature for these four clusters are unrelated in our multidimensional physical parameter space; the quoted cluster properties in the literature are therefore meaningless. We confirm the existence of visually similar NGC 1901 for which we provide a probabilistic membership analysis. An overdensity in three spatial dimensions proves to be enough to reliably detect sparse clusters, but the whole six-dimensional space must be used to identify members with high confidence, as demonstrated in the case of NGC 1901