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Berghoff, M.

Publications and source records attributed to Berghoff, M..

2 recordsLinked to original sources

Mechanical influences on in silico tumor evolution

Experimental insight and conceptual understanding of tumor growth are steadily growing and leading to new therapeutic interventions. Experiments and clinical studies are able to link single-cell properties to macroscopic tumor attributes. The development of cellular subpopulations in heterogeneous tumors can be understood as an evolutionary system with different cell types competing over both space and nutrients. However, to predict the growth trajectory and development of a tumor, fitness and trade-offs of cell properties in the context of the surroundings are required and often inaccessible. The optimum of the evolutionary trajectory provides a target for intervention, but can mostly not be identified. We propose that the optimal value of cellular properties is influenced by the tumor surrounding. Computational multiscale-modeling of tissue enables the observation of the trajectory of each cell while modeling the tumor surrounding. We model a 3D spheroid tumor and the fitness of individual cells and the evolutionary behavior of the tumor are quantified and linked to global parameters. Cell-cell adhesion and cell motility are two important mechanical properties for cell development and used as free parameters. Mechanical properties alone are able to drive the tumor towards low adhesion.We implement a dynamically changing nutrient surrounding representing the fluctuating blood-supply through blood vessel collapse and angiogenesis. We find that the evolutionary speed depends on the frequency of the fluctuations. We identify a frequency domain in which the evolutionary speed is significantly increased over a tumor with constant nutrient supply. The findings suggest that mechanically-induced fluctuations can accelerate tumor evolution. Author summaryLimited space and nutrients together with competing cell types drive an evolutionary process inside tumors. This process selects for the fittest cell types and optimizes the growing behavior for its local surroundings. An expanding tumor exerts mechanical forces on its cells and its surroundings, leading to a fluctuating nutrient supply through collapsing blood vessels. Here, we observe the influence of a dynamically changing surrounding on the evolutionary behavior of heterogeneous tumors in a high-resolution computational model. We find that the evolutionary speed depends on the frequency of the fluctuations and a fitness advantage of low-adhesion cells.

cancer biology↗

Emerging Tumor Development by Simulating Single-cell Events

Despite decades of substantial research, cancer remains a ubiquitous scourge in the industrialized world. Effective treatments require a thorough understanding of macroscopic cancerous tumor growth out of individual cells. Clinical imaging methods, however, only detect late-stage macroscopic tumors, while many quantitative experiments focus on small clusters of cancerous cells in microscopic detail but struggle to grow full tumors in-vitro. Here, we introduce the critical scale-bridging link between both these scopes. We are able to simulate the growth of mm-sized tumors composed of 1.5 million m-resolved individual cells by employing highly parallelized code on a supercomputer. We observe the competition for resources and space, which can lead to hypoxic or necrotic tissue regions. Cellular mutations and tumor stem cells can lead to tissue heterogeneity and change tumor properties. We probe the effects of different chemotherapy and radiotherapy treatments and observe selective pressure. This improved theoretical understanding of cancer growth as emerging behavior from single-cells opens new avenues for various scientific fields, ranging from developing better early-stage cancer detection devices to testing treatment regimes in-silico for personalized medicine. Author summaryExperimental and microscopy techniques are rapidly advancing biology and the observability of tissue. The theoretical understanding of tissue either focuses on a few cells or continuous tissue. Here we introduce the scale-bridging theoretical link that is able to model single cells as well as tissue consisting of millions of those cells, harvesting the power of modern supercomputers. We close the gap between single-cells and tissue through access to the full time-resolved trajectories of each cell and the emerging behavior of the tissue. We apply our framework on a generalized model for tumor growth. Tumor heterogeneity, as well as tumor stem cells are introduced, and the changes of behavior in response to cancer treatments is observed and validated.

biophysics↗