Biomedical subjects

Thomas S Deisboeck

Publications and source records attributed to Thomas S Deisboeck.

17 recordsLinked to original sources

PubMed2006

Cancer dissemination: a consequence of limited carrying capacity?

Assuming that there is feedback between an expanding cancer system and its organ-typical microenvironment, we argue here that such local tumor growth is initially guided by co-existence rather than competition with the surrounding tissue. We then present a novel concept that understands cancer dissemination as a biological mechanism to evade the specific carrying capacity limit of its host organ. This conceptual framework allows us to relate the tumor system's volumetric growth rate to the host organ's functionality-conveying composite infrastructure, and, intriguingly, already provides useful insights into several clinical findings.

Animals↗

PubMed2006

Development of a three-dimensional multiscale agent-based tumor model: simulating gene-protein interaction profiles, cell phenotypes and multicellular patterns in brain cancer.

Experimental evidence suggests that epidermal growth factor receptor (EGFR)-mediated activation of the signaling protein phospholipase Cgamma plays a critical role in a cancer cell's phenotypic decision to either proliferate or to migrate at a given point in time. Here, we present a novel three-dimensional multiscale agent-based model to simulate this cellular decision process in the context of a virtual brain tumor. Each tumor cell is equipped with an EGFR gene-protein interaction network module that also connects to a simplified cell cycle description. The simulation results show that over time proliferative and migratory cell populations not only oscillate but also directly impact the spatio-temporal expansion patterns of the entire cancer system. The percentage change in the concentration of the sub-cellular interaction network's molecular components fluctuates, and, for the 'proliferation-to-migration' switch we find that the phenotype triggering molecular profile to some degree varies as the tumor system grows and the microenvironment changes. We discuss potential implications of these findings for experimental and clinical cancer research.

Apoptosis↗

PubMed2006

Does cancer growth depend on surface extension?

We argue that volumetric growth dynamics of a solid cancer depend on the tumor system's overall surface extension. While this at first may seem evident, to our knowledge, so far no theoretical argument has been presented explaining this relationship explicitly. In here, we therefore develop a conceptual framework based on the so-called 'universal scaling law' and then support our conjecture through evaluation with experimental data. Our concept suggests not only that cancer tissue invasion operates with relatively few and thin branches of mobile cells but also that this overall tumor surface expansion, and the diffusion of nutrients that it enables, can nourish the tumor prior to the impact of neovascularization.

Animals↗

PubMed2005

The dynamic evolution of the power exponent in a universal growth model of tumors.

We have previously reported that a universal growth law, as proposed by West and collaborators for all living organisms, appears to be able to describe also the growth of tumors in vivo after an initial exponential growth phase. In contrast to the assumption of a fixed power exponent p (assumed by West et al. to be equal to 3/4), we propose in this paper a dynamic evolution of p, using experimental data from the cancer literature. In analogy with results obtained by applying scaling laws to the study of fragmentation of solids, the dynamic behaviour of p is related to the evolution of the fractal topology of neoplastic vascular systems. Our model might be applied for diagnostic purposes to mark the emergence of an efficient neo-angiogenetic structure if the results of our in silico experiments are confirmed by clinical observations.

Humans↗

PubMed2005

Tumor growth instability and the onset of invasion.

Motivated by experimental observations, we develop a mathematical model of chemotactically directed tumor growth. We present an analytical study of the model as well as a numerical one. The mathematical analysis shows that: (i) tumor cell proliferation by itself cannot generate the invasive branching behavior observed experimentally, (ii) heterotype chemotaxis provides an instability mechanism that leads to the onset of tumor invasion, and (iii) homotype chemotaxis does not provide such an instability mechanism but enhances the mean speed of the tumor surface. The numerical results not only support the assumptions needed to perform the mathematical analysis but they also provide evidence of (i), (ii), and (iii). Finally, both the analytical study and the numerical work agree with the experimental phenomena.

Animals↗

PubMed2005

The effects of EGF-receptor density on multiscale tumor growth patterns.

We studied the effects of epidermal growth factor receptor (EGFR) density on tumor growth dynamics, both on the sub- and the multi-cellular level using our previously developed model. This algorithm simulates the growth of a brain tumor using a multi-scale two-dimensional agent-based approach with an integrated transforming growth factor alpha (TGFalpha) induced EGFR-gene-protein interaction network. The results confirm that increasing cell receptor density correlates with an acceleration of the tumor system's spatio-temporal expansion dynamics. This multicellular behavior cannot be explained solely on the basis of spatial sub-cellular dynamics, which remain qualitatively similar amongst the three glioma cell lines investigated here in silico. Rather, we find that cells with higher EGFR density show an early increase in the phenotypic switching activity between proliferative and migratory traits, linked to a higher level of initial auto-stimulation by the PLCgamma-mediated TGFalpha-EGFR autocrine network. This indicates a more active protein level interaction in these chemotactically acting tumor systems and supports the role of post-translational regulation for the implemented EGFR pathway. Implications of these results for experimental cancer research are discussed.

Algorithms↗

PubMed2005

Evolutionary game theory in an agent-based brain tumor model: exploring the 'Genotype-Phenotype' link.

To investigate the genotype-phenotype link in a polyclonal cancer cell population, here we introduce evolutionary game theory into our previously developed agent-based brain tumor model. We model the heterogeneous cell population as a mixture of two distinct genotypes: the more proliferative Type A and the more migratory Type B. Our agent-based simulations reveal a phase transition in the tumor's velocity of spatial expansion linking the tumor fitness to genotypic composition. Specifically, velocity initially falls as rising payoffs reward the interactions among the more stationary Type A cells, but unexpectedly accelerates again when these A-A payoffs increase even further. At this latter accelerating stage, fewer migratory Type B cells appear to confer a competitive advantage in terms of the tumor's spatial aggression over the overall numerically dominating Type A cells, which in turn leads to an acceleration of the overall tumor dynamics while its surface roughness declines. We discuss potential implications of our findings for cancer research.

Brain Neoplasms↗

PubMed2005

Correlating velocity patterns with spatial dynamics in glioma cell migration.

Highly malignant neuroepithelial tumors are known for their extensive tissue invasion. Investigating the relationship between their spatial behavior and temporal patterns by employing detrended fluctuation analysis (DFA), we report here that faster glioma cell motility is accompanied by both greater predictability of the cells' migration velocity and concomitantly, more directionality in the cells' migration paths. Implications of this finding for both experimental and clinical cancer research are discussed.

Acceleration↗

PubMed2005

Insights from a novel tumor model: Indications for a quantitative link between tumor growth and invasion.

Both the lack of nutrient supply and rising mechanical stress exerted by the microenvironment appear to be able to cause discrepancies between the actual, observed tumor mass and that predicted by West et al.'s [A general model for ontogenetic growth. Nature 2001;413:628-31] universal growth model. Using our previously developed model we hypothesize here, that (1) solid tumor growth and cell invasion are linked, not only qualitatively but also quantitatively, that (2) the onset of invasion marks the time point when the tumor's cell density reaches a compaction maximum, and that (3) tumor cell invasion, reduction of mechanical confinement and angiogenesis can all contribute to an increase in the actual tumor mass m towards the level m(W) predicted by West et al.'s universal growth curve. These novel insights contribute to our understanding of tumorigenesis and thus may have important implications not only for experimental cancer research but also be of value for clinical purposes such as for predictions of tumor growth dynamics and treatment impact.

Cell Count↗

PubMed2004

Simulating the impact of a molecular 'decision-process' on cellular phenotype and multicellular patterns in brain tumors.

Experimental evidence indicates that human brain cancer cells proliferate or migrate, yet do not display both phenotypes at the same time. Here, we present a novel computational model simulating this cellular decision-process leading up to either phenotype based on a molecular interaction network of genes and proteins. The model's regulatory network consists of the epidermal growth factor receptor (EGFR), its ligand transforming growth factor-alpha (TGF alpha), the downstream enzyme phospholipaseC-gamma (PLC gamma) and a mitosis-associated response pathway. This network is activated by autocrine TGF alpha secretion, and the EGFR-dependent downstream signaling this step triggers, as well as modulated by an extrinsic nutritive glucose gradient. Employing a framework of mass action kinetics within a multiscale agent-based environment, we analyse both the emergent multicellular behavior of tumor growth and the single-cell molecular profiles that change over time and space. Our results show that one can indeed simulate the dichotomy between cell migration and proliferation based solely on an EGFR decision network. It turns out that these behavioral decisions on the single cell level impact the spatial dynamics of the entire cancerous system. Furthermore, the simulation results yield intriguing experimentally testable hypotheses also on the sub-cellular level such as spatial cytosolic polarization of PLC gamma towards an extrinsic chemotactic gradient. Implications of these results for future works, both on the modeling and experimental side are discussed.

Brain Neoplasms↗

PubMed2004

An error catastrophe in cancer?

A comparison between the evolution of cancer cell populations and RNA viruses reveals a number of remarkable similarities. Both display high levels of plasticity and adaptability as a consequence of high degrees of genetic variation. It has been suggested that, as it occurs with RNA viruses, there is a threshold in the levels of genetic instability affordable by cancer cells in order to be able to overcome selection barriers (Trends Genet. 15 (1999) M57). Here we explore this concept by means of a simple mathematical model. It is shown that an error threshold exists in this model, which investigates both competition between cancer cell populations and its impact on overall tumor growth dynamics. Once the threshold is reached, the highly unstable tumor cell populations, which were sustaining malignant growth, become unable to maintain their genetic information, which in turn triggers a slowed down overall tumor growth regime.

Animals↗

PubMed2004

Visualization and analysis of white matter structural asymmetry in diffusion tensor MRI data.

This work presents a method that permits the characterization, quantification, and 3D visualization of white matter structural information contained within diffusion tensor MR imaging (DT-MRI) data. In this method, regions within the brain are defined as possessing linear, planar, or spherical diffusion. Visualization of this diffusion metric data is realized by generating streamtube and streamsurface models to represent regions of linear and planar diffusion. Quantification of differences in diffusion anisotropy between different regions of interest (ROIs) is then achieved by analyzing 2D barycentric histograms created from the complete distribution of diffusion metric values measured in each region. In four healthy volunteers, there was only a small degree of asymmetry (epsilon) in the number of linear, planar, or spherical diffusion voxels between the right and left hemispheres (epsilon approximately equal to +/- 2%). However, in a patient with a metastatic brain lesion there was marked asymmetry in both linear (epsilon approximately -10%) and planar (epsilon approximately equal to 5%) diffusion between comparable ipsilateral and contralateral regions, with a significant reduction in the number of linear diffusion voxels and an increase in the number of planar diffusion voxels in the tumor-bearing hemisphere. These results demonstrate the potential of this approach to characterize brain structure in both healthy and diseased subjects.

Adenocarcinoma↗

PubMed2003

Does tumor growth follow a "universal law"?

A general model for the ontogenetic growth of living organisms has been recently proposed. Here we investigate the extension of this model to the growth of solid malignant tumors. A variety of in vitro and in vivo data are analysed and compared with the prediction of a "universal" law, relating properly rescaled tumor masses and tumor growth times. The results support the notion that tumor growth follows such a universal law. Several important implications of this finding are discussed, including its relevance for tumor metastasis and recurrence, cell turnover rates, angiogenesis and invasion.

Animals↗

PubMed2003

The impact of "search precision" in an agent-based tumor model.

Several cell surface receptors are overexpressed in malignant brain tumors and reportedly involved in tumor progression and invasion. It is unclear, however, whether such an improvement of cellular signal reception leads to a monotonic increase in the tumor system's average velocity during invasion or whether there is a maximum threshold beyond which the average velocity starts to decelerate. To gain more insight into the systemic effects of such cellular search precision modulations, this study proposes a two-dimensional agent-based model in which the spatio-temporal expansion of malignant brain tumor cells is guided by environmental heterogeneities in mechanical confinement, toxic metabolites and nutrient sources. Here, the spatial field of action is represented by an adaptive grid lattice, which corresponds to the experimental finding that tumor cells are more likely to follow each other along preformed pathways. Another prominent feature is the dual threshold concept for both nutrient level and toxicity, which determine whether cells proliferate, migrate, remain quiescent or die in the next period. The numerical results from varying the key parameters encoding the capability of tumor cells to invade and their ability to proliferate indicate an emergent behavior. Specifically, increasing invasiveness not only leads to an increase in maximum expansion velocity, but also requires a more precise spatial search process, corresponding to an improved cell signal reception, in order to obtain maximum velocity. To increase cellular invasiveness beyond the maximum that can be achieved by exclusively tuning the motility parameter, it requires an additional reduction in the cells' proliferation rate and prompts an even more biased search process. Most interestingly, however, a prominent phase transition suggests that tumor cells do not employ a 100 percent search precision to attain maximum spatial velocity. These findings argue for a selection advantage conferred by limited randomness in processing spatial search and indicate that our computational platform may prove valuable in investigating emergent, multicellular tumor patterns caused by alterations on the molecular level.

Algorithms↗

PubMed2002

Emerging patterns in tumor systems: simulating the dynamics of multicellular clusters with an agent-based spatial agglomeration model.

Brain cancer cells invade early on surrounding parenchyma, which makes it impossible to surgically remove all tumor cells and thus significantly worsens the prognosis of the patient. Specific structural elements such as multicellular clusters have been seen in experimental settings to emerge within the invasive cell system and are believed to express the systems' guidance toward nutritive sites in a heterogeneous environment. Based on these observations, we developed a novel agent-based model of spatio-temporal search and agglomeration to investigate the dynamics of cell motility and aggregation with the assumption that tumors behave as complex dynamic self-organizing biosystems. In this model, virtual cells migrate because they are attracted by higher nutrient concentrations and to avoid overpopulated areas with high levels of toxic metabolites. A specific feature of our model is the capability of cells to search both globally and locally. This concept is applied to simulate cell-surface receptor-mediated information processing of tumor cells such that a cell searching for a more growth-permissive place "learns" the information content of a brain tissue region within a two-dimensional lattice in two stages, processing first the global and then the local input. In both stages, differences in microenvironment characteristics define distinctions in energy expenditure for a moving cell and thus influence cell migration, proliferation, agglomeration, and cell death. Numerical results of our model show a phase transition leading to the emergence of two distinct spatio-temporal patterns depending on the dominant search mechanism. If global search is dominant, the result is a small number of large clusters exhibiting rapid spatial expansion but shorter lifetime of the tumor system. By contrast, if local search is dominant, the trade-off is many small clusters with longer lifetime but much slower velocity of expansion. Furthermore, in the case of such dominant local search, the model reveals an expansive advantage for tumor cell populations with a lower nutrient-depletion rate. Important implications of these results for cancer research are discussed.

Algorithms↗

PubMed2002

Growth patterns of microscopic brain tumors.

Highly malignant brain tumors such as glioblastoma multiforme form complex growth patterns in vitro in which invasive cells organize in tenuous branches. Here, we formulate a chemotaxis model for this sort of growth. A key element controlling the pattern is homotype attraction, i.e., the tendency for invasive cells to follow pathways previously explored. We investigate this in two ways: we show that there is an intrinsic instability in the model, which leads to branch formation. We also give a discrete description for the expansion of the invasive zone, and a continuum model for the nutrient supply. The results indicate that both strong heterotype chemotaxis and strong homotype chemoattraction are required for branch formation within the invasive zone. Our model thus can give a way to assess the importance of the various processes, and a way to explore and analyze transitions between different growth regimes.

Biophysical Phenomena↗

PubMed2002

Analysis and 3D reconstruction of heterogeneity in malignant brain tumors: an interdisciplinary case study using a novel computational visualization approach.

OBJECTIVE: To explore how a multidisciplinary approach, combining modern visualization and image processing techniques with innovative experimental studies, can augment the understanding of tumor development. STUDY DESIGN: We analyzed histologic sections of a microscopic brain tumor and reconstructed these slices into a 3D representation. We processed these slices to: (1) identify tumor boundaries, (2) isolate proliferating tumor cells, and (3) segment the tumor into regions based on the density of proliferating cells. We then reconstructed the 3D shape of the tumor using a constrained deformable surface approach. RESULTS: This novel method allows the analyst to (1) see specific properties of histologic slices in the 3D environment with animation, (2) switch 2D "views" dynamically, and (3) see relationships between the 3D structure and structure on a plane. CONCLUSION: Using this method to analyze a specific "case," we were also able to shed light on the limitations of a widely held assumption about the shape of expanding microscopic solid tumors as well as find more indications that such tumors behave as adaptive biosystems. Implications of these case study results, as well as future applications of the method for tumor biology research, are discussed.

Antibodies, Monoclonal↗