The molecular and phenotypic basis of the glioma invasive perivascular niche

Gliomas are devastating brain cancers that have poor prognostic outcomes for their patients. Short overall patient survival is due to a lack of durable, efficacious treatment options. Such therapeutic difficulties exist, in part, due to several glioma survival adaptations and mechanisms, which allow glioma cells to repurpose paracrine signalling pathways and ion channels within discreet microenvironments. These Darwinian adaptations facilitate invasion into brain parenchyma and perivascular space or promote evasion from anti-cancer defence mechanisms. Ultimately, this culminates in glioma repopulation and migration at distances beyond the original tumour site, which is a considerable obstacle for effective treatment. After an era of failed phase II trials targeting individual signalling pathways, coupled to our increasing knowledge of glioma sub-clonal divergence, combinatorial therapeutic approaches which target multiple molecular pathways and mechanisms will be necessary for better treatment outcomes in treating malignant gliomas. Furthermore, next-generation therapy which focuses on infiltrative tumour phenotypes and disruption of the vascular and perivascular microenvironments harbouring residual disease cells offers optimism for the localised control of malignant gliomas

Taking aim at moving targets in computational cell migration

Cell migration is central to the development and maintenance of multicellular organisms. Fundamental understanding of cell migration can, for example, direct novel therapeutic strategies to control invasive tumor cells. However, the study of cell migration yields an overabundance of experimental data that require demanding processing and analysis for results extraction. Computational methods and tools have therefore become essential in the quantification and modeling of cell migration data. We review computational approaches for the key tasks in the quantification of in vitro cell migration: image pre-processing, motion estimation and feature extraction. Moreover, we summarize the current state-of-the-art for in silico modeling of cell migration. Finally, we provide a list of available software tools for cell migration to assist researchers in choosing the most appropriate solution for their needs

Translation of the ecological trap concept to glioma therapy: the cancer cell trap concept: The cancer cell trap concept.

International audienceViewing tumors as ecosystems offers the opportunity to consider how ecological concepts can be translated to novel therapeutic perspectives. The ecological trap concept emerged approximately half a century ago when it was observed that animals can prefer an environment of low quality for survival over other available environments of higher quality. The presence of such a trap can drive a local population to extinction. The cancer cell trap concept is the translation of the ecological trap into glioma therapy. It exploits and diverts the invasive potential of glioma cells by guiding their migration towards specific locations where a local therapy can be delivered efficiently. This illustrates how an ecological concept can change therapeutic obstacles into therapeutic tools

A mesoscopic simulator to uncover heterogeneity and evolutionary dynamics in tumors

Increasingly complex in silico modeling approaches offer a way to simultaneously access cancerous processes at different spatio-temporal scales. High-level models, such as those based on partial differential equations, are computationally affordable and allow large tumor sizes and long temporal windows to be studied, but miss the discrete nature of many key underlying cellular processes. Individual-based approaches provide a much more detailed description of tumors, but have difficulties when trying to handle full-sized real cancers. Thus, there exists a trade-off between the integration of macroscopic and microscopic information, now widely available, and the ability to attain clinical tumor sizes. In this paper we put forward a stochastic mesoscopic simulation framework that incorporates key cellular processes during tumor progression while keeping computational costs to a minimum. Our framework captures a physical scale that allows both the incorporation of microscopic information, tracking the spatio-temporal emergence of tumor heterogeneity and the underlying evolutionary dynamics, and the reconstruction of clinically sized tumors from high-resolution medical imaging data, with the additional benefit of low computational cost. We illustrate the functionality of our modeling approach for the case of glioblastoma, a paradigm of tumor heterogeneity that remains extremely challenging in the clinical setting.This research has been supported by grants awarded to VMPG by James S. Mc. Donnell Foundation, United States of America, 21st Century Science Initiative in Mathematical and Complex Systems Approaches for Brain Cancer (collaborative award 220020560) and Junta de Comunidades de Castilla-La Mancha, Spain (grant number SBPLY/17/180501/000154). VMPG and GFC thank the funding from Ministerio de Ciencia e Innovacion, Spain (grant number PID2019-110895RB-I00). This research has also been supported by a grant awarded to GFC and JBB by the Junta de Comunidades de Castilla-La Mancha, Spain (grant number SBPLY/19/180501/000211). AMR received support from Asociacion Pablo Ugarte (http://www.asociacionpablougarte.es). JJS received support from Universidad de Castilla-La Mancha (grant number 2020-PREDUCLM-15634). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Receptors for Hyaluronic Acid and Poliovirus: A Combinatorial Role in Glioma Invasion?

Background: CD44 has long been associated with glioma invasion while, more recently, CD155 has been implicated in playing a similar role. Notably, these two receptors have been shown closely positioned on monocytes. Methods and Findings: In this study, an up-regulation of CD44 and CD155 was demonstrated in established and earlypassage cultures of glioblastoma. Total internal reflected fluorescence (TIRF) microscopy revealed close proximity of CD44 and CD155. CD44 antibody blocking and gene silencing (via siRNA) resulted in greater inhibition of invasion than that for CD155. Combined interference resulted in 86 % inhibition of invasion, although in these investigations no obvious evidence of synergy between CD44 and CD155 in curbing invasion was shown. Both siRNA-CD44 and siRNA-CD155 treated cells lacked processes and were rounder, while live cell imaging showed reduced motility rate compared to wild type cells. Adhesion assay demonstrated that wild type cells adhered most efficiently to laminin, whereas siRNA-treated cells (p,0.0001 for both CD44 and CD155 expression) showed decreased adhesion on several ECMs investigated. BrdU assay showed a higher proliferation of siRNA-CD44 and siRNA-CD155 cells, inversely correlated with reduced invasion. Confocal microscopy revealed overlapping of CD155 and integrins (b1, avb1 and avb3) on glioblastoma cell processes whereas siRNAtransfected cells showed consequent reduction in integrin expression with no specific staining patterns. Reduced expression of Rho GTPases, Cdc42, Rac1/2/3, RhoA and RhoB, was seen in siRNA-CD44 and siRNA-CD155 cells. In contrast t

Mathematical modelling of movement and glioma invasion

Modelling movement is an important topic in fields ranging from ecology to medicine. In particular, glioma, an often fatal brain tumour is characterised by its diffuse invasion into the surrounding normal brain tissue, enabling the tumour to escape therapy. In this thesis we focus on the mathematical modelling of glioma and movement in general. We begin by exploring the basic structure of the brain, describing glioma classification and the hallmarks of cancer as well as reviewing mathematical models of glioma. In Chapter 2 an Ordinary Differential Equation model is presented to describe the interaction between healthy and mutated cells in vivo and vitro scenarios. The model is extended to a Partial Differential Equation to cover the spatial dynamics of interaction and the possibility of travelling wave solutions. A leading hypothesis suggests that malignant glioma cells switch between proliferating and migrating phenotypes, a mechanism known as the “go or grow” hypothesis. Although the molecular mechanisms that control this switch are uncertain, it is generally assumed to depend on micro-environmental factors. In Chapter 3 we propose a simple mathematical model based on the go or grow hypothesis for brain tumours (gliomas). The model describes the competition between healthy glial cells and malignant cells, with the latter subdivided into invasive and proliferating subpopulations. Simulation and stability analysis is performed for spatial and non-spatial versions of the model. The model incorporates two types of switch between migration and proliferation glioma cells: a constant switch form and a density dependent form. In Chapter 4 we present a framework for modelling a different characteristic movement lengths based on a biased random walk in response to external control species. We use the model to understand different strategies by which a population may locate some resource in its environment. Further we consider a pilot application to glioma, showing how it can be used to model movement along different brain structures. Finally we conclude with a brief discussion that summarises the main results and highlights directions for future work

Focus on the Physics of Cancer

Despite the spectacular achievements of molecular biology in the second half of the twentieth century and the crucial advances it permitted in cancer research, the fight against cancer has brought some disillusions. It is nowadays more and more apparent that getting a global picture of the very diverse and interlinked aspects of cancer development necessitates, in synergy with these achievements, other perspectives and investigating tools. In this undertaking, multidisciplinary approaches that include quantitative sciences in general and physics in particular play a crucial role. This `focus on' collection contains 19 articles representative of the diversity and state-of-the-art of the contributions that physics can bring to the field of cancer research.Comment: Invited editorial review for the `Focus on the Physics of Cancer' published by the New journal of Physics in 2011--201

A New Method to Address Unmet Needs for Extracting Individual Cell Migration Features from a Large Number of Cells Embedded in 3D Volumes

Background: In vitro cell observation has been widely used by biologists and pharmacologists for screening molecule-induced effects on cancer cells. Computer-assisted time-lapse microscopy enables automated live cell imaging in vitro, enabling cell behavior characterization through image analysis, in particular regarding cell migration. In this context, 3D cell assays in transparent matrix gels have been developed to provide more realistic in vitro 3D environments for monitoring cell migration (fundamentally different from cell motility behavior observed in 2D), which is related to the spread of cancer and metastases. Methodology/Principal Findings: In this paper we propose an improved automated tracking method that is designed to robustly and individually follow a large number of unlabeled cells observed under phase-contrast microscopy in 3D gels. The method automatically detects and tracks individual cells across a sequence of acquired volumes, using a template matching filtering method that in turn allows for robust detection and mean-shift tracking. The robustness of the method results from detecting and managing the cases where two cell (mean-shift) trackers converge to the same point. The resulting trajectories quantify cell migration through statistical analysis of 3D trajectory descriptors. We manually validated the method and observed efficient cell detection and a low tracking error rate (6%). We also applied the method in a real biological experiment where the pro-migratory effects of hyaluronic acid (HA) were analyzed on brain cancer cells. Using collagen gels with increased HA proportions, we were able to evidence a dose-response effect on cell migration abilities. Conclusions/Significance: The developed method enables biomedical researchers to automatically and robustly quantify the pro- or anti-migratory effects of different experimental conditions on unlabeled cell cultures in a 3D environment. © 2011 Adanja et al.SCOPUS: ar.jinfo:eu-repo/semantics/publishe