Modeling invasion patterns in the glioblastoma battlefield

Modelling microtube driven invasion of glioma

Malignant gliomas are notoriously invasive, a major impediment against their successful treatment. This invasive growth has motivated the use of predictive partial differential equation models, formulated at varying levels of detail, and including (i) "proliferation-infiltration" models, (ii) "go-or-grow" models, and (iii) anisotropic diffusion models. Often, these models use macroscopic observations of a diffuse tumour interface to motivate a phenomenological description of invasion, rather than performing a detailed and mechanistic modelling of glioma cell invasion processes. Here we close this gap. Based on experiments that support an important role played by long cellular protrusions, termed tumour microtubes, we formulate a new model for microtube-driven glioma invasion. In particular, we model a population of tumour cells that extend tissue-infiltrating microtubes. Mitosis leads to new nuclei that migrate along the microtubes and settle elsewhere. A combination of steady state analysis and numerical simulation is employed to show that the model can predict an expanding tumour, with travelling wave solutions led by microtube dynamics. A sequence of scaling arguments allows us reduce the detailed model into simpler formulations, including models falling into each of the general classes (i), (ii), and (iii) above. This analysis allows us to clearly identify the assumptions under which these various models can be a posteriori justified in the context of microtube-driven glioma invasion. Numerical simulations are used to compare the various model classes and we discuss their advantages and disadvantages.

Mechanotransduction in tumor dynamics modeling

Mechanotherapy is a groundbreaking approach to impact carcinogenesis. Cells sense and respond to mechanical stimuli, translating them into biochemical signals in a process known as mechanotransduction. The impact of stress on tumor growth has been studied in the last three decades, and many papers highlight the role of mechanics as a critical self-inducer of tumor fate at the in vitro and in vivo biological levels. Meanwhile, mathematical models attempt to determine laws to reproduce tumor dynamics. This review discusses biological mechanotransduction mechanisms and mathematical-biomechanical models together. The aim is to provide a common framework for the different approaches that have emerged in the literature from the perspective of tumor avascularity and to provide insight into emerging mechanotherapies that have attracted interest in recent years.

An update on the molecular biology of glioblastoma, with clinical implications and progress in its treatment

Glioblastoma multiforme (GBM) is the most aggressive and common malignant primary brain tumor. Patients with GBM often have poor prognoses, with a median survival of ∼15 months. Enhanced understanding of the molecular biology of central nervous system tumors has led to modifications in their classifications, the most recent of which classified these tumors into new categories and made some changes in their nomenclature and grading system. This review aims to give a panoramic view of the last 3 years' findings in glioblastoma characterization, its heterogeneity, and current advances in its treatment. Several molecular parameters have been used to achieve an accurate and personalized characterization of glioblastoma in patients, including epigenetic, genetic, transcriptomic and metabolic features, as well as age- and sex-related patterns and the involvement of several noncoding RNAs in glioblastoma progression. Astrocyte-like neural stem cells and outer radial glial-like cells from the subventricular zone have been proposed as agents involved in GBM of IDH-wildtype origin, but this remains controversial. Glioblastoma metabolism is characterized by upregulation of the PI3K/Akt/mTOR signaling pathway, promotion of the glycolytic flux, maintenance of lipid storage, and other features. This metabolism also contributes to glioblastoma's resistance to conventional therapies. Tumor heterogeneity, a hallmark of GBM, has been shown to affect the genetic expression, modulation of metabolic pathways, and immune system evasion. GBM's aggressive invasion potential is modulated by cell-to-cell crosstalk within the tumor microenvironment and altered expressions of specific genes, such as ANXA2, GBP2, FN1, PHIP, and GLUT3. Nevertheless, the rising number of active clinical trials illustrates the efforts to identify new targets and drugs to treat this malignancy. Immunotherapy is still relevant for research purposes, given the amount of ongoing clinical trials based on this strategy to treat GBM, and neoantigen and nucleic acid-based vaccines are gaining importance due to their antitumoral activity by inducing the immune response. Furthermore, there are clinical trials focused on the PI3K/Akt/mTOR axis, angiogenesis, and tumor heterogeneity for developing molecular-targeted therapies against GBM. Other strategies, such as nanodelivery and computational models, may improve the drug pharmacokinetics and the prognosis of patients with GBM.

Synaptic components are required for glioblastoma progression in Drosophila

Glioblastoma (GB) is the most aggressive, lethal and frequent primary brain tumor. It originates from glial cells and is characterized by rapid expansion through infiltration. GB cells interact with the microenvironment and healthy surrounding tissues, mostly neurons and vessels. GB cells project tumor microtubes (TMs) contact with neurons, and exchange signaling molecules related to Wingless/WNT, JNK, Insulin or Neuroligin-3 pathways. This cell to cell communication promotes GB expansion and neurodegeneration. Moreover, healthy neurons form glutamatergic functional synapses with GB cells which facilitate GB expansion and premature death in mouse GB xerograph models. Targeting signaling and synaptic components of GB progression may become a suitable strategy against glioblastoma. In a Drosophila GB model, we have determined the post-synaptic nature of GB cells with respect to neurons, and the contribution of post-synaptic genes expressed in GB cells to tumor progression. In addition, we document the presence of intratumoral synapses between GB cells, and the functional contribution of pre-synaptic genes to GB calcium dependent activity and expansion. Finally, we explore the relevance of synaptic genes in GB cells to the lifespan reduction caused by GB advance. Our results indicate that both presynaptic and postsynaptic proteins play a role in GB progression and lethality.

Glioblastoma (GB) is the most frequent and aggressive type of brain tumor. It is originated from glial cells that expand and proliferate very fast in the brain. GB cells infiltrate and establish cell to cell communication with healthy neurons. Currently there is no effective treatment for GB and these tumors result incurable with an average survival of 16 months after diagnosis. Here we used a Drosophila melanogaster model to search for genetic suppressors of GB progression. The results show that genes involved in the formation of synapses are required for glial cell number increase, expansion of tumoral volume and premature death. Among these synaptic genes we found that post-synaptic genes that contribute to Neuron-GB interaction which validate previous findings in human GB. Moreover, we found electro dense structures between GB cells that are compatible with synapses and that expression of pre-synaptic genes, including brp, Lip-α and syt 1, is required for GB progression and aggressiveness. These results suggest a contribution of synapses between GB cells to disease progression, named as intratumoral synapses.

Integrin-αvβ3 as a Therapeutic Target in Glioblastoma: Back to the Future?

Glioblastoma (GBM), the most common primary malignant brain tumor, is associated with a dismal prognosis. Standard therapies including maximal surgical resection, radiotherapy, and temozolomide chemotherapy remain poorly efficient. Improving GBM treatment modalities is, therefore, a paramount challenge for researchers and clinicians. GBMs exhibit the hallmark feature of aggressive invasion into the surrounding tissue. Among cell surface receptors involved in this process, members of the integrin family are known to be key actors of GBM invasion. Upregulation of integrins was reported in both tumor and stromal cells, making them a suitable target for innovative therapies targeting integrins in GBM patients, as their impairment disrupts tumor cell proliferation and invasive capacities. Among them, integrin-αvβ3 expression correlates with high-grade GBM. Driven by a plethora of preclinical biological studies, antagonists of αvβ3 rapidly became attractive therapeutic candidates to impair GBM tumorigenesis. In this perspective, the advent of nuclear medicine is currently one of the greatest components of the theranostic concept in both preclinical and clinical research fields. In this review, we provided an overview of αvβ3 expression in GBM to emphasize the therapeutic agents developed. Advanced current and future developments in the theranostic field targeting αvβ3 are finally discussed.

Predicting cell behaviour parameters from glioblastoma on a chip images. A deep learning approach

The broad possibilities offered by microfluidic devices in relation to massive data monitoring and acquisition open the door to the use of deep learning technologies in a very promising field: cell culture monitoring. In this work, we develop a methodology for parameter identification in cell culture from fluorescence images using Convolutional Neural Networks (CNN). We apply this methodology to the in vitro study of glioblastoma (GBM), the most common, aggressive and lethal primary brain tumour. In particular, the aim is to predict the three parameters defining the go or grow GBM behaviour, which is determinant for the tumour prognosis and response to treatment. The data used to train the network are obtained from a mathematical model, previously validated with in vitro experimental results. The resulting CNN provides remarkably accurate predictions (Pearson's ρ > 0.99 for all the parameters). Besides, it proves to be sound, to filter noise and to generalise. After training and validation with synthetic data, we predict the parameters corresponding to a real image of a microfluidic experiment. The obtained results show good performance of the CNN. The proposed technique may set the first steps towards patient-specific tools, able to predict in real-time the tumour evolution for each particular patient, thanks to a combined in vitro-in silico approach.