1,720,963 research outputs found
Chemotherapy-Induced Cell-Surface GRP78 Expression as a Prognostic Marker for Invasiveness of Metastatic Triple-Negative Breast Cancer
No full textMetastasis remains the leading cause (90%) of cancer-related mortality, especially in metastatic triple-negative breast cancer (TNBC). Improved understanding of molecular drivers in the metastatic cascade is crucial, to find accurate prognostic markers for invasiveness after chemotherapy treatment. Current breast cancer chemotherapy treatments include doxorubicin and paclitaxel, inducing various effects, such as the unfolded protein response (UPR). The key regulator of the UPR is the 78-kDa glucose-regulated protein (GRP78), which is associated with metastatic disease, although, its expression level in the context of invasiveness is still controversial. We evaluate doxorubicin effects on TNBC cells, identifying GRP78 subpopulations linked to invasiveness. Specifically, we evaluate the motility and invasiveness of GRP78 positive vs. negative cell subpopulations by two different assays: the in vitro Boyden chamber migration assay and our innovative, rapid (2-3 h) clinically relevant, mechanobiology-based invasiveness assay. We validate chemotherapy-induced increase in the subpopulation of cell-surface GRP78(+) in two human, metastatic TNBC cell lines: MDA-MB-231 and MDA-MB-468. The GRP78(+) cell subpopulation exhibits reduced invasiveness and metastatic potential, as compared to whole-population control and to the GRP78(-) cell subpopulation, which are both highly invasive. Thus, using our innovative, clinically relevant assay, we rapidly (on clinical timescale) validate that GRP78(-) cells are likely linked with invasiveness, yet also demonstrate that combination of the GRP78(+) and GRP78(-) cells could increase the overall metastatic potential. Our results and approach could provide patient-personalized predictive marker for the expected benefits of chemotherapy in TNBC patients and potentially reveal non-responders to chemotherapy while also allowing evaluation of the clinical risk for metastasis.Acknowledgments
The authors thank Julia Lipovetsky for the technical assistance. The work was partially supported by the Israeli Ministry of Innovation, Science and Technology (MOST) Breakthrough Research Program (Grant no. 1001717860 awarded to Professor Daphne Weihs), by the Applebaum Foundation, by the Gerald O. Mann and the Frank and Dolores Corbett Charitable Foundations, and by the Russel Berrie Nano Institute at the Technion-IIT (all awarded to
Professor Daphne Weihs).
Funding
Open access funding provided by Technion - Israel Institute of Technology
Physical confinement and cell proximity increase cell migration rates and invasiveness: A mathematical model of cancer cell invasion through flexible channels
Full text linkCancer cell migration between different body parts is the driving force behind cancer metastasis, which is the main cause of mortality of patients. Migration of cancer cells often proceeds by penetration through narrow cavities in locally stiff, yet flexible tissues. In our previous work, we developed a model for cell geometry evolution during invasion, which we extend here to investigate whether leader and follower (cancer) cells that only interact mechanically can benefit from sequential transmigration through narrow micro-channels and cavities. We consider two cases of cells sequentially migrating through a flexible channel: leader and follower cells being closely adjacent or distant. Using Wilcoxon’s signed-rank test on the data collected from Monte Carlo simulations, we conclude that the modelled transmigration speed for the follower cell is significantly larger than for the leader cell when cells are distant, i.e. follower cells transmigrate after the leader has completed the crossing. Furthermore, it appears that there exists an optimum with respect to the width of the channel such that cell moves fastest. On the other hand, in the case of closely adjacent cells, effectively performing collective migration, the leader cell moves 12% faster since the follower cell pushes it. This work shows that mechanical interactions between cells can increase the net transmigration speed of cancer cells, resulting in increased invasiveness. In other words, interaction between cancer cells can accelerate metastatic invasion.Analysis and Stochastic
Do Cancer Cells Collaborate During Metastasis?
No full textWe consider a model for cell deformation and cellular forces that are exerted on the immediate environment. This model is applied to the transmigration of cancer cells through narrow, deformable channels. This migration process is an important and rate-determining mechanism during the metastasis of cancer
Evaluation of a mechanically-coupled reaction-diffusion model for macroscopic brain tumor growth
No full textMacroscopic growth of brain tumors has been studied by means of different computational modeling approaches. Glioblastoma multiforme (GBM), the most frequent malignant histological type, is commonly modeled as a reaction-diffusion type system, accounting for its invasive growth pattern, e.g. [1]. Purely mechanical models have been proposed to represent the mass-effect caused by the growing tumor, e.g.
[2]. Only few models, such as [3], consider both effects in a single 3D model.
We report first results of a comparative study that evaluates the ability of a simple computational model to reproduce the shape of pathologies and healthy tissue deformations found in patients.
We use the finite element method (FEM) for simulating GBM invasion into brain tissue and the mechanical interaction between tumor and healthy tissue components. Cell proliferation and invasion is modeled as a reaction-diffusion process; the simulation of the mechanic interaction relies on a linear-elastic material model. Both are coupled by relating local increase in tumor cell concentration to the generation of isotropic strain in the corresponding tissue element. The model accounts for multiple brain regions with values for proliferation, isotropic diffusion and mechanical properties derived from literature.
Tumors were seeded at multiple locations in FEM models derived from publicly available human brain atlases. Simulation results for a given tumor volume were compared to patient images, using a metric that takes into account extent and shape of the tumor, as well as healthy tissue deformation. Model parameterizations resulting in simulated tumors most similar to real-world pathologies were identified
by systematic variation of seed location and relative magnitude of diffusion and mechanical coupling.
1. Swanson et al., J. Neurol. Sci., 216 (1):1-10, 2003.
2. Hogea et al., Phys. Med. Biol., 52 (23):6893-6908, 2007.
3. Clatz et al., IEEE Trans. Med. Imag., 24 (10):1334-1346, 2005
Computational modeling of therapy on pancreatic cancer in its early stages
No full textMore than eighty percent of pancreatic cancer involves ductal adenocarcinoma with an abundant desmoplastic extracellular matrix surrounding the solid tumor entity. This aberrant tumor microenvironment facilitates a strong resistance of pancreatic cancer to medication. Although various therapeutic strategies have been reported to be effective in mice with pancreatic cancer, they still need to be tested quantitatively in wider animal-based experiments before being applied as therapies. To aid the design of experiments, we develop a cell-based mathematical model to describe cancer progression under therapy with a specific application to pancreatic cancer. The displacement of cells is simulated by solving a large system of stochastic differential equations with the Euler–Maruyama method. We consider treatment with the PEGylated drug PEGPH20 that breaks down hyaluronan in desmoplastic stroma followed by administration of the chemotherapy drug gemcitabine to inhibit the proliferation of cancer cells. Modeling the effects of PEGPH20 + gemcitabine concentrations is based on Green’s fundamental solutions of the reaction–diffusion equation. Moreover, Monte Carlo simulations are performed to quantitatively investigate uncertainties in the input parameters as well as predictions for the likelihood of success of cancer therapy. Our simplified model is able to simulate cancer progression and evaluate treatments to inhibit the progression of cancer.Numerical Analysi
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
A Cellular Automata Model of Oncolytic Virotherapy in Pancreatic Cancer
No full textOncolytic virotherapy is known as a new treatment to employ less virulent viruses to specifically target and damage cancer cells. This work presents a cellular automata model of oncolytic virotherapy with an application to pancreatic cancer. The fundamental biomedical processes (like cell proliferation, mutation, apoptosis) are modeled by the use of probabilistic principles. The migration of injected viruses (as therapy) is modeled by diffusion through the tissue. The resulting diffusion–reaction equation with smoothed point viral sources is discretized by the finite difference method and integrated by the IMEX approach. Furthermore, Monte Carlo simulations are done to quantitatively evaluate the correlations between various input parameters and numerical results. As we expected, our model is able to simulate the pancreatic cancer growth at early stages, which is calibrated with experimental results. In addition, the model can be used to predict and evaluate the therapeutic effect of oncolytic virotherapy.Numerical Analysi
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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