27,742 research outputs found

    Advances in the design and development of oncolytic measles viruses.

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    A successful oncolytic virus is one that selectively propagates and destroys cancerous tissue without causing excessive damage to the normal surrounding tissue. Oncolytic measles virus (MV) is one such virus that exhibits this characteristic and thus has rapidly emerged as a potentially useful anticancer modality. Derivatives of the Edmonston MV vaccine strain possess a remarkable safety record in humans. Promising results in preclinical animal models and evidence of biological activity in early phase trials contribute to the enthusiasm. Genetic modifications have enabled MV to evolve from a vaccine agent to a potential anticancer therapy. Specifically, alterations of the MV genome have led to improved tumor selectivity and delivery, therapeutic potency, and immune system modulation. In this article, we will review the advancements that have been made in the design and development of MV that have led to its use as a cancer therapy. In addition, we will discuss the evidence supporting its use, as well as the challenges associated with MV as a potential cancer therapeutic

    Radiotherapy planning for glioblastoma based on a tumor growth model: Improving target volume delineation

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    Glioblastoma are known to infiltrate the brain parenchyma instead of forming a solid tumor mass with a defined boundary. Only the part of the tumor with high tumor cell density can be localized through imaging directly. In contrast, brain tissue infiltrated by tumor cells at low density appears normal on current imaging modalities. In clinical practice, a uniform margin is applied to account for microscopic spread of disease. The current treatment planning procedure can potentially be improved by accounting for the anisotropy of tumor growth: Anatomical barriers such as the falx cerebri represent boundaries for migrating tumor cells. In addition, tumor cells primarily spread in white matter and infiltrate gray matter at lower rate. We investigate the use of a phenomenological tumor growth model for treatment planning. The model is based on the Fisher-Kolmogorov equation, which formalizes these growth characteristics and estimates the spatial distribution of tumor cells in normal appearing regions of the brain. The target volume for radiotherapy planning can be defined as an isoline of the simulated tumor cell density. A retrospective study involving 10 glioblastoma patients has been performed. To illustrate the main findings of the study, a detailed case study is presented for a glioblastoma located close to the falx. In this situation, the falx represents a boundary for migrating tumor cells, whereas the corpus callosum provides a route for the tumor to spread to the contralateral hemisphere. We further discuss the sensitivity of the model with respect to the input parameters. Correct segmentation of the brain appears to be the most crucial model input. We conclude that the tumor growth model provides a method to account for anisotropic growth patterns of glioblastoma, and may therefore provide a tool to make target delineation more objective and automated

    Tumor growth instability and the onset of invasion

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    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 behaviour 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.Comment: 12 pages, 8 figures, revtex
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