Journal:

Article Title: Computer Simulation of Glioma Growth and Morphology

doi: 10.1016/j.neuroimage.2007.03.008

Figure Lengend Snippet: Multiscale 3-D computer model predicts gross morphologic features of a growing glioblastoma. (A movie of this simulation is in Supporting Online Material.) A: Viable (VT) and necrotic (NT) tissue regions, and vasculature (MV: mature blood-conducting vessels in red; NV: new non-conducting vessels in blue) are shown. The time sequence (from left to right, over a period of 3 months) reveals that the morphology is affected by successive cycles of neovascularization, vasculature maturation, and vessel cooption (VC). Bar, 250 µm. B: Histology-like section of the last frame of the simulation in A (obtained by slicing horizontally through the simulated tumor) reveals viable tumor regions (white) surrounding necrotic tissue (dark). Tumor mass spatial distribution is calculated using Eq. 1 with ν = ρ and cell viability as a function of nutrient ν = σ. Input parameters were calibrated from in-vitro and ex-vivo glioma data (Summary of Materials & Equipment Used). In particular, tumor cells are estimated to proliferate at a rate λprolif =1 day-1 (Frieboes et al., 2006). C: Another view from simulation shown in Fig. 1A, right.

Article Snippet: These results are supported by numerous experimental observations in animal glioblastoma models ( Rubenstein et al., 2000 , Kunkel et al., 2001 , Lamszus et al., 2003 , Bello et al., 2004 ), and from histopathology of brain tumors in patients, and may also apply to solid tumors in general ( Cristini et al., 2005 , Frieboes et al ., 2006 ) beyond the context of brain tumors.

Techniques: Sequencing, In Vitro, Ex Vivo

Journal:

Article Title: Computer Simulation of Glioma Growth and Morphology

doi: 10.1016/j.neuroimage.2007.03.008

Figure Lengend Snippet: Multiscale model predicts that tumor tissue morphology is determined by diffusion gradients. A: Tumor histology predicted by the computer model (obtained by slicing vertically through the simulated tumor corresponding to last frame in Fig. 1A) reveals clusters of viable cells (VT) surrounding blood vessel cross-sections (MV), and zones depleted of cells or necrotic farther away (NT). Viable tissue is confined within 100–200 µm of the conducting intra-tumoral vessels and of the brain parenchyma (cross sections of conducting vessels are in red, non-conducting in blue). Bar, 200 µm. B: Calculated diffusion gradients of cell substrates corresponding to the tumor structure predicted by the simulation in A indicate high nutrient (white) in the vicinity of blood conducting vessels and low nutrient (black) otherwise, thereby determining the regions of cell viability. Legend: arbitrary units. C: Glioblastoma histologic section from a patient shows tissue structure of a large bulb-shaped tumor area. This morphology reveals viable cells (VT) cuffing and surrounding blood vessels in cross-sections (MV), and depleted inter-vessel zones (NT). The thickness of the cuff of viable cells corresponds to the diffusion gradient of oxygen and nutrients they require emanating from the vessel. These results indicate that tumor architecture in this specimen is determined by cellular metabolism and intra-tumoral diffusion gradients of required nutrients provided by the vasculature, also confirming the presence of substrate gradients and the parameter estimates for diffusion length used in the simulations (Summary of Materials & Equipment Used). Bar, 200 µm.

Article Snippet: These results are supported by numerous experimental observations in animal glioblastoma models ( Rubenstein et al., 2000 , Kunkel et al., 2001 , Lamszus et al., 2003 , Bello et al., 2004 ), and from histopathology of brain tumors in patients, and may also apply to solid tumors in general ( Cristini et al., 2005 , Frieboes et al ., 2006 ) beyond the context of brain tumors.

Techniques: Diffusion-based Assay

Journal:

Article Title: Computer Simulation of Glioma Growth and Morphology

doi: 10.1016/j.neuroimage.2007.03.008

Figure Lengend Snippet: Multiscale model predicts that tumor tissue invasion is driven by diffusion gradients. A: Detail of computer simulated glioma histology (obtained by slicing through the 3-D simulated tumor of Fig. 1) showing invasive tumor front (white) moving up towards extra-tumoral conducting neo-vessels (NV), supporting the hypothesis that diffusion gradients of cell substrates released by the neovasculature drive collective tumor cell infiltration into the brain in addition to determining the tumor structure (Figure 2). The model predicts that the movement of tumor fronts towards sources of cell substrate strongly influences glioma invasiveness. Aged vessels inside the tumor have thicker walls and thus are assumed to provide fewer nutrients than the thin-walled neovasculature at the tumor periphery (Padera et al., 2004). Conducting vessels (red), non-conducting (blue). Bar, 100 µm. B: Glioblastoma histopathology from one patient showing tumor (bottom) pushing into more normal brain (top). Note demarcated margin between tumor and brain parenchyma to the middle top of the image and green fluorescent outlines of large vascular channels deeper in the tumor. Neovascularization (NV) at the tumor-brain interface can be detected by red fluorescence from the erythrocytes inside the vessels (see materials and methods for microscopic imaging of archived tissue in H&E by fluorescence). Bar, 100 µm.

Article Snippet: These results are supported by numerous experimental observations in animal glioblastoma models ( Rubenstein et al., 2000 , Kunkel et al., 2001 , Lamszus et al., 2003 , Bello et al., 2004 ), and from histopathology of brain tumors in patients, and may also apply to solid tumors in general ( Cristini et al., 2005 , Frieboes et al ., 2006 ) beyond the context of brain tumors.

Techniques: Diffusion-based Assay, Histopathology, Fluorescence, Imaging