Showing papers by "Kristin R. Swanson published in 2012"

Direct inhibition of myosin II effectively blocks glioma invasion in the presence of multiple motogens.

Abstract: Anaplastic gliomas, the most common and malignant of primary brain tumors, frequently contain activating mutations and amplifications in promigratory signal transduction pathways. However, targeting these pathways with individual signal transduction inhibitors does not appreciably reduce tumor invasion, because these pathways are redundant; blockade of any one pathway can be overcome by stimulation of another. This implies that a more effective approach would be to target a component at which these pathways converge. In this study, we have investigated whether the molecular motor myosin II represents such a target by examining glioma invasion in a series of increasingly complex models that are sensitive to platelet-derived growth factor, epidermal growth factor, or both. Our results lead to two conclusions. First, malignant glioma cells are stimulated to invade brain through the activation of multiple signaling cascades not accounted for in simple in vitro assays. Second, even though there is a high degree of redundancy in promigratory signaling cascades in gliomas, blocking tumor invasion by directly targeting myosin II remains effective. Our results thus support our hypothesis that myosin II represents a point of convergence for signal transduction pathways that drive glioma invasion and that its inhibition cannot be overcome by other motility mechanisms.

Applying a patient-specific bio-mathematical model of glioma growth to develop virtual [18F]-FMISO-PET images.

Abstract: Glioblastoma multiforme (GBM) is a class of primary brain tumours characterized by their ability to rapidly proliferate and diffusely infiltrate surrounding brain tissue. The aggressive growth of GBM leads to the development of regions of low oxygenation (hypoxia), which can be clinically assessed through [18F]-fluoromisonidazole (FMISO) positron emission tomography (PET) imaging. Building upon the success of our previous mathematical modelling efforts, we have expanded our model to include the tumour microenvironment, specifically incorporating hypoxia, necrosis and angiogenesis. A pharmacokinetic model for the FMISO-PET tracer is applied at each spatial location throughout the brain and an analytical simulator for the image acquisition and reconstruction methods is applied to the resultant tracer activity map. The combination of our anatomical model with one for FMISO tracer dynamics and PET image reconstruction is able to produce a patient-specific virtual PET image that reproduces the image characteristics of the clinical PET scan as well as shows no statistical difference in the distribution of hypoxia within the tumour. This work establishes proof of principle for a link between anatomical (magnetic resonance image [MRI]) and molecular (PET) imaging on a patient-specific basis as well as address otherwise untenable questions in molecular imaging, such as determining the effect on tracer activity from cellular density. Although further investigation is necessary to establish the predicitve value of this technique, this unique tool provides a better dynamic understanding of the biological connection between anatomical changes seen on MRI and biochemical activity seen on PET of GBM in vivo.

Glial progenitor cell recruitment drives aggressive glioma growth: mathematical and experimental modelling

Abstract: Currently available glioma treatments remain unsuccessful at prolonging disease-free remission. Recent evidence suggests that tumour recruitment of glial progenitor cells by platelet-derived growth factor (PDGF) may play a role in the development and progression of these tumours. Building upon our recent experimental results and previous proliferation–invasion (PI) reaction–diffusion model, in this study, we created a proliferation–invasion–recruitment (PIR) model that includes a mechanism for progenitor cell recruitment, wherein paracrine PDGF signalling stimulates migration and proliferation of progenitors derived from the local brain environment. Parametrizing this mathematical model with data obtained from the PDGF-driven rat glioma model, we explored the consequences of recruitment, using the PIR model to compare the effects of high versus low PDGF secretion rates on tumour growth and invasion dynamics. The mathematical model predicts correlation between high levels of recruitment and both increased radial velocity of expansion on magnetic resonance imaging and less diffusely invasive edges. Thus, the PIR model predicts that PDGF levels correlate with tumour aggressiveness, and results are consistent with both human and experimental data, demonstrating that the effects of progenitor cell recruitment provide a novel mechanism to explain the variability in the rates of proliferation and dispersion observed in human gliomas.

Adaptive IMRT using a multiobjective evolutionary algorithm integrated with a diffusion-invasion model of glioblastoma

Abstract: We demonstrate a patient-specific method of adaptive IMRT treatment for glioblastoma using a multiobjective evolutionary algorithm (MOEA). The MOEA generates spatially optimized dose distributions using an iterative dialogue between the MOEA and a mathematical model of tumor cell proliferation, diffusion and response. Dose distributions optimized on a weekly basis using biological metrics have the potential to substantially improve and individualize treatment outcomes. Optimized dose distributions were generated using three different decision criteria for the tumor and compared with plans utilizing standard dose of 1.8 Gy/fraction to the CTV (T2-visible MRI region plus a 2.5 cm margin). The sets of optimal dose distributions generated using the MOEA approach the Pareto Front (the set of IMRT plans that delineate optimal tradeoffs amongst the clinical goals of tumor control and normal tissue sparing). MOEA optimized doses demonstrated superior performance as judged by three biological metrics according to simulated results. The predicted number of reproductively viable cells 12 weeks after treatment was found to be the best target objective for use in the MOEA. (Some figures may appear in colour only in the online journal)

Use of a novel patient-specific model of glioma growth kinetics to elucidate underlying biology as measured by gene expression microarray.

Abstract: 71 Background: Gliomas are heterogeneous diseases with a wide distribution of growth kinetics that can be estimated prior to treatment and that are prognostic for patient outcome after treatment. Coherent molecular data sets have been made available through cooperative projects such as REMBRANDT and TCGA. We apply our novel patient specific method of measuring the net proliferation and diffusion rate from routinely available preoperative MRI sequences on patients included in these publicly available data sets to assess the underlying biology with imaging. Methods: The normalized microarray data from REMBRANDT (n=475) was used to discover a set of genes differentially expressed among GBM patients when compared with lower grade gliomas (n=853). 647 of these genes were also assessed with probesets in TCGA (n=466). Of these 466 patients, 84 also had preoperative MRI imaging available through The Cancer Imaging Archive (TCIA), for which net diffusion (D) proliferation (ρ) were estimated. Differential gene expr...