The median survival for patients diagnosed with glioblastoma multiforme, the most common type of brain tumor, is less than 1 year. Animal glioma models that are more predictive of therapeutic response in human patients than traditional models and that are genetically and histologically accurate are an unmet need. The nestin tv-a (Ntv-a) genetically engineered mouse spontaneously develops glioma when infected with ALV-A expressing platelet-derived growth factor, resulting in autocrine platelet-derived growth factor signaling.
In the Ntv-a genetically engineered mouse model, T2-weighted and T1-weighted, contrast-enhanced magnetic resonance images were correlated with histology, glioma grade (high or low), and survival. Magnetic resonance imaging (MRI) was therefore used to enroll mice with high-grade gliomas into a second study that tested efficacy of the current standard of care for glioma, temozolomide (100 mg/kg qdx5 i.p., n=13).
The Ntv-a model generated a heterogeneous group of gliomas, some with high-grade growth rate and histologic characteristics and others with characteristics of lower-grade gliomas. We showed that MRI could be used to predict tumor grade and survival. Temozolomide treatment of high-grade tv-a gliomas provided a 14-day growth delay compared with vehicle controls. Diffusion MRI measurement of the apparent diffusion coefficient showed an early decrease in cellularity with temozolomide, similar to that observed in humans.
The use of MRI in the Ntv-a model allows determination of glioma grade and survival prediction, distribution of mice with specific tumor types into preclinical trials, and efficacy determination both by tumor growth and early apparent diffusion coefficient response.
"In an effort to represent the proneural, PDGF driven subtype of human GBM, this mouse model is also PDGF driven where PTEN is deleted in nestin expressing cells in an ink4/arf deficient background , , , . This PDGF driven highly proliferative mouse model has been found to exhibit pathological features similar to the human GBM subtype , , . Herein we sought to investigate the effectiveness of DW-MRI as a surrogate biomarker of treatment response in this animal model that mimics the proneural GBM class of tumors. "
[Show abstract][Hide abstract] ABSTRACT: The effectiveness of the radiosensitizer gemcitabine (GEM) was evaluated in a mouse glioma along with the imaging biomarker diffusion-weighted magnetic resonance imaging (DW-MRI) for early detection of treatment effects. A genetically engineered murine GBM model [Ink4a-Arf(-/-) Pten(loxP/loxP)/Ntv-a RCAS/PDGF(+)/Cre(+)] was treated with gemcitabine (GEM), temozolomide (TMZ) +/- ionizing radiation (IR). Therapeutic efficacy was quantified by contrast-enhanced MRI and DW-MRI for growth rate and tumor cellularity, respectively. Mice treated with GEM, TMZ and radiation showed a significant reduction in growth rates as early as three days post-treatment initiation. Both combination treatments (GEM/IR and TMZ/IR) resulted in improved survival over single therapies. Tumor diffusion values increased prior to detectable changes in tumor volume growth rates following administration of therapies. Concomitant GEM/IR and TMZ/IR was active and well tolerated in this GBM model and similarly prolonged median survival of tumor bearing mice. DW-MRI provided early changes to radiosensitization treatment warranting evaluation of this imaging biomarker in clinical trials.
PLoS ONE 04/2012; 7(4):e35857. DOI:10.1371/journal.pone.0035857 · 3.23 Impact Factor
"Even though these models have limitations, they are widely used, and in longitudinal studies the tumors are usually tracked with quantitative fluorescence/bioluminescence imaging [4, 5]. Optical methods do not capture tumor morphology, but magnetic resonance imaging (MRI), which has been applied to xenograft models, is able to acquire detailed images of implanted tumors [6, 7]. There is a clear need for the combination of a truly representative glioma model, and the imaging detail provided by MRI, to generate more relevant assessments of candidate glioma therapeutics . "
[Show abstract][Hide abstract] ABSTRACT: Magnetic resonance imaging (MRI) is the imaging modality of choice by which to monitor patient gliomas and treatment effects, and has been applied to murine models of glioma. However, a major obstacle to the development of effective glioma therapeutics has been that widely used animal models of glioma have not accurately recapitulated the morphological heterogeneity and invasive nature of this very lethal human cancer. This deficiency is being alleviated somewhat as more representative models are being developed, but there is still a clear need for relevant yet practical models that are well-characterized in terms of their MRI features. Hence we sought to chronicle the MRI profile of a recently developed, comparatively straightforward human tumor stem cell (hTSC) derived glioma model in mice using conventional MRI methods. This model reproduces the salient features of gliomas in humans, including florid neoangiogenesis and aggressive invasion of normal brain. Accordingly, the variable, invasive morphology of hTSC gliomas visualized on MRI duplicated that seen in patients, and it differed considerably from the widely used U87 glioma model that does not invade normal brain. After several weeks of tumor growth the hTSC model exhibited an MRI contrast enhancing phenotype having variable intensity and an irregular shape, which mimicked the heterogeneous appearance observed with human glioma patients. The MRI findings reported here support the use of the hTSC glioma xenograft model combined with MRI, as a test platform for assessing candidate therapeutics for glioma, and for developing novel MR methods.
Journal of Neuro-Oncology 09/2011; 104(2):473-81. DOI:10.1007/s11060-010-0517-x · 3.07 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Mathematical modeling techniques have been widely employed to understand how cancer grows, and, more recently, such approaches have been used to understand how cancer can be controlled. In this manuscript, a previously validated hybrid cellular automaton model of tumor growth in a vascularized environment is used to study the antitumor activity of several vascular-targeting compounds of known efficacy. In particular, this model is used to test the antitumor activity of a clinically used angiogenesis inhibitor (both in isolation, and with a cytotoxic chemotherapeutic) and a vascular disrupting agent currently undergoing clinical trial testing. I demonstrate that the mathematical model can make predictions in agreement with preclinical/clinical data and can also be used to gain more insight into these treatment protocols. The results presented herein suggest that vascular-targeting agents, as currently administered, cannot lead to cancer eradication, although a highly efficacious agent may lead to long-term cancer control.
Computational and Mathematical Methods in Medicine 03/2011; 2011(5):830515. DOI:10.1155/2011/830515 · 0.77 Impact Factor
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