Glioblastoma multiforme (GBM) is a rapidly progressive disease of morbidity and mortality and is the most common form of primary brain cancer in adults. Lack of appropriate in vivo models has been a major roadblock to developing effective therapies for GBM. A new highly invasive in vivo GBM model is described that was derived from a spontaneous brain tumor (VM-M3) in the VM mouse strain. Highly invasive tumor cells could be identified histologically on the hemisphere contralateral to the hemisphere implanted with tumor cells or tissue. Tumor cells were highly expressive for the chemokine receptor CXCR4 and the proliferation marker Ki-67 and could be identified invading through the pia mater, the vascular system, the ventricular system, around neurons, and over white matter tracts including the corpus callosum. In addition, the brain tumor cells were labeled with the firefly luciferase gene, allowing for non-invasive detection and quantitation through bioluminescent imaging. The VM-M3 tumor has a short incubation time with mortality occurring in 100% of the animals within approximately 15 days. The VM-M3 brain tumor model therefore can be used in a pre-clinical setting for the rapid evaluation of novel anti-invasive therapies.

BackgroundMany of the current standard therapies employed for the management of primary malignant brain cancers are largely viewed as palliative, ultimately because these conventional strategies have been shown, in many instances, to decrease patient quality of life while only offering a modest increase in the length of survival. We propose that caloric restriction (CR) is an alternative metabolic therapy for brain cancer management that will not only improve survival but also reduce the morbidity associated with disease. Although we have shown that CR manages tumor growth and improves survival through multiple molecular and biochemical mechanisms, little information is known about the role that CR plays in modulating inflammation in brain tumor tissue.Methodology/Principal FindingsPhosphorylation and activation of nuclear factor κB (NF-κB) results in the transactivation of many genes including those encoding cycloxygenase-2 (COX-2) and allograft inflammatory factor-1 (AIF-1), both of which are proteins that are primarily expressed by inflammatory and malignant cancer cells. COX-2 has been shown to enhance inflammation and promote tumor cell survival in both in vitro and in vivo studies. In the current report, we demonstrate that the p65 subunit of NF-κB was expressed constitutively in the CT-2A tumor compared with contra-lateral normal brain tissue, and we also show that CR reduces (i) the phosphorylation and degree of transcriptional activation of the NF-κB-dependent genes COX-2 and AIF-1 in tumor tissue, as well as (ii) the expression of proinflammatory markers lying downstream of NF-κB in the CT-2A malignant mouse astrocytoma, [e.g. macrophage inflammatory protein-2 (MIP-2)]. On the whole, our date indicate that the NF-κB inflammatory pathway is constitutively activated in the CT-2A astrocytoma and that CR targets this pathway and inflammation.ConclusionCR could be effective in reducing malignant brain tumor growth in part by inhibiting inflammation in the primary brain tumor.

Mature vasculature contains an endothelial cell lining with a surrounding sheath of pericytes/vascular smooth muscle cells (VSMCs). Tumor vessels are immature and lack a pericyte sheath. Colocalization of vascular endothelial growth factor receptor 2 (VEGFR-2) and platelet-derived growth factor receptor beta (PDGF-Rβ) reduces pericyte ensheathment of tumor vessels. We found that a 30% dietary restriction (DR) enhanced vessel maturation in the mouse CT-2A astrocytoma. DR reduced microvessel density and VEGF expression in the astrocytoma, while increasing recruitment of pericytes, positive for alpha-smooth muscle actin (α-SMA). Moreover, DR reduced colocalization of VEGF-R2 and PDGF-Rβ, but did not reduce total PDGF-Rβ expression. These findings suggest that DR promoted vessel normalization by preventing VEGF-induced inhibition of the PDGF signaling axis in pericytes. DR appears to shift the tumor vasculature from a leaky immature state to a more mature state. We suggest that vessel normalization could improve delivery of therapeutic drugs to brain tumors.

The diagnosis of prostate transition zone cancer (PTZC) remains a clinical challenge due to their similarity to benign prostatic hyperplasia (BPH) on MRI. The Deep Convolutional Neural Networks (DCNNs) showed high efficacy in diagnosing PTZC on medical imaging but was limited by the small data size. A transfer learning (TL) method was combined with deep learning to overcome this challenge. Materials and methodsA retrospective investigation was conducted on 217 patients enrolled from our hospital database (208 patients) and The Cancer Imaging Archive (nine patients). Using T2-weighted images (T2WIs) and apparent diffusion coefficient (ADC) maps, DCNN models were trained and compared between different TL databases (ImageNet vs. disease-related images) and protocols (from scratch, fine-tuning, or transductive transferring). ResultsPTZC and BPH can be classified through traditional DCNN. The efficacy of TL from natural images was limited but improved by transferring knowledge from the disease-related images. Furthermore, transductive TL from disease-related images had comparable efficacy to the fine-tuning method. Limitations include retrospective design and a relatively small sample size. ConclusionDeep TL from disease-related images is a powerful tool for an automated PTZC diagnostic system. In developing regions where only conventional MR scans are available, the accurate diagnosis of PTZC can be achieved via transductive deep TL from disease-related images.