Glioma Brain Tumor Research Articles

Next-Generation Sequencing Analysis of ctDNA for the Detection of Glioma and Metastatic Brain Tumors in Adults.

Background and aims: The next-generation sequencing technologies and their related assessments of circulating tumor DNA in both glioma and metastatic brain tumors remain largely limited.Methods: Based largely on a protocol approved by the institutional review board at Peking University International Hospital, the current retrospective, single-center study was conducted. Genomic DNA was extracted from blood samples or tumor tissues. With the application of NextSeq 500 instrument (Illumina), Sequencing was performed with an average coverage of 550-fold. Paired-end sequencing was employed utilized with an attempt to achieve improved sensitivity of duplicate detection and therefore to increase the detection reliability of possible fusions.Results: A total of 28 patients (21 men and 7 women) with brain tumors in the present study were involved in the study. The patients enrolled were assigned into two groups, including glioma group (n = 21) and metastatic brain tumor group (n = 7). The mean age of metastatic brain tumor group (59.86 ± 8.85 y), (43.65 ± 13.05 y) reported significantly higher results in comparison to that of glioma group (45.3 ± 12.3 years) (P < 0.05). The mutant genes in metastatic brain tumor group included ALK, MDM2, ATM, BRCA1, FGFR1, MDM4 and KRAS; however, there were no glioma-related mutant genes including MGMT, IDH1, IDH2, 1p/19q, and BRAF et al. Interesteringly, only two patient (28.3%) was detected blood ctDNA in metastatic brain tumor group; In contrast, blood ctDNA was found in ten glioma patients (47.6%) including 1p/19q, MDM2, ERBB2, IDH1, CDKN2A, CDK4, PDGFRA, CCNE1, MET. The characterizations of IDH mutations in the glioma included IDH1 mutation (p.R132H) and IDH2 mutation (p.R172K). The mutation rate of IDH in tumor tissues was 37.06 ± 8.32%, which was significantly higher than blood samples (P < 0.05).Conclusion: The present study demonstrated that the mutant genes among glioma and metastatic brain tumors are shown to be different. Moreover, the ctDNAs in the metastatic brain tumors included ALK and MDM2, and glioma-related ctDNAs included 1p/19q and MDM2 followed by frequencies of ERBB2, IDH1, CDKN2A, CDK4, PDGFRA, CCNE1, MET. These ctDNAs might be biomarkers and therapeutic responders in brain tumor.

Automated segmentation and detection of T1-weighted magnetic resonance imaging brain images of glioma brain tumor

There are numerous studies on brain imaging applications. The statistics in Malaysia showed that glioma is one of the most common type disease in brain tumor. Glioma brain tumor is an abnormal growth of glial cells inside the brain tissues which known as cerebral tissues. Radiologist commonly used Magnetic Resonance Imaging (MRI) image sequences to diagnose the brain tumor. However, manual examination of the brain tumor diagnosis by radiologist is difficult and time-consuming task as tumors are occurred in variability of shape and appearance. They will also inject a gadolinium contrast agent to enhance the image modality which will give the side effects to the patients. Therefore, this paper presents an automated segmentation and detection of MRI brain images using Sobel edge detection and mathematical morphology operations. The total of 30 glioma T1-Weighted MRI brain images are obtained from Brain Tumor Image Segmentation Benchmark (BRATS). The results of segmentation and detection are quantitatively evaluated by using Area Overlap which produced the accuracy rate of 80.2% and shows that the presented methods are promising.

Automated segmentation and detection of T1-weighted magnetic resonance imaging brain images of glioma brain tumor

Automated segmentation and detection of T1-weighted magnetic resonance imaging brain images of glioma brain tumor

A reliable ensemble-based classification framework for glioma brain tumor segmentation

Glioma is one of the most frequent primary brain tumors in adults that arise from glial cells. Automatic and accurate segmentation of glioma is critical for detecting all parts of tumor and its surrounding tissues in cancer detection and surgical planning. In this paper, we present a reliable classification framework for detection and segmentation of abnormal tissues including brain glioma tumor portions such as edemas and tumor core. This framework learns weighted features extracted from the 3D cubic neighborhoods regarding to gray-level differences that indicate the spatial relationships among voxels. In addition to intensity values in each slice, we consider sets of three consecutive slices to extract information from 3D neighbors. Classification procedure is defined based on combination of support vector machines regarding to an ensemble learning method. In order to regularize and improve the output of the classifier framework, we design a post-process procedure based on statistical concepts. The proposed framework is trained and tested with BRATS datasets, and comparative analysis is implemented. Experimental results indicate competitive performance compared to the state-of-the-art methods. The achieved accuracy is characterized by the overall mean Dice index of 88%.

Deep Multi-Scale 3D Convolutional Neural Network (CNN) for MRI Gliomas Brain Tumor Classification.

Accurate and fully automatic brain tumor grading from volumetric 3D magnetic resonance imaging (MRI) is an essential procedure in the field of medical imaging analysis for full assistance of neuroradiology during clinical diagnosis. We propose, in this paper, an efficient and fully automatic deep multi-scale three-dimensional convolutional neural network (3D CNN) architecture for glioma brain tumor classification into low-grade gliomas (LGG) and high-grade gliomas (HGG) using the whole volumetric T1-Gado MRI sequence. Based on a 3D convolutional layer and a deep network, via small kernels, the proposed architecture has the potential to merge both the local and global contextual information with reduced weights. To overcome the data heterogeneity, we proposed a preprocessing technique based on intensity normalization and adaptive contrast enhancement of MRI data. Furthermore, for an effective training of such a deep 3D network, we used a data augmentation technique. The paper studied the impact of the proposed preprocessing and data augmentation on classification accuracy.Quantitative evaluations, over the well-known benchmark (Brats-2018), attest that the proposed architecture generates the most discriminative feature map to distinguish between LG and HG gliomas compared with 2D CNN variant. The proposed approach offers promising results outperforming the recently supervised and unsupervised state-of-the-art approaches by achieving an overall accuracy of 96.49% using the validation dataset. The obtained experimental results confirm that adequate MRI's preprocessing and data augmentation could lead to an accurate classification when exploiting CNN-based approaches.

Development of automatic glioma brain tumor detection system using deep convolutional neural networks

AbstractWe have developed six convolutional neural network (CNN) models for finding optimal brain tumor detection system on high‐grade glioma and low‐grade glioma lesions from voluminous magnetic resonance imaging human brain scans. Glioma is the most common form of brain tumor. The models are constructed based on the different combinations and settings of hyperparameters with conventional CNN architecture. The six models are two layers with five epochs, five layers with dropout, five layers with stopping criteria (FLSC), FLSC and dropout (FLSCD), FLSC and batch normalization (FLSCBN), and FLSCBN and dropout. The models were trained and tested with BraTS2013 and whole brain atlas data sets. Among them, FLSCBN model yielded the best classification results for brain tumor detection. Experimental results revealed that our deep learning approach was better than the conventional state‐of‐art methods.

Label-free discrimination of glioma brain tumors in different stages by surface enhanced Raman scattering

According to the WHO classification criteria, the most common intracranial tumor gliomas can be divided into four grades based on their symptoms. Among them, Grade Ⅰ and Grade II are low-grade gliomas, and Grade III and Grade IV are high-grade gliomas. Because gliomas have a high lethal rate, they have received widespread attention in the medical field. Based on these circumstances, a rapid and facile surface enhanced Raman scattering (SERS) method using silver nano particle-decorated silver nanorod (AgNPs@AgNR) as substrates were developed for the discrimination of gliomas. Compared with SERS-active silver nanoparticles and silver nanorod substrates, the prepared AgNPs@AgNR substrates showed an outstanding SERS performance with an enhancement factor up to 1.37 × 109. Combined AgNPs@AgNR substrate with principal component analysis (PCA), we achieved rapid discrimination of healthy brain tissue and gliomas at different grades. The spectra obtained from the tissue illustrate prominently spectral differences which can be applied to identify whether it came from a healthy region or from a glioma. The most prominently difference between the SERS spectrum of healthy brain tissue and that of gliomas at different grades is the reduction in quotient of two characteristic peaks at 653 and 724 cm−1. Furthermore, healthy brain tissue and Grade II gliomas as low grade gliomas as well as Grade III and Grade IV as high-grade gliomas can be clearly distinguished by three-dimensional PCA. Preliminary results indicate that the SERS spectra based on AgNPs@AgNR substrates can be applied for a rapid identification owing to its simple preparation of specimen and high-speed spectral acquirement.

Brain tumor classification using modified local binary patterns (LBP) feature extraction methods

Automatic classification of brain tumor types is very important for accelerating the treatment process, planning and increasing the patient’s survival rate. Today, MR images are used to determine the type of brain tumor. Manual diagnosis of brain tumor type depends on the experience and sensitivity of radiologists. Therefore, researchers have developed many brain tumor classification models to minimize the human factor. In this study, two different feature extraction (nLBP and αLBP) approaches were used to classify the most common brain tumor types; Glioma, Meningioma, and Pituitary brain tumors. nLBP is formed based on the relationship for each pixel around the neighbors. The nLBP method has a d parameter that specifies the distance between consecutive neighbors for comparison. Different patterns are obtained for different d parameter values. The αLBP operator calculates the value of each pixel based on an angle value. The angle values used for calculation are 0, 45, 90 and 135. To test the proposed methods, it was applied to images obtained from the brain tumor database collected from Nanfang Hospital, Guangzhou, China, and Tianjin Medical University General Hospital between the years of 2005 and 2010. The classification process was performed by using K-Nearest Neighbor (Knn) and Artificial Neural Networks (ANN), Random Forest (RF), A1DE, Linear Discriminant Analysis (LDA) classification methods, with the feature matrices obtained with nLBP, αLBP and classical LBP from the images in the data set. The highest success rate in brain tumor classification was 95.56% with the nLBPd = 1 feature extraction method and Knn model.

Salvianolic acid A increases the accumulation of doxorubicin in brain tumors through Caveolae endocytosis

Brain glioma is one of the most common brain tumors in the central nervous system (CNS). The blood-brain tumor barrier (BTB) restricts the delivery of anti-tumor drugs into tumor tissue in the brain. Therefore, improving the transportation of antineoplastic drugs across the BTB is essential to ameliorate treatment of brain tumors. The present study was performed to explore the effect and mechanism of salvianolic acid A (Sal A) on transportation of doxorubicin (Dox) across the BTB in vivo and in vitro. By creating a brain C6 glioma model in rats, we demonstrated that Sal A significantly increased the level of Dox in brain tumor tissue as shown by liquid chromatograph mass spectrometry. Interestingly, we found that Sal A increased transendothelial electrical resistance (TEER) values of the BTB and decreased the permeability of FITC-Dextran (4kD) across the BTB in vitro. Furthermore, the expression of tight junction proteins (TJs) in glioma endothelial cells (GECs) and brain tumor microvessels were also increased, suggesting that Sal A enhanced delivery of Dox across the BTB independent of the paracellular pathway. Next, we detected that Sal A had an effect on transcellular transport of compounds across the BTB. The accumulation of FITC-labeled bovine serum albumin (FITC-BSA) was significantly increased in GECs after treatment with Sal A (10 μM) for 6h, which was inhibited after pre-treatment with methyl-β-cyclodextrin (MβCD) for 30 min. The increased delivery of Dox across the BTB was also reduced after treatment with MβCD. In addition, phosphorylation levels of protein kinase B (PKB) and tyrosine protein kinase–Src family (Src) were increased in the Sal A treatment group. Sal A up-regulated the expression level of the phosphorylation of Caveolin-1 (pCaveolin-1), and this effect was reversed by a PKB or Src inhibitor. Taken together, our study showed for the first time that Sal A facilitated the delivery of antitumor drugs into brain tumor tissues by targeting the PKB/Src/Caveolin-1 signaling pathway.

Integrin Signaling in Glioma Pathogenesis: From Biology to Therapy.

Integrins are a large family of transmembrane adhesion receptors, which play a key role in interactions of a cell with the surrounding stroma. Integrins are comprised of non-covalently linked α and β chains, which form heterodimeric receptor complexes. The signals from integrin receptors are combined with those originating from growth factor receptors and participate in orchestrating morphological changes of cells, organization of the cytoskeleton, stimulation of cell proliferation and rescuing cells from programmed cell death induced by extracellular matrix (ECM) detachment. Upon binding to specific ligands or ECM components, integrin dimers activate downstream signaling pathways, including focal adhesion kinase, phosphoinositide-3-kinase (PI3K) and AKT kinases, which regulate migration, invasion, proliferation and survival. Expression of specific integrins is upregulated in both tumor cells and stromal cells in a tumor microenvironment. Therefore, integrins became an attractive therapeutic target for many cancers, including the most common primary brain tumors—gliomas. In this review we provide an overview of the involvement of integrin signaling in glioma pathogenesis, formation of the tumor niche and brain tissue infiltration. We will summarize up-to-date therapeutic strategies for gliomas focused on interference with integrin ligand-receptor signaling.

Investigating dynamic susceptibility contrast-enhanced perfusion-weighted magnetic resonance imaging in posterior fossa tumors: differences and similarities with supratentorial tumors.

To assess the accuracy of dynamic susceptibility contrast-enhanced perfusion-weighted magnetic resonance imaging in glioma grading and brain tumor characterization of infratentorial tumors, and to investigate differences from supratentorial tumors. This retrospective study, approved by the institutional review board, included 246 patients with brain tumors (184 supratentorial, 62 infratentorial), grouped by tumor type: high-grade gliomas (HGG), low-grade gliomas (LGG), metastases (Met), and primary central nervous system lymphoma (PCNSL). Relative cerebral blood volume (rCBV) and mean percentage of signal recovery (PSR) were calculated. For statistical analyses, lesions were grouped by location and histology. Differences were tested with Mann-Whitney U tests. From ROC curves, we calculated accuracy, sensitivity, specificity, PPV, and NPV, for rCBV and PSR. For infratentorial tumors, rCBV was highly accurate in differentiating HGG from LGG (AUC = 0.938). Mean PSR showed high accuracy in differentiating PCNSL and HGG from Met (AUC = 0.978 and AUC = 0.881, respectively). Infratentorial and supratentorial tumors had similarly high rCBV in HGG, high mean PSR in PCNSL, and low mean PSR in Met. The main differences were the optimum threshold rCBV values (3.04 for supratentorial, 1.77 for infratentorial tumors) and the mean PSR, which was significantly higher in LGG than in HGG in supratentorial (p = 0.035), but not infratentorial gliomas. Using infratentorial rCBV threshold values for supratentorial tumors decreased the sensitivity and specificity. rCBV and mean PSR were useful in grading and differentiating infratentorial tumors. Proper cutoff values were important in the accuracy of perfusion-weighted imaging in posterior fossa tumors.

Coronavirus disease 2019 pandemic and its implications on triaging patients with brain tumors for surgery, radiotherapy, and chemotherapy

The coronavirus disease 2019 pandemic has the potential to overwhelm the current health-care system in our country. The oncologists face a tricky question as to whom to prioritize treatment for. Postponing cancer treatments might be associated with some risk of compromised clinical outcomes. The risks need to be considered in light of the magnitude of potential benefits, the impact of delaying standard treatment on outcomes, and competing patient-centric and infrastructure priorities. Treatment decisions during the pandemic have to be triaged and prioritized based on the existing health-care facility and the fluidity of the situation. High-priority and urgent interventions include management of suspected high-grade glioma, primary central nervous system lymphoma, and round cell tumors. Low-priority interventions include management of low-grade glioma, benign tumors, and relapsed-refractory brain tumors. Similar to other cancers, the management of patients with brain tumors also needs to be adapted and prioritized; pragmatic patient treatment pathways need to be formalized without compromising the clinical outcomes and overburdening the health infrastructure. Health-care professionals dedicated to oversee the management of patients with brain tumors may have to triage referrals and modify the management of various brain tumors, as treatment pathways will differ from region to region and country to country based on the ground level situation. This document is an endeavor to provide a basic framework to triage and prioritize brain tumor patient management pathways. As the situation is rapidly evolving, we suggest a basic treatment guideline for these patients, which is simple to adopt.

Mathematical deconvolution of CAR T-cell proliferation and exhaustion from real-time killing assay data.

Chimeric antigen receptor (CAR) T-cell therapy has shown promise in the treatment of haematological cancers and is currently being investigated for solid tumours, including high-grade glioma brain tumours. There is a desperate need to quantitatively study the factors that contribute to the efficacy of CAR T-cell therapy in solid tumours. In this work, we use a mathematical model of predator–prey dynamics to explore the kinetics of CAR T-cell killing in glioma: the Chimeric Antigen Receptor T-cell treatment Response in GliOma (CARRGO) model. The model includes rates of cancer cell proliferation, CAR T-cell killing, proliferation, exhaustion, and persistence. We use patient-derived and engineered cancer cell lines with an in vitro real-time cell analyser to parametrize the CARRGO model. We observe that CAR T-cell dose correlates inversely with the killing rate and correlates directly with the net rate of proliferation and exhaustion. This suggests that at a lower dose of CAR T-cells, individual T-cells kill more cancer cells but become more exhausted when compared with higher doses. Furthermore, the exhaustion rate was observed to increase significantly with tumour growth rate and was dependent on level of antigen expression. The CARRGO model highlights nonlinear dynamics involved in CAR T-cell therapy and provides novel insights into the kinetics of CAR T-cell killing. The model suggests that CAR T-cell treatment may be tailored to individual tumour characteristics including tumour growth rate and antigen level to maximize therapeutic benefit.

Characterization of G protein β subunit expression of human brain Glioma tumor.

Proteome indicates the protein content of a genome. Proteome analysis is effective in a new system formulation and prediction, prevention, and treatment based on protein. One of the purposes of proteomics researches is to know and understand the cancer mechanism. In this study, we separated the proteins by the Two-Dimensional Electrophorese method and analyzed and compared protein spots by statistical and software data. The spots were separated and identified by the proteins' Isoelectric PH differences, molecular weights, and data bank. In continuation, the protein profiles were clustered by MALDI-TOF-TOF and the main element was identified and confirmed. We have used site PhosphoSitePlus® to review post-translational modifications. The findings indicated that the G protein Beta subunit rate increased in the astrocytoma, oligodendroglia, and glioblastoma cerebral malignant tumors. The βγ complex formation may prevent and activates many paths of cellular growth. The βγ complex activity control of the changes after the conversion parallel to GTPase activity of this α subunit may be a formulation mechanism for the G signal path (Tab. 5, Fig. 4, Diagram 2, Ref. 29). Keywords: glioma, G protein β subunit, proteomics.

An Efficient Framework for Accurate Arterial Input Selection in DSC-MRI of Glioma Brain Tumors

Automatic and accurate arterial input function (AIF) selection has an essential role for quantification of cerebral perfusion hemodynamic parameters using dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI). The purpose of this study is to develop an optimal automatic method for arterial input function determination in DSC-MRI of glioma brain tumors by using a new preprocessing method.For this study, DSC-MR images of 43 patients with glioma brain tumors were retrieved retrospectively. Our proposed AIF selection framework consisted an effcient pre-processing step, through which non-arterial curves such as tumorous, tissue, noisy and partial-volume affected curves were excluded, followed by AIF selection through agglomerative hierarchical (AH) clustering method. The performance of automatic AIF clustering was compared with manual AIF selection performed by an experienced radiologist, based on curve shape parameters, i.e. maximum peak (MP), full-width-at-half-maximum (FWHM), M (=MP/ (TTP × FWHM)) and root mean square error (RMSE).Mean values of AIFs shape parameters were compared with those derived from manually selected AIFs by two-tailed paired t-test. The results showed statistically insignificant differences in MP, FWHM, and M parameters and lower RMSE, approving the resemblance of the selected AIF with the gold standard. The intraclass correlation coefficient and coefficients of variation percent showed a better agreement between manual AIF and our proposed AIF selection than previously proposed methods.The results of current work suggest that by using efficient preprocessing steps, the accuracy of automatic AIF selection could be improved and this method appears promising for efficient and accurate clinical applications.

Research on glioma magnetic resonance imaging segmentation based on dual-channel three-dimensional densely connected network

Focus on the inconsistency of the shape, location and size of brain glioma, a dual-channel 3-dimensional (3D) densely connected network is proposed to automatically segment brain glioma tumor on magnetic resonance images. Our method is based on a 3D convolutional neural network frame, and two convolution kernel sizes are adopted in each channel to extract multi-scale features in different scales of receptive fields. Then we construct two densely connected blocks in each pathway for feature learning and transmission. Finally, the concatenation of two pathway features was sent to classification layer to classify central region voxels to segment brain tumor automatically. We train and test our model on open brain tumor segmentation challenge dataset, and we also compared our results with other models. Experimental results show that our algorithm can segment different tumor lesions more accurately. It has important application value in the clinical diagnosis and treatment of brain tumor diseases.

Glioma brain tumor detection and segmentation using weighting random forest classifier with optimized ant colony features

AbstractThe uncontrolled growth of cells in brain regions leads to the tumor regions and these abnormal tumor regions are scanned by magnetic resonance imaging (MRI) technique as an image. This paper proposes random forest classifier based Glioma brain tumor detection and segmentation methodology using feature optimization technique. The texture features are derived from brain MRI image and these derived feature set are now optimized by ant colony optimization algorithm. These optimized set of features are trained and classified using random forest classification method. This classifier classifies the brain MRI image into Glioma or non‐Glioma image based on the optimized set of features. Furthermore, energy‐based segmentation method is applied on the classified Glioma image for segmenting the tumor regions. The proposed methodology for Glioma brain tumor stated in this paper achieves 97.7% of sensitivity, 96.5% of specificity, and 98.01% of accuracy.

Co-cultures of Glioma Stem Cells and Primary Neurons, Astrocytes, Microglia, and Endothelial Cells for Investigation of Intercellular Communication in the Brain.

Intercellular communication within complex biological and pathological systems via extracellular vesicles (EVs) and secreted factors is a highly attractive area of research. However, cell models enabling investigation of such communication in vitro are limited. Commonly utilized is the supplementation of hyper-concentrated EVs or other extracellular factors to the recipient cell cultures. This approach requires purification of the secreted complexes and is confounded by the contamination of media components. Two-chamber co-cultures of donor and recipient cells separated by a pore membrane may represent a more physiological and better-controlled system for the investigation of intercellular communication. Yet, distinct culture conditions for different neural cell types often make them incompatible for co-culturing. Here we optimized short-term co-cultures of patient-derived low-passage glioma-initiating stem cells with normal cells of the brain microenvironment, such as primary neurons, astrocytes, microglia, and brain endothelial cells. We demonstrate the culture compatibility of these cell types and internalization of glioma-derived extracellular RNA by the normal recipient cells. The presented protocols are valuable for the investigation of intercellular communication between glioma brain tumor and cells of its microenvironment, including but not limited to the EVs-mediated communication.RESEARCH IN CONTEXTCell-to-cell communication is essential in normal physiology and implicated in disease; however, experimental systems for its modeling in vitro are limited. Particularly, the investigation of communication between brain tumors and normal cells of the brain microenvironment has been challenged by the lack of adequate culture models. Here we developed co-cultures of glioma stem cells with various types of normal brain cells, including primary neurons, astrocytes, microglia, and brain endothelial cells, and demonstrated their utility for the study of intercellular communication. Detection of proposed markers in the recipient cells confirmed RNA transfer in these co-cultures.

Molecular screening and genetic diversity analysis of anticancer Azurin-encoding and Azurin-like genes in human gut microbiome deduced through cultivation-dependent and cultivation-independent studies.

Azurin, a bacteriocin produced by a human gut bacterium Pseudomonas aeruginosa, can reveal selectively cytotoxic and induce apoptosis in cancer cells. After overcoming two phase I trials, a functional region of Azurin called p28 has been approved as a drug for the treatment of brain tumor glioma by FDA. The present study aims to improve a screening procedure and assess genetic diversity of Azurin genes in P. aeruginosa and Azurin-like genes in the gut microbiome of a specific population in Vietnam and global populations. Firstly, both cultivation-dependent and cultivation-independent techniques based on genomic and metagenomic DNAs extracted from fecal samples of the healthy specific population were performed and optimized to detect Azurin genes. Secondly, the Azurin gene sequences were analyzed and compared with global populations by using bioinformatics tools. Finally, the screening procedure improved from the first step was applied for screening Azurin-like genes, followed by the protein synthesis and NCI in vitro screening for anticancer activity. As a result, this study has successfully optimized the annealing temperatures to amplify DNAs for screening Azurin genes and applying to Azurin-like genes from human gut microbiota. The novelty of this study is the first of its kind to classify Azurin genes into five different genotypes at a global scale and confirm the potential anticancer activity of three Azurin-like synthetic proteins (Cnazu1, Dlazu11, and Ruazu12). The results contribute to the procedure development applied for screening anticancer proteins from human microbiome and a comprehensive understanding of their therapeutic response at a genetic level.

Iodine nanoparticles enhance radiotherapy of intracerebral human glioma in mice and increase efficacy of chemotherapy

Gliomas and other brain tumors have evaded durable therapies, ultimately causing about 20% of all cancer deaths. Tumors are widespread in the brain at time of diagnosis, limiting surgery and radiotherapy effectiveness. Drugs are also poorly effective. Radiotherapy (RT) is limited by dose to normal tissue. However, high-atomic-number elements absorb X-rays and deposit the absorbed dose locally, even doubling (or more) the local dose. Previously we showed that gold nanoparticles (AuNPs) with RT could eradicate some brain tumors in mice and many other preclinical studies confirmed AuNPs as outstanding radioenhancers. However, impediments to clinical translation of AuNPs have been poor clearance, skin discoloration, and cost. We therefore developed iodine nanoparticles (INPs) that are almost colorless, non-toxic, lower cost, and have reasonable clearance, thus overcoming major drawbacks of AuNPs. Here we report the use of iodine nanoparticle radiotherapy (INRT) in treating advanced human gliomas (U87) grown orthotopically in nude mice resulting in a more than a doubling of median life extension compared to RT alone. Significantly, INRT also enhanced the efficacy of chemotherapy when it was combined with the chemotherapeutic agent Doxil, resulting in some longer-term survivors. While ongoing optimization studies should further improve INRT, clinical translation appears promising.