Molecular imaging coupled to pattern recognition distinguishes response to temozolomide in preclinical glioblastoma

Optimization, Characterization, and Comparison of Two Luciferase-Expressing Mouse Glioblastoma Models

Glioblastoma (GBM) is the most aggressive brain cancer. To model GBM in research, orthotopic brain tumor models, including syngeneic models like GL261 and genetically engineered mouse models like TRP, are used. In longitudinal studies, tumor growth and the treatment response are typically tracked with in vivo imaging, including bioluminescence imaging (BLI), which is quick, cost-effective, and easily quantifiable. However, BLI requires luciferase-tagged cells, and recent studies indicate that the luciferase gene can elicit an immune response, leading to tumor rejection and experimental variation. We sought to optimize the engraftment of two luciferase-expressing GBM models, GL261 Red-FLuc and TRP-mCherry-FLuc, showing differences in tumor take, with GL261 Red-FLuc cells requiring immunocompromised mice for 100% engraftment. Immunohistochemistry and MRI revealed distinct tumor characteristics: GL261 Red-FLuc tumors were well-demarcated with densely packed cells, high mitotic activity, and vascularization. In contrast, TRP-mCherry-FLuc tumors were large, invasive, and necrotic, with perivascular invasion. Quantifying the tumor volume using the HALO® AI analysis platform yielded results comparable to manual measurements, providing a standardized and efficient approach for the reliable, high-throughput analysis of luciferase-expressing tumors. Our study highlights the importance of considering tumor engraftment when using luciferase-expressing GBM models, providing insights for preclinical research design.

Long acting tariquidar loaded stearic acid-modified hydroxyapatite enhances brain penetration and antitumor effect of temozolomide

Temozolomide (TMZ) is the first line chemotherapy for glioblastoma (GBM) treatment, but the P-glycoprotein (P-gp) expressed in blood-brain barrier (BBB) will pump out TMZ from the brain leading to decreased TMZ concentration. Tariquidar (TQD), a selective and potent P-gp inhibitor, may be suitable for combination therapy to increase concentration of TMZ in brain. Hydroxyapatite (HAP) is a biodegradable material with sustained release characteristics, and stearic acid surface-modified HAP (SA-HAP) can increase hydrophobicity to facilitate TQD loading. TQD-loaded stearic acid surface-modified HAP (SA-HAP-TQD) was prepared with optimal size and high TQD loading efficiency, and in vitro release and cellular uptake of SA-HAP-TQD showed that SA-HAP-TQD were taken up into lysosome and continuously released TQD from macrophages. In vivo studies have found that over 70 % of SA-HAP was degraded and 80 % of TQD was released from SA-HAP-TQD 28 days after administration. SA-HAP-TQD could increase brain penetration of TMZ, but it would not enhance adverse effects of TMZ in healthy mice. SA-HAP-TQD and TMZ combination had longer median survival than TMZ single therapy in GL261 orthotopic model. These results suggest that SA-HAP-TQD has sustained release characteristics and are potential for improving antitumor effect with TMZ treatment.

Early pseudoprogression and progression lesions in glioblastoma patients are both metabolically heterogeneous

The standard treatment in glioblastoma includes maximal safe resection followed by concomitant radiotherapy plus chemotherapy and adjuvant temozolomide. The first follow-up study to evaluate treatment response is performed 1 month after concomitant treatment, when contrast-enhancing regions may appear that can correspond to true progression or pseudoprogression. We retrospectively evaluated 31 consecutive patients at the first follow-up after concomitant treatment to check whether the metabolic pattern assessed with multivoxel MRS was predictive of treatment response 2 months later. We extracted the underlying metabolic patterns of the contrast-enhancing regions with a blind-source separation method and mapped them over the reference images. Pattern heterogeneity was calculated using entropy, and association between patterns and outcomes was measured with Cramér's V. We identified three distinct metabolic patterns-proliferative, necrotic, and responsive, which were associated with status 2 months later. Individually, 70% of the patients showed metabolically heterogeneous patterns in the contrast-enhancing regions. Metabolic heterogeneity was not related to the regions' size and only stable patients were less heterogeneous than the rest. Contrast-enhancing regions are also metabolically heterogeneous 1 month after concomitant treatment. This could explain the reported difficulty in finding robust pseudoprogression biomarkers.

Tracking Therapy Response in Glioblastoma Using 1D Convolutional Neural Networks

BACKGROUND

Glioblastoma (GB) is a malignant brain tumour that is challenging to treat, often relapsing even after aggressive therapy. Evaluating therapy response relies on magnetic resonance imaging (MRI) following the Response Assessment in Neuro-Oncology (RANO) criteria. However, early assessment is hindered by phenomena such as pseudoprogression and pseudoresponse. Magnetic resonance spectroscopy (MRS/MRSI) provides metabolomics information but is underutilised due to a lack of familiarity and standardisation.

METHODS

This study explores the potential of spectroscopic imaging (MRSI) in combination with several machine learning approaches, including one-dimensional convolutional neural networks (1D-CNNs), to improve therapy response assessment. Preclinical GB (GL261-bearing mice) were studied for method optimisation and validation.

RESULTS

The proposed 1D-CNN models successfully identify different regions of tumours sampled by MRSI, i.e., normal brain (N), control/unresponsive tumour (T), and tumour responding to treatment (R). Class activation maps using Grad-CAM enabled the study of the key areas relevant to the models, providing model explainability. The generated colour-coded maps showing the N, T and R regions were highly accurate (according to Dice scores) when compared against ground truth and outperformed our previous method.

CONCLUSIONS

The proposed methodology may provide new and better opportunities for therapy response assessment, potentially providing earlier hints of tumour relapsing stages.

Metabolomic and Lipidomic Profiling of Gliomas-A New Direction in Personalized Therapies

Simple Summary

Gliomas comprise an extremely diverse category of brain tumors that are difficult to diagnose and treat. As a result, scientists continue to search for new treatment solutions, with personalized medicine having emerged as a particularly promising therapeutic approach. Metabolomics and its sub-discipline, lipidomics, are two scientific fields well-suited to support this search. Metabolomics focuses on the physicochemical changes in the metabolome, which include all of the small endogenous and exogenous compounds in a biological system. As such, metabolic analysis can help identify important biochemical pathways which could be the targets for new therapeutic approaches. This review examines the new directions of personalized therapies for gliomas and how metabolomic and lipidomic analysis assists in developing these strategies and monitoring their effectiveness. The discussion of new strategies is preceded by a brief overview of the current “gold standard” treatment for gliomas and the obstacles that new treatment approaches must overcome.

Abstract

In addition to being the most common primary brain tumor, gliomas are also among the most difficult to diagnose and treat. At present, the “gold standard” in glioma treatment entails the surgical resection of the largest possible portion of the tumor, followed by temozolomide therapy and radiation. However, this approach does not always yield the desired results. Additionally, the ability to cross the blood-brain barrier remains a major challenge for new potential drugs. Thus, researchers continue to search for targeted therapies that can be individualized based on the specific characteristics of each case. Metabolic and lipidomic research may represent two of the best ways to achieve this goal, as they enable detailed insights into the changes in the profile of small molecules in a biological system/specimen. This article reviews the new approaches to glioma therapy based on the analysis of alterations to biochemical pathways, and it provides an overview of the clinical results that may support personalized therapies in the future.

Establishing Imaging Biomarkers of Host Immune System Efficacy during Glioblastoma Therapy Response: Challenges, Obstacles and Future Perspectives

This hypothesis proposal addresses three major questions: (1) Why do we need imaging biomarkers for assessing the efficacy of immune system participation in glioblastoma therapy response? (2) Why are they not available yet? and (3) How can we produce them? We summarize the literature data supporting the claim that the immune system is behind the efficacy of most successful glioblastoma therapies but, unfortunately, there are no current short-term imaging biomarkers of its activity. We also discuss how using an immunocompetent murine model of glioblastoma, allowing the cure of mice and the generation of immune memory, provides a suitable framework for glioblastoma therapy response biomarker studies. Both magnetic resonance imaging and magnetic resonance-based metabolomic data (i.e., magnetic resonance spectroscopic imaging) can provide non-invasive assessments of such a system. A predictor based in nosological images, generated from magnetic resonance spectroscopic imaging analyses and their oscillatory patterns, should be translational to clinics. We also review hurdles that may explain why such an oscillatory biomarker was not reported in previous imaging glioblastoma work. Single shot explorations that neglect short-term oscillatory behavior derived from immune system attack on tumors may mislead actual response extent detection. Finally, we consider improvements required to properly predict immune system-mediated early response (1–2 weeks) to therapy. The sensible use of improved biomarkers may enable translatable evidence-based therapeutic protocols, with the possibility of extending preclinical results to human patients.

Tumor-Associated Microglia/Macrophages as a Predictor for Survival in Glioblastoma and Temozolomide-Induced Changes in CXCR2 Signaling with New Resistance Overcoming Strategy by Combination Therapy

Tumor recurrence is the main challenge in glioblastoma (GBM) treatment. Gold standard therapy temozolomide (TMZ) is known to induce upregulation of IL8/CXCL2/CXCR2 signaling that promotes tumor progression and angiogenesis. Our aim was to verify the alterations on this signaling pathway in human GBM recurrence and to investigate the impact of TMZ in particular. Furthermore, a combi-therapy of TMZ and CXCR2 antagonization was established to assess the efficacy and tolerability. First, we analyzed 76 matched primary and recurrent GBM samples with regard to various histological aspects with a focus on the role of TMZ treatment and the assessment of predictors of overall survival (OS). Second, the combi-therapy with TMZ and CXCR2-antagonization was evaluated in a syngeneic mouse tumor model with in-depth immunohistological investigations and subsequent gene expression analyses. We observed a significantly decreased infiltration of tumor-associated microglia/macrophages (TAM) in recurrent tumors, while a high TAM infiltration in primary tumors was associated with a reduced OS. Additionally, more patients expressed IL8 in recurrent tumors and TMZ therapy maintained CXCL2 expression. In mice, enhanced anti-tumoral effects were observed after combi-therapy. In conclusion, high TAM infiltration predicts a survival disadvantage, supporting findings of the tumor-promoting phenotype of TAMs. Furthermore, the combination therapy seemed to be promising to overcome CXCR2-mediated resistance.

Cancer metabolism in a snapshot: MRS(I)

The contribution of MRS(I) to the in vivo evaluation of cancer-metabolism-derived metrics, mostly since 2016, is reviewed here. Increased carbon consumption by tumour cells, which are highly glycolytic, is now being sampled by ¹³ C magnetic resonance spectroscopic imaging (MRSI) following the injection of hyperpolarized [1-¹³ C] pyruvate (Pyr). Hot-spots of, mostly, increased lactate dehydrogenase activity or flow between Pyr and lactate (Lac) have been seen with cancer progression in prostate (preclinical and in humans), brain and pancreas (both preclinical) tumours. Therapy response is usually signalled by decreased Lac/Pyr ¹³ C-labelled ratio with respect to untreated or non-responding tumour. For therapeutic agents inducing tumour hypoxia, the ¹³ C-labelled Lac/bicarbonate ratio may be a better metric than the Lac/Pyr ratio. ³¹ P MRSI may sample intracellular pH changes from brain tumours (acidification upon antiangiogenic treatment, basification at fast proliferation and relapse). The steady state tumour metabolome pattern is still in use for cancer evaluation. Metrics used for this range from quantification of single oncometabolites (such as 2-hydroxyglutarate in mutant IDH1 glial brain tumours) to selected metabolite ratios (such as total choline to N-acetylaspartate (plain ratio or CNI index)) or the whole ¹ H MRSI(I) pattern through pattern recognition analysis. These approaches have been applied to address different questions such as tumour subtype definition, following/predicting the response to therapy or defining better resection or radiosurgery limits.

Imaging of cancer lipid metabolism in response to therapy

Lipids represent a diverse array of molecules essential to the cell's structure, defense, energy, and communication. Lipid metabolism can often become dysregulated during tumor development. During cancer therapy, targeted inhibition of cell proliferation can likewise cause widespread and drastic changes in lipid composition. Molecular imaging techniques have been developed to monitor altered lipid profiles as a biomarker for cancer diagnosis and treatment response. For decades, MRS has been the dominant non-invasive technique for studying lipid metabolite levels. Recent insights into the oncogenic transformations driving changes in lipid metabolism have revealed new mechanisms and signaling molecules that can be exploited using optical imaging, mass spectrometry imaging, and positron emission tomography. These novel imaging modalities have provided researchers with a diverse toolbox to examine changes in lipids in response to a wide array of anticancer strategies including chemotherapy, radiation therapy, signal transduction inhibitors, gene therapy, immunotherapy, or a combination of these strategies. The understanding of lipid metabolism in response to cancer therapy continues to evolve as each therapeutic method emerges, and this review seeks to summarize the current field and areas of unmet needs.

Embedding MRI information into MRSI data source extraction improves brain tumour delineation in animal models

Glioblastoma is the most frequent malignant intra-cranial tumour. Magnetic resonance imaging is the modality of choice in diagnosis, aggressiveness assessment, and follow-up. However, there are examples where it lacks diagnostic accuracy. Magnetic resonance spectroscopy enables the identification of molecules present in the tissue, providing a precise metabolomic signature. Previous research shows that combining imaging and spectroscopy information results in more accurate outcomes and superior diagnostic value. This study proposes a method to combine them, which builds upon a previous methodology whose main objective is to guide the extraction of sources. To this aim, prior knowledge about class-specific information is integrated into the methodology by setting the metric of a latent variable space where Non-negative Matrix Factorisation is performed. The former methodology, which only used spectroscopy and involved combining spectra from different subjects, was adapted to use selected areas of interest that arise from segmenting the T2-weighted image. Results showed that embedding imaging information into the source extraction (the proposed semi-supervised analysis) improved the quality of the tumour delineation, as compared to those obtained without this information (unsupervised analysis). Both approaches were applied to pre-clinical data, involving thirteen brain tumour-bearing mice, and tested against histopathological data. On results of twenty-eight images, the proposed Semi-Supervised Source Extraction (SSSE) method greatly outperformed the unsupervised one, as well as an alternative semi-supervised approach from the literature, with differences being statistically significant. SSSE has proven successful in the delineation of the tumour, while bringing benefits such as 1) not constricting the metabolomic-based prediction to the image-segmented area, 2) ability to deal with signal-to-noise issues, 3) opportunity to answer specific questions by allowing researchers/radiologists define areas of interest that guide the source extraction, 4) creation of an intra-subject model and avoiding contamination from inter-subject overlaps, and 5) extraction of meaningful, good-quality sources that adds interpretability, conferring validation and better understanding of each case.

Nuclear Magnetic Resonance Spectroscopy to Identify Metabolite Biomarkers of Nonresponsiveness to Targeted Therapy in Glioblastoma Tumor Stem Cells

Glioblastoma is the most common and malignant brain tumor, and current therapies confer only modest survival benefits. A major obstacle is our ability to monitor treatment effect on tumors. Current imaging modalities are ambiguous, and repeated biopsies are not encouraged. To scout for markers of treatment response, we used NMR spectroscopy to study the effects of a survivin inhibitor on the metabolome of primary glioblastoma cancer stem cells. Applying high resolution NMR spectroscopy (¹H resonance frequency: 800.03 MHz) to just 3 million cells per sample, we achieved sensitive and high resolving determinations of, e.g., amino acids, nucleosides, and constituents of the citric acid cycle. For control samples that were cultured, prepared, and measured at varying dates, peak area relative standard deviations were 15-20%. Analyses of unfractionated lysates were performed for straightforward compound identification with COLMAR and HMDB databases. Principal component analysis revealed that citrate levels were clearly upregulated in nonresponsive cells, while lactate levels substantially decreased following treatment for both responsive and nonresponsive cells. Hence, lactate and citrate may be potential markers of successful drug uptake and poor response to survivin inhibitors, respectively. Our metabolomics approach provided alternative biomarker candidates compared to spectrometry-based proteomics, underlining benefits of complementary methodologies. These initial findings make a foundation for exploring in vivo MR spectroscopy (MRS) of brain tumors, as citrate and lactate are MRS-visible. In sum, NMR metabolomics is a tool for addressing glioblastoma.

Metronomic treatment in immunocompetent preclinical GL261 glioblastoma: effects of cyclophosphamide and temozolomide

Glioblastoma (GBM) causes poor survival in patients even when applying aggressive treatment. Temozolomide (TMZ) is the standard chemotherapeutic choice for GBM treatment, but resistance always ensues. In previous years, efforts have focused on new therapeutic regimens with conventional drugs to activate immune responses that may enhance tumor regression and prevent regrowth, for example the "metronomic" approaches. In metronomic scheduling studies, cyclophosphamide (CPA) in GL261 GBM growing subcutaneously in C57BL/6 mice was shown not only to activate antitumor CD8⁺ T-cell response, but also to induce long-term specific T-cell tumor memory. Accordingly, we have evaluated whether metronomic CPA or TMZ administration could increase survival in orthotopic GL261 in C57BL/6 mice, an immunocompetent model. Longitudinal in vivo studies with CPA (140 mg/kg) or TMZ (range 140-240 mg/kg) metronomic administration (every 6 days) were performed in tumor-bearing mice. Tumor evolution was monitored at 7 T with MRI (T₂ -weighted, diffusion-weighted imaging) and MRSI-based nosological images of response to therapy. Obtained results demonstrated that both treatments resulted in increased survival (38.6 ± 21.0 days, n = 30) compared with control (19.4 ± 2.4 days, n = 18). Best results were obtained with 140 mg/kg TMZ (treated, 44.9 ± 29.0 days, n = 12, versus control, 19.3 ± 2.3 days, n = 12), achieving a longer survival rate than previous group work using three cycles of TMZ therapy at 60 mg/kg (33.9 ± 11.7 days, n = 38). Additional interesting findings were, first, clear edema appearance during chemotherapeutic treatment, second, the ability to apply the semi-supervised source analysis previously developed in our group for non-invasive TMZ therapy response monitoring to detect CPA-induced response, and third, the necropsy findings in mice cured from GBM after high TMZ cumulative dosage (980-1400 mg/kg), which demonstrated lymphoma incidence. In summary, every 6 day administration schedule of TMZ or CPA improves survival in orthotopic GL261 GBM with respect to controls or non-metronomic therapy, in partial agreement with previous work on subcutaneous GL261.

Temozolomide arrests glioma growth and normalizes intratumoral extracellular pH

Gliomas maintain an acidic extracellular pH (pH_e), which promotes tumor growth and builds resistance to therapy. Given evidence that acidic pH_e beyond the tumor core indicates infiltration, we hypothesized that imaging the intratumoral pH_e in relation to the peritumoral pH_e can provide a novel readout of therapeutic influence on the tumor microenvironment. We used Biosensor Imaging of Redundant Deviation in Shifts (BIRDS), which utilizes chemical shifts of non-exchangeable protons from macrocyclic chelates (e.g., DOTP⁸⁻) complexed with paramagnetic thulium (Tm³⁺), to generate pH_e maps in rat brains bearing U251 tumors. Following TmDOTP⁵⁻ infusion, T₂-weighted MRI provided delineation of the tumor boundary and BIRDS was used to image the pH_e gradient between intratumoral and peritumoral regions (ΔpH_e) in both untreated and temozolomide treated (40 mg/kg) rats bearing U251 tumors. Treated rats had reduced tumor volume (p < 0.01), reduced proliferation (Ki-67 staining; p < 0.03) and apoptosis induction (cleaved Caspase-3 staining; p < 0.001) when compared to untreated rats. The ΔpH_e was significantly higher in untreated compared to treated rats (p < 0.002), suggesting that temozolomide, which induces apoptosis and hinders proliferation, also normalizes intratumoral pH_e. Thus, BIRDS can be used to map the ΔpH_e in gliomas and provide a physiological readout of the therapeutic response on the tumor microenvironment.

Metabolomics of Therapy Response in Preclinical Glioblastoma: A Multi-Slice MRSI-Based Volumetric Analysis for Noninvasive Assessment of Temozolomide Treatment

Glioblastoma (GBM) is the most common aggressive primary brain tumor in adults, with a short survival time even after aggressive therapy. Non-invasive surrogate biomarkers of therapy response may be relevant for improving patient survival. Previous work produced such biomarkers in preclinical GBM using semi-supervised source extraction and single-slice Magnetic Resonance Spectroscopic Imaging (MRSI). Nevertheless, GBMs are heterogeneous and single-slice studies could prevent obtaining relevant information. The purpose of this work was to evaluate whether a multi-slice MRSI approach, acquiring consecutive grids across the tumor, is feasible for preclinical models and may produce additional insight into therapy response. Nosological images were analyzed pixel-by-pixel and a relative responding volume, the Tumor Responding Index (TRI), was defined to quantify response. Heterogeneous response levels were observed and treated animals were ascribed to three arbitrary predefined groups: high response (HR, n = 2), TRI = 68.2 ± 2.8%, intermediate response (IR, n = 6), TRI = 41.1 ± 4.2% and low response (LR, n = 2), TRI = 13.4 ± 14.3%, producing therapy response categorization which had not been fully registered in single-slice studies. Results agreed with the multi-slice approach being feasible and producing an inverse correlation between TRI and Ki67 immunostaining. Additionally, ca. 7-day oscillations of TRI were observed, suggesting that host immune system activation in response to treatment could contribute to the responding patterns detected.

Targeting Protein Kinase CK2: Evaluating CX-4945 Potential for GL261 Glioblastoma Therapy in Immunocompetent Mice

Glioblastoma (GBM) causes poor survival in patients even with aggressive treatment. Temozolomide (TMZ) is the standard chemotherapeutic choice for GBM treatment but resistance always ensues. Protein kinase CK2 (CK2) contributes to tumour development and proliferation in cancer, and it is overexpressed in human GBM. Accordingly, targeting CK2 in GBM may benefit patients. Our goal has been to evaluate whether CK2 inhibitors (iCK2s) could increase survival in an immunocompetent preclinical GBM model. Cultured GL261 cells were treated with different iCK2s including CX-4945, and target effects evaluated in vitro. CX-4945 was found to decrease CK2 activity and Akt(S129) phosphorylation in GL261 cells. Longitudinal in vivo studies with CX-4945 alone or in combination with TMZ were performed in tumour-bearing mice. Increase in survival (p < 0.05) was found with combined CX-4945 and TMZ metronomic treatment (54.7 ± 11.9 days, n = 6) when compared to individual metronomic treatments (CX-4945: 24.5 ± 2.0 and TMZ: 38.7 ± 2.7, n = 6) and controls (22.5 ± 1.2, n = 6). Despite this, CX-4945 did not improve mice outcome when administered on every/alternate days, either alone or in combination with 3-cycle TMZ. The highest survival rate was obtained with the metronomic combined TMZ+CX-4945 every 6 days, pointing to the participation of the immune system or other ancillary mechanism in therapy response.

Brain metabolic pattern analysis using a magnetic resonance spectra classification software in experimental stroke

Background

Magnetic resonance spectroscopy (MRS) provides non-invasive information about the metabolic pattern of the brain parenchyma in vivo. The SpectraClassifier software performs MRS pattern-recognition by determining the spectral features (metabolites) which can be used objectively to classify spectra. Our aim was to develop an Infarct Evolution Classifier and a Brain Regions Classifier in a rat model of focal ischemic stroke using SpectraClassifier.

Results

A total of 164 single-voxel proton spectra obtained with a 7 Tesla magnet at an echo time of 12 ms from non-infarcted parenchyma, subventricular zones and infarcted parenchyma were analyzed with SpectraClassifier (http://gabrmn.uab.es/?q=sc). The spectra corresponded to Sprague-Dawley rats (healthy rats, n = 7) and stroke rats at day 1 post-stroke (acute phase, n = 6 rats) and at days 7 ± 1 post-stroke (subacute phase, n = 14). In the Infarct Evolution Classifier, spectral features contributed by lactate + mobile lipids (1.33 ppm), total creatine (3.05 ppm) and mobile lipids (0.85 ppm) distinguished among non-infarcted parenchyma (100% sensitivity and 100% specificity), acute phase of infarct (100% sensitivity and 95% specificity) and subacute phase of infarct (78% sensitivity and 100% specificity). In the Brain Regions Classifier, spectral features contributed by myoinositol (3.62 ppm) and total creatine (3.04/3.05 ppm) distinguished among infarcted parenchyma (100% sensitivity and 98% specificity), non-infarcted parenchyma (84% sensitivity and 84% specificity) and subventricular zones (76% sensitivity and 93% specificity).

Conclusion

SpectraClassifier identified candidate biomarkers for infarct evolution (mobile lipids accumulation) and different brain regions (myoinositol content).

Electronic supplementary material

The online version of this article (doi:10.1186/s12868-016-0328-x) contains supplementary material, which is available to authorized users.

Development of a transplantable glioma tumour model from genetically engineered mice: MRI/MRS/MRSI characterisation

The initial aim of this study was to generate a transplantable glial tumour model of low-intermediate grade by disaggregation of a spontaneous tumour mass from genetically engineered models (GEM). This should result in an increased tumour incidence in comparison to GEM animals. An anaplastic oligoastrocytoma (OA) tumour of World Health Organization (WHO) grade III was obtained from a female GEM mouse with the S100β-v-erbB/inK4a-Arf (+/-) genotype maintained in the C57BL/6 background. The tumour tissue was disaggregated; tumour cells from it were grown in aggregates and stereotactically injected into C57BL/6 mice. Tumour development was followed using Magnetic Resonance Imaging (MRI), while changes in the metabolomics pattern of the masses were evaluated by Magnetic Resonance Spectroscopy/Spectroscopic Imaging (MRS/MRSI). Final tumour grade was evaluated by histopathological analysis. The total number of tumours generated from GEM cells from disaggregated tumour (CDT) was 67 with up to 100 % penetrance, as compared to 16 % in the local GEM model, with an average survival time of 66 ± 55 days, up to 4.3-fold significantly higher than the standard GL261 glioblastoma (GBM) tumour model. Tumours produced by transplantation of cells freshly obtained from disaggregated GEM tumour were diagnosed as WHO grade III anaplastic oligodendroglioma (ODG) and OA, while tumours produced from a previously frozen sample were diagnosed as WHO grade IV GBM. We successfully grew CDT and generated tumours from a grade III GEM glial tumour. Freezing and cell culture protocols produced progression to grade IV GBM, which makes the developed transplantable model qualify as potential secondary GBM model in mice.

MRSI-based molecular imaging of therapy response to temozolomide in preclinical glioblastoma using source analysis

Characterization of glioblastoma (GB) response to treatment is a key factor for improving patients' survival and prognosis. MRI and magnetic resonance spectroscopic imaging (MRSI) provide morphologic and metabolic profiles of GB but usually fail to produce unequivocal biomarkers of response. The purpose of this work is to provide proof of concept of the ability of a semi-supervised signal source extraction methodology to produce images with robust recognition of response to temozolomide (TMZ) in a preclinical GB model. A total of 38 female C57BL/6 mice were used in this study. The semi-supervised methodology extracted the required sources from a training set consisting of MRSI grids from eight GL261 GBs treated with TMZ, and six control untreated GBs. Three different sources (normal brain parenchyma, actively proliferating GB and GB responding to treatment) were extracted and used for calculating nosologic maps representing the spatial response to treatment. These results were validated with an independent test set (7 control and 17 treated cases) and correlated with histopathology. Major differences between the responder and non-responder sources were mainly related to the resonances of mobile lipids (MLs) and polyunsaturated fatty acids in MLs (0.9, 1.3 and 2.8 ppm). Responding tumors showed significantly lower mitotic (3.3 ± 2.9 versus 14.1 ± 4.2 mitoses/field) and proliferation rates (29.8 ± 10.3 versus 57.8 ± 5.4%) than control untreated cases. The methodology described in this work is able to produce nosological images of response to TMZ in GL261 preclinical GBs and suitably correlates with the histopathological analysis of tumors. A similar strategy could be devised for monitoring response to treatment in patients. Copyright © 2016 John Wiley & Sons, Ltd.

Protein Kinase CK2 Content in GL261 Mouse Glioblastoma

Glioblastoma (GBM) is the most prevalent and aggressive human glial tumour with a median survival of 14-15 months. Temozolomide (TMZ) is the standard chemotherapeutic choice for GBM treatment. Unfortunately, chemoresistence always ensues with concomitant tumour regrowth. Protein kinase CK2 (CK2) contributes to tumour development, proliferation, and suppression of apoptosis in cancer and it is overexpressed in human GBM. Targeting CK2 in GBM treatment may benefit patients. With this translational perspective in mind, we have studied the CK2 expression level by Western blot analysis in a preclinical model of GBM: GL261 cells growing orthotopically in C57BL/6 mice. The expression level of the CK2 catalytic subunit (CK2α) was higher in tumour (about 4-fold) and in contralateral brain parenchyma (more than 2-fold) than in normal brain parenchyma (p < 0.05). In contrast, no significant changes were found in CK2 regulatory subunit (CK2β) expression, suggesting an increased unbalance of CK2α/CK2β in GL261 tumours with respect to normal brain parenchyma, in agreement with a differential role of these two subunits in tumours.