Immune system-related changes in preclinical GL261 glioblastoma under TMZ treatment : Explaining MRSI-based nosological imaging findings with RT-PCR analyses

Altres ajuts: Centro de Investigación Biomédica en Red-Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN [http://www.ciber-bbn.es/en, accessed on 18 March 2021], CB06/01/0010). UAB Predoctoral training programme (14ª Convocatoria PIF-19612, predoctoral fellowships for P.C.-P.). 2018 XARDI 00016/IU68-013944 (XarTEC SALUT).Glioblastomas (GB) are brain tumours with poor prognosis even after aggressive therapy. Previous work suggests that magnetic resonance spectroscopic imaging (MRSI) could act as a biomarker of efficient immune system attack onto GB, presenting oscillatory changes. Glioma-associated microglia/macrophages (GAMs) constitute the most abundant non-tumour cell type within the GB and can be polarised into anti-tumour (M1) or pro-tumour (M2) phenotypes. One of the mechanisms to mediate immunosuppression in brain tumours is the interaction between programmed cell death-1 ligand 1 (PD-L1) and programmed cell death-1 receptor (PD-1). We evaluated the subpopulations of GAMs in responding and control GB tumours to correlate PD-L1 expression to GAM polarisation in order to explain/validate MRSI-detected findings. Mice were evaluated by MRI/MRSI to assess the extent of response to treatment and with qPCR for GAMs M1 and M2 polarisation analyses. M1/M2 ratios and PD-L1 expression were higher in treated compared to control tumours. Furthermore, PD-L1 expression was positively correlated with the M1/M2 ratio. The oscillatory change in the GAMs prevailing population could be one of the key causes for the differential MRSI-detected pattern, allowing this to act as immune system activity biomarker in future work

Platinum-Based Nanoformulations for Glioblastoma Treatment : The Resurgence of Platinum Drugs?

Current therapies for treating Glioblastoma (GB), and brain tumours in general, are inefficient and represent numerous challenges. In addition to surgical resection, chemotherapy and radiotherapy are presently used as standards of care. However, treated patients still face a dismal prognosis with a median survival below 15-18 months. Temozolomide (TMZ) is the main chemotherapeutic agent administered; however, intrinsic or acquired resistance to TMZ contributes to the limited efficacy of this drug. To circumvent the current drawbacks in GB treatment, a large number of classical and non-classical platinum complexes have been prepared and tested for anticancer activity, especially platinum (IV)-based prodrugs. Platinum complexes, used as alkylating agents in the anticancer chemotherapy of some malignancies, are though often associated with severe systemic toxicity (i.e., neurotoxicity), especially after long-term treatments. The objective of the current developments is to produce novel nanoformulations with improved lipophilicity and passive diffusion, promoting intracellular accumulation, while reducing toxicity and optimizing the concomitant treatment of chemo-/radiotherapy. Moreover, the blood-brain barrier (BBB) prevents the access of the drugs to the brain and accumulation in tumour cells, so it represents a key challenge for GB management. The development of novel nanomedicines with the ability to (i) encapsulate Pt-based drugs and pro-drugs, (ii) cross the BBB, and (iii) specifically target cancer cells represents a promising approach to increase the therapeutic effect of the anticancer drugs and reduce undesired side effects. In this review, a critical discussion is presented concerning different families of nanoparticles able to encapsulate platinum anticancer drugs and their application for GB treatment, emphasizing their potential for increasing the effectiveness of platinum-based drugs

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

Non-invasive grading of astrocytic tumours from the relative contents of myo-inositol and glycine measured by in vivo MRS

Altres ajuts: INTERPRET (EU-IST1999-10310). This work was also partially funded by the Centro de Investigación Biomédica en Red - Bioingeniería, Biomateriales y Nanomedicina, which is an initiative of the Instituto de Salud Carlos III (Spain) co-funded by EU FEDER funds.MRI and MRS are established methodologies for evaluating intracranial lesions. One MR spectral feature suggested for in vivo grading of astrocytic tumours is the apparent myo-Inositol (mI) intensity (ca 3.55ppm) at short echo times, although glycine (gly) may also contribute in vivo to this resonance. The purpose of this study was to quantitatively evaluate the mI + gly contribution to the recorded spectral pattern in vivo and correlate it with in vitro data obtained from perchloric acid extraction of tumour biopsies. Patient spectra (n = 95) at 1.5T at short (20-31 ms) and long (135-136 ms) echo times were obtained from the INTERPRET MRS database (http://gabrmn.uab.es/interpretvalidateddb/). Phantom spectra were acquired with a comparable protocol. Spectra were automatically processed and the ratios of the (mI + gly) to Cr peak heights ((mI + gly)/Cr) calculated. Perchloric acid extracts of brain tumour biopsies were analysed by high-resolution NMR at 9.4T. The ratio (mI + gly)/Cr decreased significantly with astrocytic grade in vivo between low-grade astrocytoma (A2) and glioblastoma multiforme (GBM). In vitro results displayed a somewhat different tendency, with anaplastic astrocytomas having significantly higher (mI + gly)/Cr than A2 and GBM. The discrepancy between in vivo and in vitro data suggests that the NMR visibility of glycine in glial brain tumours is restricted in vivo

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

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

Development of robust discriminant equations for assessing subtypes of glioblastoma biopsies

In the preceding decade, various studies on glioblastoma (Gb) demonstrated that signatures obtained from gene expression microarrays correlate better with survival than with histopathological classification. However, there is not a universal consensus formula to predict patient survival. We developed a gene signature using the expression profile of 47 Gbs through an unsupervised procedure and two groups were obtained. Subsequent to a training procedure through leave-one-out cross-validation, we fitted a discriminant (linear discriminant analysis (LDA)) equation using the four most discriminant probesets. This was repeated for two other published signatures and the performance of LDA equations was evaluated on an independent test set, which contained status of IDH1 mutation, EGFR amplification, MGMT methylation and gene VEGF expression, among other clinical and molecular information. The unsupervised local signature was composed of 69 probesets and clearly defined two Gb groups, which would agree with primary and secondary Gbs. This hypothesis was confirmed by predicting cases from the independent data set using the equations developed by us. The high survival group predicted by equations based on our local and one of the published signatures contained a significantly higher percentage of cases displaying IDH1 mutation and non-amplification of EGFR. In contrast, only the equation based on the published signature showed in the poor survival group a significant high percentage of cases displaying a hypothesised methylation of MGMT gene promoter and overexpression of gene VEGF. We have produced a robust equation to confidently discriminate Gb subtypes based in the normalised expression level of only four genes

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

Contribució a la millora del diagnòstic i de la valoració pronòstica de tumors cerebrals humans

Consultable des del TDXTítol obtingut de la portada digitalitzadaEls tumors cerebrals humans són relativament freqüents a persones d'edat avançada. El seu correcte diagnòstic té gran importància per garantir procediments terapèutics o decisió clínica adequats. La espectroscòpia per ressonància magnètica nuclear in vivo és una eina per al diagnòstic no invasiu amb gran potencial davant la sospita de lesió intracerebral. Es pot adquirir en la mateixa exploració que les imatges per ressonància magnètica amb un petit augment del temps total. En canvi, l'espectroscòpia d'alta resolució ha estat molt utilitzada en l'anàlisis d'extractes de metabòlits de biòpsies tumorals o bé de fluids corporals per a una millor resolució i separació dels diferents components moleculars de la mostra. Aquesta tesi tracta de l'anàlisi in vivo i in vitro de patrons espectrals de tumors cerebrals humans i també de lesions cístiques associades a aquests tumors. L'anàlisi d'espectres in vivo ha emprat també la metodologia de reconeixement de patrons espectrals. En l'estudi de lesions cístiques, hem desenvolupat un classificador automàtic basat en patrons espectrals in vivo per distingir entre lesions malignes, benignes i abscessos. Els líquids cístics han estat estudiats quimicament. S'han detectat àcid siàlic i àcid hexurònic, compostos moltes vegades presents a elements macromoleculars de matriu extracel·lar. S'han realitzat extraccions de metabòlits solubles en àcid perclòric (PCA) dels líquids per observar-ne el patró espectral. Els principals resultats d'aquest apartat són que el senyal observat in vivo a 2,03 ppm, generalment associat a N-acetilaspartat, correspon en bona part a un element macromolecular, possiblement siàlic associat a proteïnes. S'han analitzat els patrons in vivo i en dissolucions model a camp clínic (1,5 Teslas) de mio-inositol i glicina, donada la seva importància en la gradació de tumors astrocítics i en la discriminació d'altres tipus tumorals com hemangiopericitomes i meningiomes, i s'ha dissenyat una corba de calibració amb dissolucions model per determinar les quantitats relatives de cadascun a l'espectre in vivo. Això permetrà la comparació d'aquests valors amb els obtinguts a l'anàlisi de biòpsies in vitro. En aquest apartat, s'ha comprovat que la quantitat relativa de mio-inositol disminueix amb l'augment del grau tumoral. També es van plantejar, a l'anàlisi de patrons espectrals a camp clínic, fórmules discriminatòries experimentals («classificadors»), com la basada en mio-inositol/glicina i lípids per a discriminar glioblastoma de metàstasis amb una millora de la discriminació diferencial per 22% dels casos de glioblastoma. Les anàlisis in vitro s'han fet mitjançant extraccions de metabòlits solubles en PCA amb posterior avaluació del patró espectral mitjançant una anàlisi estadística, per detectar metabòlits útils en la discriminació futura de tipus tumorals difícils de diferenciar en exploracions in vivo. A més, s'han realitzat tests preliminars de reconeixement de patrons espectrals in vitro. S'han obtingut les evidències per entendre les diferències observades in vivo. Finalment, la detecció de concentracions de glicina in vivo als tumors astrocítics de grau baix, més reduïda que la obtinguda in vitro, suggereix una restricció de la mobilitat de la glicina al teixit intacte, possiblement degut a la seva associació amb macromolècules cel·lulars. A més, aquesta Tesi també tracta de la col·laboració al desenvolupament d'una eina d'auxili al diagnòstic de tumors cerebrals, basada en patrons espectrals de RMN de 1H, anàlisi d'imatges per ressonància magnètica, anatomies patològiques i dades clíniques, associada al projecte INTERPRET (International Network for Pattern Recognition of Tumours using Magnetic Resonance, http://carbon.uab.es/INTERPRET). Aquesta eina es troba en procés de certificació industrial i està sent sotmesa a avaluació en diferents centres clínics.Human cerebral tumours are relatively frequent neoplasms. access to closed cranial spaces is difficult and then proper diagnositc is important to ensure correct therapeutic proceedings. In vivo magnetic resonance spectroscopy is an important diagnostic tool for abnormal intracraneal masses. Spectra can be acquired in the same magnetic resonance imaging exploration, with little additional time. On the other hand, high resolution spectroscopy, by another hand, has been widely used in perchloric extracts analysis and for body liquids and fluids to achieve a better resolution and separation of different sample compounds. This thesis deals with the in vivo and in vitro analyses of spectral patterns of human cerebral tumours and also cystic lesions associated with these tumours or with infectious abscesses. Pattern recognition techniques have also been applied to in vivo spectral analyses of data. We have developed an automatized classifier based in the spectral pattern recorded in vivo from cystic lesions to distinguish between malignant and benign tumours and abscesses. Cystic fluids have also been chemically analysed and we have found sialic and hexuronic acids, often present in macromolecular components of the extracellular matrix. We have also carried out perchloric acid (PCA) extraction of soluble metabolites of cyst fluids to characterize its spectral pattern. Main results of this part were that the 2,03 ppm signal in vivo, generally attributed to N-acetyl aspartate, is mainly due to a macromolecular component, possibly protein-bound sialic acid. Furthermore, we have analysed in vivo and model solution spectral patterns (clinical field, 1,5 Teslas) of myo-inositol (mI) and glycine (gly), due to their possible importance in astrocytic tumour grading and also for discrimination of other tumoral types as hemangioperycitomas and meningiomas. We have also designed a calibration curve with model solutions to calculate de mI/gly in in vivo spectra. This has allowed comparison of these values with in vitro ones, obtained from biopsy PCA extractanalyses. In this part, we have verified that the relative concentration of myo-inositol decreases with increasing tumoral grade. In in vivo spectral pattern analysis, we have also proposed experimental discriminative formulas (classifiers), as one based in myo-inositol/glycine and lipids, to discriminate glioblastomas from metastasis with an improvement of discrimination in 22% of the glioblastoma cases. In vitro analyses have been carried out with PCA-soluble metabolites, with further evaluation of spectral pattern by means of statistical analyses, in order to detect metabolites potentially useful in discrimination of tumoral types difficult to be performed in toward?¿? clinical practice. Moreover, preliminary studies with pattern recognition techniques on the in vitro data have been started. These studies will provide evidence-based facts to understand the in vivo detected differences. We have confirmed previous results, such as the high myo-inositol concentration in hemangioperycitomas, being a very important differential diagnostic feature. Finally, detection of the in vivo apparent glycine concentration in astrocytic tumours, lower than the in vitro detected concentration, suggests a reduction of glycine mobility in intact tissue, possibly due to its association with cell macromolecules. This thesis also describes the collaboration in the development of a decision-support-system to aid cerebral tumour diagnosis, based in 1H-RMN spectral patterns, magnetic resonance imaging analysis, anatomical pathology and clinical data, associated to the INTERPRET project (International Network for Pattern Recognition of Tumours using Magnetic Resonance, http://carbon.uab.es/INTERPRET). This system is presently in process of industrial certification and its performance is being evaluated at several clinical centres

Tracking Therapy Response in Glioblastoma Using 1D Convolutional Neural Networks

Glioblastoma (GB) is a malignant brain tumour with no cure, even after the best treatment. The evaluation of a therapy response is usually based on magnetic resonance imaging (MRI), but it lacks precision in early stages, and doctors must wait several weeks until they are confident information is produced, facing an uncertain time window. Magnetic resonance spectroscopy (MRS/MRSI) can provide additional information about tumours and their environment but is not widely used in clinical settings since the spectroscopy format is not standardised as MRI is, and doctors are not familiarised with outputs/interpretation. This study aims to improve the assessment of the treatment response in GB using MRSI data and machine learning, including state-of-the-art one-dimensional convolutional neural networks. Preclinical (murine) GB data were used for developing models that successfully identified tumour regions regarding their response to treatment (or the lack thereof). These models were accurate and outperformed previous methods, potentially providing new opportunities for GB patient management. 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