Unsupervised learning for vascular heterogeneity assessment of glioblastoma based on magnetic resonance imaging: The Hemodynamic Tissue Signature

[ES] El futuro de la imagen médica está ligado a la inteligencia artificial. El análisis manual de imágenes médicas es hoy en día una tarea ardua, propensa a errores y a menudo inasequible para los humanos, que ha llamado la atención de la comunidad de Aprendizaje Automático (AA). La Imagen por Resonancia Magnética (IRM) nos proporciona una rica variedad de representaciones de la morfología y el comportamiento de lesiones inaccesibles sin una intervención invasiva arriesgada. Sin embargo, explotar la potente pero a menudo latente información contenida en la IRM es una tarea muy complicada, que requiere técnicas de análisis computacional inteligente. Los tumores del sistema nervioso central son una de las enfermedades más críticas estudiadas a través de IRM. Específicamente, el glioblastoma representa un gran desafío, ya que, hasta la fecha, continua siendo un cáncer letal que carece de una terapia satisfactoria. Del conjunto de características que hacen del glioblastoma un tumor tan agresivo, un aspecto particular que ha sido ampliamente estudiado es su heterogeneidad vascular. La fuerte proliferación vascular del glioblastoma, así como su robusta angiogénesis han sido consideradas responsables de la alta letalidad de esta neoplasia. Esta tesis se centra en la investigación y desarrollo del método Hemodynamic Tissue Signature (HTS): un método de AA no supervisado para describir la heterogeneidad vascular de los glioblastomas mediante el análisis de perfusión por IRM. El método HTS se basa en el concepto de hábitat, que se define como una subregión de la lesión con un perfil de IRM que describe un comportamiento fisiológico concreto. El método HTS delinea cuatro hábitats en el glioblastoma: el hábitat HAT, como la región más perfundida del tumor con captación de contraste; el hábitat LAT, como la región del tumor con un perfil angiogénico más bajo; el hábitat IPE, como la región adyacente al tumor con índices de perfusión elevados; y el hábitat VPE, como el edema restante de la lesión con el perfil de perfusión más bajo. La investigación y desarrollo de este método ha originado una serie de contribuciones enmarcadas en esta tesis. Primero, para verificar la fiabilidad de los métodos de AA no supervisados en la extracción de patrones de IRM, se realizó una comparativa para la tarea de segmentación de gliomas de grado alto. Segundo, se propuso un algoritmo de AA no supervisado dentro de la familia de los Spatially Varying Finite Mixture Models. El algoritmo propone una densidad a priori basada en un Markov Random Field combinado con la función probabilística Non-Local Means, para codificar la idea de que píxeles vecinos tienden a pertenecer al mismo objeto. Tercero, se presenta el método HTS para describir la heterogeneidad vascular del glioblastoma. El método se ha aplicado a casos reales en una cohorte local de un solo centro y en una cohorte internacional de más de 180 pacientes de 7 centros europeos. Se llevó a cabo una evaluación exhaustiva del método para medir el potencial pronóstico de los hábitats HTS. Finalmente, la tecnología desarrollada en la tesis se ha integrado en la plataforma online ONCOhabitats (https://www.oncohabitats.upv.es). La plataforma ofrece dos servicios: 1) segmentación de tejidos de glioblastoma, y 2) evaluación de la heterogeneidad vascular del tumor mediante el método HTS. Los resultados de esta tesis han sido publicados en diez contribuciones científicas, incluyendo revistas y conferencias de alto impacto en las áreas de Informática Médica, Estadística y Probabilidad, Radiología y Medicina Nuclear y Aprendizaje Automático. También se emitió una patente industrial registrada en España, Europa y EEUU. Finalmente, las ideas originales concebidas en esta tesis dieron lugar a la creación de ONCOANALYTICS CDX, una empresa enmarcada en el modelo de negocio de los companion diagnostics de compuestos farmacéuticos.[EN] The future of medical imaging is linked to Artificial Intelligence (AI). The manual analysis of medical images is nowadays an arduous, error-prone and often unaffordable task for humans, which has caught the attention of the Machine Learning (ML) community. Magnetic Resonance Imaging (MRI) provides us with a wide variety of rich representations of the morphology and behavior of lesions completely inaccessible without a risky invasive intervention. Nevertheless, harnessing the powerful but often latent information contained in MRI acquisitions is a very complicated task, which requires computational intelligent analysis techniques. Central nervous system tumors are one of the most critical diseases studied through MRI. Specifically, glioblastoma represents a major challenge, as it remains a lethal cancer that, to date, lacks a satisfactory therapy. Of the entire set of characteristics that make glioblastoma so aggressive, a particular aspect that has been widely studied is its vascular heterogeneity. The strong vascular proliferation of glioblastomas, as well as their robust angiogenesis and extensive microvasculature heterogeneity have been claimed responsible for the high lethality of the neoplasm. This thesis focuses on the research and development of the Hemodynamic Tissue Signature (HTS) method: an unsupervised ML approach to describe the vascular heterogeneity of glioblastomas by means of perfusion MRI analysis. The HTS builds on the concept of habitats. A habitat is defined as a sub-region of the lesion with a particular MRI profile describing a specific physiological behavior. The HTS method delineates four habitats within the glioblastoma: the HAT habitat, as the most perfused region of the enhancing tumor; the LAT habitat, as the region of the enhancing tumor with a lower angiogenic profile; the potentially IPE habitat, as the non-enhancing region adjacent to the tumor with elevated perfusion indexes; and the VPE habitat, as the remaining edema of the lesion with the lowest perfusion profile. The research and development of the HTS method has generated a number of contributions to this thesis. First, in order to verify that unsupervised learning methods are reliable to extract MRI patterns to describe the heterogeneity of a lesion, a comparison among several unsupervised learning methods was conducted for the task of high grade glioma segmentation. Second, a Bayesian unsupervised learning algorithm from the family of Spatially Varying Finite Mixture Models is proposed. The algorithm integrates a Markov Random Field prior density weighted by the probabilistic Non-Local Means function, to codify the idea that neighboring pixels tend to belong to the same semantic object. Third, the HTS method to describe the vascular heterogeneity of glioblastomas is presented. The HTS method has been applied to real cases, both in a local single-center cohort of patients, and in an international retrospective cohort of more than 180 patients from 7 European centers. A comprehensive evaluation of the method was conducted to measure the prognostic potential of the HTS habitats. Finally, the technology developed in this thesis has been integrated into an online open-access platform for its academic use. The ONCOhabitats platform is hosted at https://www.oncohabitats.upv.es, and provides two main services: 1) glioblastoma tissue segmentation, and 2) vascular heterogeneity assessment of glioblastomas by means of the HTS method. The results of this thesis have been published in ten scientific contributions, including top-ranked journals and conferences in the areas of Medical Informatics, Statistics and Probability, Radiology & Nuclear Medicine and Machine Learning. An industrial patent registered in Spain, Europe and EEUU was also issued. Finally, the original ideas conceived in this thesis led to the foundation of ONCOANALYTICS CDX, a company framed into the business model of companion diagnostics for pharmaceutical compounds.[CA] El futur de la imatge mèdica està lligat a la intel·ligència artificial. L'anàlisi manual d'imatges mèdiques és hui dia una tasca àrdua, propensa a errors i sovint inassequible per als humans, que ha cridat l'atenció de la comunitat d'Aprenentatge Automàtic (AA). La Imatge per Ressonància Magnètica (IRM) ens proporciona una àmplia varietat de representacions de la morfologia i el comportament de lesions inaccessibles sense una intervenció invasiva arriscada. Tanmateix, explotar la potent però sovint latent informació continguda a les adquisicions de IRM esdevé una tasca molt complicada, que requereix tècniques d'anàlisi computacional intel·ligent. Els tumors del sistema nerviós central són una de les malalties més crítiques estudiades a través de IRM. Específicament, el glioblastoma representa un gran repte, ja que, fins hui, continua siguent un càncer letal que manca d'una teràpia satisfactòria. Del conjunt de característiques que fan del glioblastoma un tumor tan agressiu, un aspecte particular que ha sigut àmpliament estudiat és la seua heterogeneïtat vascular. La forta proliferació vascular dels glioblastomes, així com la seua robusta angiogènesi han sigut considerades responsables de l'alta letalitat d'aquesta neoplàsia. Aquesta tesi es centra en la recerca i desenvolupament del mètode Hemodynamic Tissue Signature (HTS): un mètode d'AA no supervisat per descriure l'heterogeneïtat vascular dels glioblastomas mitjançant l'anàlisi de perfusió per IRM. El mètode HTS es basa en el concepte d'hàbitat, que es defineix com una subregió de la lesió amb un perfil particular d'IRM, que descriu un comportament fisiològic concret. El mètode HTS delinea quatre hàbitats dins del glioblastoma: l'hàbitat HAT, com la regió més perfosa del tumor amb captació de contrast; l'hàbitat LAT, com la regió del tumor amb un perfil angiogènic més baix; l'hàbitat IPE, com la regió adjacent al tumor amb índexs de perfusió elevats, i l'hàbitat VPE, com l'edema restant de la lesió amb el perfil de perfusió més baix. La recerca i desenvolupament del mètode HTS ha originat una sèrie de contribucions emmarcades a aquesta tesi. Primer, per verificar la fiabilitat dels mètodes d'AA no supervisats en l'extracció de patrons d'IRM, es va realitzar una comparativa en la tasca de segmentació de gliomes de grau alt. Segon, s'ha proposat un algorisme d'AA no supervisat dintre de la família dels Spatially Varying Finite Mixture Models. L'algorisme proposa un densitat a priori basada en un Markov Random Field combinat amb la funció probabilística Non-Local Means, per a codificar la idea que els píxels veïns tendeixen a pertànyer al mateix objecte semàntic. Tercer, es presenta el mètode HTS per descriure l'heterogeneïtat vascular dels glioblastomas. El mètode HTS s'ha aplicat a casos reals en una cohort local d'un sol centre i en una cohort internacional de més de 180 pacients de 7 centres europeus. Es va dur a terme una avaluació exhaustiva del mètode per mesurar el potencial pronòstic dels hàbitats HTS. Finalment, la tecnologia desenvolupada en aquesta tesi s'ha integrat en una plataforma online ONCOhabitats (https://www.oncohabitats.upv.es). La plataforma ofereix dos serveis: 1) segmentació dels teixits del glioblastoma, i 2) avaluació de l'heterogeneïtat vascular dels glioblastomes mitjançant el mètode HTS. Els resultats d'aquesta tesi han sigut publicats en deu contribucions científiques, incloent revistes i conferències de primer nivell a les àrees d'Informàtica Mèdica, Estadística i Probabilitat, Radiologia i Medicina Nuclear i Aprenentatge Automàtic. També es va emetre una patent industrial registrada a Espanya, Europa i els EEUU. Finalment, les idees originals concebudes en aquesta tesi van donar lloc a la creació d'ONCOANALYTICS CDX, una empresa emmarcada en el model de negoci dels companion diagnostics de compostos farmacèutics.En este sentido quiero agradecer a las diferentes instituciones y estructuras de financiación de investigación que han contribuido al desarrollo de esta tesis. En especial quiero agradecer a la Universitat Politècnica de València, donde he desarrollado toda mi carrera acadèmica y científica, así como al Ministerio de Ciencia e Innovación, al Ministerio de Economía y Competitividad, a la Comisión Europea, al EIT Health Programme y a la fundación Caixa ImpulseJuan Albarracín, J. (2020). Unsupervised learning for vascular heterogeneity assessment of glioblastoma based on magnetic resonance imaging: The Hemodynamic Tissue Signature [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/149560TESI

Unsupervised glioblastoma segmentation based on multiparametric Magnetic Resonance Imaging (MRI)

[EN] Design and evaluation of an automated unsupervised segmentation method for brain tumour, specifically glioblastoma tumour, based on Magnetic Resonance Imaging (MRI). A preprocessing and feature extraction pipeline based on the state of the art techniques for MRI is proposed. Several unsupervised classification algorithms are studied and evaluated, considering structured and non structured classification algorithms. An original postprocessing method is designed to automatically identify the pathological classes of a segmentation. The unsupervised method is evaluated with a real public reference dataset, against consolidated supervised approaches.[ES] Diseño y evaluación de un método de segmentación automática no supervisada de tumor cerebral, en concreto glioblastomas, mediante imágenes de Resonancia Magnética (RM). Se propone un pipeline de preprocesamiento y extracción de carácterísticas basado en técnicas del estado del arte en imágenes de RM. Se estudian y evaluan distintos algoritmos de clasificación no supervisada, diferenciando entre algoritmos de clasificación estructurada y clasificación no estructurada. Se diseña y propone un postproceso original para la identificación automática de clases patológicas en una segmentación. Se evalua el método no supervisado mediante a una base de datos pública real de referencia, en la que participan metodos consolidados principalmente supervisados.Juan Albarracín, J. (2014). Unsupervised glioblastoma segmentation based on multiparametric Magnetic Resonance Imaging (MRI). http://hdl.handle.net/10251/51064Archivo delegad

Diseño, análisis y optimización de un sistema de reconocimiento de imágenes basadas en contenido para imagen publicitaria

Juan Albarracín, J. (2011). Diseño, análisis y optimización de un sistema de reconocimiento de imágenes basadas en contenido para imagen publicitaria. http://hdl.handle.net/10251/13959.Archivo delegad

Non-local spatially varying finite mixture models for image segmentation

[EN] In this work, we propose a new Bayesian model for unsupervised image segmentation based on a combination of the spatially varying finite mixture models (SVFMMs) and the non-local means (NLM) framework. The probabilistic NLM weighting function is successfully integrated into a varying Gauss¿Markov random field, yielding a prior density that adaptively imposes a local regularization to simultaneously preserve edges and enforce smooth constraints in homogeneous regions of the image. Two versions of our model are proposed: a pixel-based model and a patch-based model, depending on the design of the probabilistic NLM weighting function. Contrary to previous methods proposed in the literature, our approximation does not introduce new parameters to be estimated into the model, because the NLM weighting function is completely known once the neighborhood of a pixel is fixed. The proposed model can be estimated in closed-form solution via a maximum a posteriori (MAP) estimation in an expectation¿maximization scheme. We have compared our model with previously proposed SVFMMs using two public datasets: the Berkeley Segmentation dataset and the BRATS 2013 dataset. The proposed model performs favorably to previous approaches in the literature, achieving better results in terms of Rand Index and Dice metrics in our experiments.This study is partially supported by Secretaria de Estado de Investigacion, Desarrollo e Innovacion (DPI2016-80054-R, TIN2013-43457-R) and Agencia Valenciana de la Innovacion (INNVAL10/18/048). E.F.G was supported by the European Union's Horizon 2020 research and innovation programme under the Marie Skodowska-Curie grant agreement (No. 844646) and also acknowledges the support of NVIDIA GPU Grant Program.Juan -Albarracín, J.; Fuster García, E.; Juan, A.; Garcia-Gomez, JM. (2021). Non-local spatially varying finite mixture models for image segmentation. Statistics and Computing. 31(1):1-10. https://doi.org/10.1007/s11222-020-09988-w11031

Deep ensemble multitask classification of emergency medical call incidents combining multimodal data improves emergency medical dispatch

[EN] The objective of this work was to develop a predictive model to aid non-clinical dispatchers to classify emergency medical call incidents by their life-threatening level (yes/no), admissible response delay (undelayable, minutes, hours, days) and emergency system jurisdiction (emergency system/primary care) in real time. We used a total of 1 244 624 independent incidents from the Valencian emergency medical dispatch service in Spain, compiled in retrospective from 2009 to 2012, including clinical features, demographics, circumstantial factors and free text dispatcher observations. Based on them, we designed and developed DeepEMC2, a deep ensemble multitask model integrating four subnetworks: three specialized to context, clinical and text data, respectively, and another to ensemble the former. The four subnetworks are composed in turn by multi-layer perceptron modules, bidirectional long short-term memory units and a bidirectional encoding representations from transformers module. DeepEMC2 showed a macro F1-score of 0.759 in life-threatening classification, 0.576 in admissible response delay and 0.757 in emergency system jurisdiction. These results show a substantial performance increase of 12.5 %, 17.5 % and 5.1 %, respectively, with respect to the current in-house triage protocol of the Valencian emergency medical dispatch service. Besides, DeepEMC2 significantly outperformed a set of baseline machine learning models, including naive bayes, logistic regression, random forest and gradient boosting (¿ = 0.05). Hence, DeepEMC2 is able to: 1) capture information present in emergency medical calls not considered by the existing triage protocol, and 2) model complex data dependencies not feasible by the tested baseline models. Likewise, our results suggest that most of this unconsidered information is present in the free text dispatcher observations. To our knowledge, this study describes the first deep learning model undertaking emergency medical call incidents classification. Its adoption in medical dispatch centers would potentially improve emergency dispatch processes, resulting in a positive impact in patient wellbeing and health services sustainability.This work has been supported by the Valencian agency for security and emergency response project A1800173041, the Ministry of Science, Innovation and Universities of Spain program FPU18/06441 and the EU Horizon 2020 project InAdvance 825750Ferri-Borredà, P.; Sáez Silvestre, C.; Felix-De Castro, A.; Juan-Albarracín, J.; Blanes-Selva, V.; Sánchez-Cuesta, P.; Garcia-Gomez, JM. (2021). Deep ensemble multitask classification of emergency medical call incidents combining multimodal data improves emergency medical dispatch. Artificial Intelligence in Medicine. 117:1-13. https://doi.org/10.1016/j.artmed.2021.102088S11311

Local detection of microvessels in IDH-wildtype glioblastoma using relative cerebral blood volume: an imaging marker useful for astrocytoma grade 4 classification

[EN] Background The microvessels area (MVA), derived from microvascular proliferation, is a biomarker useful for high-grade glioma classification. Nevertheless, its measurement is costly, labor-intense, and invasive. Finding radiologic correlations with MVA could provide a complementary non-invasive approach without an extra cost and labor intensity and from the first stage. This study aims to correlate imaging markers, such as relative cerebral blood volume (rCBV), and local MVA in IDH-wildtype glioblastoma, and to propose this imaging marker as useful for astrocytoma grade 4 classification. Methods Data from 73 tissue blocks belonging to 17 IDH-wildtype glioblastomas and 7 blocks from 2 IDH-mutant astrocytomas were compiled from the Ivy GAP database. MRI processing and rCBV quantification were carried out using ONCOhabitats methodology. Histologic and MRI co-registration was done manually with experts' supervision, achieving an accuracy of 88.8% of overlay. Spearman's correlation was used to analyze the association between rCBV and microvessel area. Mann-Whitney test was used to study differences of rCBV between blocks with presence or absence of microvessels in IDH-wildtype glioblastoma, as well as to find differences with IDH-mutant astrocytoma samples. Results Significant positive correlations were found between rCBV and microvessel area in the IDH-wildtype blocks (p < 0.001), as well as significant differences in rCBV were found between blocks with microvascular proliferation and blocks without it (p < 0.0001). In addition, significant differences in rCBV were found between IDH-wildtype glioblastoma and IDH-mutant astrocytoma samples, being 2-2.5 times higher rCBV values in IDH-wildtype glioblastoma samples. Conclusions The proposed rCBV marker, calculated from diagnostic MRIs, can detect in IDH-wildtype glioblastoma those regions with microvessels from those without it, and it is significantly correlated with local microvessels area. In addition, the proposed rCBV marker can differentiate the IDH mutation status, providing a complementary non-invasive method for high-grade glioma classification.This work was funded by grants from the National Plan for Scientific and Technical Research and Innovation 2017-2020, No. PID2019-104978RB-I00) (JMGG); H2020-SC1-2016-CNECT Project (No. 727560) (JMGG), and H2020SC1-BHC-2018-2020 (No. 825750) (JMGG). M.A.T was supported by DPI201680054-R (Programa Estatal de Promocion del Talento y su Empleabilidad en I + D + i). EFG was supported by the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No 844646. The funding body played no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.Álvarez-Torres, MDM.; Fuster García, E.; Juan-Albarracín, J.; Reynes, G.; Aparici-Robles, F.; Ferrer Lozano, J.; Garcia-Gomez, JM. (2022). Local detection of microvessels in IDH-wildtype glioblastoma using relative cerebral blood volume: an imaging marker useful for astrocytoma grade 4 classification. BMC Cancer. 22(1):1-13. https://doi.org/10.1186/s12885-021-09117-411322

Glioblastoma: Vascular Habitats Detected at Preoperative Dynamic Susceptibility-weighted Contrast-enhanced Perfusion MR Imaging Predict Survival

[EN] Purpose: To determine if preoperative vascular heterogeneity of glioblastoma is predictive of overall survival of patients undergoing standard-of-care treatment by using an unsupervised multiparametric perfusion-based habitat-discovery algorithm. Materials and Methods: Preoperative magnetic resonance (MR) imaging including dynamic susceptibility-weighted contrast material-enhanced perfusion studies in 50 consecutive patients with glioblastoma were retrieved. Perfusion parameters of glioblastoma were analyzed and used to automatically draw four reproducible habitats that describe the tumor vascular heterogeneity: high-angiogenic and low-angiogenic regions of the enhancing tumor, potentially tumor-infiltrated peripheral edema, and vasogenic edema. Kaplan-Meier and Cox proportional hazard analyses were conducted to assess the prognostic potential of the hemodynamic tissue signature to predict patient survival. Results: Cox regression analysis yielded a significant correlation between patients' survival and maximum relative cerebral blood volume (rCBV(max)) and maximum relative cerebral blood flow (rCBF(max)) in high-angiogenic and low-angiogenic habitats (P < .01, false discovery rate-corrected P < .05). Moreover, rCBF(max) in the potentially tumor-infiltrated peripheral edema habitat was also significantly correlated (P < .05, false discovery rate-corrected P < .05). Kaplan-Meier analysis demonstrated significant differences between the observed survival of populations divided according to the median of the rCBV(max) or rCBF(max) at the high-angiogenic and low-angiogenic habitats (log-rank test P < .05, false discovery rate-corrected P < .05), with an average survival increase of 230 days. Conclusion: Preoperative perfusion heterogeneity contains relevant information about overall survival in patients who undergo standard-of-care treatment. The hemodynamic tissue signature method automatically describes this heterogeneity, providing a set of vascular habitats with high prognostic capabilities.Study supported by H2020 European Institute of Innovation and Technology (POC-2016.SPAIN-07) and Universitat Politecnica de Valencia (PAID-10-14). J.J.A., E.F.G., and J.M.G.G. supported by Secretaria de Estado de Investigacion, Desarrollo e Innovacion (DPI2016-80054-R, TIN2013-43457-R). E.F.G. supported by CaixaImpulse program from Fundacio Bancaria "la Caixa" (LCF/TR/CI16/10010016). E.F.G and A.A.B. supported by the Universitat Politecnica de Valencia Instituto Investigacion Sanitaria de La Fe (C05).Juan -Albarracín, J.; Fuster García, E.; Pérez-Girbés, A.; Aparici-Robles, F.; Alberich Bayarri, A.; Revert Ventura, AJ.; Martí Bonmatí, L.... (2018). Glioblastoma: Vascular Habitats Detected at Preoperative Dynamic Susceptibility-weighted Contrast-enhanced Perfusion MR Imaging Predict Survival. Radiology. 287(3):944-954. https://doi.org/10.1148/radiol.2017170845S944954287

Short-Term Low Temperature Induces Nitro-Oxidative Stress that Deregulates the NADP-Malic Enzyme Function by Tyrosine Nitration in Arabidopsis thaliana

Low temperature (LT) negatively affects plant growth and development via the alteration of the metabolism of reactive oxygen and nitrogen species (ROS and RNS). Among RNS, tyrosine nitration, the addition of an NO2 group to a tyrosine residue, can modulate reduced nicotinamide-dinucleotide phosphate (NADPH)-generating systems and, therefore, can alter the levels of NADPH, a key cofactor in cellular redox homeostasis. NADPH also acts as an indispensable electron donor within a wide range of enzymatic reactions, biosynthetic pathways, and detoxification processes, which could affect plant viability. To extend our knowledge about the regulation of this key cofactor by this nitric oxide (NO)-related post-translational modification, we analyzed the effect of tyrosine nitration on another NADPH-generating enzyme, the NADP-malic enzyme (NADP-ME), under LT stress. In Arabidopsis thaliana seedlings exposed to short-term LT (4 °C for 48 h), a 50% growth reduction accompanied by an increase in the content of superoxide, nitric oxide, and peroxynitrite, in addition to diminished cytosolic NADP-ME activity, were found. In vitro assays confirmed that peroxynitrite inhibits cytosolic NADP-ME2 activity due to tyrosine nitration. The mass spectrometric analysis of nitrated NADP-ME2 enabled us to determine that Tyr-73 was exclusively nitrated to 3-nitrotyrosine by peroxynitrite. The in silico analysis of the Arabidopsis NADP-ME2 protein sequence suggests that Tyr73 nitration could disrupt the interactions between the specific amino acids responsible for protein structure stability. In conclusion, the present data show that short-term LT stress affects the metabolism of ROS and RNS, which appears to negatively modulate the activity of cytosolic NADP-ME through the tyrosine nitration processThis research was funded by ERDF grants co-financed by the Ministry of Economy and Competitiveness (project PGC2018-096405-B-I00) and the Junta de Andalucía (group BIO286) in Spain. Research in FJ-C lab is supported by an ERDF-co-financed grant from the Ministry of Economy and Competitiveness (AGL2015-65104-P) and Junta de Andalucía (group BIO-192), Spain. Postdoctoral researcher J.B.-M. was funded by the Ministry of Economy and Competitiveness (Spain) within Juan de la Cierva-Incorporación program (IJCI-2015-23438)

Differential effect of vascularity between long- and short-term survivors with IDH1/2 wild-type glioblastoma

[EN] Introduction: IDH1/2 wt glioblastoma (GB) represents the most lethal tumour of the central nervous system. Tumour vascularity is associated with overall survival (OS), and the clinical relevance of vascular markers, such as rCBV, has already been validated. Nevertheless, molecular and clinical factors may have different influences on the beneficial effect of a favourable vascular signature. Purpose: To evaluate the association between the rCBV and OS of IDH1/2 wt GB patients for long-term survivors (LTSs) and short-term survivors (STSs). Given that initial high rCBV may affect the patient's OS in follow-up stages, we will assess whether a moderate vascularity is beneficial for OS in both groups of patients. Materials and methods: Ninety-nine IDH1/2 wt GB patients were divided into LTSs (OS >= 400 days) and STSs (OS < 400 days). Mann-Whitney and Fisher, uni- and multiparametric Cox, Aalen's additive regression and Kaplan-Meier tests were carried out. Tumour vascularity was represented by the mean rCBV of the high angiogenic tumour (HAT) habitat computed through the haemodynamic tissue signature methodology (available on the ONCOhabitats platform). Results: For LTSs, we found a significant association between a moderate value of rCBV(mean) and higher OS (uni- and multiparametric Cox and Aalen's regression) (p = 0.0140, HR = 1.19; p = 0.0085, HR = 1.22) and significant stratification capability (p = 0.0343). For the STS group, no association between rCBV(mean) and survival was observed. Moreover, no significant differences (p > 0.05) in gender, age, resection status, chemoradiation, or MGMT methylation were observed between LTSs and STSs. Conclusion: We have found different prognostic and stratification effects of the vascular marker for the LTS and STS groups. We propose the use of rCBV(mean) at HAT as a vascular marker clinically relevant for LTSs with IDH1/2 wt GB and maybe as a potential target for randomized clinical trials focused on this group of patients.DPI2016-80054-R (Programa Estatal de Promocion del Talento y su Empleabilidad en I +D+i).; European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 844646; H2020-SC1-BHC-2018-2020 (No. 825750); MTS4up project (National Plan for Scientific and Technical Research and Innovation 2013-2016, No. DPI2016-80054-R); European Union's Horizon 2020 research and innovation programme under Marie Sklodowska-Curie, Grant/Award Number: 844646; Research Council of Norway, Grant/Award Number: 261984; South-Eastern Norway Regional Health Authority, Grant/Award Number: 2017073; European Research Council (ERC) under the European Union's Horizon 2020, Grant/Award Number: 758657Álvarez-Torres, MDM.; Fuster García, E.; Reynes, G.; Juan-Albarracín, J.; Chelebian-Kocharyan, EA.; Oleaga, L.; Pineda, J.... (2021). Differential effect of vascularity between long- and short-term survivors with IDH1/2 wild-type glioblastoma. NMR in Biomedicine. 34(4):1-11. https://doi.org/10.1002/nbm.446211134

MGMT methylation may benefit overall survival in patients with moderately vascularized glioblastomas

[EN] Objectives To assess the combined role of tumor vascularity, estimated from perfusion MRI, andMGMTmethylation status on overall survival (OS) in patients with glioblastoma. Methods A multicentric international dataset including 96 patients from NCT03439332 clinical study were used to study the prognostic relationships betweenMGMTand perfusion markers. Relative cerebral blood volume (rCBV) in the most vascularized tumor regions was automatically obtained from preoperative MRIs using ONCOhabitats online analysis service. Cox survival regression models and stratification strategies were conducted to define a subpopulation that is particularly favored byMGMTmethylation in terms of OS. Results rCBV distributions did not differ significantly (p > 0.05) in the methylated and the non-methylated subpopulations. In patients with moderately vascularized tumors (rCBV 10.73), however, there was no significant effect ofMGMTmethylation (HR = 1.72,p = 0.10, AUC = 0.56). Conclusions Our results indicate the existence of complementary prognostic information provided byMGMTmethylation and rCBV. Perfusion markers could identify a subpopulation of patients who will benefit the most fromMGMTmethylation. Not considering this information may lead to bias in the interpretation of clinical studies.Open Access funding provided by University of Oslo (incl Oslo University Hospital). This study has received funding from MTS4up project (National Plan for Scientific and Technical Research and Innovation 2013-2016, No. DPI2016-80054-R) (JMGG); H2020-SC12016-CNECT Project (No. 727560) (JMGG), H2020-SC1-BHC-20182020 (No. 825750) (JMGG), the European Research Council (ERC) under the European Union's Horizon 2020 (Grant Agreement No. 758657), the South-Eastern Norway Regional Health Authority Grants 2017073 and 2013069, the Research Council of Norway Grants 261984 (KEE). M.A.T was supported by Programa Estatal de Promocion del Talento y su Empleabilidad en I+D+i (DPI2016-80054-R). E.F.G was supported by the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement (No. 844646).Fuster García, E.; Lorente Estellés, D.; Álvarez-Torres, MDM.; Juan-Albarracín, J.; Chelebian-Kocharyan, EA.; Rovira, A.; Auger Acosta, C.... (2021). MGMT methylation may benefit overall survival in patients with moderately vascularized glioblastomas. European Radiology. 31(3):1738-1747. https://doi.org/10.1007/s00330-020-07297-41738174731