Predicting Survival in Glioblastoma Patients Using Diffusion MR Imaging Metrics-A Systematic Review

Glioblastoma and radiotherapy: A multicenter AI study for Survival Predictions from MRI (GRASP study)

BACKGROUND

The aim was to predict survival of glioblastoma at 8 months after radiotherapy (a period allowing for completing a typical course of adjuvant temozolomide), by applying deep learning to the first brain MRI after radiotherapy completion.

METHODS

Retrospective and prospective data were collected from 206 consecutive glioblastoma, isocitrate dehydrogenase -wildtype patients diagnosed between March 2014 and February 2022 across 11 UK centers. Models were trained on 158 retrospective patients from 3 centers. Holdout test sets were retrospective (n = 19; internal validation), and prospective (n = 29; external validation from 8 distinct centers). Neural network branches for T2-weighted and contrast-enhanced T1-weighted inputs were concatenated to predict survival. A nonimaging branch (demographics/MGMT/treatment data) was also combined with the imaging model. We investigated the influence of individual MR sequences; nonimaging features; and weighted dense blocks pretrained for abnormality detection.

RESULTS

The imaging model outperformed the nonimaging model in all test sets (area under the receiver-operating characteristic curve, AUC P = .038) and performed similarly to a combined imaging/nonimaging model (P > .05). Imaging, nonimaging, and combined models applied to amalgamated test sets gave AUCs of 0.93, 0.79, and 0.91. Initializing the imaging model with pretrained weights from 10 000s of brain MRIs improved performance considerably (amalgamated test sets without pretraining 0.64; P = .003).

CONCLUSIONS

A deep learning model using MRI images after radiotherapy reliably and accurately determined survival of glioblastoma. The model serves as a prognostic biomarker identifying patients who will not survive beyond a typical course of adjuvant temozolomide, thereby stratifying patients into those who might require early second-line or clinical trial treatment.

Application of RNA-based therapeutics in glioma: A review

Despite the extensive advancements made in the field of cancer therapy, the outlook of individuals suffering from glioblastoma multiforme remains highly detrimental. The absence of specific treatments for cancerous cells significantly hinders the effectiveness of conventional anticancer techniques. Multiple research studies have demonstrated that the suppression of specific genes or the augmentation of therapeutic proteins through RNA-based therapeutics may represent a valuable approach when combined with chemotherapy or immunotherapy. In recent years, there has been a significant increase in the application of RNA therapeutics in conjunction with chemotherapy and immunotherapy. This emerging field has become a prominent area of research for advancing various types of cancer treatments. The present investigation provides an in-depth overview of the classification and application of RNA therapy, focusing on the mechanisms of RNA antitumor treatment and the current status of clinical studies on RNA drugs.

Standardized brain tumor imaging protocols for clinical trials: current recommendations and tips for integration

Standardized MRI acquisition protocols are crucial for reducing the measurement and interpretation variability associated with response assessment in brain tumor clinical trials. The main challenge is that standardized protocols should ensure high image quality while maximizing the number of institutions meeting the acquisition requirements. In recent years, extensive effort has been made by consensus groups to propose different "ideal" and "minimum requirements" brain tumor imaging protocols (BTIPs) for gliomas, brain metastases (BM), and primary central nervous system lymphomas (PCSNL). In clinical practice, BTIPs for clinical trials can be easily integrated with additional MRI sequences that may be desired for clinical patient management at individual sites. In this review, we summarize the general concepts behind the choice and timing of sequences included in the current recommended BTIPs, we provide a comparative overview, and discuss tips and caveats to integrate additional clinical or research sequences while preserving the recommended BTIPs. Finally, we also reflect on potential future directions for brain tumor imaging in clinical trials.

Functional Magnetic Resonance Imaging and Diffusion Tensor Imaging-Tractography in Resective Brain Surgery: Lesion Coverage Strategies and Patient Outcomes

Diffusion tensor imaging (DTI)-tractography and functional magnetic resonance imaging (fMRI) have dynamically entered the presurgical evaluation context of brain surgery during the past decades, providing novel perspectives in surgical planning and lesion access approaches. However, their application in the presurgical setting requires significant time and effort and increased costs, thereby raising questions regarding efficiency and best use. In this work, we set out to evaluate DTI-tractography and combined fMRI/DTI-tractography during intra-operative neuronavigation in resective brain surgery using lesion-related preoperative neurological deficit (PND) outcomes as metrics. We retrospectively reviewed medical records of 252 consecutive patients admitted for brain surgery. Standard anatomical neuroimaging protocols were performed in 127 patients, 69 patients had additional DTI-tractography, and 56 had combined DTI-tractography/fMRI. fMRI procedures involved language, motor, somatic sensory, sensorimotor and visual mapping. DTI-tractography involved fiber tracking of the motor, sensory, language and visual pathways. At 1 month postoperatively, DTI-tractography patients were more likely to present either improvement or preservation of PNDs (p = 0.004 and p = 0.007, respectively). At 6 months, combined DTI-tractography/fMRI patients were more likely to experience complete PND resolution (p < 0.001). Low-grade lesion patients (N = 102) with combined DTI-tractography/fMRI were more likely to experience complete resolution of PNDs at 1 and 6 months (p = 0.001 and p < 0.001, respectively). High-grade lesion patients (N = 140) with combined DTI-tractography/fMRI were more likely to have PNDs resolved at 6 months (p = 0.005). Patients with motor symptoms (N = 80) were more likely to experience complete remission of PNDs at 6 months with DTI-tractography or combined DTI-tractography/fMRI (p = 0.008 and p = 0.004, respectively), without significant difference between the two imaging protocols (p = 1). Patients with sensory symptoms (N = 44) were more likely to experience complete PND remission at 6 months with combined DTI-tractography/fMRI (p = 0.004). The intraoperative neuroimaging modality did not have a significant effect in patients with preoperative seizures (N = 47). Lack of PND worsening was observed at 6 month follow-up in patients with combined DTI-tractography/fMRI. Our results strongly support the combined use of DTI-tractography and fMRI in patients undergoing resective brain surgery for improving their postoperative clinical profile.

Potential of pre-operative MRI features in glioblastoma to predict for molecular stem cell subtype and patient overall survival

AIM OF THE STUDY

A molecular signature based on 10 mRNA abundances that characterizes the mesenchymal-to-proneural phenotype of glioblastoma stem(like) cells (GSCs) enriched in primary culture has been previously established. As this phenotype has been proposed to be prognostic for disease outcome the present study aims to identify features of the preoperative MR imaging that may predict the GSC phenotype of individual tumors.

MATERIAL/METHODS

Molecular mesenchymal-to-proneural mRNA signatures and intrinsic radioresistance (SF4, survival fraction at 4 Gy) of primary GSC-enriched cultures were associated with survival data and pre-operative MR imaging of the corresponding glioblastoma patients of a prospective cohort (n = 24). The analyzed imaging parameters comprised linear vectors derived from tumor volume, necrotic volume and edema as contoured manually.

RESULTS

A necrosis/tumor vector ratio and to a weaker extent the product of this ratio and the edema vector were identified to correlate with the mesenchymal-to-proneural mRNA signature and the SF4 of the patient-derived GSC cultures. Importantly, both parameter combinations were predictive for overall survival of the whole patient cohort. Moreover, the combination of necrosis/tumor vector ratio and edema vector differed significantly between uni- and multifocally recurring tumors.

CONCLUSION

Features of the preoperative MR images may reflect the molecular signature of the GSC population and might be used in the future as a prognostic factor and for treatment stratification especially in the MGMT promotor-unmethylated sub-cohort of glioblastoma patients.

Radiomics in the diagnosis of glioblastoma

Radiomics is a process of extracting many quantitative data obtained from medical images and analysing them. In neuroradiology it may be used to discover magnetic resonance imaging (MRI) features of glioblastomas that are impossible to identify by human vision alone. In this article, the authors describe the methodology and their first experience in creating a predictive model based on radiomic features obtained from the preoperative MRI examination of patients with glioblastoma. Early identification of malignant glioblastoma subtypes characterized by different prognoses and responses to treatment would greatly facilitate the implementation of targeted therapy, which appears to be the future of glioblastoma treatment.

Isotoxic dose escalated radiotherapy for glioblastoma based on diffusion-weighted MRI and tumor control probability-an in-silico study

OBJECTIVES

Glioblastoma (GBM) is the most common malignant primary brain tumor with local recurrence after radiotherapy (RT), the most common mode of failure. Standard RT practice applies the prescription dose uniformly across tumor volume disregarding radiological tumor heterogeneity. We present a novel strategy using diffusion-weighted (DW-) MRI to calculate the cellular density within the gross tumor volume (GTV) in order to facilitate dose escalation to a biological target volume (BTV) to improve tumor control probability (TCP).

METHODS

The pre-treatment apparent diffusion coefficient (ADC) maps derived from DW-MRI of ten GBM patients treated with radical chemoradiotherapy were used to calculate the local cellular density based on published data. Then, a TCP model was used to calculate TCP maps from the derived cell density values. The dose was escalated using a simultaneous integrated boost (SIB) to the BTV, defined as the voxels for which the expected pre-boost TCP was in the lowest quartile of the TCP range for each patient. The SIB dose was chosen so that the TCP in the BTV increased to match the average TCP of the whole tumor.

RESULTS

By applying a SIB of between 3.60 Gy and 16.80 Gy isotoxically to the BTV, the cohort's calculated TCP increased by a mean of 8.44% (ranging from 7.19 to 16.84%). The radiation dose to organ at risk is still under their tolerance.

CONCLUSIONS

Our findings indicate that TCPs of GBM patients could be increased by escalating radiation doses to intratumoral locations guided by the patient's biology (i.e., cellularity), moreover offering the possibility for personalized RT GBM treatments.

ADVANCES IN KNOWLEDGE

A personalized and voxel level SIB radiotherapy method for GBM is proposed using DW-MRI, which can increase the tumor control probability and maintain organ at risk dose constraints.

Artificial intelligence-based locoregional markers of brain peritumoral microenvironment

In malignant primary brain tumors, cancer cells infiltrate into the peritumoral brain structures which results in inevitable recurrence. Quantitative assessment of infiltrative heterogeneity in the peritumoral region, the area where biopsy or resection can be hazardous, is important for clinical decision making. Here, we derive a novel set of Artificial intelligence (AI)-based markers capturing the heterogeneity of tumor infiltration, by characterizing free water movement restriction in the peritumoral region using Diffusion Tensor Imaging (DTI)-based free water volume fraction maps. We leverage the differences in the peritumoral region of metastasis and glioblastomas, the former consisting of vasogenic versus the latter containing infiltrative edema, to extract a voxel-wise deep learning-based peritumoral microenvironment index (PMI). Descriptive characteristics of locoregional hubs of uniformly high PMI values are then extracted as AI-based markers to capture distinct aspects of infiltrative heterogeneity. The proposed markers are utilized to stratify patients' survival and IDH1 mutation status on a population of 275 adult-type diffuse gliomas (CNS WHO grade 4). Our results show significant differences in the proposed markers between patients with different overall survival and IDH1 mutation status (t test, Wilcoxon rank sum test, linear regression; p < 0.01). Clustering of patients using the proposed markers reveals distinct survival groups (logrank; p < 10-5, Cox hazard ratio = 1.82; p < 0.005). Our findings provide a panel of markers as surrogates of infiltration that might capture novel insight about underlying biology of peritumoral microstructural heterogeneity, providing potential biomarkers of prognosis pertaining to survival and molecular stratification, with applicability in clinical decision making.

Prognostication of overall survival in patients with brain metastases using diffusion tensor imaging and dynamic susceptibility contrast-enhanced MRI

OBJECTIVES

To investigate the prognostic utility of DTI and DSC-PWI perfusion-derived parameters in brain metastases patients.

METHODS

Retrospective analyses of DTI-derived parameters (MD, FA, CL, CP, and CS) and DSC-perfusion PWI-derived rCBVmax from 101 patients diagnosed with brain metastases prior to treatment were performed. Using semi-automated segmentation, DTI metrics and rCBVmax were quantified from enhancing areas of the dominant metastatic lesion. For each metric, patients were classified as short- and long-term survivors based on analysis of the best coefficient for each parameter and percentile to separate the groups. Kaplan-Meier analysis was used to compare mOS between these groups. Multivariate survival analysis was subsequently conducted. A correlative histopathologic analysis was performed in a subcohort (n = 10) with DTI metrics and rCBVmax on opposite ends of the spectrum.

RESULTS

Significant differences in mOS were observed for MDmin (p < 0.05), FA (p < 0.01), CL (p < 0.05), and CP (p < 0.01) and trend toward significance for rCBVmax (p = 0.07) between the two risk groups, in the univariate analysis. On multivariate analysis, the best predictive survival model was comprised of MDmin (p = 0.05), rCBVmax (p < 0.05), RPA (p < 0.0001), and number of lesions (p = 0.07). On histopathology, metastatic tumors showed significant differences in the amount of stroma depending on the combination of DTI metrics and rCBVmax values. Patients with high stromal content demonstrated poorer mOS.

CONCLUSION

Pretreatment DTI-derived parameters, notably MDmin and rCBVmax, are promising imaging markers for prognostication of OS in patients with brain metastases. Stromal cellularity may be a contributing factor to these differences.

ADVANCES IN KNOWLEDGE

The correlation of DTI-derived metrics and perfusion MRI with patient outcomes has not been investigated in patients with treatment naïve brain metastasis. DTI and DSC-PWI can aid in therapeutic decision-making by providing additional clinical guidance.

The relationship between radiomics and pathomics in Glioblastoma patients: Preliminary results from a cross-scale association study

Glioblastoma multiforme (GBM) typically exhibits substantial intratumoral heterogeneity at both microscopic and radiological resolution scales. Diffusion Weighted Imaging (DWI) and dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) are two functional MRI techniques that are commonly employed in clinic for the assessment of GBM tumor characteristics. This work presents initial results aiming at determining if radiomics features extracted from preoperative ADC maps and post-contrast T1 (T1C) images are associated with pathomic features arising from H&E digitized pathology images. 48 patients from the public available CPTAC-GBM database, for which both radiology and pathology images were available, were involved in the study. 91 radiomics features were extracted from ADC maps and post-contrast T1 images using PyRadiomics. 65 pathomic features were extracted from cell detection measurements from H&E images. Moreover, 91 features were extracted from cell density maps of H&E images at four different resolutions. Radiopathomic associations were evaluated by means of Spearman's correlation (ρ) and factor analysis. p values were adjusted for multiple correlations by using a false discovery rate adjustment. Significant cross-scale associations were identified between pathomics and ADC, both considering features (n = 186, 0.45 < ρ < 0.74 in absolute value) and factors (n = 5, 0.48 < ρ < 0.54 in absolute value). Significant but fewer ρ values were found concerning the association between pathomics and radiomics features (n = 53, 0.5 < ρ < 0.65 in absolute value) and factors (n = 2, ρ = 0.63 and ρ = 0.53 in absolute value). The results of this study suggest that cross-scale associations may exist between digital pathology and ADC and T1C imaging. This can be useful not only to improve the knowledge concerning GBM intratumoral heterogeneity, but also to strengthen the role of radiomics approach and its validation in clinical practice as "virtual biopsy", introducing new insights for omics integration toward a personalized medicine approach.

A Systematic Review of the Current Status and Quality of Radiomics for Glioma Differential Diagnosis

Simple Summary

Gliomas can be difficult to discern clinically and radiologically from other brain lesions (either neoplastic or non-neoplastic) since their clinical manifestations as well as preoperative imaging features often overlap and appear misleading. Radiomics could be extremely helpful for non-invasive glioma differential diagnosis (DDx). However, implementation in clinical practice is still distant and concerns have been raised regarding the methodological quality of radiomic studies. In this context, we aimed to summarize the current status and quality of radiomic studies concerning glioma DDx in a systematic review. In total, 42 studies were selected and examined in our work. Our study revealed that, despite promising and encouraging results, current studies on radiomics for glioma DDx still lack the quality required to allow its introduction into clinical practice. This work could provide new insights and help to reach a consensus on the use of the radiomic approach for glioma DDx.

Abstract

Radiomics is a promising tool that may increase the value of imaging in differential diagnosis (DDx) of glioma. However, implementation in clinical practice is still distant and concerns have been raised regarding the methodological quality of radiomic studies. Therefore, we aimed to systematically review the current status of radiomic studies concerning glioma DDx, also using the radiomics quality score (RQS) to assess the quality of the methodology used in each study. A systematic literature search was performed to identify original articles focused on the use of radiomics for glioma DDx from 2015. Methodological quality was assessed using the RQS tool. Spearman’s correlation (ρ) analysis was performed to explore whether RQS was correlated with journal metrics and the characteristics of the studies. Finally, 42 articles were selected for the systematic qualitative analysis. Selected articles were grouped and summarized in terms of those on DDx between glioma and primary central nervous system lymphoma, those aiming at differentiating glioma from brain metastases, and those based on DDx of glioma and other brain diseases. Median RQS was 8.71 out 36, with a mean RQS of all studies of 24.21%. Our study revealed that, despite promising and encouraging results, current studies on radiomics for glioma DDx still lack the quality required to allow its introduction into clinical practice. This work could provide new insights and help to reach a consensus on the use of the radiomic approach for glioma DDx.

Lichen-Derived Compounds and Extracts as Biologically Active Substances with Anticancer and Neuroprotective Properties

Lichens are a source of chemical compounds with valuable biological properties, structurally predisposed to penetration into the central nervous system (CNS). Hence, our research aimed to examine the biological potential of lipophilic extracts of Parmelia sulcata, Evernia prunastri, Cladonia uncialis, and their major secondary metabolites, in the context of searching for new therapies for CNS diseases, mainly glioblastoma multiforme (GBM). The extracts selected for the study were standardized for their content of salazinic acid, evernic acid, and (−)-usnic acid, respectively. The extracts and lichen metabolites were evaluated in terms of their anti-tumor activity, i.e., cytotoxicity against A-172 and T98G cell lines and anti-IDO1, IDO2, TDO activity, their anti-inflammatory properties exerted by anti-COX-2 and anti-hyaluronidase activity, antioxidant activity, and anti-acetylcholinesterase and anti-butyrylcholinesterase activity. The results of this study indicate that lichen-derived compounds and extracts exert significant cytotoxicity against GBM cells, inhibit the kynurenine pathway enzymes, and have anti-inflammatory properties and weak antioxidant and anti-cholinesterase properties. Moreover, evernic acid and (−)-usnic acid were shown to be able to cross the blood-brain barrier. These results demonstrate that lichen-derived extracts and compounds, especially (−)-usnic acid, can be regarded as prototypes of pharmacologically active compounds within the CNS, especially suitable for the treatment of GBM.

A Predictive Clinical-Radiomics Nomogram for Survival Prediction of Glioblastoma Using MRI

Glioblastoma (GBM) is the most common and aggressive primary brain tumor in adult patients with a median survival of around one year. Prediction of survival outcomes in GBM patients could represent a huge step in treatment personalization. The objective of this study was to develop machine learning (ML) algorithms for survival prediction of GBM patient. We identified a radiomic signature on a training-set composed of data from the 2019 BraTS challenge (210 patients) from MRI retrieved at diagnosis. Then, using this signature along with the age of the patients for training classification models, we obtained on test-sets AUCs of 0.85, 0.74 and 0.58 (0.92, 0.88 and 0.75 on the training-sets) for survival at 9-, 12- and 15-months, respectively. This signature was then validated on an independent cohort of 116 GBM patients with confirmed disease relapse for the prediction of patients surviving less or more than the median OS of 22 months. Our model insured an AUC of 0.71 (0.65 on train). The Kaplan–Meier method showed significant OS difference between groups (log-rank p = 0.05). These results suggest that radiomic signatures may improve survival outcome predictions in GBM thus creating a solid clinical tool for tailoring therapy in this population.

Multiregional Sequencing of IDH-WT Glioblastoma Reveals High Genetic Heterogeneity and a Dynamic Evolutionary History

Simple Summary

Glioblastoma is the most common and aggressive primary brain malignancy in adults. In addition to extensive inter-patient heterogeneity, glioblastoma shows intra-tumor extensive cellular and molecular heterogeneity, both spatially and temporally. This heterogeneity is one of the main reasons for the poor prognosis and overall survival. Moreover, it raises the important question of whether the molecular characterization of a single biopsy sample, as performed in standard diagnostics, actually represents the entire lesion. In this study, we sequenced the whole exome of nine spatially different cancer regions of three primary glioblastomas. We characterized their mutational profiles and copy number alterations, with implications for our understanding of tumor biology in relation to clonal architecture and evolutionary dynamics, as well as therapeutically relevant alterations.

Abstract

Glioblastoma is one of the most common and lethal primary neoplasms of the brain. Patient survival has not improved significantly over the past three decades and the patient median survival is just over one year. Tumor heterogeneity is thought to be a major determinant of therapeutic failure and a major reason for poor overall survival. This work aims to comprehensively define intra- and inter-tumor heterogeneity by mapping the genomic and mutational landscape of multiple areas of three primary IDH wild-type (IDH-WT) glioblastomas. Using whole exome sequencing, we explored how copy number variation, chromosomal and single loci amplifications/deletions, and mutational burden are spatially distributed across nine different tumor regions. The results show that all tumors exhibit a different signature despite the same diagnosis. Above all, a high inter-tumor heterogeneity emerges. The evolutionary dynamics of all identified mutations within each region underline the questionable value of a single biopsy and thus the therapeutic approach for the patient. Multiregional collection and subsequent sequencing are essential to try to address the clinical challenge of precision medicine. Especially in glioblastoma, this approach could provide powerful support to pathologists and oncologists in evaluating the diagnosis and defining the best treatment option.

Permeability of Hypogymnia physodes Extract Component-Physodic Acid through the Blood-Brain Barrier as an Important Argument for Its Anticancer and Neuroprotective Activity within the Central Nervous System

Simple Summary

Central nervous system (CNS) diseases, including tumors such as glioblastomas and neurodegenerative diseases, such as Alzheimer’s disease, are some of the greatest challenges of modern medicine. Therefore, our study aimed to evaluate the anticancer and neuroprotective activity of the extract from a common European lichen Hypogymnia physodes and of its compound-physodic acid. The examined substances were cytotoxic against the glioblastoma cell lines A-172, T98G, and U-138 MG. Both substances strongly inhibited hyaluronidase, and diminished cyclooxygenase-2 activity (H. physodes extract), enzymes expressed in patients with malignant glioma. Furthermore, H. physodes extract inhibited tyrosinase activity, the enzyme linked to neurodegenerative diseases. The tested substances exhibited antioxidant activity, however, acetylcholinesterase and butyrylcholinesterase inhibitory activity were not high. We proved that physodic acid can cross the blood–brain barrier. We conclude that physodic acid and H. physodes extract should be regarded as promising agents with anticancer, chemopreventive, and neuroprotective activities, especially concerning CNS.

Abstract

Lichen secondary metabolites are characterized by huge pharmacological potential. Our research focused on assessing the anticancer and neuroprotective activity of Hypogymnia physodes acetone extract (HP extract) and physodic acid, its major component. The antitumor properties were evaluated by cytotoxicity analysis using A-172, T98G, and U-138 MG glioblastoma cell lines and by hyaluronidase and cyclooxygenase-2 (COX-2) inhibition. The neuroprotective potential was examined using COX-2, tyrosinase, acetylcholinesterase (AChE), and butyrylcholinesterase (BChE) activity tests. Moreover, the antioxidant potential of the tested substances was examined, and the chemical composition of the extract was analyzed. For physodic acid, the permeability through the blood–brain barrier using Parallel Artificial Membrane Permeability Assay for the Blood–Brain Barrier assay (PAMPA-BBB) was assessed. Our study shows that the tested substances strongly inhibited glioblastoma cell proliferation and hyaluronidase activity. Besides, HP extract diminished COX-2 and tyrosinase activity. However, the AChE and BChE inhibitory activity of HP extract and physodic acid were mild. The examined substances exhibited strong antioxidant activity. Importantly, we proved that physodic acid crosses the blood–brain barrier. We conclude that physodic acid and H. physodes should be regarded as promising agents with anticancer, chemopreventive, and neuroprotective activities, especially regarding the central nervous system diseases.

The Triad Hsp60-miRNAs-Extracellular Vesicles in Brain Tumors: Assessing Its Components for Understanding Tumorigenesis and Monitoring Patients

Brain tumors have a poor prognosis and progress must be made for developing efficacious treatments, but for this to occur their biology and interaction with the host must be elucidated beyond current knowledge. What has been learned from other tumors may be applied to study brain tumors, for example, the role of Hsp60, miRNAs, and extracellular vesicles (EVs) in the mechanisms of cell proliferation and dissemination, and resistance to immune attack and anticancer drugs. It has been established that Hsp60 increases in cancer cells, in which it occurs not only in the mitochondria but also in the cytosol and plasma-cell membrane and it is released in EVs into the extracellular space and in circulation. There is evidence suggesting that these EVs interact with cells near and far from their original cell and that this interaction has an impact on the functions of the target cell. It is assumed that this crosstalk between cancer and host cells favors carcinogenesis in various ways. We, therefore, propose to study the triad Hsp60-related miRNAs-EVs in brain tumors and have standardized methods for the purpose. These revealed that EVs with Hsp60 and related miRNAs increase in patients’ blood in a manner that reflects disease status. The means are now available to monitor brain tumor patients by measuring the triad and to dissect its effects on target cells in vitro, and in experimental models in vivo.