Intratumoral heterogeneity in glioblastoma: don't forget the peritumoral brain zone

Intratumoral Cell Heterogeneity in Patient-Derived Glioblastoma Cell Lines Revealed by Single-Cell RNA-Sequencing

Glioblastoma cell lines derived from different patients are widely used in tumor biology research and drug screening. A key feature of glioblastoma is the high level of inter- and intratumor heterogeneity that accounts for treatment resistance. Our aim was to investigate whether intratumor heterogeneity is maintained in cell models. Single-cell RNA sequencing was used to investigate the cellular composition of a tumor sample and six patient-derived glioblastoma cell lines. Three cell lines preserved the mutational profile of the original tumor, whereas three others differed from their precursors. Copy-number variation analysis showed significantly rearranged genomes in all the cell lines and in the tumor sample. The tumor had the most complex cell composition, including cancer cells and microenvironmental cells. Cell lines with a conserved genome had less diverse cellularity, and during cultivation, a relative increase in the stem-cell-derived progenitors was noticed. Cell lines with genomes different from those of the primary tumors mainly contained neural progenitor cells and microenvironmental cells. The establishment of cell lines without the driver mutations that are intrinsic to the original tumors may be related to the selection of clones or cell populations during cultivation. Thus, patient-derived glioblastoma cell lines differ substantially in their cellular profile, which should be taken into account in translational studies.

Biomarkers of immunotherapy in glioblastoma

Glioblastoma (GBM) is the most common primary brain cancer, comprising half of all malignant brain tumors. Patients with GBM have a poor prognosis, with a median survival of 14-15 months. Current therapies for GBM, including chemotherapy, radiotherapy, and surgical resection, remain inadequate. Novel therapies are required to extend patient survival. Although immunotherapy has shown promise in other cancers, including melanoma and non-small lung cancer, its efficacy in GBM has been limited to subsets of patients. Identifying biomarkers of immunotherapy response in GBM could help stratify patients, identify new therapeutic targets, and develop more effective treatments. This article reviews existing and emerging biomarkers of clinical response to immunotherapy in GBM. The scope of this review includes immune checkpoint inhibitor and antitumoral vaccination approaches, summarizing the variety of molecular, cellular, and computational methodologies that have been explored in the setting of anti-GBM immunotherapies.

Exploring the prognostic value and biological pathways of transcriptomics and radiomics patterns in glioblastoma multiforme

Objectives

To develop a multi-omics prognostic model integrating transcriptomics and radiomics for predicting overall survival in patients with glioblastoma multiforme (GBM), and investigate the biological pathways of radiomics patterns.

Materials and methods

Transcription profiles of GBM patients and normal controls were used to obtain differentially expressed mRNAs and long non-coding RNAs (lncRNAs). Radiomics features were extracted from magnetic resonance imaging (MRI). Least absolute shrinkage and selection operator (LASSO) Cox regression was employed to select survival-associated features for the construction of transcriptomics and radiomics signatures. Genes associated with GBM prognosis were identified through the analysis of lncRNA-mRNA co-expression networks and Weighted Gene Co-expression Network Analysis (WGCNA), and their biological pathways were investigated using Genomes enrichment analysis. Transcriptomics, radiomics, and clinical data were integrated to evaluate the multi-omics prognostic model's performance.

Results

LASSO Cox regression yielded 21 survival-related features, including 19 transcriptomics features and 2 radiomics features. Based on transcriptomics and radiomics signature, GBM patients were classified as high-risk or low-risk. The genes obtained from the co-expression network screen were associated with microtubule binding, while those from the WGCNA screen were associated with growth factor receptor binding. In the training set, the AUC values for the multi-omics model and clinical model were 0.964 and 0.830, respectively, while in the validation set, they were 0.907 and 0.787. The multi-omics prognostic model outperformed the clinical prognostic model.

Conclusions

The co-expression network and WGCNA methods revealed genes associated with multiple biological pathways in GBM. The multi-omics prognostic model demonstrated excellent performance and indicated significant potential for clinical application.

Diffusion tensor imaging in detecting gliomas sub-regions of infiltration, local and remote recurrences: a systematic review

Given that glioma cells tend to infiltrate and migrate along WM tracts, leading to demyelination and axonal injuries, Diffusion Tensor Imaging (DTI) emerged as a promising tool for identifying major "high-risk areas" of recurrence within the peritumoral brain zone (PBZ) or at a distance throughout the adjacents white matter tracts. Of our systematic review is to answer the following research question: In patients with brain tumor, is DTI able to recognizes within the peri-tumoral brain zone (PBZ) areas more prone to local (near the surgical cavity) or remote recurrence compared to the conventional imaging techniques?. We conducted a comprehensive literature search to identify relevant studies in line with the PRISMA-P (Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols) guidelines. 15 papers were deemed compatible with our research question and included. To enhance the paper's readability, we have categorized our findings into two distinct groups: the first delves into the role of DTI in detecting PBZ sub-regions of infiltration and local recurrences (n = 8), while the second group explores the feasibility of DTI in detecting white matter tract infiltration and remote recurrences (n = 7). DTI values and, within a broader framework, radiomics investigations can provide precise, voxel-by-voxel insights into the state of PBZ and recurrences. Better defining the regions at risk for potential recurrence within the PBZ and along WM bundles will allow targeted therapy.

Abnormal vascular structure and function within brain metastases is linked to pembrolizumab resistance

BACKGROUND

We recently conducted a phase 2 trial (NCT028865685) evaluating intracranial efficacy of pembrolizumab for brain metastases (BM) of diverse histologies. Our study met its primary efficacy endpoint and illustrates that pembrolizumab exerts promising activity in a select group of patients with BM. Given the importance of aberrant vasculature in mediating immunosuppression, we explored the relationship between immune checkpoint inhibitor (ICI) efficacy and vascular architecture in the hopes of identifying potential mechanisms of intracranial ICI response or resistance for BM.

METHODS

Using Vessel Architectural Imaging, a histologically validated quantitative metric for in vivo tumor vascular physiology, we analyzed dual-echo DSC/DCE MRI for 44 patients on trial. Tumor and peri-tumor cerebral blood volume/flow, vessel size, arterial and venous dominance, and vascular permeability were measured before and after treatment with pembrolizumab.

RESULTS

BM that progressed on ICI were characterized by a highly aberrant vasculature dominated by large-caliber vessels. In contrast, ICI-responsive BM possessed a more structurally balanced vasculature consisting of both small and large vessels, and there was a trend toward a decrease in under-perfused tissue, suggesting a reversal of the negative effects of hypoxia. In the peri-tumor region, the development of smaller blood vessels, consistent with neo-angiogenesis, was associated with tumor growth before radiographic evidence of contrast enhancement on anatomical MRI.

CONCLUSIONS

This study, one of the largest functional imaging studies for BM, suggests that vascular architecture is linked with ICI efficacy. Studies identifying modulators of vascular architecture, and effects on immune activity, are warranted and may inform future combination treatments.

Identifying key factors for predicting O6-Methylguanine-DNA methyltransferase status in adult patients with diffuse glioma: a multimodal analysis of demographics, radiomics, and MRI by variable Vision Transformer

PURPOSE

This study aimed to perform multimodal analysis by vision transformer (vViT) in predicting O6-methylguanine-DNA methyl transferase (MGMT) promoter status among adult patients with diffuse glioma using demographics (sex and age), radiomic features, and MRI.

METHODS

The training and test datasets contained 122 patients with 1,570 images and 30 patients with 484 images, respectively. The radiomic features were extracted from enhancing tumors (ET), necrotic tumor cores (NCR), and the peritumoral edematous/infiltrated tissues (ED) using contrast-enhanced T1-weighted images (CE-T1WI) and T2-weighted images (T2WI). The vViT had 9 sectors; 1 demographic sector, 6 radiomic sectors (CE-T1WI ET, CE-T1WI NCR, CE-T1WI ED, T2WI ET, T2WI NCR, and T2WI ED), 2 image sectors (CE-T1WI, and T2WI). Accuracy and area under the curve of receiver-operating characteristics (AUC-ROC) were calculated for the test dataset. The performance of vViT was compared with AlexNet, GoogleNet, VGG16, and ResNet by McNemar and Delong test. Permutation importance (PI) analysis with the Mann-Whitney U test was performed.

RESULTS

The accuracy was 0.833 (95% confidence interval [95%CI]: 0.714-0.877) and the area under the curve of receiver-operating characteristics was 0.840 (0.650-0.995) in the patient-based analysis. The vViT had higher accuracy than VGG16 and ResNet, and had higher AUC-ROC than GoogleNet (p<0.05). The ED radiomic features extracted from the T2-weighted image demonstrated the highest importance (PI=0.239, 95%CI: 0.237-0.240) among all other sectors (p<0.0001).

CONCLUSION

The vViT is a competent deep learning model in predicting MGMT status. The ED radiomic features of the T2-weighted image demonstrated the most dominant contribution.

Integrative analysis of transcriptome and proteome profiles in primary and recurrent glioblastoma

PURPOSE

Glioblastoma (GBM) is the most common and aggressive primary brain tumor characterized by poor prognosis and high recurrence. The underlying molecular mechanism that drives tumor progression and recurrence is unclear. This study is intended to look for molecular and biological changes that play a key role in GBM recurrence.

EXPERIMENTAL DESIGN

An integrative transcriptomic and proteomic analysis was performed on three primary GBM and three recurrent GBM tissues. Omics analyses were conducted using label-free quantitative proteomics and whole transcriptome sequencing.

RESULTS

A significant difference was found between primary GBM and recurrent GBM at the transcriptional level. Similar to other omics studies of cancer, a weak overlap was observed between transcriptome and proteome, and Procollagen C-Endopeptidase Enhancer 2 (PCOLCE2) was observed to be upregulated at mRNA and protein levels. Analysis of public cancer database revealed that high expression of PCOLCE2 is associated with poor prognosis of patients with GBM and that it may be a potential prognostic indicator. Functional and environmental enrichment analyses revealed significantly altered signaling pathways related to energy metabolism, including mitochondrial ATP synthesis-coupled electron transport and oxidative phosphorylation.

CONCLUSIONS AND CLINICAL RELEVANCE

This study provides new insights into the recurrence process of GBM through combined transcriptomic and proteomic analyses, complementing the existing GBM transcriptomic and proteomic data and suggesting that integrated multi-omics analyses may reveal new disease features of GBM.

The Potential of the Fibronectin Inhibitor Arg-Gly-Asp-Ser in the Development of Therapies for Glioblastoma

Glioblastoma (GBM) is the most lethal and common malignant primary brain tumor in adults. An important feature that supports GBM aggressiveness is the unique composition of its extracellular matrix (ECM). Particularly, fibronectin plays an important role in cancer cell adhesion, differentiation, proliferation, and chemoresistance. Thus, herein, a hydrogel with mechanical properties compatible with the brain and the ability to disrupt the dynamic and reciprocal interaction between fibronectin and tumor cells was produced. High-molecular-weight hyaluronic acid (HMW-HA) functionalized with the inhibitory fibronectin peptide Arg-Gly-Asp-Ser (RGDS) was used to produce the polymeric matrix. Liposomes encapsulating doxorubicin (DOX) were also included in the hydrogel to kill GBM cells. The resulting hydrogel containing liposomes with therapeutic DOX concentrations presented rheological properties like a healthy brain. In vitro assays demonstrated that unmodified HMW-HA hydrogels only caused GBM cell killing after DOX incorporation. Conversely, RGDS-functionalized hydrogels displayed per se cytotoxicity. As GBM cells produce several proteolytic enzymes capable of disrupting the peptide-HA bond, we selected MMP-2 to illustrate this phenomenon. Therefore, RGDS internalization can induce GBM cell apoptosis. Importantly, RGDS-functionalized hydrogel incorporating DOX efficiently damaged GBM cells without affecting astrocyte viability, proving its safety. Overall, the results demonstrate the potential of the RGDS-functionalized hydrogel to develop safe and effective GBM treatments.

Intratumoral and Peritumoral Multiparametric MRI-Based Radiomics Signature for Preoperative Prediction of Ki-67 Proliferation Status in Glioblastoma: A Two-Center Study

RATIONALE AND OBJECTIVES

To assess the predictive ability of intratumoral and peritumoral multiparametric magnetic resonance imaging (MRI)-based radiomics signature (RS) for preoperative prediction of Ki-67 proliferation status in glioblastoma. MATERIALS AND METHODS: A total of 205 patients with glioblastoma at two institutions were retrospectively analyzed. Data from institution 1 (n = 158) were used to develop the predictive model, and as an internal test dataset, data from institution 2 (n = 47) constitute the external test dataset. Feature selection was performed using spearman correlation coefficient, univariate ranking method, and the least absolute shrinkage and selection operator algorithm. RSs were established using a logistic regression algorithm. The predictive performance of the RSs was assessed using calibration curve, decision curve analysis (DCA), and area under the curve (AUC).

RESULTS

In the RSs based on single-parametric (contrast-enhanced T1-weighted image, T2-weighted image, or apparent diffusion coefficient maps), the AUCs of intratumoral, peritumoral, and combined area (intratumoral and peritumoral) were 0.60-0.67, with no significant difference among them. The RSs that using multiparametric features (integrating the previously mentioned three sequences) showed improved AUC compared to the single-parametric RSs; AUC reached 0.75-0.89. Among them, the multiparametric RS based on radiomics features of the combined area (Multi-Com) exhibited the highest performance, with an internal test dataset AUC of 0.89 (95% confidence interval (CI) 0.75-1.00) and an external test dataset AUC of 0.88 (95% CI 0.78-0.97). The calibration curve and DCA display RS (Multi-Com) have good calibration ability and clinical applicability.

CONCLUSION

The multiparametric MRI-based RS combining intratumoral and peritumoral features can serve as a noninvasive and effective tool for preoperative assessment of Ki-67 proliferation status in glioblastoma.

Preoperative vascular heterogeneity based on dynamic susceptibility contrast MRI in predicting spatial pattern of locally recurrent high-grade gliomas

OBJECTIVES

To investigate if spatial recurrence pattern is associated with patient prognosis, and whether MRI vascular habitats can predict spatial pattern.

METHODS

In this retrospective study, 69 patients with locally recurrent high-grade gliomas (HGGs) were included. The cohort was divided into intra-resection cavity recurrence (ICR) and extra-resection cavity recurrence (ECR) patterns, according to the distance between the location of the recurrent tumor and the resection cavity or surgical region. Four vascular habitats, high angiogenic tumor, low angiogenic tumor, infiltrated peripheral edema, and vasogenic peripheral edema, were segmented and vascular heterogeneity parameters were analyzed. The survival and diagnostic performance under different spatial recurrence patterns were analyzed by Kaplan-Meier and ROC. A nomogram model was constructed by regression analysis and validated by bootstrapping technique.

RESULTS

Progression-free survival (PFS) and overall survival (OS) were longer for ICR (n = 32) than those for ECR (n = 37) (median PFS: 8 vs. 5 months, median OS: 17 vs. 13 months, p < 0.05). MRI vascular habitat analyses showed ECR had higher median relative cerebral blood volume (rCBVmedian) at each habitat than ICR (all p < 0.01). The rCBVmedian at IPE had good diagnostic performance (AUC: 0.727, 95%CI: 0.607, 0.828). The AUC of the nomogram based on MRI vascular habitats and clinical factors was 0.834 (95%CI: 0.726, 0.913) and was confirmed as 0.833 (95%CI: 0.830, 0.836) by bootstrapping validation.

CONCLUSIONS

The spatial pattern of locally recurrent HGGs is associated with prognosis. MRI vascular heterogeneity parameter could be used as a non-invasive imaging marker to predict spatial recurrence pattern.

CLINICAL RELEVANCE STATEMENT

Vascular heterogeneity parameters based on MRI vascular habitat analyses can non-invasively predict the spatial patterns of locally recurrent high-grade gliomas, providing a new diagnostic basis for clinicians to develop the extent of surgical resection and postoperative radiotherapy planning.

KEY POINTS

• Intra-resection cavity pattern was associated with longer progression-free survival and overall survival in locally recurrent high-grade gliomas. • Higher vascular heterogeneities in extra-resection cavity recurrence than in intra-resection cavity recurrence and the vascular heterogeneity parameters had good diagnostic performance in discriminating spatial recurrence pattern. • A nomogram model based on MRI vascular habitats and clinical factors had good performance in predicting spatial recurrence pattern.

Therapeutic Hypothermia in Treating Glioblastoma: A Review

Glioblastoma (GBM) is the most commonly occurring of all malignant central nervous system (CNS) tumors in adults. Considering the low median survival of only ∼15 months and poor prognosis in GBM patients, despite surgical resection with adjuvant radiation and chemotherapy, it is vital to seek brand new and innovative treatment in combination with already existing methods. Hypothermia participates in many metabolic pathways, inflammatory responses, and apoptotic processes, while also promoting the integrity of neurons. Following the successful application of therapeutic hypothermia across a spectrum of disorders such as traumatic CNS injury, cardiac arrest, and epilepsy, several clinical trials have set to evaluate the potency of hypothermia in treating a variety of cancers, including breast and ovaries cancer. In regard to primary neoplasms and more specifically, GBM, hypothermia has recently shown promising results as an auxiliary treatment, reinforcing chemotherapy's efficacy. In this review, we discuss the recent advances in utilizing hypothermia as treatment for GBM and other cancers.

Distinguishing Tumor Cell Infiltration and Vasogenic Edema in the Peritumoral Region of Glioblastoma at the Voxel Level via Conventional MRI Sequences

RATIONALE AND OBJECTIVES

The peritumoral region of glioblastoma (GBM) is composed of infiltrating tumor cells and vasogenic edema, which are difficult to distinguish manually on MRI. To distinguish tumor cell infiltration and vasogenic edema in GBM peritumoral regions, it is crucial to develop a method that is precise, effective, and widely applicable.

MATERIALS AND METHODS

We retrieved the image characteristics of 379,730 voxels (marker of tumor infiltration) from 28 non-enhanced gliomas and 365,262 voxels (marker of edema) from the peritumoral edema region of 14 meningiomas on conventional MRI sequences (T1-weighted image, the contrast-enhancing T1-weighted image, the T2-weighted image, the T2-fluid attenuated inversion recovery image, and the apparent diffusion coefficient map). Using the SVM classifier, a model for predicting tumor cell infiltration and vasogenic edema at the voxel level was developed. The accuracy of the model's predictions was then evaluated using 15 GBM patients who underwent stereotactic biopsies.

RESULTS

The area under the curve (AUC), accuracy, sensitivity, and specificity of the prediction model were 0.93, 0.84, 0.83, and 0.85 in the training set, and 0.90, 0.82, 0.83, and 0.83 in the test set (704,992 voxels), respectively. The pathology verification of 28 biopsy points with an accuracy of 0.79.

CONCLUSION

At the voxel level, it seems possible to forecast tumor cell infiltration and vasogenic edema in the peritumoral region of GBM based on conventional MRI sequences.

Peritumoral brain zone in glioblastoma: biological, clinical and mechanical features

Glioblastoma is a highly aggressive and invasive tumor that affects the central nervous system (CNS). With a five-year survival rate of only 6.9% and a median survival time of eight months, it has the lowest survival rate among CNS tumors. Its treatment consists of surgical resection, subsequent fractionated radiotherapy and concomitant and adjuvant chemotherapy with temozolomide. Despite the implementation of clinical interventions, recurrence is a common occurrence, with over 80% of cases arising at the edge of the resection cavity a few months after treatment. The high recurrence rate and location of glioblastoma indicate the need for a better understanding of the peritumor brain zone (PBZ). In this review, we first describe the main radiological, cellular, molecular and biomechanical tissue features of PBZ; and subsequently, we discuss its current clinical management, potential local therapeutic approaches and future prospects.

Prognostic Value of Apparent Diffusion Coefficient (ADC) in Patients with Diffuse Gliomas

This study aimed to evaluate potential posttreatment changes in ADC values within the tissue surrounding the enhancing lesion, particularly in areas not exhibiting MRI characteristics of involvement. Additionally, the objective was to investigate the correlations among ADC values, treatment response, and survival outcomes in individuals diagnosed with gliomas. This retrospective study included a total of 49 patients that underwent either stereotactic biopsy or maximal surgical resection. Histologically confirmed as Grade III or IV gliomas, all cases adhered to the 2016 and 2021 WHO classifications, with subsequent radio-chemotherapy administered post-surgery. Patients were divided into two groups: short and long survival groups. Baseline and follow-up MRI scans were obtained on a 1.5 T MRI scanner. Two ROI circles were positioned near the enhancing area, one ROI in the NAWM ipsilateral to the neoplasm and another symmetrically in the contralateral hemisphere on ADC maps. At follow-up there was a significant difference in both ipsilateral and contralateral NAWM between the two groups, -0.0857 (p = 0.004) and -0.0607 (p = 0.037), respectively. There was a weak negative correlation between survival and ADC values in ipsilateral and contralateral NAWM at the baseline with the correlation coefficient -0.328 (p = 0.02) and -0.302 (p = 0.04), respectively. The correlation was stronger at the follow-up. The findings indicate that ADC values in normal-appearing white matter (NAWM) may function as a prognostic biomarker in patients with diffuse gliomas.

Peritumoral Radiomics for Identification of Telomerase Reverse Transcriptase Promoter Mutation in Patients With Glioblastoma Based on Preoperative MRI

Purpose: To evaluate the value of intra- and peritumoral deep learning (DL) features based on multi-parametric magnetic resonance imaging (MRI) for identifying telomerase reverse transcriptase (TERT) promoter mutation in glioblastoma (GBM). Methods: In this study, we included 229 patients with GBM who underwent preoperative MRI in two hospitals between November 2016 and September 2022. We used four 2D Convolutional Neural Networks (GoogLeNet, DenseNet121, VGG16, and MobileNetV3-Large) to extract intra- and peritumoral DL features. The Mann-Whitney U test, Pearson correlation analysis, least absolute shrinkage and selection operator, and logistic regression analysis were used for feature selection and construction of DL radiomics (DLR) signatures in different regions. These multi-parametric and multi-region signatures were combined to identify TERT promoter mutation. The area under the receiver operating characteristic curve (AUC) was used to evaluate the effects of the signatures. Results: The signatures based on the DL features from the peritumoral regions with expansion distances of 2 mm, 8 mm, and 10 mm using the GoogLeNet architecture correlated with the optimal AUC values (test set: .823, .753, and .768) in the T2-weighted, T1-weighted contrast-enhanced, and T1-weighted images. Using the stacking fusion method, DLR with multi-parameter and multi-region fusion achieved the best discrimination with AUC values of .948 and .902 in the training and test sets, respectively. Conclusions: The radiomics model based on the fusion of multi-parameter MRI intra- and peritumoral DLR signatures may help to identify TERT promoter mutation in patients with GBM.

Implantable SDF-1α-loaded silk fibroin hyaluronic acid aerogel sponges as an instructive component of the glioblastoma ecosystem: Between chemoattraction and tumor shaping into resection cavities

In view of inevitable recurrences despite resection, glioblastoma (GB) is still an unmet clinical need. Dealing with the stromal-cell derived factor 1-alpha (SDF-1α)/CXCR4 axis as a hallmark of infiltrative GB tumors and with the resection cavity situation, the present study described the effects and relevance of a new engineered micro-nanostructured SF-HA-Hep aerogel sponges, made of silk fibroin (SF), hyaluronic acid (HA) and heparin (Hep) and loaded with SDF-1α, to interfere with the GB ecosystem and residual GB cells, attracting and confining them in a controlled area before elimination. 70 µm-pore sponges were designed as an implantable scaffold to trap GB cells. They presented shape memory and fit brain cavities. Histological results after implantation in brain immunocompetent Fischer rats revealed that SF-HA-Hep sponges are well tolerated for more than 3 months while moderately and reversibly colonized by immuno-inflammatory cells. The use of human U87MG GB cells overexpressing the CXCR4 receptor (U87MG-CXCR4+) and responding to SDF-1α allowed demonstrating directional GB cell attraction and colonization of the device in vitro and in vivo in orthotopic resection cavities in Nude rats. Not modifying global survival, aerogel sponge implantation strongly shaped U87MG-CXCR4+ tumors in cavities in contrast to random infiltrative growth in controls. Overall, those results support the interest of SF-HA-Hep sponges as modifiers of the GB ecosystem dynamics acting as "cell meeting rooms" and biocompatible niches whose properties deserve to be considered toward the development of new clinical procedures. STATEMENT OF SIGNIFICANCE: Brain tumor glioblastoma (GB) is one of the worst unmet clinical needs. To prevent the relapse in the resection cavity situation, new implantable biopolymer aerogel sponges loaded with a chemoattractant molecule were designed and preclinically tested as a prototype targeting the interaction between the initial tumor location and its attraction by the peritumoral environment. While not modifying global survival, biocompatible SDF1-loaded hyaluronic acid and silk fibroin sponges induce directional GB cell attraction and colonization in vitro and in rats in vivo. Interestingly, they strongly shaped GB tumors in contrast to random infiltrative growth in controls. These results provide original findings on application of exogenous engineered niches that shape tumors and serve as cell meeting rooms for further clinical developments.

Unlocking Bevacizumab's Potential: rCBVmax as a Predictive Biomarker for Enhanced Survival in Glioblastoma IDH-Wildtype Patients

BACKGROUND

Aberrant vascular architecture and angiogenesis are hallmarks of glioblastoma IDH-wildtype, suggesting that these tumors are suitable for antiangiogenic therapy. Bevacizumab was FDA-approved in 2009 following promising results in two clinical trials. However, its use for recurrent glioblastomas remains a subject of debate, as it does not universally improve patient survival.

PURPOSES

In this study, we aimed to analyze the influence of tumor vascularity on the benefit provided by BVZ and propose preoperative rCBVmax at the high angiogenic tumor habitat as a predictive biomarker to select patients who can benefit the most.

METHODS

Clinical and MRI data from 106 patients with glioblastoma IDH-wildtype have been analyzed. Thirty-nine of them received BVZ, and the remaining sixty-seven did not receive a second-line treatment. The ONCOhabitats method was used to automatically calculate rCBV.

RESULTS

We found a median survival from progression of 305 days longer for patients with moderate vascular tumors who received BVZ than those who did not receive any second-line treatment. This contrasts with patients with high-vascular tumors who only presented a median survival of 173 days longer when receiving BVZ. Furthermore, better responses to BVZ were found for the moderate-vascular group with a higher proportion of patients alive at 6, 12, 18, and 24 months after progression.

CONCLUSIONS

We propose rCBVmax as a potential biomarker to select patients who can benefit more from BVZ after tumor progression. In addition, we propose a threshold of 7.5 to stratify patients into moderate- and high-vascular groups to select the optimal second-line treatment.

Tumor Multiregional Mean Apparent Propagator (MAP) Features in Evaluating Gliomas-A Comparative Study With Diffusion Kurtosis Imaging (DKI)

BACKGROUND

Glioma classification affects treatment and prognosis. Reliable imaging methods for preoperatively evaluating gliomas are essential.

PURPOSE

To evaluate tumor multiregional mean apparent propagator (MAP) features in glioma diagnosis and to compare those with diffusion-kurtosis imaging (DKI).

STUDY TYPE

Retrospective study.

SUBJECTS

70 untreated glioma patients (31 LGGs (low-grade gliomas), 34 women; mean age, 47 ± 12 years, training (60%, n = 42) and testing cohorts (40%, n = 28)).

FIELD STRENGTH/SEQUENCE

3-T, diffusion-MRI using q-space Cartesian grid sampling with 11 different b-values.

ASSESSMENT

Tumor multiregional MAP (mean squared displacement (MSD); q-space inverse variance (QIV); non-Gaussianity (NG); axial/radial non-Gaussianity (NGAx, NGRad); return-to-origin/axis/plane probability (RTOP, RTAP, and RTPP)); and DKI metrics (axial/mean/radial kurtosis (AK, MK, and RK)) on tumor parenchyma (TP) and peritumoral areas (PT) in histopathologically gliomas grading and genotyping were assessed.

STATISTICAL TESTS

Mann-Whitney U; Kruskal-Wallis; Benjamini-Hochberg; Bonferroni-correction; receiver operating curve (ROC) and area under curve (AUC); DeLong's test; Random Forest (RF). P value<0.05 was considered statistically significant after multiple comparisons correction.

RESULTS

Compared with LGGs, MSD, and QIV were significantly lower in TP, whereas NG, NGAx, NGRad, RTOP, RTAP, RTPP, and DKI metrics were significantly higher in HGGs (high-grade gliomas) (P ≤ 0.007), as well as in isocitrate-dehydrogenase (IDH)-mutated than IDH-wildtype gliomas (P ≤ 0.039). These trends were reversed for PT (tumor grades, P ≤ 0.011; IDH-mutation status, P ≤ 0.012). ROC analysis showed that, in TP, DKI metrics performed best in TP (AUC 0.83), whereas in PT, RTPP performed best (AUC 0.77) in glioma grading. AK performed best in TP (AUC 0.77), whereas MSD and RTPP performed best in PT (AUC 0.73) in IDH genotyping. Further RF analysis with DKI and MAP demonstrated good performance in grading (AUC 0.91, Accuracy 82%) and IDH genotyping (AUC 0.87, Accuracy 79%).

DATA CONCLUSION

Tumor multiregional MAP features could effectively evaluate gliomas. The performance of MAP may be similar to DKI in TP, while in PT, MAP may outperform DKI.

LEVEL OF EVIDENCE

4 TECHNICAL EFFICACY: Stage 2.

Assessment of 13 essential and toxic trace elements in tumor and peritumoral brain tissues from human glioblastoma

Trace elements within the brain are important for proper neurological function, but their imbalance has been rarely investigated in glioblastoma. This study enrolled a total of 14 patients with glioblastoma, and the tumor and peritumoral brain tissues were collected while undergoing surgery. The concentrations of Mg, Ca, Cr, Mn, Fe, Co, Cu, Zn, Se, As, Cd, Tl and Pb were determined using a well-evaluated ICP-MS method. The Cu- and Cd-binding proteomes were further analyzed using the anatomic transcriptional atlas from Ivy GAP. Histological evaluation was based on rubeanic acid staining and immunohistochemistry, respectively. The 13 trace element concentrations were obtained, and the highest were Ca, Mn, Fe, Zn and Cu, ranging from a few to dozens of ug/g. Correlation analysis suggested the existence of two intra-correlated clusters: essential metals (Cu-Ca-Zn-Mg) and heavy metals (Pb-As-Cd-Tl-Co-Cr-Mn). Compared to the tumor samples, significantly higher levels of Cu and Cd were observed in the peritumoral region. Further analysis of the Cu- and Cd-binding proteins from the anatomic view suggested that DBH and NOS1 were obviously increased in the leading edge than the central tumor region. Consistent with the above findings, histological evaluation of Cu and DBH further confirmed more copper and DBH expressions in the peritumoral area compared to the tumor core. Trace elements differ in tumor and peritumoral brain zone in glioblastoma, which may associate with tumor angiogenesis.

Glioblastomas with and without peritumoral fluid-attenuated inversion recovery (FLAIR) hyperintensity present morphological and microstructural differences on conventional MR images

OBJECTIVES

Glioblastoma (GB) without peritumoral fluid-attenuated inversion recovery (FLAIR) hyperintensity is atypical and its characteristics are barely known. The aim of this study was to explore the differences in pathological and MRI-based intrinsic features (including morphologic and first-order features) between GBs with peritumoral FLAIR hyperintensity (PFH-bearing GBs) and GBs without peritumoral FLAIR hyperintensity (PFH-free GBs).

METHODS

In total, 155 patients with pathologically diagnosed GBs were retrospectively collected, which included 110 PFH-bearing GBs and 45 PFH-free GBs. The pathological and imaging data were collected. The Visually AcceSAble Rembrandt Images (VASARI) features were carefully evaluated. The first-order radiomics features from the tumor region were extracted from FLAIR, apparent diffusion coefficient (ADC), and T1CE (T1-contrast enhanced) images. All parameters were compared between the two groups of GBs.

RESULTS

The pathological data showed more alpha thalassemia/mental retardation syndrome X-linked (ATRX)-loss in PFH-free GBs compared to PFH-bearing ones (p < 0.001). Based on VASARI evaluation, PFH-free GBs had larger intra-tumoral enhancing proportion and smaller necrotic proportion (both, p < 0.001), more common non-enhancing tumor (p < 0.001), mild/minimal enhancement (p = 0.003), expansive T1/FLAIR ratio (p < 0.001) and solid enhancement (p = 0.009), and less pial invasion (p = 0.010). Moreover, multiple ADC- and T1CE-based first-order radiomics features demonstrated differences, especially the lower intensity heterogeneity in PFH-free GBs (for all, adjusted p < 0.05).

CONCLUSIONS

Compared to PFH-bearing GBs, PFH-free ones demonstrated less immature neovascularization and lower intra-tumoral heterogeneity, which would be helpful in clinical treatment stratification.

CLINICAL RELEVANCE STATEMENT

Glioblastomas without peritumoral FLAIR hyperintensity show less immature neovascularization and lower heterogeneity leading to potential higher treatment benefits due to less drug resistance and treatment failure.

KEY POINTS

• The study explored the differences between glioblastomas with and without peritumoral FLAIR hyperintensity. • Glioblastomas without peritumoral FLAIR hyperintensity showed less necrosis and contrast enhancement and lower intensity heterogeneity. • Glioblastomas without peritumoral FLAIR hyperintensity had less immature neovascularization and lower tumor heterogeneity.

Comparison of MRI radiomics-based machine learning survival models in predicting prognosis of glioblastoma multiforme

Objective

To compare the performance of radiomics-based machine learning survival models in predicting the prognosis of glioblastoma multiforme (GBM) patients.

Methods

131 GBM patients were included in our study. The traditional Cox proportional-hazards (CoxPH) model and four machine learning models (SurvivalTree, Random survival forest (RSF), DeepSurv, DeepHit) were constructed, and the performance of the five models was evaluated using the C-index.

Results

After the screening, 1792 radiomics features were obtained. Seven radiomics features with the strongest relationship with prognosis were obtained following the application of the least absolute shrinkage and selection operator (LASSO) regression. The CoxPH model demonstrated that age (HR = 1.576, p = 0.037), Karnofsky performance status (KPS) score (HR = 1.890, p = 0.006), radiomics risk score (HR = 3.497, p = 0.001), and radiomics risk level (HR = 1.572, p = 0.043) were associated with poorer prognosis. The DeepSurv model performed the best among the five models, obtaining C-index of 0.882 and 0.732 for the training and test set, respectively. The performances of the other four models were lower: CoxPH (0.663 training set / 0.635 test set), SurvivalTree (0.702/0.655), RSF (0.735/0.667), DeepHit (0.608/0.560).

Conclusion

This study confirmed the superior performance of deep learning algorithms based on radiomics relative to the traditional method in predicting the overall survival of GBM patients; specifically, the DeepSurv model showed the best predictive ability.

Current Knowledge about the Peritumoral Microenvironment in Glioblastoma

Glioblastoma is a deadly disease, with a mean overall survival of less than 2 years from diagnosis. Recurrence after gross total surgical resection and adjuvant chemo-radiotherapy almost invariably occurs within the so-called peritumoral brain zone (PBZ). The aim of this narrative review is to summarize the most relevant findings about the biological characteristics of the PBZ currently available in the medical literature. The PBZ presents several peculiar biological characteristics. The cellular landscape of this area is different from that of healthy brain tissue and is characterized by a mixture of cell types, including tumor cells (seen in about 30% of cases), angiogenesis-related endothelial cells, reactive astrocytes, glioma-associated microglia/macrophages (GAMs) with anti-inflammatory polarization, tumor-infiltrating lymphocytes (TILs) with an "exhausted" phenotype, and glioma-associated stromal cells (GASCs). From a genomic and transcriptomic point of view, compared with the tumor core and healthy brain tissue, the PBZ presents a "half-way" pattern with upregulation of genes related to angiogenesis, the extracellular matrix, and cellular senescence and with stemness features and downregulation in tumor suppressor genes. This review illustrates that the PBZ is a transition zone with a pre-malignant microenvironment that constitutes the base for GBM progression/recurrence. Understanding of the PBZ could be relevant to developing more effective treatments to prevent GBM development and recurrence.

Pretreatment MR-based radiomics in patients with glioblastoma: A systematic review and meta-analysis of prognostic endpoints

PURPOSE

Recent studies have shown promise of MR-based radiomics in predicting the survival of patients with untreated glioblastoma. This study aimed to comprehensively collate evidence to assess the prognostic value of radiomics in glioblastoma.

METHODS

PubMed-MEDLINE, Embase, and Web of Science were searched to find original articles investigating the prognostic value of MR-based radiomics in glioblastoma published up to July 14, 2023. Concordance indexes (C-indexes) and Cox proportional hazards ratios (HRs) of overall survival (OS) and progression-free survival (PFS) were pooled via random-effects modeling. For studies aimed at classifying long-term and short-term PFS, a hierarchical regression model was used to calculate pooled sensitivity and specificity. Between-study heterogeneity was assessed using the Higgin inconsistency index (I2). Subgroup regression analysis was performed to find potential factors contributing to heterogeneity. Publication bias was assessed via funnel plots and the Egger test.

RESULTS

Among 1371 abstracts, 18 and 17 studies were included for qualitative and quantitative data synthesis, respectively. Respective pooled C-indexes and HRs for OS were 0.65 (95 % confidence interval [CI], 0.58-0.72) and 2.88 (95 % CI, 2.28-3.64), whereas those for PFS were 0.61 (95 % CI, 0.55-0.66) and 2.78 (95 % CI, 1.91-4.03). Among 4 studies that predicted short-term PFS, the pooled sensitivity and specificity were 0.77 (95 % CI, 0.58-0.89) and 0.60 (95 % CI, 0.45-0.73), respectively. There was a substantial between-study heterogeneity among studies with the survival endpoint of OS C-index (n = 9, I2 = 83.8 %). Publication bias was not observed overall.

CONCLUSION

Pretreatment MR-based radiomics provided modest prognostic value in both OS and PFS in patients with glioblastoma.

Neuropsychological Evaluation and Functional Magnetic Resonance Imaging Tasks in the Preoperative Assessment of Patients with Brain Tumors: A Systematic Review

BACKGROUND

Current surgical treatment of gliomas relies on a function-preserving, maximally safe resection approach. Functional Magnetic Resonance Imaging (fMRI) is a widely employed technology for this purpose. A preoperative neuropsychological evaluation should accompany this exam. However, only a few studies have reported both neuropsychological tests and fMRI tasks for preoperative planning-the current study aimed to systematically review the scientific literature on the topic.

METHODS

PRISMA guidelines were followed. We included studies that reported both neuropsychological tests and fMRI. Exclusion criteria were: no brain tumors, underage patients, no preoperative assessment, resting-state fMRI only, or healthy sample population/preclinical studies.

RESULTS

We identified 123 papers, but only 15 articles were included. Eight articles focused on language; three evaluated cognitive performance; single papers studied sensorimotor cortex, prefrontal functions, insular cortex, and cerebellar activation. Two qualitative studies focused on visuomotor function and language. According to some authors, there was a strong correlation between performance in presurgical neuropsychological tests and fMRI. Several papers suggested that selecting well-adjusted and individualized neuropsychological tasks may enable the development of personalized and more efficient protocols. The fMRI findings may also help identify plasticity phenomena to avoid unintentional damage during neurosurgery.

CONCLUSIONS

Most studies have focused on language, the most commonly evaluated cognitive function. The correlation between neuropsychological and fMRI results suggests that altered functions during the neuropsychological assessment may help identify patients who could benefit from an fMRI and, possibly, functions that should be tested. Neuropsychological evaluation and fMRI have complementary roles in the preoperative assessment.

Evaluation of Temozolomide and Fingolimod Treatments in Glioblastoma Preclinical Models

Glioblastomas are malignant brain tumors which remain lethal due to their aggressive and invasive nature. The standard treatment combines surgical resection, radiotherapy, and chemotherapy using Temozolomide, albeit with a minor impact on patient prognosis (15 months median survival). New therapies evaluated in preclinical translational models are therefore still required to improve patient survival and quality of life. In this preclinical study, we evaluated the effect of Temozolomide in different models of glioblastoma. We also aimed to investigate the efficacy of Fingolimod, an immunomodulatory drug for multiple sclerosis also described as an inhibitor of the sphingosine-1-phosphate (S1P)/S1P receptor axis. The effects of Fingolimod and Temozolomide were analyzed with in vitro 2D and 3D cellular assay and in vivo models using mouse and human glioblastoma cells implanted in immunocompetent or immunodeficient mice, respectively. We demonstrated both in in vitro and in vivo models that Temozolomide has a varied effect depending on the tumor type (i.e., U87MG, U118MG, U138MG, and GL261), demonstrating sensitivity, acquired resistance, and purely resistant tumor phenotypes, as observed in patients. Conversely, Fingolimod only reduced in vitro 2D tumor cell growth and increased cytotoxicity. Indeed, Fingolimod had little or no effect on 3D spheroid cytotoxicity and was devoid of effect on in vivo tumor progression in Temozolomide-sensitive models. These results suggest that the efficacy of Fingolimod is dependent on the glioblastoma tumor microenvironment. Globally, our data suggest that the response to Temozolomide varies depending on the cancer model, consistent with its clinical activity, whereas the potential activity of Fingolimod may merit further evaluation.

Unravelling the Glioblastoma Tumour Microenvironment: Can Aptamer Targeted Delivery Become Successful in Treating Brain Cancers?

The key challenges to treating glioblastoma multiforme (GBM) are the heterogeneous and complex nature of the GBM tumour microenvironment (TME) and difficulty of drug delivery across the blood-brain barrier (BBB). The TME is composed of various neuronal and immune cells, as well as non-cellular components, including metabolic products, cellular interactions, and chemical compositions, all of which play a critical role in GBM development and therapeutic resistance. In this review, we aim to unravel the complexity of the GBM TME, evaluate current therapeutics targeting this microenvironment, and lastly identify potential targets and therapeutic delivery vehicles for the treatment of GBM. Specifically, we explore the potential of aptamer-targeted delivery as a successful approach to treating brain cancers. Aptamers have emerged as promising therapeutic drug delivery vehicles with the potential to cross the BBB and deliver payloads to GBM and brain metastases. By targeting specific ligands within the TME, aptamers could potentially improve treatment outcomes and overcome the challenges associated with larger therapies such as antibodies.

Supramaximal resection based on en-bloc technique reduces tumor burden and prolongs survival in primary supratentorial lobar glioblastoma

PURPOSE

Resection beyond the contrast-enhanced zone contributed to reduce tumor burden and prolong survival in glioblastomas. The optimal extent of resection (EOR) and how to achieve it are worthy of continuous investigation for obtaining a satisfactory balance between maximal resection and the preservation of neurological function.

METHODS

A total of 340 adult supratentorial lobar glioblastomas (included astrocytoma, WHO 4, IDH mutation and glioblastoma) were retrospectively evaluated. The clinical data, EOR, technique of resection, postoperative complications, overall survival (OS) and progression-free survival (PFS) were assessed by univariate, multivariate and propensity score matched analysis. Histological staining was performed to comprehend the effect of the membranous structures and the cell distribution in tumoral and peritumoral regions.

RESULTS

Supramaximal resection (SMR) was confirmed as resection with 100% EORCE and > 50% EORnCE in glioblastomas by Cox proportional hazards model. Histological results showed SMR reduced the cell density of surgical edge compared to total resection. En-bloc technique based on membranous structures, which had blocking effect on tumoral invasion, contributed to achieve SMR. Moreover, applying en-bloc technique and achieving SMR did not additionally deteriorate neurological function and had similarly effects on the improvement of neurological function. Multivariate analysis confirmed that IDH1 status, technique of resection and EOR were independently correlated with PFS, and > 64 years old, IDH1 status, technique of resection, EOR and preoperative NIHSS were independently correlated with OS.

CONCLUSIONS

Applying en-bloc technique and achieving SMR, which could reduce tumor burden and did not increase additional complications, both had remarkedly positive effects on clinical outcomes in patients with primary supratentorial lobar glioblastomas.

Structural and functional vascular dysfunction within brain metastases is linked to pembrolizumab inefficacy

Structurally and functionally aberrant vasculature is a hallmark of tumor angiogenesis and treatment resistance. Given the synergistic link between aberrant tumor vasculature and immunosuppression, we analyzed perfusion MRI for 44 patients with brain metastases (BM) undergoing treatment with pembrolizumab. To date, vascular-immune communication, or the relationship between immune checkpoint inhibitor (ICI) efficacy and vascular architecture, has not been well-characterized in human imaging studies. We found that ICI-responsive BM possessed a structurally balanced vascular makeup, which was linked to improved vascular efficiency and an immune-stimulatory microenvironment. In contrast, ICI-resistant BM were characterized by a lack of immune cell infiltration and a highly aberrant vasculature dominated by large-caliber vessels. Peri-tumor region analysis revealed early functional changes predictive of ICI resistance before radiographic evidence on conventional MRI. This study was one of the largest functional imaging studies for BM and establishes a foundation for functional studies that illuminate the mechanisms linking patterns of vascular architecture with immunosuppression, as targeting these aspects of cancer biology may serve as the basis for future combination treatments.

Discrimination Between Glioblastoma and Solitary Brain Metastasis Using Conventional MRI and Diffusion-Weighted Imaging Based on a Deep Learning Algorithm

This study aims to develop and validate a deep learning (DL) model to differentiate glioblastoma from single brain metastasis (BM) using conventional MRI combined with diffusion-weighted imaging (DWI). Preoperative conventional MRI and DWI of 202 patients with solitary brain tumor (104 glioblastoma and 98 BM) were retrospectively obtained between February 2016 and September 2022. The data were divided into training and validation sets in a 7:3 ratio. An additional 32 patients (19 glioblastoma and 13 BM) from a different hospital were considered testing set. Single-MRI-sequence DL models were developed using the 3D residual network-18 architecture in tumoral (T model) and tumoral + peritumoral regions (T&P model). Furthermore, the combination model based on conventional MRI and DWI was developed. The area under the receiver operating characteristic curve (AUC) was used to assess the classification performance. The attention area of the model was visualized as a heatmap by gradient-weighted class activation mapping technique. For the single-MRI-sequence DL model, the T2WI sequence achieved the highest AUC in the validation set with either T models (0.889) or T&P models (0.934). In the combination models of the T&P model, the model of DWI combined with T2WI and contrast-enhanced T1WI showed increased AUC of 0.949 and 0.930 compared with that of single-MRI sequences in the validation set, respectively. And the highest AUC (0.956) was achieved by combined contrast-enhanced T1WI, T2WI, and DWI. In the heatmap, the central region of the tumoral was hotter and received more attention than other areas and was more important for differentiating glioblastoma from BM. A conventional MRI-based DL model could differentiate glioblastoma from solitary BM, and the combination models improved classification performance.

Optimizing maximum resection of glioblastoma: Raman spectroscopy versus 5-aminolevulinic acid

OBJECTIVE

The objective of this study was to assess and compare the potential of 5-aminolevulinic acid (5-ALA) and Raman spectroscopy (RS) in detecting tumor-infiltrated brain in patients with glioblastoma (GBM).

METHODS

Between July 2020 and October 2021, the authors conducted a prospective clinical trial with 15 patients who underwent neurosurgical treatment of newly diagnosed and histologically verified GBM. A solid contrast-enhancing tumor core and peritumoral tissue were investigated intraoperatively for cancer cells by using 5-ALA and RS to achieve pathology-tailored maximum resection. In each case, a minimum of 10 biopsies were sampled from navigation-guided areas. Two neuropathologists examined the biopsies for the presence of neoplastic cells. The detection performance of 5-ALA and RS alone and in combination was assessed. Pre- and postoperative MRI, Karnofsky Performance Status (KPS), and National Institutes of Health Stroke Scale (NIHSS) scores were compared, and median progression-free survival (PFS) was evaluated.

RESULTS

A total of 185 biopsy samples were harvested from the contrast-enhancing tumor core (n = 19) and peritumoral tissue (n = 166). In the tumor core, 5-ALA and RS each showed a sensitivity of 100%. In the peritumoral tissue, 5-ALA was less sensitive than RS in detecting cancer (46% vs 69%) but showed higher specificity (81% vs 57%). When the two methods were combined, the accuracy of tumor detection was increased by about 10%. Pathology-tailored resection led to a 52% increase in resection volume comparing the volume of preoperative contrast enhancement with the postoperative resection cavity on MRI (p = 0.0123). Eloquent brain involvement prevented gross-total resection in 4 patients. Four weeks after surgery, mean KPS (p = 0.7637) and NIHSS scores (p = 0.3146) were not significantly different from preoperative values. Of the 13 patients who had received postoperative chemoradiotherapy, 4 did not show any progression after a median follow-up of 14 months. The remaining 9 patients had a median PFS of 8 months.

CONCLUSIONS

According to the study data, RS is capable of detecting tumor-infiltrated brain with higher sensitivity but lower specificity than the current standard of 5-ALA. With further technological and workflow advancements, RS in combination with protoporphyrin IX fluorescence may contribute to pathology-tailored glioma resection in the future.

Clinical impact of anti-inflammatory microglia and macrophage phenotypes at glioblastoma margins

Glioblastoma is a devastating brain cancer for which effective treatments are required. Tumour-associated microglia and macrophages promote glioblastoma growth in an immune-suppressed microenvironment. Most recurrences occur at the invasive margin of the surrounding brain, yet the relationships between microglia/macrophage phenotypes, T cells and programmed death-ligand 1 (an immune checkpoint) across human glioblastoma regions are understudied. In this study, we performed a quantitative immunohistochemical analysis of 15 markers of microglia/macrophage phenotypes (including anti-inflammatory markers triggering receptor expressed on myeloid cells 2 and CD163, and the low-affinity-activating receptor CD32a), T cells, natural killer cells and programmed death-ligand 1, in 59 human IDH1-wild-type glioblastoma multi-regional samples (n = 177; 1 sample at tumour core, 2 samples at the margins: the infiltrating zone and leading edge). Assessment was made for the prognostic value of markers; the results were validated in an independent cohort. Microglia/macrophage motility and activation (Iba1, CD68), programmed death-ligand 1 and CD4+ T cells were reduced, and homeostatic microglia (P2RY12) were increased in the invasive margins compared with the tumour core. There were significant positive correlations between microglia/macrophage markers CD68 (phagocytic)/triggering receptor expressed on myeloid cells 2 (anti-inflammatory) and CD8+ T cells in the invasive margins but not in the tumour core (P < 0.01). Programmed death-ligand 1 expression was associated with microglia/macrophage markers (including anti-inflammatory) CD68, CD163, CD32a and triggering receptor expressed on myeloid cells 2, only in the leading edge of glioblastomas (P < 0.01). Similarly, there was a positive correlation between programmed death-ligand 1 expression and CD8+ T-cell infiltration in the leading edge (P < 0.001). There was no relationship between CD64 (a receptor for autoreactive T-cell responses) and CD8+/CD4+ T cells, or between the microglia/macrophage antigen presentation marker HLA-DR and microglial motility (Iba1) in the tumour margins. Natural killer cell infiltration (CD335+) correlated with CD8+ T cells and with CD68/CD163/triggering receptor expressed on myeloid cells 2 anti-inflammatory microglia/macrophages at the leading edge. In an independent large glioblastoma cohort with transcriptomic data, positive correlations between anti-inflammatory microglia/macrophage markers (triggering receptor expressed on myeloid cells 2, CD163 and CD32a) and CD4+/CD8+/programmed death-ligand 1 RNA expression were validated (P < 0.001). Finally, multivariate analysis showed that high triggering receptor expressed on myeloid cells 2, programmed death-ligand 1 and CD32a expression at the leading edge were significantly associated with poorer overall patient survival (hazard ratio = 2.05, 3.42 and 2.11, respectively), independent of clinical variables. In conclusion, anti-inflammatory microglia/macrophages, CD8+ T cells and programmed death-ligand 1 are correlated in the invasive margins of glioblastoma, consistent with immune-suppressive interactions. High triggering receptor expressed on myeloid cells 2, programmed death-ligand 1 and CD32a expression at the human glioblastoma leading edge are predictors of poorer overall survival. Given substantial interest in targeting microglia/macrophages, together with immune checkpoint inhibitors in cancer, these data have major clinical implications.

Machine learning predicts histologic type and grade of canine gliomas based on MRI texture analysis

Conventional MRI features of canine gliomas subtypes and grades significantly overlap. Texture analysis (TA) quantifies image texture based on spatial arrangement of pixel intensities. Machine learning (ML) models based on MRI-TA demonstrate high accuracy in predicting brain tumor types and grades in human medicine. The aim of this retrospective, diagnostic accuracy study was to investigate the accuracy of ML-based MRI-TA in predicting canine gliomas histologic types and grades. Dogs with histopathological diagnosis of intracranial glioma and available brain MRI were included. Tumors were manually segmented across their entire volume in enhancing part, non-enhancing part, and peri-tumoral vasogenic edema in T2-weighted (T2w), T1-weighted (T1w), FLAIR, and T1w postcontrast sequences. Texture features were extracted and fed into three ML classifiers. Classifiers' performance was assessed using a leave-one-out cross-validation approach. Multiclass and binary models were built to predict histologic types (oligodendroglioma vs. astrocytoma vs. oligoastrocytoma) and grades (high vs. low), respectively. Thirty-eight dogs with a total of 40 masses were included. Machine learning classifiers had an average accuracy of 77% for discriminating tumor types and of 75.6% for predicting high-grade gliomas. The support vector machine classifier had an accuracy of up to 94% for predicting tumor types and up to 87% for predicting high-grade gliomas. The most discriminative texture features of tumor types and grades appeared related to the peri-tumoral edema in T1w images and to the non-enhancing part of the tumor in T2w images, respectively. In conclusion, ML-based MRI-TA has the potential to discriminate intracranial canine gliomas types and grades.

Functional Graphene for Peritumoral Brain Microenvironment Modulation Therapy in Glioblastoma

Peritumoral brain invasion is the main target to cure glioblastoma. Chemoradiotherapy and targeted therapies fail to combat peritumoral relapse. Brain inaccessibility and tumor heterogeneity explain this failure, combined with overlooking the peritumor microenvironment. Reduce graphene oxide (rGO) provides a unique opportunity to modulate the local brain microenvironment. Multimodal graphene impacts are reported on glioblastoma cells in vitro but fail when translated in vivo because of low diffusion. This issue is solved by developing a new rGO formulation involving ultramixing during the functionalization with polyethyleneimine (PEI) leading to the formation of highly water-stable rGO-PEI. Wide mice brain diffusion and biocompatibility are demonstrated. Using an invasive GL261 model, an anti-invasive effect is observed. A major unexpected modification of the peritumoral area is also observed with the neutralization of gliosis. In vitro, mechanistic investigations are performed using primary astrocytes and cytokine array. The result suggests that direct contact of rGO-PEI_UT neutralizes astrogliosis, decreasing several proinflammatory cytokines that would explain a bystander tumor anti-invasive effect. rGO also significantly downregulates several proinvasive/protumoral cytokines at the tumor cell level. The results open the way to a new microenvironment anti-invasive nanotherapy using a new graphene nanomaterial that is optimized for in vivo brain delivery.

Mechanisms of Resistance and Current Treatment Options for Glioblastoma Multiforme (GBM)

Glioblastoma multiforme (GBM) is a highly aggressive form of brain cancer that is difficult to treat due to its resistance to both radiation and chemotherapy. This resistance is largely due to the unique biology of GBM cells, which can evade the effects of conventional treatments through mechanisms such as increased resistance to cell death and rapid regeneration of cancerous cells. Additionally, the blood–brain barrier makes it difficult for chemotherapy drugs to reach GBM cells, leading to reduced effectiveness. Despite these challenges, there are several treatment options available for GBM. The standard of care for newly diagnosed GBM patients involves surgical resection followed by concurrent chemoradiotherapy and adjuvant chemotherapy. Emerging treatments include immunotherapy, such as checkpoint inhibitors, and targeted therapies, such as bevacizumab, that attempt to attack specific vulnerabilities in GBM cells. Another promising approach is the use of tumor-treating fields, a type of electric field therapy that has been shown to slow the growth of GBM cells. Clinical trials are ongoing to evaluate the safety and efficacy of these and other innovative treatments for GBM, intending to improve with outcomes for patients.

Ensemble Inversion for Brain Tumor Growth Models With Mass Effect

We propose a method for extracting physics-based biomarkers from a single multiparametric Magnetic Resonance Imaging (mpMRI) scan bearing a glioma tumor. We account for mass effect, the deformation of brain parenchyma due to the growing tumor, which on its own is an important radiographic feature but its automatic quantification remains an open problem. In particular, we calibrate a partial differential equation (PDE) tumor growth model that captures mass effect, parameterized by a single scalar parameter, tumor proliferation, migration, while localizing the tumor initiation site. The single-scan calibration problem is severely ill-posed because the precancerous, healthy, brain anatomy is unknown. To address the ill-posedness, we introduce an ensemble inversion scheme that uses a number of normal subject brain templates as proxies for the healthy precancer subject anatomy. We verify our solver on a synthetic dataset and perform a retrospective analysis on a clinical dataset of 216 glioblastoma (GBM) patients. We analyze the reconstructions using our calibrated biophysical model and demonstrate that our solver provides both global and local quantitative measures of tumor biophysics and mass effect. We further highlight the improved performance in model calibration through the inclusion of mass effect in tumor growth models-including mass effect in the model leads to 10% increase in average dice coefficients for patients with significant mass effect. We further evaluate our model by introducing novel biophysics-based features and using them for survival analysis. Our preliminary analysis suggests that including such features can improve patient stratification and survival prediction.

Persistent Peri-Ablation Blood-Brain Barrier Opening After Laser Interstitial Thermal Therapy for Brain Tumors

Purpose Laser interstitial thermal therapy (LITT) is a minimally invasive, image-guided, cytoreductive procedure to treat recurrent glioblastoma. This study implemented dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) methods and employed a model selection paradigm to localize and quantify post-LITT blood-brain barrier (BBB) permeability in the ablation vicinity. Serum levels of neuron-specific enolase (NSE), a peripheral marker of increased BBB permeability, were measured. Methods Seventeen patients were enrolled in the study. Using an enzyme-linked immunosorbent assay, serum NSE was measured preoperatively, 24 hours postoperatively, and at two, eight, 12, and 16 weeks postoperatively, depending on postoperative adjuvant treatment. Of the 17 patients, four had longitudinal DCE-MRI data available, from which blood-to-brain forward volumetric transfer constant (Ktrans) data were assessed. Imaging was performed preoperatively, 24 hours postoperatively, and between two and eight weeks postoperatively. Results Serum NSE increased at 24 hours following ablation (p=0.04), peaked at two weeks, and returned to baseline by eight weeks postoperatively. Ktrans was found to be elevated in the peri-ablation periphery 24 hours after the procedure. This increase persisted for two weeks. Conclusion Following the LITT procedure, serum NSE levels and peri-ablation Ktrans estimated from DCE-MRI demonstrated increases during the first two weeks after ablation, suggesting transiently increased BBB permeability.

Surgical management of Glioma Grade 4: technical update from the neuro-oncology section of the Italian Society of Neurosurgery (SINch®): a systematic review

PURPOSE

The extent of resection (EOR) is an independent prognostic factor for overall survival (OS) in adult patients with Glioma Grade 4 (GG4). The aim of the neuro-oncology section of the Italian Society of Neurosurgery (SINch®) was to provide a general overview of the current trends and technical tools to reach this goal.

METHODS

A systematic review was performed. The results were divided and ordered, by an expert team of surgeons, to assess the Class of Evidence (CE) and Strength of Recommendation (SR) of perioperative drugs management, imaging, surgery, intraoperative imaging, estimation of EOR, surgery at tumor progression and surgery in elderly patients.

RESULTS

A total of 352 studies were identified, including 299 retrospective studies and 53 reviews/meta-analysis. The use of Dexamethasone and the avoidance of prophylaxis with anti-seizure medications reached a CE I and SR A. A preoperative imaging standard protocol was defined with CE II and SR B and usefulness of an early postoperative MRI, with CE II and SR B. The EOR was defined the strongest independent risk factor for both OS and tumor recurrence with CE II and SR B. For intraoperative imaging only the use of 5-ALA reached a CE II and SR B. The estimation of EOR was established to be fundamental in planning postoperative adjuvant treatments with CE II and SR B and the stereotactic image-guided brain biopsy to be the procedure of choice when an extensive surgical resection is not feasible (CE II and SR B).

CONCLUSIONS

A growing number of evidences evidence support the role of maximal safe resection as primary OS predictor in GG4 patients. The ongoing development of intraoperative techniques for a precise real-time identification of peritumoral functional pathways enables surgeons to maximize EOR minimizing the post-operative morbidity.

Glioblastoma: A Retrospective Analysis of the Role of the Maximal Surgical Resection on Overall Survival and Progression Free Survival

Background: Glioblastoma (GBM) is the most common and aggressive primary brain tumor in adults; despite advances in the understanding of GBM pathogenesis, significant achievements in treating this disease are still lacking. The aim of this study was to evaluate the prognostic significance of the extent of surgical resection (EOR), beyond the neoplastic mass, on the overall survival (OS). Methods: A retrospective review of a single-institution glioblastoma patient database (January 2012–September 2021) was undertaken. The series is composed of 64 patients who underwent surgery at the University Department of Neurosurgery of Ancona; the series was divided into four groups based on the amount of tumor mass excision with the fluid-attenuated inversion recovery (FLAIR) abnormalities (SUPr-supratotal resection, GTR-gross total resection, STR-subtotal resection, BIOPSY). The hypothesis was that the maximal resection of FLAIR abnormalities may improve the overall survival compared to the resection of the visible T1 contrast-enhanced neoplastic area only. Results: In the univariate analysis, SUPr and GTR are correlated with the overall survival (p = 0.001); the percentage of total neoplastic removal threshold conditioning outcome was 90% (p = 0.027). These results were confirmed by the multivariate analysis. Conclusions: Maximal surgical resection, when feasible, involving areas of FLAIR abnormalities represents an advantageous approach for the OS in GBM patients.

The peritumoral brain zone in glioblastoma: where we are and where we are going

Glioblastoma (GBM) is the most aggressive and invasive primary brain tumor. Current therapies are not curative, and patients' outcomes remain poor with an overall survival of 20.9 months after surgery. The typical growing pattern of GBM develops by infiltrating the surrounding apparent normal brain tissue within which the recurrence is expected to appear in the majority of cases. Thus, in the last decades, an increased interest has developed to investigate the cellular and molecular interactions between GBM and the peritumoral brain zone (PBZ) bordering the tumor tissue. The aim of this review is to provide up-to-date knowledge about the oncogenic properties of the PBZ to highlight possible druggable targets for more effective treatment of GBM by limiting the formation of recurrence, which is almost inevitable in the majority of patients. Starting from the description of the cellular components, passing through the illustration of the molecular profiles, we finally focused on more clinical aspects, represented by imaging and radiological details. The complete picture that emerges from this review could provide new input for future investigations aimed at identifying new effective strategies to eradicate this still incurable tumor.

Bio-Prospecting of Crude Leaf Extracts from Thirteen Plants of Brazilian Cerrado Biome on Human Glioma Cell Lines

(1) Background: Malignant gliomas are aggressive tumors characterized by fast cellular growth and highly invasive properties. Despite all biological and clinical advances in therapy, the standard treatment remains essentially palliative. Therefore, searching for alternative therapies that minimize adverse symptoms and improve glioblastoma patients' outcomes is imperative. Natural products represent an essential source in the discovery of such new drugs. Plants from the cerrado biome have been receiving increased attention due to the presence of secondary metabolites with significant therapeutic potential. (2) Aim: This study provides data on the cytotoxic potential of 13 leaf extracts obtained from plants of 5 families (Anacardiaceae, Annonaceae, Fabaceae, Melastomataceae e Siparunaceae) found in the Brazilian cerrado biome on a panel of 5 glioma cell lines and one normal astrocyte. (3) Methods: The effect of crude extracts on cell viability was evaluated by MTS assay. Mass spectrometry (ESI FT-ICR MS) was performed to identify the secondary metabolites classes presented in the crude extracts and partitions. (4) Results: Our results revealed the cytotoxic potential of Melastomataceae species Miconia cuspidata, Miconia albicans, and Miconia chamissois. Additionally, comparing the four partitions obtained from M. chamissois crude extract indicates that the chloroform partition had the greatest cytotoxic activity against the glioma cell lines. The partitions also showed a mean IC₅₀ close to chemotherapy, temozolomide; nevertheless, lower toxicity against normal astrocytes. Analysis of secondary metabolites classes presented in these crude extracts and partitions indicates the presence of phenolic compounds. (5) Conclusions: These findings highlight M. chamissois chloroform partition as a promising component and may guide the search for the development of additional new anticancer therapies.

MRI-derived radiomics to guide post-operative management of glioblastoma: Implication for personalized radiation treatment volume delineation

Background

The glioblastoma's bad prognosis is primarily due to intra-tumor heterogeneity, demonstrated from several studies that collected molecular biology, cytogenetic data and more recently radiomic features for a better prognostic stratification. The GLIFA project (GLIoblastoma Feature Analysis) is a multicentric project planned to investigate the role of radiomic analysis in GB management, to verify if radiomic features in the tissue around the resection cavity may guide the radiation target volume delineation.

Materials and methods

We retrospectively analyze from three centers radiomic features extracted from 90 patients with total or near total resection, who completed the standard adjuvant treatment and for whom we had post-operative images available for features extraction. The Manual segmentation was performed on post gadolinium T1w MRI sequence by 2 radiation oncologists and reviewed by a neuroradiologist, both with at least 10 years of experience. The Regions of interest (ROI) considered for the analysis were: the surgical cavity ± post-surgical residual mass (CTV_cavity); the CTV a margin of 1.5 cm added to CTV_cavity and the volume resulting from subtracting the CTV_cavity from the CTV was defined as CTV_Ring. Radiomic analysis and modeling were conducted in RStudio. Z-score normalization was applied to each radiomic feature. A radiomic model was generated using features extracted from the Ring to perform a binary classification and predict the PFS at 6 months. A 3-fold cross-validation repeated five times was implemented for internal validation of the model.

Results

Two-hundred and seventy ROIs were contoured. The proposed radiomic model was given by the best fitting logistic regression model, and included the following 3 features: F_cm_merged.contrast, F_cm_merged.info.corr.2, F_rlm_merged.rlnu. A good agreement between model predicted probabilities and observed outcome probabilities was obtained (p-value of 0.49 by Hosmer and Lemeshow statistical test). The ROC curve of the model reported an AUC of 0.78 (95% CI: 0.68-0.88).

Conclusion

This is the first hypothesis-generating study which applies a radiomic analysis focusing on healthy tissue ring around the surgical cavity on post-operative MRI. This study provides a preliminary model for a decision support tool for a customization of the radiation target volume in GB patients in order to achieve a margin reduction strategy.

Integrated analysis of genome-wide DNA methylation and cancer-associated fibroblasts identified prognostic biomarkers and immune checkpoint blockade in lower grade gliomas

Background

Cancer-associated fibroblasts (CAFs) are vital components of prominent cellular components in lower-grade gliomas (LGGs) that contribute to LGGs' progression, treatment resistance, and immunosuppression. Epigenetic modification and immunity have significant implications for tumorigenesis and development.

Methods

We combined aberrant methylation and CAFs abundances to build a prognostic model and the impact on the biological properties of LGGs. Grouping based on the median CAFs abundances score of samples in the TCGA-LGGs dataset, differentially expressed genes and aberrantly methylated genes were combined for subsequent analysis.

Results

We identified five differentially methylated and expressed genes (LAT32, SWAP70, GSAP, EMP3, and SLC2A10) and established a prognostic gene signature validated in the CGGA-LGGs dataset. Immunohistochemistry (IHC) and in vitro tests were performed to verify these expressions. The high-risk group increased in tumor-promoting immune cells and tumor mutational burden. Notably, risk stratification had different ICB sensitivities in LGGs, and there were also significant sensitivity differences for temozolomide and the other three novel chemotherapeutic agents.

Conclusion

Our study reveals characteristics of CAFs in LGGs, refines the direct link between epigenetics and tumor stroma, and might provide clinical implications for guiding tailored anti-CAFs therapy in combination with immunotherapy for LGGs patients.

MRI radiomic features of peritumoral edema may predict the recurrence sites of glioblastoma multiforme

Background and purpose

As one of the most aggressive malignant tumor in the central nervous system, the main cause of poor outcome of glioblastoma (GBM) is recurrence, a non-invasive method which can predict the area of recurrence pre-operation is necessary.To investigate whether there is radiological heterogeneity within peritumoral edema and identify the reproducible radiomic features predictive of the sites of recurrence of glioblastoma(GBM), which may be of value to optimize patients' management.

Materials and methods

The clinical information and MR images (contrast-enhanced T1 weighted and FLAIR sequences) of 22 patients who have been histologically proven glioblastoma, were retrospectively evaluated. Kaplan-Meier methods was used for survival analysis. Oedematous regions were manually segmented by an expert into recurrence region, non-recurrence region. A set of 94 radiomic features were obtained from each region using the function of analyzing MR image of 3D slicer. Paired t test was performed to identify the features existing significant difference. Subsequently, the data of two patients from TCGA database was used to evaluate whether these features have clinical value.

Results

Ten features with significant differences between the recurrence and non-recurrence subregions were identified and verified on two individual patients from the TCGA database with pathologically confirmed diagnosis of GBM.

Conclusions

Our results suggested that heterogeneity does exist in peritumoral edema, indicating that the radiomic features of peritumoral edema from routine MR images can be utilized to predict the sites of GBM recurrence. Our findings may further guide the surgical treatment strategy for GBM.

Nomogram Model for Predicting the Prognosis of High-Grade Glioma in Adults Receiving Standard Treatment: A Retrospective Cohort Study

OBJECTIVES

To identify the critical factors associated with the progression-free survival (PFS) and overall survival (OS) of high-grade glioma (HGG) in adults who have received standard treatment and establish a novel graphical nomogram and an online dynamic nomogram.

PATIENTS AND METHODS

This is a retrospective study of adult HGG patients receiving standard treatment (surgery, postoperative radiotherapy, and temozolomide (TMZ) chemotherapy) at Huashan Hospital, Fudan University between January 2017 and December 2019. We used uni- and multi-variable COX models to identify the significant prognostic factors for PFS and OS. Based on the significant predictors, graphical and online nomograms were established.

RESULTS

A total of 246 patients were enrolled in the study based on the inclusion criteria. The average PFS and OS were 22.99 ± 11.43 and 30.51 ± 13.73 months, respectively. According to the multi-variable COX model, age, extent of resection (EOR), and IDH mutation were associated with PFS and OS, while edema index (EI) was relevant to PFS. In addition, patients with IDH and TERT promoter co-mutations had longer PFSs and OSs, and no apparent survival benefit was found in the long-cycle TMZ adjuvant chemotherapy compared with the standard Stupp protocol. Based on these critical factors, a graphical nomogram and online nomogram were developed for predicting PFS and OS, respectively. The calibration curve showed favorable consistency between the predicted and actual survival rates. C-index and time-dependent AUC showed good discrimination abilities.

CONCLUSIONS

We identified the significant predictors for the PFS and OS of HGG adults receiving standard treatment and established user-friendly nomogram models to assist neurosurgeons in optimizing clinical management and treatment strategies.

Postoperative magnetic resonance imaging signal changes in middle cerebral peduncle after vestibular schwannoma surgery

BACKGROUND AND PURPOSE

Preoperative compression of middle cerebellar peduncle (MCP) is often observed in vestibular schwannomas. Its re-expansion is expected after tumour resection, however, frequently its thickness remains unchanged or undergoes further atrophy. Similarly, increased MCP FLAIR signal is often observed and thought to be associated with intraoperative MCP injury. This study investigates the dynamics of MCP FLAIR signal changes over time and their implications in long-term MCP atrophy.

MATERIALS AND METHODS

Retrospective analysis of patients operated between 2011 and 2019 was performed. Measurements of FLAIR signals and MCP thickness were performed preoperatively, postoperatively and at follow-up.

RESULTS

28 patients (15 females, mean age 51.94 years) were included. The mean follow-up was 23.98 months. The mean tumour size was 2.99 cm. The MCP FLAIR signal was elevated preoperatively in 10 (35.7%) patients and further increased postoperatively in 22 (78.6%), followed by its decrease at follow up (7 patients, 25%). An immediate postoperative re-expansion of middle cerebellar peduncle was observed in 24 (85.7%) patients. No association between tumour size and preoperative FLAIR was established, however tumour size was negatively associated with the MCP thickness. A significant negative association between a postoperative FLAIR and follow-up thickness (p < 0.001) was noted, even if controlling for tumour size and both tumour size and preoperative MCP thickness.

CONCLUSION

In patients with vestibular schwannomas undergoing surgical resection, the middle cerebellar peduncle FLAIR signal seems to associated with long term thickness of MCP, regardless of its initial size, however does not seem to correlate with the clinical outcome.

Management of Recurrent Glioblastomas: What Can We Learn from the French Glioblastoma Biobank?

Safe maximal resection followed by radiotherapy plus concomitant and adjuvant temozolomide (TMZ) is universally accepted as the first-line treatment for glioblastoma (GB), but no standard of care has yet been defined for managing recurrent GB (rGB). We used the French GB biobank (FGB) to evaluate the second-line options currently used, with a view to defining the optimal approach and future directions in GB research. We retrospectively analyzed data for 338 patients with de novo isocitrate dehydrogenase (IDH)-wildtype GB recurring after TMZ chemoradiotherapy. Cox proportional hazards models and Kaplan-Meier analyses were used to investigate survival outcomes. Median overall survival after first surgery (OS1) was 19.8 months (95% CI: 18.5-22.0) and median OS after first progression (OS2) was 9.9 months (95% CI: 8.8-10.8). Two second-line options were noted for rGB patients in the FGB: supportive care and treatments, with systemic treatment being the treatment most frequently used. The supportive care option was independently associated with a shorter OS2 (p < 0.001). None of the systemic treatment regimens was unequivocally better than the others for rGB patients. An analysis of survival outcomes based on time to first recurrence (TFR) after chemoradiotherapy indicated that survival was best for patients with a long TFR (≥18 months; median OS1: 44.3 months (95% CI: 41.7-56.4) and median OS2: 13.0 months (95% CI: 11.2-17.7), but that such patients constituted only a small proportion of the total patient population (13.0%). This better survival appeared to be more strongly associated with response to first-line treatment than with response to second-line treatment, indicating that the recurring tumors were more aggressive and/or resistant than the initial tumors in these patients. In the face of high rates of treatment failure for GB, the establishment of well-designed large cohorts of primary and rGB samples, with the help of biobanks, such as the FGB, taking into account the TFR and survival outcomes of GB patients, is urgently required for solid comparative biological analyses to drive the discovery of novel prognostic and/or therapeutic clinical markers for GB.

Predicting the true extent of glioblastoma based on probabilistic tractography

Glioblastoma is the most frequent type of primary brain tumors. Despite the advanced therapy, most of the patients die within 2 years after the diagnosis. The tumor has a typical appearance on MRI: a central hypointensity surrounded by an inhomogeneous, ring-shaped contrast enhancement along its border. Too small to be recognized by MRI, detached individual tumor cells migrate along white matter fiber tracts several centimeters away from the edge of the tumor. Usually these cells are the source of tumor recurrence. If the infiltrated brain areas could be identified, longer survival time could be achieved through supratotal resection and individually planned radiation therapy. Probabilistic tractography is an advanced imaging method that can potentially be used to identify infiltrated pathways, thus the real extent of the glioblastoma. Our study consisted of twenty high grade glioma patients. Probabilistic tractography was started from the tumor. The location of tumor recurrence on follow-up MRI was considered as the primary infiltrated white matter tracts. The results of probabilistic tractography were evaluated at thirteen different thresholds. The overlap with the tumor recurrence of each threshold level was then defined to calculate the sensitivity and specificity. In the group level, sensitivity (81%) and specificity (90%) were the most reliable at 5% threshold level. There were two outliers in the study group, both with high specificity and very low sensitivity. According to our results, probabilistic tractography can help to define the true extent of the glioblastoma at the time of diagnosis with high sensitivity and specificity. Individually planned surgery and irradiation could provide a better chance of survival in these patients.

Precision Killing of Sinoporphyrin Sodium-Mediated Photodynamic Therapy against Malignant Tumor Cells

Photodynamic therapy (PDT) has significant advantages in the treatment of malignant tumors, such as high efficiency, minimal invasion and less side effects, and it can preserve the integrity and quality of the organs. The power density, irradiation time and photosensitizer (PS) concentration are three main parameters that play important roles in killing tumor cells. However, until now, the underlying relationships among them for PDT outcomes have been unclear. In this study, human malignant glioblastoma U-118MG and melanoma A375 cells were selected, and the product of the power density, irradiation time and PS concentration was defined as the total photodynamic parameter (TPP), in order to investigate the mechanisms of PS sinoporphyrin sodium (DVDMS)-mediated PDT (DVDMS-PDT). The results showed that the survival rates of the U-118MG and A375 cells were negatively correlated with the TPP value in the curve, and the correlation exactly filed an e-exponential function. Moreover, according to the formula, we realized controllable killing effects of the tumor cells by randomly adjusting the three parameters, and we finally verified the accuracy and repeatability of the formula. In conclusion, the establishment and implementation of a newly functional relationship among the PDT parameters are essential for predicting PDT outcomes and providing personalized precise treatment, and they are contributive to the development of PDT dosimetry.