Survival and low-grade glioma: the emergence of genetic information

Identifying and understanding how people living with a lower-grade glioma engage in self-management

PURPOSE

Lower-grade gliomas (LGG) are mostly diagnosed in working-aged adults and rarely cured. LGG patients may face chronic impairments (e.g. fatigue, cognitive deficits). Self-management can improve clinical and psychosocial outcomes, yet how LGG patients self-manage the consequences of their tumour and its treatment is not fully understood. This study, therefore, aimed to identify and understand how LGG patients engage in the self-management of their condition.

METHODS

A diverse group of 28 LGG patients (age range 22-69 years; male n = 16, female n = 12; mean time since diagnosis = 8.7 years) who had completed primary treatment, were recruited from across the United Kingdom. Semi-structured interviews were conducted. Informed by a self-management strategy framework developed in cancer, directed content analysis identified and categorised self-management types and strategies used by patients.

RESULTS

Overall, 20 self-management strategy types, comprising 123 self-management strategies were reported; each participant detailed extensive engagement in self-management. The most used strategy types were 'using support' (n = 28), 'creating a healthy environment' (n = 28), 'meaning making' (n = 27), and 'self-monitoring' (n = 27). The most used strategies were 'accepting the tumour and its consequences' (n = 26), 'receiving support from friends (n = 24) and family' (n = 24), and 'reinterpreting negative consequences' (n = 24).

CONCLUSIONS

This study provides a comprehensive understanding of the strategies used by LGG patients to self-manage their health and wellbeing, with a diverse, and substantial number of self-management strategies reported.

IMPLICATIONS FOR CANCER SURVIVORS

The findings will inform the development of a supported self-management intervention for LGG patients, which will be novel for this patient group.

Lipid metabolic rewiring in glioma‑associated microglia/macrophages (Review)

Gliomas are the most prevailing brain malignancy in both children and adults. Microglia, which are resident in the central nervous system (CNS), are distributed throughout the brain and serve an important role in the immunity of the CNS. Microglial cells exhibit varying phenotypic and metabolic properties during different stages of glioma development, making them a highly dynamic cell population. In particular, glioma‑associated microglia/macrophages (GAMs) can alter their metabolic characteristics and influence malignancies in response to the signals they receive. The significance of macrophage metabolic reprogramming in tumor growth is becoming increasingly acknowledged in recent years. However, to the best of our knowledge, there is currently a scarcity of data from investigations into the lipid metabolic profiles of microglia/macrophages in the glioma setting. Therefore, the present review aims to provide a thorough review of the role that lipid metabolism serves in tumor‑associated macrophages. In addition, it outlines potential targets for therapy based on lipid metabolism. The present review aims to serve as a reference source for future investigations into GAMs.

Effects of Raupya Suvarna Sutashekhara, a herbo-mineral-metallic formulation as adjunct Oral Ayurvedic Medicine on long-term survival in patients of malignant brain tumor

BACKGROUND

The incidence of brain tumors poses a significant threat, particularly with high-grade tumors that exhibit rapid growth and can significantly impair the patient's quality of life, despite treatment modalities. Ayurveda, a natural system of medicines helps to enhance health benefits when used as a complementary therapy in combination with conventional treatment through various herbal, herbo-mineral or herbo-mineral-metallic formulations. One of such formulations, Raupya Suvarna Sutshekhara (RSS), has Raupya bhasma and Suvarna bhasma (incinerated Silver and Gold respectively) ingredients and they are described as Rasayana (immunomodulatory) and possess Medhya (enhances the brain functions and intelligence) activity.

OBJECTIVE

The study documents effects of Raupya Suvarna Sutshekhara in alleviating symptoms of malignant brain tumors.

MATERIALS AND METHODS

This retrospective cohort study was done for malignant brain tumor patients (n = 110) registered at our centre who were treated with Raupya Suvarna Sutshekhara 125 mg-250 mg (a herbo-mineral metallic formulation) as a treatment of choice of neural tonic along with supporting Ayurvedic medicines. Treatment response evaluation period ranged from 3 months to 5 years (median period 29 weeks). Clinical symptoms, weight, Karnofsky Performance Score (KPS), Quality of Life (QoL) score, and survival data with respect to treatment with RSS were analyzed.

RESULTS

RSS showed a very significant (p < 0.001) effect on symptoms related to memory loss, headache, imbalance, loss of appetite, and generalized weakness while significant (p < 0.05) decrease in urinary incontinence, seizures, difficulty in thinking/articulating, and weakness in one part/side of the body. However, a not-quite significant change was seen in symptom confusion/disorientation. No significant improvement was seen in vision changes, facial numbness or tingling, swallowing difficulties, and tingling in extremities. Body weight and KPS also showed improvement. Patients with treatment between 2 and 5 years showed median survival up to 62 months.

CONCLUSIONS

It was observed that adjunct Ayurvedic treatment with RSS helped to reduce the severity of symptoms due to the tumor itself or side-effects of conventional treatments, maintain the quality of life of malignant brain tumor patients and has shown to increase survival with respect to the duration of the treatment.

Evaluation of an Image-based Classification Model to Identify Glioma Subtypes Using Arterial Spin Labeling Perfusion MRI On the Publicly Available UCSF Glioma Dataset

PURPOSE

Glioma is a complex cancer comprising various subtypes and mutations, which may have different metabolic characteristics that can potentially be investigated and identified using perfusion imaging. Therefore, the aim of this work was to use radiomics and machine learning analysis of arterial spin labeling MRI data to automatically differentiate glioma subtypes and mutations.

METHODS

A total of 495 Arterial Spin Labeling (ASL) perfusion imaging datasets from the UCSF Glioma database were used in this study. These datasets were segmented to delineate the tumor volume and classified according to tumor grade, pathological diagnosis, and IDH status. Perfusion image data was obtained from a 3T MRI scanner using pseudo-continuous ASL. High level texture features were extracted for each ASL dataset using PyRadiomics after tumor volume segmentation and then analyzed using a machine learning framework consisting of ReliefF feature ranking and logistic model tree classification algorithms.

RESULTS

The results of the evaluation revealed balanced accuracies for the three endpoints ranging from 55.76% (SD = 4.28, 95% CI: 53.90-57.65) for the tumor grade using 25.4 ± 37.21 features, 62.53% (SD = 2.86, 95% CI: 61.27-63.78) for the mutation status with 23.3 ± 29.17 picked features, and 80.97% (SD = 1.83, 95% CI: 80.17-81.78) for the pathological diagnosis which used 47.3 ± 32.72 selected features.

CONCLUSIONS

Radiomics and machine learning analysis of ASL perfusion data in glioma patients hold potential for aiding in the diagnosis and treatment of glioma, mainly for discerning glioblastoma from astrocytoma, while performance for tumor grading and mutation status appears limited.

Exploring intranasal drug delivery via nanocarriers: A promising glioblastoma therapy

Glioblastoma is one of the most recurring types of glioma, having the highest mortality rate among all other gliomas. Traditionally, the standard course of treatment for glioblastoma involved maximum surgical resection, followed by chemotherapy and radiation therapy. Nanocarriers have recently focused on enhancing the chemotherapeutic administration to the brain to satisfy unmet therapeutic requirements for treating brain-related disorders. Due to the significant drawbacks and high recurrence rates of gliomas, intranasal administration of nanocarrier systems presents several advantages. These include low toxicity, non-invasiveness, and the ability to cross the blood-brain barrier. By customizing their size, encasing them with mucoadhesive agents, or undergoing surface modification that encourages movement over the nose's mucosa, we can exceptionally engineer nanocarriers for intranasal administration. Olfactory and trigeminal nerves absorb drugs administered nasally and transport them to the brain, serving as the primary delivery mechanism for nose-to-brain administration. This review sums up the latest developments in chemotherapeutic nanocarriers, such as metallic nanoparticles, polymeric nanoparticles, nanogels, nano vesicular carriers, genetic material-based nanocarriers, and polymeric micelles. These nanocarriers have demonstrated efficient drug delivery from the nose to the brain, effectively overcoming mucociliary clearance. However, challenges persist, such as limitations in targeted chemotherapy and restricted drug loading capacity for intranasal administration. Additionally, the review addresses regulatory considerations and prospects for these innovative drug delivery systems.

Ollier Disease, Acute Myeloid Leukemia, and Brain Glioma: IDH as the Common Denominator

Ollier disease (OD), acute myeloid leukemia (AML), and brain glioma (BG) are three apparently completely different neoplasms in terms of histopathology, clinic, natural history, and management, but they can affect the same patient. This study aimed to identify the common molecular pathways involved in the pathogenesis of all three diseases and discuss their current and potential role as therapeutic targets. A detailed and comprehensive systematic literature review according to PRISMA guidelines on OD patients harboring BG and/or AML was made. In addition, the unique case of a patient affected by all three considered diseases has been added to our case series. Demographic, pathological, treatment, and outcome data were analyzed and discussed, mainly focusing on the molecular findings. Twenty-eight studies reported thirty-three patients affected by OD and BG, and only one study reported one patient with OD and AML, while only our patient harbored all three pathologies. The IDH R132H mutation was the only genetic alteration shared by all three pathologies and was simultaneously detected in enchondromas and brain glioma in 100% (3/3) of OD patients with BG and also in the neoplastic blood cells of the single patient hosting all three diseases. The IDH1-R132H gene mutation is the etiopathogenetic common denominator among three apparently different tumors coexisting in the same patient. The adoption of mutant-specific IDH1 inhibitor molecules could represent a potential panacea for these conditions in the era of targeted therapies. Further studies with larger clinical series are needed to confirm our results and hypothesis.

Dosimetric comparison of advanced radiation techniques for scalp-sparing in low-grade gliomas

BACKGROUND

Alopecia causes significant distress for patients and negatively impacts quality of life for low-grade glioma (LGG) patients. We aimed to compare and evaluate variations in dose distribution for scalp-sparing in LGG patients with proton therapy and photon therapy, namely intensity-modulated proton therapy (IMPT), intensity-modulated radiotherapy (IMRT), volumetric modulated arc therapy (VMAT), and helical tomotherapy (HT).

METHODS

This retrospective study utilized a dataset comprising imaging data from 22 patients with LGG who underwent postoperative radiotherapy. Treatment plans were generated for each patient with scalp-optimized (SO) approaches and scalp-non-optimized (SNO) approaches using proton techniques and photons techniques; all plans adhered to the same dose constraint of delivering a total radiation dose of 54.04 Gy to the target volume. All treatment plans were subsequently analyzed.

RESULTS

All the plans generated in this study met the dose constraints for the target volume and OARs. The SO plans resulted in reduced maximum scalp dose (Dmax), mean scalp dose (Dmean), and volume of the scalp receiving 30 Gy (V30) and 40 Gy (V40) compared with SNO plans in all radiation techniques. Among all radiation techniques, the IMPT plans exhibited superior performance compared to other plans for dose homogeneity as for SO plans. Also, IMPT showed lower values for Dmean and Dmax than all photon radiation techniques.

CONCLUSION

Our study provides evidence that the SO approach is a feasible technique for reducing scalp radiation dose. However, it is imperative to conduct prospective trials to assess the benefits associated with this approach.

Genomic, epigenomic and transcriptomic landscape of glioblastoma

The mostly aggressive and extremely malignant type of central nervous system is Glioblastoma (GBM), which is characterized by an extremely short average survival time of lesser than 16 months. The primary cause of this phenomenon can be attributed to the extensively altered genome of GBM, which is characterized by the dysregulation of numerous critical signaling pathways and epigenetics regulations associated with proliferation, cellular growth, survival, and apoptosis. In light of this, different genetic alterations in critical signaling pathways and various epigenetics regulation mechanisms are associated with GBM and identified as distinguishing markers. Such GBM prognostic alterations are identified in PI3K/AKT, p53, RTK, RAS, RB, STAT3 and ZIP4 signaling pathways, metabolic pathway (IDH1/2), as well as alterations in epigenetic regulation genes (MGMT, CDKN2A-p16INK4aCDKN2B-p15INK4b). The exploration of innovative diagnostic and therapeutic approaches that specifically target these pathways is utmost importance to enhance the future medication for GBM. This study provides a comprehensive overview of dysregulated epigenetic mechanisms and signaling pathways due to mutations, methylation, and copy number alterations of in critical genes in GBM with prevalence and emphasizing their significance.

Prognostic value of four immune-related genes in lower-grade gliomas: a biomarker discovery study

Introduction

The tumor microenvironment and IRGs are highly correlated with tumor occurrence, progression, and prognosis. However, their roles in grade II and III gliomas, termed LGGs in this study, remain to be fully elucidated. Our research aims to develop immune-related features for risk stratification and prognosis prediction in LGG.

Methods

Using the ssGSEA method, we assessed the immune characteristics of the LGG population. We conducted differential analysis using LGG samples from the TCGA database and normal samples from GTEx, identifying 412 differentially expressed immune-related genes (DEIRGs). Subsequently, we utilized univariate Cox, LASSO, and multivariate Cox regression analyses to establish both a gene predictive model and a nomogram predictive model.

Results

Here, we found that the ESTIMATE score, immune score and stromal score of high-immunity, high-grade and isocitrate dehydrogenase (IDH) wild-type glioma were higher than those of the corresponding group, and the tumor purity was lower. Higher ESTIMATE scores, stromal scores and immune scores indicated a poor prognosis in patients with LGG. Our four-gene prognostic model demonstrated superior accuracy compared to other molecular features. Validation using the CGGA as a testing set and the combined TCGA and CGGA cohort confirmed its robust prognostic value. Additionally, a nomogram integrating the prognostic model and clinical variables showed enhanced predictive capability.

Discussion

Our study highlights the prognostic significance of the identified four DEIRGs (KLRC3, MR1, PDIA2, and RFXAP) in LGG patients. The predictive model and nomogram developed herein offer valuable tools for personalized treatment strategies in LGG. Future research should focus on further validating these findings and exploring the functional roles of these DEIRGs within the LGG tumor microenvironment.

Evaluation of Single and Combined Temozolomide and Doxorubicin Treatment Responses in Low- and High-Grade Glioma In Vitro

BACKGROUND

Astrocytoma, the most common type of glioma, can histologically be low or high grade. Treatment recommendations for astrocytic tumors are based on the histopathological and molecular phenotype. For grade 2 astrocytoma, the combination of radiotherapy and adjuvant chemotherapy with procarbazine, lomustine, and vincristine (PCV) is better than radiotherapy alone. Temozolomide (TMZ) is being increasingly recognized as a replacement for PCV in brain tumor therapy, due to the lower myelotoxicity. TMZ is currently a well-established first-line treatment for grade 3 astrocytoma, grade 4 astrocytoma, and glioblastoma and it is also sporadically used for grade 2 astrocytoma. However, TMZ faces multiple challenges such as adverse effects and drug resistance.

METHODS

In this study, we compared the cytotoxic effect induced by TMZ and doxorubicin (DOXO), alone and in combination, on a low-grade astrocytoma cell line (AC1B) and a high-grade glioma cell line (GB1B).

RESULTS

We found that TMZ and DOXO, each produced a cytotoxic effect in monotherapy. GB1B cell line was more sensitive to the treatment than AC1B cells, at a 7- and 10-day exposure to the DOXO. However, when the duration of the treatment was extended to 14 days, GB1B cells became more resistant to DOXO treatment, compared to AC1B cells. Regarding the treatment with TMZ, GB1B exhibited greater resistance to TMZ compared to AC1B, across all studied intervals and the resistance to treatment of GB1B increased with longer exposure time. However, in combined therapy, the drugs did not exert a synergistic effect on any astrocytic cell line.

CONCLUSIONS

The current data suggest that both TMZ and DOXO exhibit efficient therapeutic effects on low- and high-grade glioma cells. However, no synergistic effect was observed for combined therapy.

Facilitating complete resection of intrinsic motor cortex glioma with titration of high-frequency cortico-subcortical mapping train count informed by navigated transcranial magnetic stimulation: illustrative case

BACKGROUND

The dilemma of neuro-oncological surgery involving suspected eloquent cortex is to maximize the extent of resection while minimizing neurological morbidity, referred to as the "onco-functional balance." Diffuse lower-grade gliomas are capable of infiltrating or displacing neural function within cortical regions and subcortical white matter tracts, which can render classical anatomic associations of eloquent function misleading.

OBSERVATIONS

This study employed presurgical navigated transcranial magnetic stimulation (nTMS) to determine the motor eloquence of a diffuse lower-grade glioma at the superior frontal gyrus extending and intrinsic to the primary motor cortex in a 45-year-old female. Positive nTMS findings were confirmed intraoperatively with high-frequency direct cortico-subcortical stimulation (HF-DCS). Modification of the HF-DCS train count from train-of-five to train-of-two permitted resection beyond classic anatomical boundaries and conventional HF-DCS safe stopping criteria.

LESSONS

Anatomical correlates of function can inaccurately inform the surgical management of diffuse lower-grade glioma, which represents the utmost opportunity for progression-free survival. Integrating an individually tailored nTMS-DCS surgical strategy contributed to complete resection, negating the requirement for adjuvant therapy. Serial nTMS follow-up may assist with the characterization of tumor-induced functional reorganization. https://thejns.org/doi/10.3171/CASE24197.

Uses of artificial intelligence in glioma: A systematic review

Glioma is the most prevalent type of primary brain tumor in adults. The use of artificial intelligence (AI) in glioma is increasing and has exhibited promising results. The present study performed a systematic review of the applications of AI in glioma as regards diagnosis, grading, prediction of genotype, progression and treatment response using different databases. The aim of the present study was to demonstrate the trends (main directions) of the recent applications of AI within the field of glioma, and to highlight emerging challenges in integrating AI within clinical practice. A search in four databases (Scopus, PubMed, Wiley and Google Scholar) yielded a total of 42 articles specifically using AI in glioma and glioblastoma. The articles were retrieved and reviewed, and the data were summarized and analyzed. The majority of the articles were from the USA (n=18) followed by China (n=11). The number of articles increased by year reaching the maximum number in 2022. The majority of the articles studied glioma as opposed to glioblastoma. In terms of grading, the majority of the articles were about both low-grade glioma (LGG) and high-grade glioma (HGG) (n=23), followed by HGG/glioblastoma (n=13). Additionally, three articles were about LGG only; two articles did not specify the grade. It was found that one article had the highest sample size among the other studies, reaching 897 samples. Despite the limitations and challenges that face AI, the use of AI in glioma has increased in recent years with promising results, with a variety of applications ranging from diagnosis, grading, prognosis prediction, and reaching to treatment and post-operative care.

Crosstalk of different cell-death patterns predicts prognosis and drug sensitivity in glioma

BACKGROUND

Glioma is a malignant brain tumor originating from glial cells, and there still a challenge to accurately predict the prognosis. Programmed cell death (PCD) plays a key role in tumorigenesis and immune response. However, the crosstalk and potential role of various PCDs in prognosis and tumor microenvironment remains unknown. Therefore, we comprehensively discussed the relationship between different models of PCD and the prognosis of glioma and provided new ideas for the optimal targeted therapy of glioma.

MATERIALS AND METHODS

We compared and analyzed the role of 14 PCD patterns on the prognosis from different levels. We constructed the cell death risk score (CDRS) index and conducted a comprehensive analysis of CDRS and TME characteristics, clinical characteristics, and drug response.

RESULTS

Effects of different PCDs at the genomic, functional, and immune microenvironment levels were discussed. CDRS index containing 6 gene signatures and a nomogram were established. High CDRS is associated with a worse prognosis. Through transcriptome and single-cell data, we found that patients with high CDRS showed stronger immunosuppressive characteristics. Moreover, the high-CDRS group was resistant to the traditional glioma chemotherapy drug Vincristine, but more sensitive to the Temozolomide and the clinical experimental drug Bortezomib. In addition, we identified 19 key potential therapeutic targets during malignant differentiation of tumor cells.

CONCLUSION

Overall, we provide the first systematic description of the role of 14 PCDs in glioma. A new CDRS model was built to predict the prognosis and to provide a new idea for the targeted therapy of glioma.

Motor seizures confer overall survival benefit in who grade 2 glioma

OBJECTIVE

The prevalence of epilepsy in World Health Organization (WHO) grade 2 glioma is high, with seizures being the presenting symptom in 60%-90%. We explore the epidemiology of seizures in this patient population in a regional neurosurgical center.

METHODS

Electronic health records of patients with histologically-proven WHO grade 2 glioma (n = 228) were reviewed between 1997 and 2021, with data collected including patient demographics, epilepsy prevalence, and seizure semiology. The influence of seizure type on overall survival was calculated using a Cox proportional hazards model.

RESULTS

Overall, 197 of 228 patients (86.4%) were diagnosed with epilepsy-either at presentation or during the course of their disease. Male patients were more likely than female patients to be diagnosed with epilepsy (91.1% vs 77.1%, p = .003) and, in those with epilepsy, more likely to experience at least one focal to bilateral tonic-clonic seizure (69.4% vs 54.1%, p = .05). Patients with left-sided tumors were twice as likely to have experienced a focal to bilateral tonic-clonic seizure (p = .02, odds ratio [OR] = .47). Predominantly experiencing seizures with motor activity appeared to confer better overall survival, with a 65% decrease in the risk of death 10 years post diagnosis (hazard ratio [HR] = .35, p = .02). This is despite accounting for previously described prognostic markers including tumor histology/genetics, time from diagnosis to surgery, and the extent of tumor resection.

SIGNIFICANCE

Motor seizure activity is a frequent feature in WHO grade 2 glioma and appears to confer a survival benefit regardless of histology or surgical factors. Seizures due to dominant hemisphere tumors may be more likely to propagate and cause bilateral tonic-clonic activity.

"It changes everything": Understanding how people experience the impact of living with a lower-grade glioma

Background

Quantitative studies show people living with a lower-grade glioma (LGG) often report low health-related quality of life. However, it is unclear how this impact is experienced; resulting supportive care needs are also poorly understood. We explored how people experience the impact of living long-term with an LGG, to help identify potential supportive care needs.

Methods

We conducted semi-structured interviews with a diverse group of people with LGG (n = 28) across the United Kingdom, who had completed primary treatment (male n = 16, female n = 12, mean age 54.6 years, mean time since diagnosis 8.7 years). Interviews were transcribed and inductive thematic analysis was conducted.

Results

Four themes relating to the impact experiences of people with LGG were generated: "Emotional response to the diagnosis," "Living with the 'What ifs'," "Changing relationships," and "Faltering independence." These reflect participants' experiences with symptoms (eg, fatigue, seizures) and impairments (eg, motor dysfunction, cognitive deficits), and how these, in turn, drive impacts on daily living (including on work, relationships, social activities, and transport). Participants spoke about their experiences with profound emotion throughout.

Conclusions

People with LGG can experience wide-ranging everyday impacts and may have extensive supportive care needs. This study highlights how this impact is experienced and what it means to people with LGG. Best practice suggestions for conducting comprehensive needs assessments tailored to those with LGG, and the development of personalized plans to meet those needs, would be a critical step to ensure that people with LGG are best supported in living with their condition.

GMAlignNet: multi-scale lightweight brain tumor image segmentation with enhanced semantic information consistency

Although the U-shaped architecture, represented by UNet, has become a major network model for brain tumor segmentation, the repeated convolution and sampling operations can easily lead to the loss of crucial information. Additionally, directly fusing features from different levels without distinction can easily result in feature misalignment, affecting segmentation accuracy. On the other hand, traditional convolutional blocks used for feature extraction cannot capture the abundant multi-scale information present in brain tumor images. This paper proposes a multi-scale feature-aligned segmentation model called GMAlignNet that fully utilizes Ghost convolution to solve these problems. Ghost hierarchical decoupled fusion unit and Ghost hierarchical decoupled unit are used instead of standard convolutions in the encoding and decoding paths. This transformation replaces the holistic learning of volume structures by traditional convolutional blocks with multi-level learning on a specific view, facilitating the acquisition of abundant multi-scale contextual information through low-cost operations. Furthermore, a feature alignment unit is proposed that can utilize semantic information flow to guide the recovery of upsampled features. It performs pixel-level semantic information correction on misaligned features due to feature fusion. The proposed method is also employed to optimize three classic networks, namely DMFNet, HDCNet, and 3D UNet, demonstrating its effectiveness in automatic brain tumor segmentation. The proposed network model was applied to the BraTS 2018 dataset, and the results indicate that the proposed GMAlignNet achieved Dice coefficients of 81.65%, 90.07%, and 85.16% for enhancing tumor, whole tumor, and tumor core segmentation, respectively. Moreover, with only 0.29 M parameters and 26.88G FLOPs, it demonstrates better potential in terms of computational efficiency and possesses the advantages of lightweight. Extensive experiments on the BraTS 2018, BraTS 2019, and BraTS 2020 datasets suggest that the proposed model exhibits better potential in handling edge details and contour recognition.

Elucidating the impact of parthanatos-related microRNAs on the tumoral immune microenvironment and clinical outcome in low-grade gliomas

Parthanatos, a cell death mechanism triggered by PARP-1 activation, is implicated in oncogenic processes, yet their role in low-grade gliomas (LGG) remains poorly understood. This research investigates Parthanatos-related miRNAs' prognostic and immunomodulatory potential, alongside their influence on therapeutic outcomes in LGGs. Comprehensive miRNA and mRNA profiles of LGG patients were extracted from TCGA and CGGA databases, integrating clinical parameters to identify Parthanatos-associated miRNAs. IHC data validated the expression levels of Parthanatos-related genes in glioma versus normal brain tissues. Protein-protein interaction networks and Spearman correlation analysis facilitated the identification of key miRNAs. Parthanatos-related miRNA indices (PMI) were screened using Lasso and assessed for their accuracy in predicting prognosis, comparing their associated potential molecular functions and heterogeneity of the immune microenvironment. Drug sensitivity was assessed between different groups and optimal therapeutic agents were predicted. Validate the expression levels of key miRNAs by qPCR. Ninety-one miRNAs significantly associated with Parthanatos were screened, through which a PMI prognosis model of nine miRNAs was constructed. The PMI score was able to independently predict the prognosis of patients with LGG, and the nomogram constructed based on the PMI provided a practical tool for clinical prediction of patient prognosis. The proportion of immune response was lower in patients in the high-risk group, and there were significant differences in drug sensitivity between different risk classes, while drugs such as Fasudil were identified as the most promising therapeutic agents for patients in the high-risk group. Our findings highlight the critical role of Parthanatos-associated miRNAs in the progression and treatment of LGG, offering novel insights into their prognostic value and therapeutic potential.

A multi-omics analysis-based model to predict the prognosis of low-grade gliomas

Lower-grade gliomas (LGGs) exhibit highly variable clinical behaviors, while classic histology characteristics cannot accurately reflect the authentic biological behaviors, clinical outcomes, and prognosis of LGGs. In this study, we carried out analyses of whole exome sequencing, RNA sequencing and DNA methylation in primary vs. recurrent LGG samples, and also combined the multi-omics data to construct a prognostic prediction model. TCGA-LGG dataset was searched for LGG samples. 523 samples were used for whole exome sequencing analysis, 532 for transcriptional analysis, and 529 for DNA methylation analysis. LASSO regression was used to screen genes with significant association with LGG survival from the frequently mutated genes, differentially expressed genes, and differentially methylated genes, whereby a prediction model for prognosis of LGG was further constructed and validated. The most frequently mutated diver genes in LGGs were IDH1 (77%), TP53 (48%), ATRX (37%), etc. Top significantly up-regulated genes were C6orf15, DAO, MEOX2, etc., and top significantly down-regulated genes were DMBX1, GPR50, HMX2, etc. 2077 genes were more and 299 were less methylated in recurrent vs. primary LGG samples. Thirty-nine genes from the above analysis were included to establish a prediction model of survival, which showed that the high-score group had a very significantly shorter survival than the low-score group in both training and testing sets. ROC analysis showed that AUC was 0.817 for the training set and 0.819 for the testing set. This study will be beneficial to accurately predict the survival of LGGs to identify patients with poor prognosis to take specific treatment as early, which will help improve the treatment outcomes and prognosis of LGG.

A TGF-β signaling-related lncRNA signature for prediction of glioma prognosis, immune microenvironment, and immunotherapy response

AIMS

The dysregulation of TGF-β signaling is a crucial pathophysiological process in tumorigenesis and progression. LncRNAs have diverse biological functions and are significant participants in the regulation of tumor signaling pathways. However, the clinical value of lncRNAs related to TGF-β signaling in glioma is currently unclear.

METHODS

Data on glioma's RNA-seq transcriptome, somatic mutation, DNA methylation data, and clinicopathological information were derived from the CGGA and TCGA databases. A prognostic lncRNA signature was constructed by Cox and LASSO regression analyses. TIMER2.0 database was utilized to deduce immune infiltration characteristics. "ELMER v.2" was used to reconstruct TF-methylation-gene regulatory network. Immunotherapy and chemotherapy response predictions were implemented by the TIDE algorithm and GDSC database, respectively. In vitro and in vivo experiments were conducted to verify the results and clarify the regulatory mechanism of lncRNA.

RESULTS

In glioma, a TGF-β signaling-related 15-lncRNA signature was constructed, including AC010173.1, HOXA-AS2, AC074286.1, AL592424.1, DRAIC, HOXC13-AS, AC007938.1, AC010729.1, AC013472.3, AC093895.1, AC131097.4, AL606970.4, HOXC-AS1, AGAP2-AS1, and AC002456.1. This signature proved to be a reliable prognostic tool, with high risk indicating an unfavorable prognosis and being linked to malignant clinicopathological and genomic mutation traits. Risk levels were associated with different immune infiltration landscapes, where high risk was indicative of high levels of macrophage infiltration. In addition, high risk also suggested better immunotherapy and chemotherapy response. cg05987823 was an important methylation site in glioma progression, and AP-1 transcription factor family participated in the regulation of signature lncRNA expression. AGAP2-AS1 knockdown in in vitro and in vivo experiments inhibited the proliferation, migration, and invasion of glioma cells, as well as the growth of glioma, by downregulating the expression levels of NF-κB and ERK 1/2 in the TGF-β signaling pathway.

CONCLUSIONS

A prognostic lncRNA signature of TGF-β signaling was established in glioma, which can be used for prognostic judgment, immune infiltration status inference, and immunotherapy response prediction. AGAP2-AS1 plays an important role in glioma progression.

Barriers and facilitators to self-management in people living with a lower-grade glioma

PURPOSE

Self-management can have clinical and quality-of-life benefits. However, people with lower-grade gliomas (LGG) may face chronic tumour- and/or treatment-related symptoms and impairments (e.g. cognitive deficits, seizures), which could influence their ability to self-manage. Our study aimed to identify and understand the barriers and facilitators to self-management in people with LGG.

METHODS

We conducted semi-structured interviews with 28 people with LGG across the United Kingdom, who had completed primary treatment. Sixteen participants were male, mean age was 50.4 years, and mean time since diagnosis was 8.7 years. Interviews were audio-recorded and transcribed. Following inductive open coding, we deductively mapped codes to Schulman-Green et al.'s framework of factors influencing self-management, developed in chronic illness.

RESULTS

Data suggested extensive support for all five framework categories ('Personal/lifestyle characteristics', 'Health status', 'Resources', 'Environmental characteristics', 'Healthcare system'), encompassing all 18 factors influencing self-management. How people with LGG experience many of these factors appears somewhat distinct from other cancers; participants described multiple, often co-occurring, challenges, primarily with knowledge and acceptance of their incurable condition, the impact of seizures and cognitive deficits, transport difficulties, and access to (in)formal support. Several factors were on a continuum, for example, sufficient knowledge was a facilitator, whereas lack thereof, was a barrier to self-management.

CONCLUSIONS

People with LGG described distinctive experiences with wide-ranging factors influencing their ability to self-manage.

IMPLICATIONS FOR CANCER SURVIVORS

These findings will improve awareness of the potential challenges faced by people with LGG around self-management and inform development of self-management interventions for this population.

High costs, low quality of life, reduced survival, and room for improving treatment: an analysis of burden and unmet needs in glioma

Gliomas are a group of heterogeneous tumors that account for substantial morbidity, mortality, and costs to patients and healthcare systems globally. Survival varies considerably by grade, histology, biomarkers, and genetic alterations such as IDH mutations and MGMT promoter methylation, and treatment, but is poor for some grades and histologies, with many patients with glioblastoma surviving less than a year from diagnosis. The present review provides an introduction to glioma, including its classification, epidemiology, economic and humanistic burden, as well as treatment options. Another focus is on treatment recommendations for IDH-mutant astrocytoma, IDH-mutant oligodendroglioma, and glioblastoma, which were synthesized from recent guidelines. While recommendations are nuanced and reflect the complexity of the disease, maximum safe resection is typically the first step in treatment, followed by radiotherapy and/or chemotherapy using temozolomide or procarbazine, lomustine, and vincristine. Immunotherapies and targeted therapies currently have only a limited role due to disappointing clinical trial results, including in recurrent glioblastoma, for which the nitrosourea lomustine remains the de facto standard of care. The lack of treatment options is compounded by frequently suboptimal clinical practice, in which patients do not receive adequate therapy after resection, including delayed, shortened, or discontinued radiotherapy and chemotherapy courses due to treatment side effects. These unmet needs will require significant efforts to address, including a continued search for novel treatment options, increased awareness of clinical guidelines, improved toxicity management for chemotherapy, and the generation of additional and more robust clinical and health economic evidence.

Immunity in malignant brain tumors: Tumor entities, role of immunotherapy, and specific contribution of myeloid cells to the brain tumor microenvironment

Malignant brain tumors lack effective treatment, that can improve their poor overall survival achieved with standard of care. Advancement in different cancer treatments has shifted the focus in brain tumor research and clinical trials toward immunotherapy-based approaches. The investigation of the immune cell landscape revealed a dominance of myeloid cells in the tumor microenvironment. Their exact roles and functions are the subject of ongoing research. Current evidence suggests a complex interplay of tumor cells and myeloid cells with competing functions toward support vs. control of tumor growth. Here, we provide a brief overview of the three most abundant brain tumor entities: meningioma, glioma, and brain metastases. We also describe the field of ongoing immunotherapy trials and their results, including immune checkpoint inhibitors, vaccination studies, oncolytic viral therapy, and CAR-T cells. Finally, we summarize the phenotypes of microglia, monocyte-derived macrophages, border-associated macrophages, neutrophils, and potential novel therapy targets.

11 C-Acetate PET/CT for Reactive Astrogliosis Outperforms 11 C-Methionine PET/CT in Glioma Classification and Survival Prediction

PURPOSE

11 C-acetate (ACE) PET/CT visualizes reactive astrogliosis in tumor microenvironment. This study compared 11 C-ACE and 11 C-methionine (MET) PET/CT for glioma classification and predicting patient survival.

PATIENTS AND METHODS

In this prospective study, a total of 142 patients with cerebral gliomas underwent preoperative MRI, 11 C-MET PET/CT, and 11 C-ACE PET/CT. Tumor-to-contralateral cortex (TNR MET ) and tumor-to-choroid plexus ratios (TNR ACE ) were calculated for 11 C-MET and 11 C-ACE. The Kruskal-Wallis test and Bonferroni post hoc analysis were used to compare the differences in 11 C-TNR MET and 11 C-TNR ACE . The Cox proportional hazards regression analysis and classification and regression tree models were used to assess progression-free survival (PFS) and overall survival (OS).

RESULTS

The median 11 C-TNR MET and 11 C-TNR ACE for oligodendrogliomas (ODs), IDH1 -mutant astrocytomas, IDH1 -wildtype astrocytomas, and glioblastomas were 2.75, 1.40, 2.30, and 3.70, respectively, and 1.40, 1.20, 1.77, and 2.87, respectively. The median 11 C-TNR MET was significantly different among the groups, except between ODs and IDH1 -wildtype astrocytomas, whereas the median 11 C-TNR ACE was significantly different among all groups. The classification and regression tree model identified 4 risk groups ( IDH1 -mutant with 11 C-TNR ACE ≤ 1.4, IDH1 -mutant with 11 C-TNR ACE > 1.4, IDH1 -wildtype with 11 C-TNR ACE ≤ 1.8, and IDH1 -wildtype with 11 C-TNR ACE > 1.8), with median PFS of 52.7, 44.5, 25.9, and 8.9 months, respectively. Using a 11 C-TNR ACE cutoff of 1.4 for IDH1 -mutant gliomas and a 11 C-TNR ACE cutoff of 2.0 for IDH1 -wildtype gliomas, all gliomas were divided into 4 groups with median OS of 52.7, 46.8, 27.6, and 12.0 months, respectively. Significant differences in PFS and OS were observed among the 4 groups after correcting for multiple comparisons.

CONCLUSIONS

11 C-ACE PET/CT is better for glioma classification and survival prediction than 11 C-MET PET/CT, highlighting its potential role in cerebral glioma patients.

Personalized treatment selection via product partition models with covariates

Precision medicine is an approach for disease treatment that defines treatment strategies based on the individual characteristics of the patients. Motivated by an open problem in cancer genomics, we develop a novel model that flexibly clusters patients with similar predictive characteristics and similar treatment responses; this approach identifies, via predictive inference, which one among a set of treatments is better suited for a new patient. The proposed method is fully model based, avoiding uncertainty underestimation attained when treatment assignment is performed by adopting heuristic clustering procedures, and belongs to the class of product partition models with covariates, here extended to include the cohesion induced by the normalized generalized gamma process. The method performs particularly well in scenarios characterized by considerable heterogeneity of the predictive covariates in simulation studies. A cancer genomics case study illustrates the potential benefits in terms of treatment response yielded by the proposed approach. Finally, being model based, the approach allows estimating clusters' specific response probabilities and then identifying patients more likely to benefit from personalized treatment.

Intramedullary Spinal Cord Tumors: Whole-Genome Sequencing to Assist Management and Prognosis

Intramedullary spinal cord tumors (IMSCTs) harbor unique genetic mutations which may play a role in prognostication and management. To this end, we present the largest cohort of IMSCTs with genetic characterization in the literature from our multi-site institutional registry. A total of 93 IMSCT patient records were reviewed from the years 1999 to 2020. Out of these, 61 complied with all inclusion criteria, 14 of these patients had undergone genetic studies with 8 undergoing whole-genomic sequencing. Univariate analyses were used to assess any factors associated with progression-free survival (PFS) using the Cox proportional hazards model. Firth's penalized likelihood approach was used to account for the low event rates. Fisher's exact test was performed to compare whole-genome analyses and specific gene mutations with progression. PFS (months) was given as a hazard ratio. Only the absence of copy neutral loss of heterozygosity (LOH) was shown to be significant (0.05, p = 0.008). Additionally, higher risk of recurrence/progression was associated with LOH (p = 0.0179). Our results suggest LOH as a genetic predictor of shorter progression-free survival, particularly within ependymoma and glioblastoma tumor types. Further genomic research with larger multi-institutional datasets should focus on these mutations as possible prognostic factors.

Neuro-oncology Treatment Strategies for Primary Glial Tumors

Primary brain tumors underwent reclassification in the 2021 World Health Organization update, relying on molecular findings (especially isocitrate dehydrogenase mutations and chromosomal changes in 1p, 19q, gain of chromosome 7 and loss of chromosome 10). Newer entities have also been described including histone 3 mutant midline gliomas. These updated pathologic classifications improve prognostication and reliable diagnosis, but may confuse interpretation of prior clinical trials and require reclassification of patients diagnosed in the past. For patients over seventy, multiple studies have now confirmed the utility of shorter courses of radiation, and the risk of post-operative delirium. Ongoing studies are comparing proton to photon radiation. Long term follow up of prior clinical trials have confirmed the roles and length of chemotherapy (mainly temozolomide) in different tumors, as well as the wearable novottf device. New oral isocitrate dehydrogenase inhibitors have also shown efficacy in clinical trials.

Nose-to-brain drug delivery for the treatment of glioblastoma multiforme: nanotechnological interventions

Glioblastoma multiforme (GBM) is the most aggressive malignant brain tumor with a short survival rate. Extensive research is underway for the last two decades to find an effective treatment for GBM but the tortuous pathophysiology, development of chemoresistance, and presence of BBB are the major challenges, prompting scientists to look for alternative targets and delivery strategies. Therefore, the nose to brain delivery emerged as an unorthodox and non-invasive route, which delivers the drug directly to the brain via the olfactory and trigeminal pathways and also bypasses the BBB and hepatic metabolism of the drug. However, mucociliary clearance, low administration volume, and less permeability of nasal mucosa are the obstacles retrenching the brain drug concentration. Thus, nanocarrier delivery through this route may conquer these limitations because of their unique surface characteristics and smaller size. In this review, we have emphasized the advantages and limitations of nanocarrier technologies such as polymeric, lipidic, inorganic, and miscellaneous nanoparticles used for nose-to-brain drug delivery against GBM in the past 10 years. Furthermore, recent advances, patents, and clinical trials are highlighted. However, most of these studies are in the early stages, so translating their outcomes into a marketed formulation would be a milestone in the better progression and survival of glioma patients.

Quality of life after surgery for lower grade gliomas

BACKGROUND

Few large studies have investigated quality of life (QOL) for adults diagnosed with lower grade glioma (LGG).

METHODS

QOL was assessed for 320 adults with LGG (World Health Organization grade 2/3) enrolled in the International Low Grade Glioma Registry by using the Medical Outcomes Study 36-Item Short Form health survey. Data on symptoms were also collected. QOL outcomes were examined by treatment group and also compared to those from a population-based case-control study of meningioma (the Meningioma Consortium), in which 1722 meningioma cases diagnosed among residents of Connecticut, Massachusetts, California, Texas, and North Carolina from May 1, 2006 through March 14, 2013 were enrolled and frequency matched to 1622 controls by age, sex, and geography.

RESULTS

The LGG sample average age is 45 years at the time of interview and 53.1% male. Almost 55% of patients had received radiation and chemotherapy (primarily temozolomide); 32.4% had received neither treatment. Two-thirds of participants with LGG report difficulty with speaking, memory, or thinking, and over one of three reports personality change or difficulty driving. After controlling for age and other comorbidities, individuals with LGG report levels of physical, emotional, and mental health functioning below those reported in a meningioma as well as a general healthy population.

CONCLUSIONS

Despite being relatively young, persons with LGG report significantly reduced QOL compared to persons with nonmalignant brain tumors and to a control population, which highlights the need to better acknowledge and manage these symptoms for this group of patients diagnosed in the prime of life.

Adult-type Diffuse Gliomas

OBJECTIVE

This article highlights key aspects of the diagnosis and management of adult-type diffuse gliomas, including glioblastomas and IDH-mutant gliomas relevant to the daily practice of the general neurologist.

LATEST DEVELOPMENTS

The advances in molecular characterization of gliomas have translated into more accurate prognostication and tumor classification. Gliomas previously categorized by histological appearance solely as astrocytomas or oligodendrogliomas are now also defined by molecular features. Furthermore, ongoing clinical trials have incorporated these advances to tailor more effective treatments for specific glioma subtypes.

ESSENTIAL POINTS

Despite recent insights into the molecular aspects of gliomas, these tumors remain incurable. Care for patients with these complex tumors requires a multidisciplinary team in which the general neurologist has an important role. Efforts focus on translating the latest data into more effective therapies that can prolong survival.

Tumor radiogenomics in gliomas with Bayesian layered variable selection

We propose a statistical framework to analyze radiological magnetic resonance imaging (MRI) and genomic data to identify the underlying radiogenomic associations in lower grade gliomas (LGG). We devise a novel imaging phenotype by dividing the tumor region into concentric spherical layers that mimics the tumor evolution process. MRI data within each layer is represented by voxel-intensity-based probability density functions which capture the complete information about tumor heterogeneity. Under a Riemannian-geometric framework these densities are mapped to a vector of principal component scores which act as imaging phenotypes. Subsequently, we build Bayesian variable selection models for each layer with the imaging phenotypes as the response and the genomic markers as predictors. Our novel hierarchical prior formulation incorporates the interior-to-exterior structure of the layers, and the correlation between the genomic markers. We employ a computationally-efficient Expectation-Maximization-based strategy for estimation. Simulation studies demonstrate the superior performance of our approach compared to other approaches. With a focus on the cancer driver genes in LGG, we discuss some biologically relevant findings. Genes implicated with survival and oncogenesis are identified as being associated with the spherical layers, which could potentially serve as early-stage diagnostic markers for disease monitoring, prior to routine invasive approaches. We provide a R package that can be used to deploy our framework to identify radiogenomic associations.

Identification of immune-related hub genes and construction of an immune-related gene prognostic index for low-grade glioma

Low-grade glioma (LGG) poses significant management challenges and has a dismal prognosis. While immunotherapy has shown significant promise in cancer treatment, its progress in glioma has confronted with challenges. In our study, we aimed to develop an immune-related gene prognostic index (IRGPI) which could be used to evaluate the response and efficacy of LGG patients with immunotherapy. We included a total of 529 LGG samples from TCGA database and 1152 normal brain tissue samples from the GTEx database. Immune-related differentially expressed genes (DEGs) were screened. Then, we used weighted gene co-expression network analysis (WGCNA) to identify immune-related hub genes in LGG patients and performed Cox regression analysis to construct an IRGPI. The median IRGPI was used as the cut-off value to categorize LGG patients into IRGPI-high and low subgroups, and the molecular and immune mechanism in IRGPI-defined subgroups were analysed. Finally, we explored the relationship between IRGPI-defined subgroups and immunotherapy related indicators in patients after immunotherapy. Three genes (RHOA, NFKBIA and CCL3) were selected to construct the IRGPI. In a survival analysis using TCGA cohort as a training set, patients in the IRGPI-low subgroup had a better OS than those in IRGPI-high subgroup, consistent with the results in CGGA cohort. The comprehensive results showed that IRGPI-low subgroup had a more abundant activated immune cell population and lower TIDE score, higher MSI, higher TMB score, lower T cell dysfunction score, more likely benefit from ICIs therapy. IRGPI is a promising biomarker in the field of LGG ICIs therapy to distinguish the prognosis, the molecular and immunological characteristics of patients.

Neuron-oligodendroglial interactions in health and malignant disease

Experience sculpts brain structure and function. Activity-dependent modulation of the myelinated infrastructure of the nervous system has emerged as a dimension of adaptive change during childhood development and in adulthood. Myelination is a richly dynamic process, with neuronal activity regulating oligodendrocyte precursor cell proliferation, oligodendrogenesis and myelin structural changes in some axonal subtypes and in some regions of the nervous system. This myelin plasticity and consequent changes to conduction velocity and circuit dynamics can powerfully influence neurological functions, including learning and memory. Conversely, disruption of the mechanisms mediating adaptive myelination can contribute to cognitive impairment. The robust effects of neuronal activity on normal oligodendroglial precursor cells, a putative cellular origin for many forms of glioma, indicates that dysregulated or 'hijacked' mechanisms of myelin plasticity could similarly promote growth in this devastating group of brain cancers. Indeed, neuronal activity promotes the pathogenesis of many forms of glioma in preclinical models through activity-regulated paracrine factors and direct neuron-to-glioma synapses. This synaptic integration of glioma into neural circuits is central to tumour growth and invasion. Thus, not only do neuron-oligodendroglial interactions modulate neural circuit structure and function in the healthy brain, but neuron-glioma interactions also have important roles in the pathogenesis of glial malignancies.

MrGPS: an m6A-related gene pair signature to predict the prognosis and immunological impact of glioma patients

N6-methyladenosine (m6A) RNA methylation is the predominant epigenetic modification for mRNAs that regulates various cancer-related pathways. However, the prognostic significance of m6A modification regulators remains unclear in glioma. By integrating the TCGA lower-grade glioma (LGG) and glioblastoma multiforme (GBM) gene expression data, we demonstrated that both the m6A regulators and m6A-target genes were associated with glioma prognosis and activated various cancer-related pathways. Then, we paired m6A regulators and their target genes as m6A-related gene pairs (MGPs) using the iPAGE algorithm, among which 122 MGPs were significantly reversed in expression between LGG and GBM. Subsequently, we employed LASSO Cox regression analysis to construct an MGP signature (MrGPS) to evaluate glioma prognosis. MrGPS was independently validated in CGGA and GEO glioma cohorts with high accuracy in predicting overall survival. The average area under the receiver operating characteristic curve (AUC) at 1-, 3- and 5-year intervals were 0.752, 0.853 and 0.831, respectively. Combining clinical factors of age and radiotherapy, the AUC of MrGPS was much improved to around 0.90. Furthermore, CIBERSORT and TIDE algorithms revealed that MrGPS is indicative for the immune infiltration level and the response to immune checkpoint inhibitor therapy in glioma patients. In conclusion, our study demonstrated that m6A methylation is a prognostic factor for glioma and the developed prognostic model MrGPS holds potential as a valuable tool for enhancing patient management and facilitating accurate prognosis assessment in cases of glioma.

Integrating bulk and single-cell RNA sequencing data reveals epithelial-mesenchymal transition molecular subtype and signature to predict prognosis, immunotherapy efficacy, and drug candidates in low-grade gliomas

Objective: Epithelial-mesenchymal transition (EMT) is a tightly regulated and dynamic process occurring in both embryonic development and tumor progression. Our study aimed to comprehensively explore the molecular subtypes, immune landscape, and prognostic signature based on EMT-related genes in low-grade gliomas (LGG) in order to facilitate treatment decision-making and drug discovery. Methods: We curated EMT-related genes and performed molecular subtyping with consensus clustering algorithm to determine EMT expression patterns in LGG. The infiltration level of diverse immune cell subsets was evaluated by implementing the single-sample gene set enrichment analysis (ssGSEA) and ESTIMATE algorithms. The distinctions in clinical characteristics, mutation landscape, and immune tumor microenvironment (TME) among the subtypes were subjected to further investigation. Gene Set Variation Analysis (GSVA) was performed to explore the biological pathways that were involved in subtypes. The chemo drug sensitivity and immunotherapy of subtypes were estimated through GDSC database and NTP algorithm. To detect EMT subtype-related prognostic gene modules, the analysis of weighted gene co-expression network (WGCNA) was performed. The LASSO algorithm was utilized to construct a prognostic risk model, and its efficacy was verified through an independent CGGA dataset. Finally, the expression of the hub genes from the prognostic model was evaluated through the single-cell dataset and in-vitro experiment. Results: The TCGA-LGG dataset revealed the creation of two molecular subtypes that presented different prognoses, clinical implications, TME, mutation landscapes, chemotherapy, and immunotherapy. A three-gene signature (SLC39A1, CTSA and CLIC1) based on EMT expression pattern were established through WGCNA analysis. Low-risk patients showed a positive outlook, increased immune cell presence, and higher expression of immune checkpoint proteins. In addition, several promising drugs, including birinapant, fluvastatin, clofarabine, dasatinib, tanespimycin, TAK-733, GDC-0152, AZD8330, trametinib and ingenol-mebutate had great potential to the treatment of high risk patients. Finally, CTSA and CLIC1 were highly expressed in monocyte cell through single-cell RNA sequencing analysis. Conclusion: Our research revealed non-negligible role of EMT in the TME diversity and complexity of LGG. A prognostic signature may contribute to the personalized treatment and prognostic determination.

BIRC5: A novel therapeutic target for lung cancer stem cells and glioma stem cells

Baculoviral inhibitor of apoptosis repeat containing 5 (BIRC5) is also known as survivin. BIRC5, a member of the apoptosis inhibitor (IAP) family, negatively regulates apoptosis or programmed cell death by inhibiting caspase activation. Due to these properties, overexpression of BIRC5 enables specific survival and division associated with cancer malignancies. In addition, BIRC5 is highly expressed in stem cells, but not present at all in terminally differentiated cells. On this basis, there is speculation that BIRC5 may be involved in the regulation of cancer stem cells (CSCs), but few study results have been reported. In addition, the molecular mechanisms of BIRC5 regulation are not yet well understood. Through the present study, it was confirmed that BIRC5 is a key factor regulating CSCs and epithelial to mesenchymal transition (EMT). BIRC5 was simultaneously overexpressed in lung cancer stem cells (LCSCs) and glioma stem cells (GSCs), and when the expression was suppressed, the characteristics of CSCs disappeared. In addition, plasminogen activator inhibitor-1 (PAI-1), a secreted factor regulated by BIRC5, is involved in signaling mechanisms that regulate cancer stem cells and EMT, and PAI-1 forms an autocrine chain. Based on these results, BIRC5 is proposed as a novel therapeutic target protein for LCSCs and GSCs.

A pyroptosis gene-based prognostic model for predicting survival in low-grade glioma

Background

Pyroptosis, a lytic form of programmed cell death initiated by inflammasomes, has been reported to be closely associated with tumor proliferation, invasion and metastasis. However, the roles of pyroptosis genes (PGs) in low-grade glioma (LGG) remain unclear.

Methods

We obtained information for 1,681 samples, including the mRNA expression profiles of LGGs and normal brain tissues and the relevant corresponding clinical information from two public datasets, TCGA and GTEx, and identified 45 differentially expressed pyroptosis genes (DEPGs). Among these DEPGs, nine hub pyroptosis genes (HPGs) were identified and used to construct a genetic risk scoring model. A total of 476 patients, selected as the training group, were divided into low-risk and high-risk groups according to the risk score. The area under the curve (AUC) values of the receiver operating characteristic (ROC) curves verified the accuracy of the model, and a nomogram combining the risk score and clinicopathological characteristics was used to predict the overall survival (OS) of LGG patients. In addition, a cohort from the Gene Expression Omnibus (GEO) database was selected as a validation group to verify the stability of the model. qRT-PCR was used to analyze the gene expression levels of nine HPGs in paracancerous and tumor tissues from 10 LGG patients.

Results

Survival analysis showed that, compared with patients in the low-risk group, patients in the high-risk group had a poorer prognosis. A risk score model combining PG expression levels with clinical features was considered an independent risk factor. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses indicated that immune-related genes were enriched among the DEPGs and that immune activity was increased in the high-risk group.

Conclusion

In summary, we successfully constructed a model to predict the prognosis of LGG patients, which will help to promote individualized treatment and provide potential new targets for immunotherapy.

MAPK signaling pathway-based glioma subtypes, machine-learning risk model, and key hub proteins identification

An early diagnosis and precise prognosis are critical for the treatment of glioma. The mitogen‑activated protein kinase (MAPK) signaling pathway potentially affects glioma, but the exploration of the clinical values of the pathway remains lacking. We accessed data from TCGA, GTEx, CGGA, etc. Up-regulated MAPK signaling pathway genes in glioma were identified and used to cluster the glioma subtypes using consensus clustering. The subtype differences in survival, cancer stemness, and the immune microenvironment were analyzed. A prognostic model was trained with the identified genes using the LASSO method and was validated with three external cohorts. The correlations between the risk model and cancer-associated signatures in cancer were analyzed. Key hub genes of the gene set were identified by hub gene analysis and survival analysis. 47% of the MAPK signaling pathway genes were overexpressed in glioma. Subtypes based on these genes were distinguished in survival, cancer stemness, and the immune microenvironment. A risk model was calculated with high confidence in the prediction of overall survival and was correlated with multiple cancer-associated signatures. 12 hub genes were identified and 8 of them were associated with survival. The MAPK signaling pathway was overexpressed in glioma with prognostic value.

Liquid Biopsy in Low-Grade Glioma: A Systematic Review and a Proposal for a Clinical Utility Score

Liquid biopsy research on Low-Grade gliomas (LGG) has remained less conspicuous than that on other malignant brain tumors. Reliable serum markers would be precious for diagnosis, follow- up and treatment. We propose a clinical utility score (CUS) for biomarkers in LGG that mirrors their clinical usefulness. We conducted a PRISMA review. We examined each biomarker classifying them by CUS and Level of Evidence (LOE). We identified four classes of biomarkers: (1). Circulating protein-(a) vitronectin discriminates LGG from HGG (Sn:98%, Sp:91%, CUS: 3, LOE: III), (b) CTLA-4 discriminates LGG from HGG, (cutoff: 220.43 pg/ml, Sn: 82%, Sp: 78%, CUS:3, LOE:III), (c) pre-operative TGF b1 predict astrocytoma (cutoff: 2.52 ng/ml, Sn: 94.9%, Sp: 100%, CUS:3, LOE:VI). (2). micro-RNA (miR)-(a) miR-16 discriminates between WHO IV and WHO II and III groups (AUC = 0.98, CUS:3, LOE: III), (b) miR-454-3p is higher in HGG than in LGG (p = 0.013, CUS:3, LOE: III), (c) miR-210 expression is related to WHO grades (Sn 83.2%, Sp 94.3%, CUS: 3, LOE: III). (3). Circulating DNA-(a) IDH1R132H mutation detected in plasma by combined COLD and digital PCR (Sn: 60%, Sp: 100%, CUS: 3, LOE: III). 4. Exosomes-(a) SDC1 serum levels could discriminate GBM from LGG (Sn: 71%, Sp: 91%, CUS: 2C, LOE: VI). Our investigation showed that miRs appear to have the highest clinical utility. The LOE of the studies assessed is generally low. A combined approach between different biomarkers and traditional diagnostics may be considered. We identified four main classes of biomarkers produced by LGG. We examined each biomarker, classifying them by clinical utility score (CUS) and level of evidence (LOE). Micro-RNA (miRs) appears to have the highest CUS and LOE.

Evaluation of chromosome 1p/19q deletion by Fluorescence in Situ Hybridization (FISH) as prognostic factors in malignant glioma patients on treatment with alkylating chemotherapy

BACKGROUND

Either deletion or co-deletion of chromosomal arms 1p or 19q is a characteristic and early genetic event in oligodendroglial tumors that is associated with a better prognosis and enhanced response to therapy. Information of 1p/19q status is now regarded as the standard of care when managing oligodendroglial tumors for therapeutic options in anticipation of the increased survival and progression-free survival times associated with it. Keeping this in view, we first time attempted to establish the FISH based detection of 1p/19q deletion in glioma tissue samples to evaluate its role and involvement in the disease.

METHOD

Overall 39 glioma cases of different histologies were evaluated by fluorescence in situ hybridization (FISH) technique using specific FISH probes with Olympus BX43 fluorescent microscope to detect chromosomes 1p and 19q or co-deletions therein.

RESULTS

Of the 39 glioma samples, overall 27 (69.2%) were found to have deletion either in 1p, 19q or both. Deletions were observed in 23.0%, 7.6% and 38.4% in 1p, 19q and 1p/19q co-deletions respectively. Overall oligidendrioglioma presented with 53.8% (21 of 39) deletions, astrocytoma group showed 12.8% and GBM accounted for 2.5% deletions. Overall survival and disease free survival was seen significantly better in oligidendrioglioma and astrocytoma with deleted tumors as compared to non-deleted ones (p<0.05).

CONCLUSION

Allelic losses on 1p and 19q, either discretely or shared, were more frequent in classic oligodendrogliomas than in either astrocytoma or Glioblastoma with better survival and response to therapy.

Long-Term Tumor Control Following Targeted Alpha Therapy (TAT) of Low-Grade Gliomas (LGGs): A New Treatment Paradigm?

The median survival time has been reported to vary between 5 and 8 years in low-grade (WHO grade 2) astrocytoma, and between 10 and 15 years for grade 2 oligodendroglioma. Targeted alpha therapy (TAT), using the modified peptide vector [213Bi]Bi/[225Ac]Ac-DOTA-substance P, has been developed to treat glioblastoma (GBM), a prevalent malignant brain tumor. In order to assess the risk of late neurotoxicity, assuming that reduced tumor cell proliferation and invasion should directly translate into good responses in low-grade gliomas (LGGs), a limited number of patients with diffuse invasive astrocytoma (n = 8) and oligodendroglioma (n = 3) were offered TAT. In two oligodendroglioma patients, TAT was applied as a second-line treatment for tumor progression, 10 years after targeted beta therapy using [90Y]Y-DOTA-substance P. The radiopharmaceutical was locally injected directly into the tumor via a stereotactic insertion of a capsule-catheter system. The activity used for radiolabeling was 2-2.5 GBq of Bismuth-213 and 17 to 35 MBq of Actinium-225, mostly applied in a single fraction. The recurrence-free survival times were in the range of 2 to 16 years (median 11 years) in low-grade astrocytoma (n = 8), in which TAT was administered following a biopsy or tumor debulking. Regarding oligodendroglioma, the recurrence-free survival time was 24 years in the first case treated, and 4 and 5 years in the two second-line cases. In conclusion, TAT leads to long-term tumor control in the majority of patients with LGG, and recurrence has so far not manifested in patients with low-grade (grade 2) astrocytomas who received TAT as a first-line therapy. We conclude that targeted alpha therapy has the potential to become a new treatment paradigm in LGG.

Unravelling benzazepines and aminopyrimidine as multi-target therapeutic repurposing drugs for EGFR V774M mutation in neuroglioma patients

Introduction

Neuroglioma, a classification encompassing tumors arising from glial cells, exhibits variable aggressiveness and depends on tumor grade and stage. Unraveling the EGFR gene alterations, including amplifications (unaltered), deletions, and missense mutations (altered), is emerging in glioma. However, the precise understanding of emerging EGFR mutations and their role in neuroglioma remains limited. This study aims to identify specific EGFR mutations prevalent in neuroglioma patients and investigate their potential as therapeutic targets using FDA-approved drugs for repurposing approach.

Methods

Neuroglioma patient's data were analyzed to identify the various mutations and survival rates. High throughput virtual screening (HTVS) of FDA-approved (1615) drugs using molecular docking and simulation was executed to determine the potential hits.

Results

Neuroglioma patient samples (n=4251) analysis reveals 19% EGFR alterations with most missense mutations at V774M in exon 19. The Kaplan-Meier plots show that the overall survival rate was higher in the unaltered group than in the altered group. Docking studies resulted the best hits based on each target's higher docking score, minimum free energy (MMGBSA), minimum kd, ki, and IC50 values. MD simulations and their trajectories show that compounds ZINC000011679756 target unaltered EGFR and ZINC000003978005 targets altered EGFR, whereas ZINC000012503187 (Conivaptan, Benzazepine) and ZINC000068153186 (Dabrafenib, aminopyrimidine) target both the EGFRs. The shortlisted compounds demonstrate favorable residual interactions with their respective targets, forming highly stable complexes. Moreover, these shortlisted compounds have drug- like properties as assessed by ADMET profiling.

Conclusion

Therefore, compounds (ZINC000012503187 and ZINC000068153186) can effectively target both the unaltered/altered EGFRs as multi-target therapeutic repurposing drugs towards neuroglioma.

Forkhead box transcription factors (FOXOs and FOXM1) in glioma: from molecular mechanisms to therapeutics

Glioma is the most aggressive and malignant type of primary brain tumor, comprises the majority of central nervous system deaths, and is categorized into different subgroups according to its histological characteristics, including astrocytomas, oligodendrogliomas, glioblastoma multiforme (GBM), and mixed tumors. The forkhead box (FOX) transcription factors comprise a collection of proteins that play various roles in numerous complex molecular cascades and have been discovered to be differentially expressed in distinct glioma subtypes. FOXM1 and FOXOs have been recognized as crucial transcription factors in tumor cells, including glioma cells. Accumulating data indicates that FOXM1 acts as an oncogene in various types of cancers, and a significant part of studies has investigated its function in glioma. Although recent studies considered FOXO subgroups as tumor suppressors, there are pieces of evidence that they may have an oncogenic role. This review will discuss the subtle functions of FOXOs and FOXM1 in gliomas, dissecting their regulatory network with other proteins, microRNAs and their role in glioma progression, including stem cell differentiation and therapy resistance/sensitivity, alongside highlighting recent pharmacological progress for modulating their expression.

An evaluation of biobanking and therapeutic clinical trial representation among adult glioma patients from rural and urban Utah

Background

Social determinants of health (SDOHs)-specifically those related to rurality, health care accessibility, and income-may play as-yet-unidentified roles in prognosis for glioma patients, and their impact on access to clinical trials is important to understand. We examined SDOHs of patients enrolled in glioma clinical trials and evaluate disparities in trial participation and outcomes between rural and urban patients.

Methods

We retrospectively identified patients enrolled in glioma clinical trials at Huntsman Cancer Institute (HCI) from May 2012 to May 2022 to evaluate clinical trial participation. We used multivariable models to evaluate SDOHs and geographic information system mapping to assess representation across Utah's counties. We utilized the most recent 10-year datasets of patients treated for glioma at HCI and from the Utah Cancer Registry to analyze survival and incidence, respectively.

Results

A total of 570 participants (68 trials) resided in Utah, 84.4% from urban counties, 13.5% from rural counties, and 2.1% from frontier (least-populous) counties. Nineteen counties (65.5%) were underrepresented in trials (enrolled participants vs. eligible), 1 (3.5%) was represented in a near-1:1 ratio, and 9 (31.0%) were overrepresented. Counties with greater enrollment had greater population densities, highest per-capita income, and proximity to HCI. Among patients treated at HCI, patients from rural/frontier counties had equivalent survival with urban patients across nearly all glioma types, including glioblastomas, despite underrepresentation in clinical trials.

Conclusions

By highlighting disparities in clinical trial enrollment, our results can support efforts to improve recruitment in underrepresented regions, which can assist providers in delivering equitable care for all patients.

A transfer learning approach on MRI-based radiomics signature for overall survival prediction of low-grade and high-grade gliomas

Lower-grade gliomas (LGG) can eventually progress to glioblastoma (GBM) and death. In the context of the transfer learning approach, we aimed to train and test an MRI-based radiomics model for predicting survival in GBM patients and validate it in LGG patients. From each patient's 704 MRI-based radiomics features, we chose seventeen optimal radiomics signatures in the GBM training set (n = 71) and used these features in both the GBM testing set (n = 31) and LGG validation set (n = 107) for further analysis. Each patient's risk score, calculated based on those optimal radiomics signatures, was chosen to represent the radiomics model. We compared the radiomics model with clinical, gene status models, and combined model integrating radiomics, clinical, and gene status in predicting survival. The average iAUCs of combined models in training, testing, and validation sets were respectively 0.804, 0.878, and 0.802, and those of radiomics models were 0.798, 0.867, and 0.717. The average iAUCs of gene status and clinical models ranged from 0.522 to 0.735 in all three sets. The radiomics model trained in GBM patients can effectively predict the overall survival of GBM and LGG patients, and the combined model improved this ability.

Applying Shear Wave and Magnetic Resonance Elastography to Grade Brain Tumors: Systematic Review and Meta-Analysis

BACKGROUND

Reports find that magnetic resonance elastography (MRE) and shear wave elastography (SWE) can classify intracranial tumors according to stiffness. However, systematic syntheses of these articles are lacking. In this report, a systematic review and meta-analysis was performed to evaluate whether SWE and MRE can predict meningioma and glioma grades.

METHODS

PubMed and Scopus were searched between February 10, 2022. and March 2, 2022. using manual search criteria. Eight out of 106 non-duplicate records were included, encompassing 84 patients with low-grade tumors (age 42 ± 13 years, 71% female) and 92 patients with high-grade tumors (age 50 ± 13 years, 42% female). Standardized mean difference in stiffness between high-grade and low-grade tumors were measured using a forest plot. The I2, χ2, and t tests were performed, and bubble plots were constructed to measure heterogeneity. An adapted QUADAS-2 scale evaluated study quality. Additionally, a funnel plot was constructed, and an Egger's intercept test determined study bias.

RESULTS

Low-grade tumors were stiffer than high-grade tumors (Cohen's D = -1.25; 95% CI -1.88, -0.62). Moderate heterogeneity was observed (I2 = 67%; P = 0.006) but controlling for publication year (I2 = 0.2%) and age (I2 = 0.0%-17%) reduced heterogeneity. Included studies revealed unclear or high bias for the reference standard and flow and timing (>50%).

CONCLUSIONS

Elastography techniques have potential to grade tumors intraoperatively and postoperatively. More studies are needed to evaluate the clinical utility of these technologies.

SLC10A3 Is a Prognostic Biomarker and Involved in Immune Infiltration and Programmed Cell Death in Lower Grade Glioma

BACKGROUND

The association between SLC10A3 (solute carrier family 10 member 3) and lower grade glioma (LGG) remains unclear.

METHODS

We used public databases and bioinformatics analysis to analyze SLC10A3. These included The Cancer Genome Atlas, Genotype-Tissue Expansion, Chinese Glioma Genome Atlas, Human Protein Atlas, GeneCards, cBioPortal, Search Tool for the Retrieval of Interacting Genes/Proteins, Gene Expression Profiling Interactive Analysis, Tumor Immune Estimation Resource, Tumor-Immune System Interaction Database, receiver operating characteristic curve analysis, Kaplan-Meier analysis, Cox analysis, nomograms, calibration plots, gene ontology/Kyoto Encyclopedia of Genes and Genomes enrichment analysis, gene set enrichment analysis, single-sample gene set enrichment analysis, and Spearman's correlation analysis.

RESULTS

SLC10A3 was upregulated in adrenocortical carcinoma, glioblastoma, and LGG and was associated with good overall survival (OS) in adrenocortical carcinoma and poor OS in LGG and glioblastoma. SLC10A3 was increased with increased World Health Organization grade, upregulated in isocitrate dehydrogenase-wild type, 1p/19q (chromosome arms 1p and 19q) non-co-deleted, and higher in astrocytoma. Patients with LGG were grouped by the occurrence of the clinical outcome endpoints (i.e., OS, disease-specific survival [DSS], and progression-free interval events). Genetic alterations in SLC10A3 were associated with poor progression-free survival in LGG. Most of clinical characteristics were associated with the SLC10A3 expression level. SLC10A3 with diagnostic and prognostic value (OS, DSS, and progression-free interval) was an independent prognostic factor in LGG. Moreover, Nomograms (WHO grade, 1p/19q codeletion, age and SLC10A3) had moderately accurate predictive for OS and DSS. Functional analysis showed that SLC10A3 might participate in the transport of multiple substances, neurogenic signaling, immune response, and programmed cell death in LGG. SLC10A3 correlated with immune infiltration in LGG and moderately correlated with the gene signature of pyroptosis, lysosome-dependent cell death, necroptosis, apoptosis, ferroptosis, alkaliptosis, and autophagy-dependent cell death.

CONCLUSIONS

SLC10A3 is a potential diagnostic and prognostic biomarker for LGG and might be associated with substance transport, neurogenic signaling, immune infiltration, and programmed cell death in LGG.

Experiences of work for people living with a grade 2/3 oligodendroglioma: a qualitative analysis within the Ways Ahead study

OBJECTIVES

This study aimed to explore the work experiences of people living with an oligodendroglioma.

DESIGN

This was a descriptive qualitative study. One-time semi-structured interviews exploring supportive care needs were conducted; work was discussed at various points throughout each interview. An inductive thematic analysis was undertaken.

SETTING

Participants were recruited across the UK through four National Health Service hospitals and the Brain Tumour Charity research involvement networks.

PARTICIPANTS

19 people with grade 2 or 3 oligodendroglioma (mean age 52 years; male n=11). At diagnosis, 16 participants were working, 2 studying and 1 retired. At the interview (mean time since diagnosis 9.6 years; range 1-18 years), seven participants were working, eight retired (four on medical grounds) and four unable to work due to illness.

RESULTS

Seven themes were constructed: (1) physical and cognitive limitations; (2) work ability and productivity; (3) work accommodations; (4) changing roles; (5) attitudes of clients and coworkers; (6) feelings and ambitions; and (7) financial implications. Fatigue, seizures and cognitive deficits influenced work ability. A stressful work environment could exacerbate symptoms. Changes in job roles and work environment were often required. Employer and coworker support were integral to positive experiences. Work changes could result in financial stress and strain.

CONCLUSIONS

This study has highlighted, for the first time, influences on work experiences in this understudied population. These findings have implications for clinicians and employers, when considering the importance of work in rehabilitation for people with oligodendrogliomas, and the individually assessed adjustments required to accommodate them, should employment be desired.

A radiomics-incorporated deep ensemble learning model for multi-parametric MRI-based glioma segmentation

Objective.To develop a deep ensemble learning (DEL) model with radiomics spatial encoding execution for improved glioma segmentation accuracy using multi-parametric magnetic resonance imaging (mp-MRI).Approach.This model was developed using 369 glioma patients with a four-modality mp-MRI protocol: T1, contrast-enhanced T1 (T1-Ce), T2, and FLAIR. In each modality volume, a 3D sliding kernel was implemented across the brain to capture image heterogeneity: 56 radiomic features were extracted within the kernel, resulting in a fourth-order tensor. Each radiomic feature can then be encoded as a 3D image volume, namely a radiomic feature map (RFM). For each patient, all RFMs extracted from all four modalities were processed using principal component analysis for dimension reduction, and the first four principal components (PCs) were selected. Next, a DEL model comprised of four U-Net sub-models was trained for the segmentation of a region-of-interest: each sub-model utilizes the mp-MRI and one of the four PCs as a five-channel input for 2D execution. Last, four softmax probability results given by the DEL model were superimposed and binarized using Otsu's method as the segmentation results. Three DEL models were trained to segment the enhancing tumor (ET), tumor core (TC), and whole tumor (WT), respectively. The segmentation results given by the proposed ensemble were compared to the mp-MRI-only U-Net results.Main Results.All three radiomics-incorporated DEL models were successfully implemented: compared to the mp-MRI-only U-net results, the dice coefficients of ET (0.777 → 0.817), TC (0.742 → 0.757), and WT (0.823 → 0.854) demonstrated improvement. The accuracy, sensitivity, and specificity results demonstrated similar patterns.Significance.The adopted radiomics spatial encoding execution enriches the image heterogeneity information that leads to the successful demonstration of the proposed DEL model, which offers a new tool for mp-MRI-based medical image segmentation.

Cellular senescence in glioma

INTRODUCTION

Glioma is the most common primary brain tumor and is often associated with treatment resistance and poor prognosis. Standard treatment typically involves radiotherapy and temozolomide-based chemotherapy, both of which induce cellular senescence-a tumor suppression mechanism.

DISCUSSION

Gliomas employ various mechanisms to bypass or escape senescence and remain in a proliferative state. Importantly, senescent cells remain viable and secrete a large number of factors collectively known as the senescence-associated secretory phenotype (SASP) that, paradoxically, also have pro-tumorigenic effects. Furthermore, senescent cells may represent one form of tumor dormancy and play a role in glioma recurrence and progression.

CONCLUSION

In this article, we delineate an overview of senescence in the context of gliomas, including the mechanisms that lead to senescence induction, bypass, and escape. Furthermore, we examine the role of senescent cells in the tumor microenvironment and their role in tumor progression and recurrence. Additionally, we highlight potential therapeutic opportunities for targeting senescence in glioma.

A neural ordinary differential equation model for visualizing deep neural network behaviors in multi-parametric MRI-based glioma segmentation

PURPOSE

To develop a neural ordinary differential equation (ODE) model for visualizing deep neural network behavior during multi-parametric MRI-based glioma segmentation as a method to enhance deep learning explainability.

METHODS

By hypothesizing that deep feature extraction can be modeled as a spatiotemporally continuous process, we implemented a novel deep learning model, Neural ODE, in which deep feature extraction was governed by an ODE parameterized by a neural network. The dynamics of (1) MR images after interactions with the deep neural network and (2) segmentation formation can thus be visualized after solving the ODE. An accumulative contribution curve (ACC) was designed to quantitatively evaluate each MR image's utilization by the deep neural network toward the final segmentation results. The proposed Neural ODE model was demonstrated using 369 glioma patients with a 4-modality multi-parametric MRI protocol: T1, contrast-enhanced T1 (T1-Ce), T2, and FLAIR. Three Neural ODE models were trained to segment enhancing tumor (ET), tumor core (TC), and whole tumor (WT), respectively. The key MRI modalities with significant utilization by deep neural networks were identified based on ACC analysis. Segmentation results by deep neural networks using only the key MRI modalities were compared to those using all four MRI modalities in terms of Dice coefficient, accuracy, sensitivity, and specificity.

RESULTS

All Neural ODE models successfully illustrated image dynamics as expected. ACC analysis identified T1-Ce as the only key modality in ET and TC segmentations, while both FLAIR and T2 were key modalities in WT segmentation. Compared to the U-Net results using all four MRI modalities, the Dice coefficient of ET (0.784→0.775), TC (0.760→0.758), and WT (0.841→0.837) using the key modalities only had minimal differences without significance. Accuracy, sensitivity, and specificity results demonstrated the same patterns.

CONCLUSION

The Neural ODE model offers a new tool for optimizing the deep learning model inputs with enhanced explainability. The presented methodology can be generalized to other medical image-related deep-learning applications.