Blood based metabolic markers of glioma from pre-diagnosis to surgery.

Gliomas are highly complex and metabolically active brain tumors associated with poor prognosis. Recent reports have found altered levels of blood metabolites during early tumor development, suggesting that tumor development could be detected several years before clinical manifestation. In this study, we performed metabolite analyses of blood samples collected from healthy controls and future glioma patients, up to eight years before glioma diagnosis, and on the day of glioma surgery. We discovered that metabolites related to early glioma development were associated with an increased energy turnover, as highlighted by elevated levels of TCA-related metabolites such as fumarate, malate, lactate and pyruvate in pre-diagnostic cases. We also found that metabolites related to glioma progression at surgery were primarily high levels of amino acids and metabolites of amino acid catabolism, with elevated levels of 11 amino acids and two branched-chain alpha-ketoacids, ketoleucine and ketoisoleucine. High amino acid turnover in glioma tumor tissue is currently utilized for PET imaging, diagnosis and delineation of tumor margins. By examining blood-based metabolic progression patterns towards disease onset, we demonstrate that this high amino acid turnover is also detectable in a simple blood sample. These findings provide additional insight of metabolic alterations during glioma development and progression.

Consensus guidelines for the management of adult high-grade gliomas for low and middle-income countries.

High-grade glioma (HGG), a formidable and often incurable disease, presents an even greater challenge in low- and middle-income countries (LMICs) where resources and medical expertise are scarce. This scarcity not only exacerbates the suffering of patients but also contributes to poorer clinical outcomes. Particularly in LMICs, the underrepresentation of the population in clinical trials and the additional hurdles posed by financial constraints underscore an urgent need for contextspecific management strategies. In response, we have rigorously evaluated recent guidelines from leading medical societies, adapting them to suit the specific needs and limitations of the local context in Pakistan. This effort, undertaken in collaboration with local physicians, aims to provide a comprehensive, standardised approach to diagnose, treat, and follow-up with HGG patients. By focussing on the best available clinical evidence and judicious use of limited resources, we strive to improve patient care and outcomes in these challenging settings.

Consensus guidelines for the management of adult low-grade gliomas for low and middle-income countries.

Low-grade gliomas (LGG) are brain tumors of glial cells origin. They are grade 1 and grade 2 tumors according to the WHO classification. Diagnosis of LGG is made through imaging, histopathological analysis, and use of molecular markers. Imaging alone does not establish the grade of the tumor and thus a histopathological examination of tissue is crucial in establishing the definite histopathological diagnosis. Clinical presentation varies according to the location and size of the tumor. Surgical resection is strongly recommended in LGG over observation to improve overall survival as surgery leads to greater benefit due to progression-free survival. Radiation has shown benefits in LGG patients in randomized controlled trials and chemotherapy with temozolomide has also shown good results. This paper covers the principles of low-grade gliomas management and summarizes the recommendations for the LMICs.

The Prognostic Value of Preoperative Inflammatory Markers for Pathological Grading of Glioma Patients.

Introduction: The independent diagnostic value of inflammatory markers neutrophil to lymphocyte ratio (NLR) and platelet to lymphocyte ratio (PLR) and the diagnostic efficacy of NLR, derived neutrophil to lymphocyte ratio (dNLR), PLR, and lymphocyte-to-monocyte ratio (LMR) in glioma cases remain unclear. We investigated the correlation of preoperative peripheral blood inflammatory markers with pathological grade, Ki-67 Proliferation Index, and IDH-1 gene phenotype in patients with glioma, focusing on tumor grade and prognosis. Methods: We retrospectively analyzed the clinical, pathological, and laboratory data of 334 patients with glioma with varying grades and 345 with World Health Organization (WHO I) meningioma who underwent initial surgery at the Affiliated Hospital of Jining Medical University from December 2019 to December 2021. The diagnostic value of peripheral blood inflammatory markers for glioma was investigated. Results: The proportion of men smoking and drinking was significantly higher in the glioma group than in the meningioma group (P < .05); in contrast, the age and body mass index (Kg/m2) were significantly lower in the glioma group (P = .01). Significant differences were noted in the pathological grade (WHO II, III, and IV), Ki-67 Proliferation Index, and peripheral blood inflammatory markers such as lymphocyte median, NLR, dNLR, and PLR between the groups (P < .05). No significant correlation existed between peripheral blood inflammatory factors and IDH-1 gene mutation status or tumor location in patients with glioma (P > .05). LMR, NLR, dNLR, and PLR, varied significantly among different glioma types (P < .05). White blood cell (WBC) count, neutrophil, NLR, and dNLR correlated positively with glioma risk. Further, WBC, neutrophil, NLR, dNLR, and LMR had a high diagnostic efficiency. Conclusion: Peripheral blood inflammatory markers, serving as noninvasive biomarkers, offer high sensitivity and specificity for diagnosing glioma, differentiating it from meningioma, diagnosing GBM, and distinguishing GBM from low-grade glioma. These markers may be implemented as routine screening tools.

Clinical utility of a blood based assay for the detection of IDH1.R132H-mutant gliomas.

Glioma represents the most common central nervous system neoplasm in adults. Current classification scheme utilizes molecular alterations, particularly IDH1.R132H, to stratify lesions into distinct prognostic groups. Identification of the single nucleotide variant through traditional tissue biopsy assessment poses procedural risks and does not fully reflect the heterogeneous and evolving tumor landscape. Here, we introduce a liquid biopsy assay, mt-IDH1dx. The blood-based test allows minimally invasive detection of tumor-derived extracellular vesicle RNA using only 2 ml plasma volume. We perform rigorous, blinded validation testing across the study population (n = 133), comprising of IDH1.R132H patients (n = 80), IDH1 wild-type gliomas (n = 44), and age matched healthy controls (n = 9). Results from our plasma testing demonstrate an overall sensitivity of 75.0% (95% CI: 64.1%-84.0%), specificity 88.7% (95% CI: 77.0%-95.7%), positive predictive value 90.9%, and negative predictive value 70.1% compared to the tissue gold standard. In addition to fundamental diagnostic applications, the study also highlights the utility of mt-IDH1dx platform for blood-based monitoring and surveillance, offering valuable prognostic information. Finally, the optimized workflow enables rapid and efficient completion of both tumor tissue and plasma testing in under 4 hours from the time of sampling.

Unraveling morphology, methylation profiling, and diagnostic challenges in BRAF-Mutant pediatric glial and glioneuronal tumors.

OBJECTIVES: To elucidate the relationship between DNA methylation profiling (DMP) and pathological diagnosis (PD) in pediatric glial and glioneuronal tumors with B-Raf proto-oncogene, serine/threonine kinase (BRAF) mutations, addressing their diagnostic challenges. METHODS: This retrospective study, conducted in Saudi Arabia, analyzed 47 cases from the Children's Brain Tumor Network online database using scanned images, next-generation sequencing data, and methylation profiles processed using the Heidelberg methylation brain tumor classifiers v12.5 and v12.8. The data was last access on 10 November 2023. RESULTS: The highest prevalence of BRAF mutations was observed in pilocytic astrocytoma and ganglioglioma. The DMP was consistent with PD in 23 cases, but discrepancies emerged in others, including diagnostic changes in diffuse leptomeningeal glioneuronal tumor and polymorphous low-grade neuroepithelial tumor of the young. A key inconsistency appeared between a pilocytic astrocytoma MC and a glioneuronal tumor PD. Two high-grade astrocytomas were misclassified as pleomorphic xanthoastrocytomas. Additionally, low variant allelic frequency in gangliogliomas likely contributed to misclassifications as control in 5 cases. CONCLUSION: This study emphasized the importance of integrating DMP with PD in diagnosing pediatric glial and glioneuronal tumors with BRAF mutations. Although DMP offers significant diagnostic insights, its limitations, particularly in cases with low tumor content, necessitate cautious interpretation, as well as its use as a complementary diagnostic tool, rather than a definitive method.

TIM-3/CD68 double-high expression in Glioma: Prognostic characteristics and potential therapeutic approaches.

BACKGROUND: Immunotherapy has revolutionized the treatment of various types of tumors, but there has been no breakthrough in the treatment of gliomas. The aim of this study is to discover valuable immunotherapy target in glioma, analyze its expression in glioma and the related microenvironment, explore potential immunotherapy strategies, and propose new possibilities for the treatment of gliomas. METHODS: Immunohistochemistry (IHC) and multiplex fluorescence immunohistochemistry (mIHC) were used to analyze the expression of common immune markers and checkpoints in 187 glioma patients from Sun Yat-sen University Caner Center (SYSUCC). Bioinformatics analysis was used to examine the expression of TIM-3 in different macrophages using the Chinese Glioma Genome Atlas (CGGA) single-cell sequencing database. The Kaplan-Meier curve was used to predict the prognostic value of samples with high TIM-3 and CD68 expression. The R package was used to analyze the somatic mutation status and the sensitivity of small molecule inhibitors in TIM-3/CD68 double-high expression samples. RESULTS: TIM-3 is a relatively highly expressed immune checkpoint in glioma. Unlike other tumors, TIM-3 is mainly expressed on macrophages in the glioma microenvironment. TIM-3/CD68 double-high expression suggests poor survival in glioma and may be a new upgrade marker in both IDH-mutant glioma and IDH-wildtype low-grade glioma (LGG) glioma (P < 0.01). Exploring the combination of TIM-3 inhibitors and p38 MAPK inhibitor may be a potential treatment direction for TIM-3/CD68 double high expression gliomas in the future. CONCLUSIONS: The combination of TIM-3 and CD68 holds significant importance as a potential target for both prognosis and therapeutic intervention in glioma.

Streamlined Intraoperative Brain Tumor Classification and Molecular Subtyping in Stereotactic Biopsies Using Stimulated Raman Histology and Deep Learning.

PURPOSE: Recent artificial intelligence algorithms aided intraoperative decision-making via stimulated Raman histology (SRH) during craniotomy. This study assesses deep learning algorithms for rapid intraoperative diagnosis from SRH images in small stereotactic-guided brain biopsies. It defines a minimum tissue sample size threshold to ensure diagnostic accuracy. EXPERIMENTAL DESIGN: A prospective single-center study examined 121 SRH images from 84 patients with unclear intracranial lesions undergoing stereotactic brain biopsy. Unprocessed, label-free samples were imaged using a portable fiber laser Raman scattering microscope. Three deep learning models were tested to (i) identify tumorous/nontumorous tissue as qualitative biopsy control; (ii) subclassify into high-grade glioma (central nervous system World Health Organization grade 4), diffuse low-grade glioma (central nervous system World Health Organization grades 2-3), metastases, lymphoma, or gliosis; and (iii) molecularly subtype IDH and 1p/19q statuses of adult-type diffuse gliomas. Model predictions were evaluated against frozen section analysis and final neuropathologic diagnoses. RESULTS: The first model identified tumorous/nontumorous tissue with 91.7% accuracy. Sample size on slides impacted accuracy in brain tumor subclassification (81.6%, κ = 0.72 frozen section; 73.9%, κ = 0.61 second model), with SRH images being smaller than hematoxylin and eosin images (4.1 ± 2.5 mm2 vs. 16.7 ± 8.2 mm2, P < 0.001). SRH images with more than 140 high-quality patches and a mean squeezed sample of 5.26 mm2 yielded 89.5% accuracy in subclassification and 93.9% in molecular subtyping of adult-type diffuse gliomas. CONCLUSIONS: Artificial intelligence-based SRH image analysis is non-inferior to frozen section analysis in detecting and subclassifying brain tumors during small stereotactic-guided biopsies once a critical squeezed sample size is reached. Beyond frozen section analysis, it enables valid molecular glioma subtyping, allowing faster treatment decisions in the future; however, refinement is needed for long-term application.

Role of Machine Learning in Liquid Biopsy of Brain Tumours.

Liquid biopsy has multiple benefits and is used extensively in other fields of oncology, but its role in neuro-oncology has been limited so far. Multiple tumour-derived materials like circulating tumour cells (CTCs), tumour-educated platelets (TEPs), cell-free DNA (cfDNA), circulating tumour DNA (ctDNA), and miRNA are studied in CSF, blood (plasma, serum) or urine. Large and complex amounts of data from liquid biopsy can be simplified by machine learning using various algorithms. By using this technique, we can diagnose brain tumours and differentiate low versus highgrade glioma and true progression from pseudo-progression. The potential of liquid biopsy in brain tumours has not been extensively studied, but it has a bright future in the coming years. Here, we present a literature review on the role of machine learning in liquid biopsy of brain tumours.

Brain gliomas: Diagnostic and therapeutic issues and the prospects of drug-targeted nano-delivery technology.

Glioma is the most common intracranial malignant tumor, with severe difficulty in treatment and a low patient survival rate. Due to the heterogeneity and invasiveness of tumors, lack of personalized clinical treatment design, and physiological barriers, it is often difficult to accurately distinguish gliomas, which dramatically affects the subsequent diagnosis, imaging treatment, and prognosis. Fortunately, nano-delivery systems have demonstrated unprecedented capabilities in diagnosing and treating gliomas in recent years. They have been modified and surface modified to efficiently traverse BBB/BBTB, target lesion sites, and intelligently release therapeutic or contrast agents, thereby achieving precise imaging and treatment. In this review, we focus on nano-delivery systems. Firstly, we provide an overview of the standard and emerging diagnostic and treatment technologies for glioma in clinical practice. After induction and analysis, we focus on summarizing the delivery methods of drug delivery systems, the design of nanoparticles, and their new advances in glioma imaging and treatment in recent years. Finally, we discussed the prospects and potential challenges of drug-delivery systems in diagnosing and treating glioma.

Biomarkers in Cerebrospinal Fluid for the Diagnosis and Monitoring of Gliomas.

Gliomas are the most common type of malignant brain tumor and are characterized by a plethora of heterogeneous molecular alterations. Current treatments require the emergence of reliable biomarkers that will aid personalized treatment decisions and increase life expectancy. Glioma tissues are not as easily accessible as other solid tumors; therefore, detecting prominent biomarkers in biological fluids is necessary. Cerebrospinal fluid (CSF) circulates adjacent to the cerebral parenchyma and holds promise for discovering useful prognostic, diagnostic, and predictive biomarkers. In this review, we summarize extensive research regarding the role of circulating DNA, tumor cells, proteins, microRNAs, metabolites, and extracellular vesicles as potential CSF biomarkers for glioma diagnosis, prognosis, and monitoring. Future studies should address discrepancies and issues of specificity regarding CSF biomarkers, as well as the validation of candidate biomarkers.

Comprehensive application of AI algorithms with TCR NGS data for glioma diagnosis.

T-cell receptor (TCR) detection can examine the extent of T-cell immune responses. Therefore, the article analyzed characteristic data of glioma obtained by DNA-based TCR high-throughput sequencing, to predict the disease with fewer biomarkers and higher accuracy. We downloaded data online and obtained six TCR-related diversity indices to establish a multidimensional classification system. By comparing actual presence of the 602 correlated sequences, we obtained two-dimensional and multidimensional datasets. Multiple classification methods were utilized for both datasets with the classification accuracy of multidimensional data slightly less to two-dimensional datasets. This study reduced the TCR ß sequences through feature selection methods like RFECV (Recursive Feature Elimination with Cross-Validation). Consequently, using only the presence of these three sequences, the classification AUC value of 96.67% can be achieved. The combination of the three correlated TCR clones obtained at a source data threshold of 0.1 is: CASSLGGNTEAFF_TRBV12_TRBJ1-1, CASSYSDTGELFF_TRBV6_TRBJ2-2, and CASSLTGNTEAFF_TRBV12_TRBJ1-1. At 0.001, the combination is: CASSLGETQYF_TRBV12_TRBJ2-5, CASSLGGNQPQHF_TRBV12_TRBJ1-5, and CASSLSGNTIYF_TRBV12_TRBJ1-3. This method can serve as a potential diagnostic and therapeutic tool, facilitating diagnosis and treatment of glioma and other cancers.

Prognostic value of C-reactive protein in patients with glioma: a meta-analysis.

Background: In this meta-analysis, we investigated C-reactive protein (CRP)'s role in glioma prognosis prediction.Methods: We conducted the current meta-analysis in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines for systematic reviews and meta-analyses. An analysis of the prognostic effect of CRP on glioma was conducted using combined hazard ratios (HRs) and 95% confidence intervals (CIs).Results: The combined data suggested that a higher CRP level was markedly related to poor overall survival in glioma (hazard ratio: 1.73; 95% CI: 1.22-2.46; p = 0.002).Conclusion: According to results in the present meta-analysis, elevated CRP levels were significantly related to inferior overall survival in glioma.

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Intraoperative rapid molecular diagnosis aids glioma subtyping and guides precise surgical resection.

OBJECTIVE: The molecular era of glioma diagnosis and treatment has arrived, and a single rapid histopathology is no longer sufficient for surgery. This study sought to present an automatic integrated gene detection system (AIGS), which enables rapid intraoperative detection of IDH/TERTp mutations. METHODS: A total of 78 patients with gliomas were included in this study. IDH/TERTp mutations were detected intraoperatively using AIGS in 41 of these patients, and they were guided to surgical resection (AIGS detection group). The remaining 37 underwent histopathology-guided conventional surgical resection (non-AIGS detection group). The clinical utility of this technique was evaluated by comparing the accuracy of glioma subtype diagnosis before and after TERTp mutation results were obtained by pathologists and the extent of resection (EOR) and patient prognosis for molecular pathology-guided glioma surgery. RESULTS: With NGS/Sanger sequencing and chromosome detection as the gold standard, the accuracy of AIGS results was 100%. And the timing was well matched to the intraoperative rapid pathology report. After obtaining the TERTp mutation detection results, the accuracy of the glioma subtype diagnosis made by the pathologists increased by 19.51%. Molecular pathology-guided surgical resection of gliomas significantly increased EOR (99.06% vs. 93.73%, p < 0.0001) and also improved median OS (26.77 vs. 13.47 months, p = 0.0289) and median PFS (15.90 vs. 10.57 months, p = 0.0181) in patients with glioblastoma. INTERPRETATION: Using AIGS intraoperatively to detect IDH/TERTp mutations to accurately diagnose glioma subtypes can help achieve maximum safe resection of gliomas, which in turn improves the survival prognosis of patients.

Breaking new ground: can artificial intelligence and machine learning transform papillary glioneuronal tumor diagnosis?

Papillary glioneuronal tumors (PGNTs), classified as Grade I by the WHO in 2016, present diagnostic challenges due to their rarity and potential for malignancy. Xiaodan Du et al.'s recent study of 36 confirmed PGNT cases provides critical insights into their imaging characteristics, revealing frequent presentation with headaches, seizures, and mass effect symptoms, predominantly located in the supratentorial region near the lateral ventricles. Lesions often appeared as mixed cystic and solid masses with septations or as cystic masses with mural nodules. Given these complexities, artificial intelligence (AI) and machine learning (ML) offer promising advancements for PGNT diagnosis. Previous studies have demonstrated AI's efficacy in diagnosing various brain tumors, utilizing deep learning and advanced imaging techniques for rapid and accurate identification. Implementing AI in PGNT diagnosis involves assembling comprehensive datasets, preprocessing data, extracting relevant features, and iteratively training models for optimal performance. Despite AI's potential, medical professionals must validate AI predictions, ensuring they complement rather than replace clinical expertise. This integration of AI and ML into PGNT diagnostics could significantly enhance preoperative accuracy, ultimately improving patient outcomes through more precise and timely interventions.

Serum beta2-microglobulin acts as a biomarker for severity and prognosis in glioma patients: a preliminary clinical study.

BACKGROUND: Gliomas are the deadliest malignant tumors of the adult central nervous system. We previously discovered that beta2-microglobulin (B2M) is abnormally upregulated in glioma tissues and that it exerts a range of oncogenic effects. Besides its tissue presence, serum B2M levels serve as biomarkers for various diseases. This study aimed to explore whether serum B2M levels can be used in the diagnosis and prognosis of gliomas. METHODS: Medical records from 246 glioma patients were retrospectively analyzed. The relationship between preoperative serum B2M levels and clinicopathological features was examined. Kaplan-Meier analysis, alongside uni- and multivariate Cox regression, assessed the association between B2M levels, systemic inflammatory markers, and glioma patient prognosis. Receiver operating characteristic (ROC) curve analysis evaluated the diagnostic significance of these biomarkers specifically for glioblastoma (GBM). RESULTS: Patients with malignant gliomas exhibited elevated preoperative serum B2M levels. Glioma patients with high serum B2M levels experienced shorter survival times. Multivariate Cox analysis determined the relationship between B2M levels (hazard ratio = 1.92, 95% confidence interval: 1.05-3.50, P = 0.034) and the overall survival of glioma patients. B2M demonstrated superior discriminatory power in distinguishing between GBM and non-GBM compared to inflammation indicators. Moreover, postoperative serum B2M levels were lower than preoperative levels in the majority of glioma patients. CONCLUSIONS: High preoperative serum B2M levels correlated with malignant glioma and a poor prognosis. Serum B2M shows promise as a novel biomarker for predicting patient prognosis and reflecting the therapeutic response.

Analysis of Metabolic Changes in Endogenous Metabolites and Diagnostic Biomarkers for Various Diseases Using Liquid Chromatography and Mass Spectrometry.

Analysis of endogenous metabolites in various diseases is useful for searching diagnostic biomarkers and elucidating the molecular mechanisms of pathophysiology. The author and collaborators have developed some LC/tandem mass spectrometry (LC/MS/MS) methods for metabolites and applied them to disease-related samples. First, we identified urinary conjugated cholesterol metabolites and serum N-palmitoyl-O-phosphocholine serine as useful biomarkers for Niemann-Pick disease type C (NPC). For the purpose of intraoperative diagnosis of glioma patients, we developed the LC/MS/MS analysis methods for 2-hydroxyglutaric acid or cystine and found that they could be good differential biomarkers. For renal cell carcinoma, we searched for various biomarkers for early diagnosis, malignancy evaluation and recurrence prediction by global metabolome analysis and targeted LC/MS/MS analysis. In pathological analysis, we developed a simultaneous LC/MS/MS analysis method for 13 steroid hormones and applied it to NPC cells, we found 6 types of reductions in NPC model cells. For non-alcoholic steatohepatitis (NASH), model mice were prepared with special diet and plasma bile acids were measured, and as a result, hydrophilic bile acids were significantly increased. In addition, we developed an LC/MS/MS method for 17 sterols and analyzed liver cholesterol metabolites and found a decrease in phytosterols and cholesterol synthetic markers and an increase in non-enzymatic oxidative sterols in the pre-onset stage of NASH. We will continue to challenge themselves to add value to clinical practice based on cutting-edge analytical chemistry methodology.

Novel insights toward diagnosis and treatment of glioneuronal and neuronal tumors in young adults.

Aim: Glioneuronal and neuronal tumors are rare primary central nervous system malignancies with heterogeneous features. Due to the rarity of these malignancies diagnosis and treatment remains a clinical challenge. Methods: Here we performed a narrative review aimed to investigate the principal issues concerning the diagnosis, pathology, and clinical management of glioneuronal tumors. Results: Diagnostic criteria have been recently overturned thanks to a better characterization on a histological and molecular biology level. The study of genomic alterations occurring within these tumors has allowed us to identify potential therapeutic targets including BRAF, FGFR, and PDGFRA. Conclusion: Techniques allowing molecular sequencing DNA methylation assessment of the disease are essential diagnostic tools. Targeting agents should be included in the therapeutic armamentarium after loco-regional treatment failure.

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The Clinical Utility of a Tiered Approach to Pediatric Glioma Molecular Characterization for Resource-Limited Settings.

PURPOSE: Molecular characterization is key to optimally diagnose and manage cancer. The complexity and cost of routine genomic analysis have unfortunately limited its use and denied many patients access to precision medicine. A possible solution is to rationalize use-creating a tiered approach to testing which uses inexpensive techniques for most patients and limits expensive testing to patients with the highest needs. Here, we tested the utility of this approach to molecularly characterize pediatric glioma in a cost- and time-sensitive manner. METHODS: We used a tiered testing pipeline of immunohistochemistry (IHC), customized fusion panels or fluorescence in situ hybridization (FISH), and targeted RNA sequencing in pediatric gliomas. Two distinct diagnostic algorithms were used for low- and high-grade gliomas (LGGs and HGGs). The percentage of driver alterations identified, associated testing costs, and turnaround time (TAT) are reported. RESULTS: The tiered approach successfully characterized 96% (95 of 99) of gliomas. For 82 LGGs, IHC, targeted fusion panel or FISH, and targeted RNA sequencing solved 35% (29 of 82), 29% (24 of 82), and 30% (25 of 82) of cases, respectively. A total of 64% (53 of 82) of samples were characterized without targeted RNA sequencing. Of 17 HGG samples, 13 were characterized by IHC and four were characterized by targeted RNA sequencing. The average cost per sample was more affordable when using the tiered approach as compared with up-front targeted RNA sequencing in LGG ($405 US dollars [USD] v $745 USD) and HGGs ($282 USD v $745 USD). The average TAT per sample was also shorter using the tiered approach (10 days for LGG, 5 days for HGG v 14 days for targeted RNA sequencing). CONCLUSION: Our tiered approach molecularly characterized 96% of samples in a cost- and time-sensitive manner. Such an approach may be feasible in neuro-oncology centers worldwide, particularly in resource-limited settings.