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.

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.

Decision making for health-related research outcomes that alter diagnosis: A model from paediatric brain tumours.

AIMS: The question of how to handle clinically actionable outcomes from retrospective research studies is poorly explored. In neuropathology, this problem is exacerbated by ongoing refinement in tumour classification. We sought to establish a disclosure threshold for potential revised diagnoses as determined by the neuro-oncology speciality. METHODS: As part of a previous research study, the diagnoses of 73 archival paediatric brain tumour samples were reclassified according to the WHO 2016 guidelines. To determine the disclosure threshold and clinical actionability of pathology-related findings, we conducted a result-evaluation approach within the ethical framework of BRAIN UK using a surrogate clinical multidisciplinary team (MDT) of neuro-oncology specialists. RESULTS: The MDT identified key determinants impacting decision-making, including anticipated changes to patient management, time elapsed since initial diagnosis, likelihood of the patient being alive and absence of additional samples since cohort inception. Ultimately, none of our research findings were considered clinically actionable, largely due to the cohort's historic archival and high-risk nature. From this experience, we developed a decision-making framework to determine if research findings indicating a change in diagnosis require reporting to the relevant clinical teams. CONCLUSIONS: Ethical issues relating to the use of archival tissue for research and the potential to identify actionable findings must be carefully considered. We have established a structured framework to assess the actionability of research data relating to patient diagnosis. While our specific findings are most applicable to the pathology of poor prognostic brain tumour groups in children, the model can be adapted to a range of disease settings, for example, other diseases where research is dependent on retrospective tissue cohorts, and research findings may have implications for patients and families, such as other tumour types, epilepsy-related pathology, genetic disorders and degenerative diseases.

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.

Diagnosis of pediatric central nervous system tumors using methylation profiling of cfDNA from cerebrospinal fluid.

Pediatric central nervous system tumors remain challenging to diagnose. Imaging approaches do not provide sufficient detail to discriminate between different tumor types, while the histopathological examination of tumor tissue shows high inter-observer variability. Recent studies have demonstrated the accurate classification of central nervous system tumors based on the DNA methylation profile of a tumor biopsy. However, a brain biopsy holds significant risk of bleeding and damaging the surrounding tissues. Liquid biopsy approaches analyzing circulating tumor DNA show high potential as an alternative and less invasive tool to study the DNA methylation pattern of tumors. Here, we explore the potential of classifying pediatric brain tumors based on methylation profiling of the circulating cell-free DNA (cfDNA) in cerebrospinal fluid (CSF). For this proof-of-concept study, we collected cerebrospinal fluid samples from 19 pediatric brain cancer patients via a ventricular drain placed for reasons of increased intracranial pressure. Analyses on the cfDNA showed high variability of cfDNA quantities across patients ranging from levels below the limit of quantification to 40 ng cfDNA per milliliter of CSF. Classification based on methylation profiling of cfDNA from CSF was correct for 7 out of 20 samples in our cohort. Accurate results were mostly observed in samples of high quality, more specifically those with limited high molecular weight DNA contamination. Interestingly, we show that centrifugation of the CSF prior to processing increases the fraction of fragmented cfDNA to high molecular weight DNA. In addition, classification was mostly correct for samples with high tumoral cfDNA fraction as estimated by computational deconvolution (> 40%). In summary, analysis of cfDNA in the CSF shows potential as a tool for diagnosing pediatric nervous system tumors especially in patients with high levels of tumoral cfDNA in the CSF. Further optimization of the collection procedure, experimental workflow and bioinformatic approach is required to also allow classification for patients with low tumoral fractions in the CSF.

Oligodendroglioma: a neurological perspective in Sub-saharan Africa.

Gliomas are a kind of brain cancer that develops from glial cells. Glial cells provide nourishment and energy to nerve cells, and they also preserve the blood-brain barrier. A primary cancer of the central nervous system (CNS) is oligodendroglioma. This suggests that it originates in the brain or spinal cord. While oligodendrogliomas can strike anyone at any age, the age range of 35 to 44 is when they most commonly occur. Oligodendrogliomas are rare in young people and more common in men than women. Based on anecdotal data, patients with oligodendroglioma may present management challenges in Africa. There are delays in diagnosis and referrals due to the scarcity of neuroimaging facilities. A wide range of strategies have been put forth to improve pathology services in low- and middle-income nations. Adequate mentorship, short-term visitor programs, overcoming supply chain constraints, establishing training standards, and establishing the role of pathologists in cancer screening and early diagnosis have all been proposed as solutions to this problem. To sum up, oligodendroglioma is one of the low-grade gliomas this study looked at. Brain cancer is a serious public health concern in Africa. Improved options for screening and therapy are required to better address this problem.

Extraction of Unstructured Electronic Health Records to Evaluate Glioblastoma Treatment Patterns.

PURPOSE: Data on lines of therapy (LOTs) for cancer treatment are important for clinical oncology research, but LOTs are not explicitly recorded in electronic health records (EHRs). We present an efficient approach for clinical data abstraction and a flexible algorithm to derive LOTs from EHR-based medication data on patients with glioblastoma multiforme (GBM). METHODS: Nonclinicians were trained to abstract the diagnosis of GBM from EHRs, and their accuracy was compared with abstraction performed by clinicians. The resulting data were used to build a cohort of patients with confirmed GBM diagnosis. An algorithm was developed to derive LOTs using structured medication data, accounting for the addition and discontinuation of therapies and drug class. Descriptive statistics were calculated and time-to-next-treatment (TTNT) analysis was performed using the Kaplan-Meier method. RESULTS: Treating clinicians as the gold standard, nonclinicians abstracted GBM diagnosis with a sensitivity of 0.98, specificity 1.00, positive predictive value 1.00, and negative predictive value 0.90, suggesting that nonclinician abstraction of GBM diagnosis was comparable with clinician abstraction. Of 693 patients with a confirmed diagnosis of GBM, 246 patients contained structured information about the types of medications received. Of them, 165 (67.1%) received a first-line therapy (1L) of temozolomide, and the median TTNT from the start of 1L was 179 days. CONCLUSION: We described a workflow for extracting diagnosis of GBM and LOT from EHR data that combines nonclinician abstraction with algorithmic processing, demonstrating comparable accuracy with clinician abstraction and highlighting the potential for scalable and efficient EHR-based oncology research.

11C-methionine in the diagnostics and management of glioblastoma patients with rapid early progression: nonrandomized, open label, prospective clinical trial (GlioMET).

BACKGROUND: Glioblastoma (GBM) is the most common and aggressive primary brain cancer. The treatment of GBM consists of a combination of surgery and subsequent oncological therapy, i.e., radiotherapy, chemotherapy, or their combination. If postoperative oncological therapy involves irradiation, magnetic resonance imaging (MRI) is used for radiotherapy treatment planning. Unfortunately, in some cases, a very early worsening (progression) or return (recurrence) of the disease is observed several weeks after the surgery and is called rapid early progression (REP). Radiotherapy planning is currently based on MRI for target volumes definitions in many radiotherapy facilities. However, patients with REP may benefit from targeting radiotherapy with other imaging modalities. The purpose of the presented clinical trial is to evaluate the utility of 11C-methionine in optimizing radiotherapy for glioblastoma patients with REP. METHODS: This study is a nonrandomized, open-label, parallel-setting, prospective, monocentric clinical trial. The main aim of this study was to refine the diagnosis in patients with GBM with REP and to optimize subsequent radiotherapy planning. Glioblastoma patients who develop REP within approximately 6 weeks after surgery will undergo 11C-methionine positron emission tomography (PET/CT) examinations. Target volumes for radiotherapy are defined using both standard planning T1-weighted contrast-enhanced MRI and PET/CT. The primary outcome is progression-free survival defined using RANO criteria and compared to a historical cohort with REP treated without PET/CT optimization of radiotherapy. DISCUSSION: PET is one of the most modern methods of molecular imaging. 11C-Methionine is an example of a radiolabelled (carbon 11) amino acid commonly used in the diagnosis of brain tumors and in the evaluation of response to treatment. Optimized radiotherapy may also have the potential to cover those regions with a high risk of subsequent progression, which would not be identified using standard-of-care MRI for radiotherapy planning. This is one of the first study focused on radiotherapy optimization for subgroup of patinets with REP. TRIAL REGISTRATION: NCT05608395, registered on 8.11.2022 in clinicaltrials.gov; EudraCT Number: 2020-000640-64, registered on 26.5.2020 in clinicaltrialsregister.eu. Protocol ID: MOU-2020-01, version 3.2, date 18.09.2020.

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.

The clinical, molecular, and therapeutic implications of time from primary diagnosis to brain metastasis in lung and breast cancer patients.

PURPOSE: Lung cancer (LC) and breast cancer (BC) are the most common causes of brain metastases (BMs). Time from primary diagnosis to BM (TPDBM) refers to the time interval between initial LC or BC diagnosis and development of BM. This research aims to identify clinical, molecular, and therapeutic risk factors associated with shorter TPDBM. METHODS: We retrospectively reviewed all diagnosed LC and BC patients with BM at Harbin Medical University Cancer Hospital from 2016 to 2020. A total of 570 patients with LC brain metastasis (LCBM) and 173 patients with breast cancer brain metastasis (BCBM) patients who met the inclusion criteria were enrolled for further analysis. BM free survival time curves were generated using Kaplan-Meier analyses. Univariate and multivariate Cox regression analyses were applied to identify risk factors associated with earlier development of BM in LC and BC, respectively. RESULTS: The median TPDBM was 5.3 months in LC and 44.4 months in BC. In multivariate analysis, clinical stage IV and M1 stage were independent risk factors for early development of LCBM. LC patients who received chemotherapy, targeted therapy, pulmonary radiotherapy, and pulmonary surgery had longer TPDBM. For BC patients, age ≥ 50 years, Ki67 ≥ 0.3, HER2 positive or triple-negative breast cancer subtype, advanced N stage, and no mastectomy were correlated with shorter TPDBM. CONCLUSIONS: This single-institutional study helps identify patients who have a high risk of developing BM early. For these patients, early detection and intervention could have clinical benefits.

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.

In situ brain tumor detection using a Raman spectroscopy system-results of a multicenter study.

Safe and effective brain tumor surgery aims to remove tumor tissue, not non-tumoral brain. This is a challenge since tumor cells are often not visually distinguishable from peritumoral brain during surgery. To address this, we conducted a multicenter study testing whether the Sentry System could distinguish the three most common types of brain tumors from brain tissue in a label-free manner. The Sentry System is a new real time, in situ brain tumor detection device that merges Raman spectroscopy with machine learning tissue classifiers. Nine hundred and seventy-six in situ spectroscopy measurements and colocalized tissue specimens were acquired from 67 patients undergoing surgery for glioblastoma, brain metastases, or meningioma to assess tumor classification. The device achieved diagnostic accuracies of 91% for glioblastoma, 97% for brain metastases, and 96% for meningiomas. These data show that the Sentry System discriminated tumor containing tissue from non-tumoral brain in real time and prior to resection.

An anion exchange membrane sensor detects EGFR and its activity state in plasma CD63 extracellular vesicles from patients with glioblastoma.

We present a quantitative sandwich immunoassay for CD63 Extracellular Vesicles (EVs) and a constituent surface cargo, EGFR and its activity state, that provides a sensitive, selective, fluorophore-free and rapid alternative to current EV-based diagnostic methods. Our sensing design utilizes a charge-gating strategy, with a hydrophilic anion exchange membrane functionalized with capture antibodies and a charged silica nanoparticle reporter functionalized with detection antibodies. With sensitivity and robustness enhancement by the ion-depletion action of the membrane, this hydrophilic design with charged reporters minimizes interference from dispersed proteins, thus enabling direct plasma analysis without the need for EV isolation or sensor blocking. With a LOD of 30 EVs/µL and a high relative sensitivity of 0.01% for targeted proteomic subfractions, our assay enables accurate quantification of the EV marker, CD63, with colocalized EGFR by an operator/sample insensitive universal normalized calibration. We analysed untreated clinical samples of Glioblastoma to demonstrate this new platform. Notably, we target both total and "active" EGFR on EVs; with a monoclonal antibody mAb806 that recognizes a normally hidden epitope on overexpressed or mutant variant III EGFR. Analysis of samples yielded an area-under-the-curve (AUC) value of 0.99 and a low p-value of 0.000033, surpassing the performance of existing assays and markers.

Unlocking Glioblastoma Vulnerabilities with CRISPR-Based Genetic Screening.

Glioblastoma (GBM) is the most common malignant brain tumor in adults. Despite advancements in treatment, the prognosis for patients with GBM remains poor due to its aggressive nature and resistance to therapy. CRISPR-based genetic screening has emerged as a powerful tool for identifying genes crucial for tumor progression and treatment resistance, offering promising targets for tumor therapy. In this review, we provide an overview of the recent advancements in CRISPR-based genetic screening approaches and their applications in GBM. We highlight how these approaches have been used to uncover the genetic determinants of GBM progression and responsiveness to various therapies. Furthermore, we discuss the ongoing challenges and future directions of CRISPR-based screening methods in advancing GBM research.

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.

[Box: see text].

Extracavitary primary effusion lymphoma presenting as a solitary brain mass.

Primary effusion lymphoma (PEL) is an uncommon B-cell lymphoma associated with human herpesvirus 8 and comprises 3-4% of all HIV-related lymphomas. It traditionally presents as a pleural, pericardial, and/or peritoneal effusion, though it can occasionally manifest as an extracavitary or solid mass in the absence of an effusion. The extracavitary or solid variant of primary effusion lymphoma has been reported in the skin, gastrointestinal tract, lung, and lymph nodes. However, very few cases have been reported in the central nervous system. We describe a case of extracavitary or solid variant of primary effusion lymphoma presenting as a brain mass in an HIV-positive man, highlighting the clinicopathologic and immunophenotypic findings of a rare entity.

Primary effusion lymphoma (PEL) is an uncommon and aggressive form of large B-cell lymphoma with a grim outlook, making up less than 1% of all lymphomas. PEL is linked to human herpesvirus 8 and predominantly impacts individuals with HIV or weakened immune systems. The typical presentation of PEL involves cancerous fluid accumulating in the chest or abdominal cavities. Occasionally, PEL can appear as a solid mass outside these cavities, termed extracavitary PEL (EC-PEL). The case we are describing highlights the difficulties in diagnosing PEL/EC-PEL. It is crucial for healthcare providers to consider EC-PEL when dealing with human herpesvirus 8-positive B-cell lymphomas, especially when patients have weakened immune systems and an unusual clinical scenario involving a solid mass, as seen in this case.

Blood Neurofilament Light Chain and Glial Fibrillary Acidic Protein as Promising Screening Biomarkers for Brain Metastases in Patients with Lung Cancer.

The diagnosis of brain metastases (BMs) in patients with lung cancer (LC) predominantly relies on magnetic resonance imaging (MRI), a method that is constrained by high costs and limited accessibility. This study explores the potential of serum neurofilament light chain (sNfL) and serum glial fibrillary acidic protein (sGFAP) as screening biomarkers for BMs in LC patients. We conducted a retrospective analysis of 700 LC cases at the National Cancer Center, Korea, from July 2020 to June 2022, measuring sNfL and sGFAP levels at initial LC diagnosis. The likelihood of BM was evaluated using multivariate analysis and a predictive nomogram. Additionally, we prospectively monitored 177 samples from 46 LC patients initially without BM. Patients with BMs (n= 135) had significantly higher median sNfL (52.5 pg/mL) and sGFAP (239.2 pg/mL) levels compared to those without BMs (n = 565), with medians of 17.8 pg/mL and 141.1 pg/mL, respectively (p < 0.001 for both). The nomogram, incorporating age, sNfL, and sGFAP, predicted BM with an area under the curve (AUC) of 0.877 (95% CI 0.84-0.914), showing 74.8% sensitivity and 83.5% specificity. Over nine months, 93% of samples from patients without BM remained below the cutoff, while all patients developing BMs showed increased levels at detection. A nomogram incorporating age, sNfL, and sGFAP provides a valuable tool for identifying LC patients at high risk for BM, thereby enabling targeted MRI screenings and enhancing diagnostic efficiency.

Glioma.

Gliomas are primary brain tumours that are thought to develop from neural stem or progenitor cells that carry tumour-initiating genetic alterations. Based on microscopic appearance and molecular characteristics, they are classified according to the WHO classification of central nervous system (CNS) tumours and graded into CNS WHO grades 1-4 from a low to high grade of malignancy. Diffusely infiltrating gliomas in adults comprise three tumour types with distinct natural course of disease, response to treatment and outcome: isocitrate dehydrogenase (IDH)-mutant and 1p/19q-codeleted oligodendrogliomas with the best prognosis; IDH-mutant astrocytomas with intermediate outcome; and IDH-wild-type glioblastomas with poor prognosis. Pilocytic astrocytoma is the most common glioma in children and is characterized by circumscribed growth, frequent BRAF alterations and favourable prognosis. Diffuse gliomas in children are divided into clinically indolent low-grade tumours and high-grade tumours with aggressive behaviour, with histone 3 K27-altered diffuse midline glioma being the leading cause of glioma-related death in children. Ependymal tumours are subdivided into biologically and prognostically distinct types on the basis of histology, molecular biomarkers and location. Although surgery, radiotherapy and alkylating agent chemotherapy are the mainstay of glioma treatment, individually tailored strategies based on tumour-intrinsic dominant signalling pathways have improved outcome in subsets of patients.