Potential biomarkers of childhood brain tumor identified by proteomics of cerebrospinal fluid from extraventricular drainage (EVD)

Tumor Dormancy and Reactivation: The Role of Heat Shock Proteins

Tumors are a heterogeneous group of cell masses originating in various organs or tissues. The cellular composition of the tumor cell mass interacts in an intricate manner, influenced by humoral, genetic, molecular, and tumor microenvironment cues that dictate tumor growth or suppression. As a result, tumors undergo a period of a dormant state before their clinically discernible stage, which surpasses the clinical dormancy threshold. Moreover, as a genetically imprinted strategy, early-seeder cells, a distinct population of tumor cells, break off to dock nearby or extravasate into blood vessels to secondary tissues, where they form disseminated solitary dormant tumor cells with reversible capacity. Among the various mechanisms underlying the dormant tumor mass and dormant tumor cell formation, heat shock proteins (HSPs) might play one of the most important roles in how the dormancy program plays out. It is known that numerous aberrant cellular processes, such as malignant transformation, cancer cell stemness, tumor invasion, metastasis, angiogenesis, and signaling pathway maintenance, are influenced by the HSPs. An accumulating body of knowledge suggests that HSPs may be involved in the angiogenic switch, immune editing, and extracellular matrix (ECM) remodeling cascades, crucial genetically imprinted strategies important to the tumor dormancy initiation and dormancy maintenance program. In this review, we highlight the biological events that orchestrate the dormancy state and the body of work that has been conducted on the dynamics of HSPs in a tumor mass, as well as tumor cell dormancy and reactivation. Additionally, we propose a conceptual framework that could possibly underlie dormant tumor reactivation in metastatic relapse.

Proteomic insights into paediatric cancer: Unravelling molecular signatures and therapeutic opportunities

Survival rates in some paediatric cancers have improved greatly over recent decades, in part due to the identification of diagnostic, prognostic and predictive molecular signatures, and the development of risk-directed therapies. However, other paediatric cancers have proved difficult to treat, and there is an urgent need to identify novel biomarkers that reveal therapeutic opportunities. The proteome is the total set of expressed proteins present in a cell or tissue at a point in time, and is vastly more dynamic than the genome. Proteomics holds significant promise for cancer research, as proteins are ultimately responsible for cellular phenotype and are the target of most anticancer drugs. Here, we review the discoveries, opportunities and challenges of proteomic analyses in paediatric cancer, with a focus on mass spectrometry (MS)-based approaches. Accelerating incorporation of proteomics into paediatric precision medicine has the potential to improve survival and quality of life for children with cancer.

Mass spectrometry-based proteomics of cerebrospinal fluid in pediatric central nervous system malignancies: a systematic review with meta-analysis of individual patient data

BACKGROUND

The cerebrospinal fluid (CSF) proteome could offer important insights into central nervous system (CNS) malignancies. To advance proteomic research in pediatric CNS cancer, the current study aims to (1) evaluate past mass spectrometry-based workflows and (2) synthesize previous CSF proteomic data, focusing on both qualitative summaries and quantitative re-analysis. MAIN: In our analysis of 11 studies investigating the CSF proteome in pediatric patients with acute lymphoblastic leukemia (ALL) or primary brain tumors, we observed significant methodological variability. This variability negatively affects comparative analysis of the included studies, as per GRADE criteria for quality of evidence. The qualitative summaries covered 161 patients and 134 non-tumor controls, while the application of validation cohort varied among the studies. The quantitative re-analysis comprised 15 B-ALL vs 6 "healthy" controls and 15 medulloblastoma patients vs 22 non-tumor controls. Certain CSF proteins were identified as potential indicators of specific malignancies or stages of neurotoxicity during chemotherapy, yet definitive conclusions were impeded by inconsistent data. There were no proteins with statistically significant differences when comparing cases versus controls that were corroborated across studies where quantitative reanalysis was feasible. From a gene ontology enrichment, we observed that age disparities between unmatched case and controls may mislead to protein correlations more indicative of age-related CNS developmental stages rather than neuro-oncological disease. Despite efforts to batch correct (HarmonizR) and impute missing values, merging of dataset proved unfeasible and thereby limited meaningful data integration across different studies.

CONCLUSION

Infrequent publications on rare pediatric cancer entities, which often involve small sample sizes, are inherently prone to result in heterogeneous studies-particularly when conducted within a rapidly evolving field like proteomics. As a result, obtaining clear evidence, such as CSF proteome biomarkers for CNS dissemination or early-stage neurotoxicity, is currently impractical. Our general recommendations comprise the need for standardized methodologies, collaborative efforts, and improved data sharing in pediatric CNS malignancy research. We specifically emphasize the possible importance of considering natural age-related variations in CSF due to different CNS development stages when matching cases and controls in future studies.

Recommendations for reproducibility of cerebrospinal fluid extracellular vesicle studies

Cerebrospinal fluid (CSF) is a clear, transparent fluid derived from blood plasma that protects the brain and spinal cord against mechanical shock, provides buoyancy, clears metabolic waste and transports extracellular components to remote sites in the brain. Given its contact with the brain and the spinal cord, CSF is the most informative biofluid for studies of the central nervous system (CNS). In addition to other components, CSF contains extracellular vesicles (EVs) that carry bioactive cargoes (e.g., lipids, nucleic acids, proteins), and that can have biological functions within and beyond the CNS. Thus, CSF EVs likely serve as both mediators of and contributors to communication in the CNS. Accordingly, their potential as biomarkers for CNS diseases has stimulated much excitement for and attention to CSF EV research. However, studies on CSF EVs present unique challenges relative to EV studies in other biofluids, including the invasive nature of CSF collection, limited CSF volumes and the low numbers of EVs in CSF as compared to plasma. Here, the objectives of the International Society for Extracellular Vesicles CSF Task Force are to promote the reproducibility of CSF EV studies by providing current reporting and best practices, and recommendations and reporting guidelines, for CSF EV studies. To accomplish this, we created and distributed a world-wide survey to ISEV members to assess methods considered 'best practices' for CSF EVs, then performed a detailed literature review for CSF EV publications that was used to curate methods and resources. Based on responses to the survey and curated information from publications, the CSF Task Force herein provides recommendations and reporting guidelines to promote the reproducibility of CSF EV studies in seven domains: (i) CSF Collection, Processing, and Storage; (ii) CSF EV Separation/Concentration; (iii) CSF EV Size and Number Measurements; (iv) CSF EV Protein Studies; (v) CSF EV RNA Studies; (vi) CSF EV Omics Studies and (vii) CSF EV Functional Studies.

Identification of Central Nervous System Oncologic Disease Biomarkers in EVs from Cerebrospinal Fluid (CSF) of Pediatric Patients: A Pilot Neuro-Proteomic Study

Cerebrospinal fluid (CSF) is a biochemical-clinical window into the brain. Unfortunately, its wide dynamic range, low protein concentration, and small sample quantity significantly limit the possibility of using it routinely. Extraventricular drainage (EVD) of CSF allows us to solve quantitative problems and to study the biological role of extracellular vesicles (EVs). In this study, we implemented bioinformatic analysis of our previous data of EVD of CSF and its EVs obtained from congenital hydrocephalus with the aim of identifying a comprehensive list of potential tumor and non-tumor biomarkers of central nervous system diseases. Among all proteins identified, those enriched in EVs are associated with synapses, synaptosomes, and nervous system diseases including gliomas, embryonal tumors, and epilepsy. Among these EV-enriched proteins, given the broad consensus present in the recent scientific literature, we validated syntaxin-binding protein 1 (STXBP1) as a marker of malignancy in EVD of CSF and its EVs from patients with pilocytic astrocytoma and medulloblastoma. Our results show that STXBP1 is negatively enriched in EVs compared to non-tumor diseases and its downregulation correlates with adverse outcomes. Further experiments are needed to validate this and other EV markers in the blood of pediatric patients for translational medicine applications.

Evolving Diagnostic and Treatment Strategies for Pediatric CNS Tumors: The Impact of Lipid Metabolism

Pediatric neurological tumors are a heterogeneous group of cancers, many of which carry a poor prognosis and lack a "standard of care" therapy. While they have similar anatomic locations, pediatric neurological tumors harbor specific molecular signatures that distinguish them from adult brain and other neurological cancers. Recent advances through the application of genetics and imaging tools have reshaped the molecular classification and treatment of pediatric neurological tumors, specifically considering the molecular alterations involved. A multidisciplinary effort is ongoing to develop new therapeutic strategies for these tumors, employing innovative and established approaches. Strikingly, there is increasing evidence that lipid metabolism is altered during the development of these types of tumors. Thus, in addition to targeted therapies focusing on classical oncogenes, new treatments are being developed based on a broad spectrum of strategies, ranging from vaccines to viral vectors, and melitherapy. This work reviews the current therapeutic landscape for pediatric brain tumors, considering new emerging treatments and ongoing clinical trials. In addition, the role of lipid metabolism in these neoplasms and its relevance for the development of novel therapies are discussed.

Autophagy, molecular chaperones, and unfolded protein response as promoters of tumor recurrence

Tumor recurrence is a paradoxical function of a machinery, whereby a small proportion of the cancer cell population enters a resistant, dormant state, persists long-term in this condition, and then transitions to proliferation. The dormant phenotype is typical of cancer stem cells, tumor-initiating cells, disseminated tumor cells, and drug-tolerant persisters, which all demonstrate similar or even equivalent properties. Cancer cell dormancy and its conversion to repopulation are regulated by several protein signaling systems that inhibit or induce cell proliferation and provide optimal interrelations between cancer cells and their special niche; these systems act in close connection with tumor microenvironment and immune response mechanisms. During dormancy and reawakening periods, cell proteostasis machineries, autophagy, molecular chaperones, and the unfolded protein response are recruited to protect refractory tumor cells from a wide variety of stressors and therapeutic insults. Proteostasis mechanisms functionally or even physically interfere with the main regulators of tumor relapse, and the significance of these interactions and implications in the tumor recurrence phases are discussed in this review.

Biomarkers and Corresponding Biosensors for Childhood Cancer Diagnostics

Although tremendous progress has been made in treating childhood cancer, it is still one of the leading causes of death in children worldwide. Because cancer symptoms overlap with those of other diseases, it is difficult to predict a tumor early enough, which causes cancers in children to be more aggressive and progress more rapidly than in adults. Therefore, early and accurate detection methods are urgently needed to effectively treat children with cancer therapy. Identification and detection of cancer biomarkers serve as non-invasive tools for early cancer screening, prevention, and treatment. Biosensors have emerged as a potential technology for rapid, sensitive, and cost-effective biomarker detection and monitoring. In this review, we provide an overview of important biomarkers for several common childhood cancers. Accordingly, we have enumerated the developed biosensors for early detection of pediatric cancer or related biomarkers. This review offers a restructured platform for ongoing research in pediatric cancer diagnostics that can contribute to the development of rapid biosensing techniques for early-stage diagnosis, monitoring, and treatment of children with cancer and reduce the mortality rate.

Artificial intelligence applications in pediatric oncology diagnosis

Artificial intelligence (AI) algorithms have been applied in abundant medical tasks with high accuracy and efficiency. Physicians can improve their diagnostic efficiency with the assistance of AI techniques for improving the subsequent personalized treatment and surveillance. AI algorithms fundamentally capture data, identify underlying patterns, achieve preset endpoints, and provide decisions and predictions about real-world events with working principles of machine learning and deep learning. AI algorithms with sufficient graphic processing unit power have been demonstrated to provide timely diagnostic references based on preliminary training of large amounts of clinical and imaging data. The sample size issue is an inevitable challenge for pediatric oncology considering its low morbidity and individual heterogeneity. However, this problem may be solved in the near future considering the exponential advancements of AI algorithms technically to decrease the dependence of AI operation on the amount of data sets and the efficiency of computing power. For instance, it could be a feasible solution by shifting convolutional neural networks (CNNs) from adults and sharing CNN algorithms across multiple institutions besides original data. The present review provides important insights into emerging AI applications for the diagnosis of pediatric oncology by systematically overviewing of up-to-date literature.

Advances in the Treatment of Pediatric Brain Tumors

Pediatric brain tumors are the most common solid malignancies in children. Advances in the treatment of pediatric brain tumors have come in the form of imaging, biopsy, surgical techniques, and molecular profiling. This has led the way for targeted therapies and immunotherapy to be assessed in clinical trials for the most common types of pediatric brain tumors. Here we review the latest efforts and challenges in targeted molecular therapy, immunotherapy, and newer modalities such as laser interstitial thermal therapy.

Cerebrospinal Fluid biomarkers in pediatric brain tumors: A systematic review

Central nervous system (CNS) tumors are the leading cause of cancer death in pediatric patients. Though these tumors typically require invasive surgical procedures to diagnose, cerebrospinal fluid (CSF) liquid biopsy presents a potential method for rapid and noninvasive detection of markers of CNS malignancy. To characterize molecular biomarkers that can be used in the diagnosis, prognosis, and monitoring of pediatric cancer patients, a literature review was conducted in accordance with PRISMA guidelines. PubMed and EMBASE were searched for the terms biomarkers, liquid biopsy, cerebrospinal fluid, pediatric central nervous system tumor, and their synonyms. Studies including pediatric patients with CSF sampling for tumor evaluation were included. Studies were excluded if they did not have full text or if they were case studies, methodology reports, in languages other than English, or animal studies. Our search revealed 163 articles of which 42 were included. Proteomic, genomic, and small molecule markers associated with CNS tumors were identified for further analysis and development of detection tools.

Weighted Gene Co-Expression Network Analysis and Support Vector Machine Learning in the Proteomic Profiling of Cerebrospinal Fluid from Extraventricular Drainage in Child Medulloblastoma

Medulloblastoma (MB) is the most common pediatric malignant central nervous system tumor. Overall survival in MB depends on treatment tuning. There is aneed for biomarkers of residual disease and recurrence. We analyzed the proteome of waste cerebrospinal fluid (CSF) from extraventricular drainage (EVD) from six children bearing various subtypes of MB and six controls needing EVD insertion for unrelated causes. Samples included total CSF, microvesicles, exosomes, and proteins captured by combinatorial peptide ligand library (CPLL). Liquid chromatography-coupled tandem mass spectrometry proteomics identified 3560 proteins in CSF from control and MB patients, 2412 (67.7%) of which were overlapping, and 346 (9.7%) and 805 (22.6%) were exclusive. Multidimensional scaling analysis discriminated samples. The weighted gene co-expression network analysis (WGCNA) identified those modules functionally associated with the samples. A ranked core of 192 proteins allowed distinguishing between control and MB samples. Machine learning highlighted long-chain fatty acid transport protein 4 (SLC27A4) and laminin B-type (LMNB1) as proteins that maximized the discrimination between control and MB samples. Machine learning WGCNA and support vector machine learning were able to distinguish between MB versus non-tumor/hemorrhagic controls. The two potential protein biomarkers for the discrimination between control and MB may guide therapy and predict recurrences, improving the MB patients' quality of life.

Proteomics and metabolomics approach in adult and pediatric glioma diagnostics

The diagnosis of glioma is mainly based on imaging methods that do not distinguish between stage and subtype prior to histopathological analysis. Patients with gliomas are generally diagnosed in the symptomatic stage of the disease. Additionally, healing scar tissue may be mistakenly identified based on magnetic resonance imaging (MRI) as a false positive tumor recurrence in postoperative patients. Current knowledge of molecular alterations underlying gliomagenesis and identification of tumoral biomarkers allow for their use as discriminators of the state of the organism. Moreover, a multiomics approach provides the greatest spectrum and the ability to track physiological changes and can serve as a minimally invasive method for diagnosing asymptomatic gliomas, preceding surgery and allowing for the initiation of prophylactic treatment. It is important to create a vast biomarker library for adults and pediatric patients due to their metabolic differences. This review focuses on the most promising proteomic, metabolomic and lipidomic glioma biomarkers, their pathways, the interactions, and correlations that can be considered characteristic of tumor grade or specific subtype.

Utilization of Cerebrospinal Fluid Proteome Analysis in the Diagnosis of Meningioma: A Systematic Review

Meningiomas have been classified as the most commonly occurring primary brain tumors. Although the majority of meningiomas are benign and slow-progressing, the tumors that grow to a larger size are associated with various risks during surgical procedures. Early detection of meningiomas is crucial to the treatment as those detected early can be treated through non-invasive methods. Due to their benign nature, meningiomas contain homogeneous protein biomarkers that can be easily identified. Cerebrospinal fluid (CSF) has a high protein composition which can be used to diagnose various brain tumors. Because CSF comes into direct contact with the brain during its functioning, it is one of the factors that makes it an important source of different biomarkers. An analysis of biochemical changes occurring in the CSF can be useful in assessing the condition of the periventricular white matter and the parenchyma.

In this review, PubMed, Medline, PubMed Central, and Google Scholar were used to identify studies discussing meningiomas regarding their assessment, types, diagnosis, and treatment, with more attention directed towards the application of CSF proteome analysis in diagnosis. Priority was given to studies published within the last 15 years. The following keywords were used in the literature search: “cerebrospinal fluid,” “meningiomas,” “brain tumors,” “primary brain tumors,” “protein biomarkers,” “proteome analysis,” and “diagnosis.” Subsequently, the 15 most relevant studies were selected for inclusion in the review. We excluded studies discussing different types of non-brain tumors as well as older articles. The selected studies also underwent a quality appraisal process using corresponding assessment tools. The selected articles were highly informative about meningiomas and the processes of diagnosis and treatment that are currently in use as well as those that are being developed or implemented. The use of CSF proteins in the diagnostic process is also discussed in this review. The studies also describe proteomics as a less invasive procedure that allows for the analysis of entire proteins and the projection of diagnostic images with higher resolutions that aid in the diagnosis.