Novel Gene Fusions in Glioblastoma Tumor Tissue and Matched Patient Plasma

SNARE proteins: Core engines of membrane fusion in cancer

Vesicles are loaded with a variety of cargoes, including membrane proteins, secreted proteins, signaling molecules, and various enzymes, etc. Not surprisingly, vesicle transport is essential for proper cellular life activities including growth, division, movement and cellular communication. Soluble N-ethylmaleimide-sensitive factor attachment protein receptors (SNAREs) mediate membrane fusion of vesicles with their target compartments that is fundamental for cargo delivery. Recent studies have shown that multiple SNARE family members are aberrantly expressed in human cancers and actively contribute to malignant proliferation, invasion, metastasis, immune evasion and treatment resistance. Here, the localization and function of SNARE proteins in eukaryotic cells are firstly mapped. Then we summarize the expression and regulation of SNAREs in cancer, and describe their contribution to cancer progression and mechanisms, and finally we propose engineering botulinum toxin as a strategy to target SNAREs for cancer treatment.

Exploring Regorafenib Responsiveness and Uncovering Molecular Mechanisms in Recurrent Glioblastoma Tumors through Longitudinal In Vitro Sampling

Glioblastoma, a deadly brain tumor, shows limited response to standard therapies like temozolomide (TMZ). Recent findings from the REGOMA trial underscore a significant survival improvement offered by Regorafenib (REGO) in recurrent glioblastoma. Our study aimed to propose a 3D ex vivo drug response precision medicine approach to investigate recurrent glioblastoma sensitivity to REGO and elucidate the underlying molecular mechanisms involved in tumor resistance or responsiveness to treatment. Three-dimensional glioblastoma organoids (GB-EXPs) obtained from 18 patients' resected recurrent glioblastoma tumors were treated with TMZ and REGO. Drug responses were evaluated using NAD(P)H FLIM, stratifying tumors as responders (Resp) or non-responders (NRs). Whole-exome sequencing was performed on 16 tissue samples, and whole-transcriptome analysis on 13 GB-EXPs treated and untreated. We found 35% (n = 9) and 77% (n = 20) of tumors responded to TMZ and REGO, respectively, with no instances of TMZ-Resp being REGO-NRs. Exome analysis revealed a unique mutational profile in REGO-Resp tumors compared to NR tumors. Transcriptome analysis identified distinct expression patterns in Resp and NR tumors, impacting Rho GTPase and NOTCH signaling, known to be involved in drug response. In conclusion, recurrent glioblastoma tumors were more responsive to REGO compared to TMZ treatment. Importantly, our approach enables a comprehensive longitudinal exploration of the molecular changes induced by treatment, unveiling promising biomarkers indicative of drug response.

Molecular Analysis of Biliary Tract Cancers with the Custom 3' RACE-Based NGS Panel

The technique 3' rapid amplification of cDNA ends (3' RACE) allows for detection of translocations with unknown gene partners located at the 3' end of the chimeric transcript. We composed a 3' RACE-based RNA sequencing panel for the analysis of FGFR1-4 gene rearrangements, detection of activating mutations located within FGFR1-4, IDH1/2, ERBB2 (HER2), KRAS, NRAS, BRAF, and PIK3CA genes, and measurement of the expression of ERBB2, PD-L1, and FGFR1-4 transcripts. This NGS panel was utilized for the molecular profiling of 168 biliary tract carcinomas (BTCs), including 83 intrahepatic cholangiocarcinomas (iCCAs), 44 extrahepatic cholangiocarcinomas (eCCAs), and 41 gallbladder adenocarcinomas (GBAs). The NGS failure rate was 3/168 (1.8%). iCCAs, but not other categories of BTCs, were characterized by frequent FGFR2 alterations (17/82, 20.7%) and IDH1/2 mutations (23/82, 28%). Other potentially druggable events included ERBB2 amplifications or mutations (7/165, 4.2% of all successfully analyzed BTCs) and BRAF p.V600E mutations (3/165, 1.8%). In addition to NGS, we analyzed microsatellite instability (MSI) using the standard five markers and revealed this event in 3/158 (1.9%) BTCs. There were no instances of ALK, ROS1, RET, and NTRK1-3 gene rearrangements or MET exon 14 skipping mutations. Parallel analysis of 47 iCCA samples with the Illumina TruSight Tumor 170 kit confirmed good performance of our NGS panel. In conclusion, targeted RNA sequencing coupled with the 3' RACE technology is an efficient tool for the molecular diagnostics of BTCs.

Assessment and prediction of glioblastoma therapy response: challenges and opportunities

Glioblastoma is the most aggressive type of primary adult brain tumour. The median survival of patients with glioblastoma remains approximately 15 months, and the 5-year survival rate is <10%. Current treatment options are limited, and the standard of care has remained relatively constant since 2011. Over the last decade, a range of different treatment regimens have been investigated with very limited success. Tumour recurrence is almost inevitable with the current treatment strategies, as glioblastoma tumours are highly heterogeneous and invasive. Additionally, another challenging issue facing patients with glioblastoma is how to distinguish between tumour progression and treatment effects, especially when relying on routine diagnostic imaging techniques in the clinic. The specificity of routine imaging for identifying tumour progression early or in a timely manner is poor due to the appearance similarity of post-treatment effects. Here, we concisely describe the current status and challenges in the assessment and early prediction of therapy response and the early detection of tumour progression or recurrence. We also summarize and discuss studies of advanced approaches such as quantitative imaging, liquid biomarker discovery and machine intelligence that hold exceptional potential to aid in the therapy monitoring of this malignancy and early prediction of therapy response, which may decisively transform the conventional detection methods in the era of precision medicine.

Transcriptomic analyses of patient peripheral blood with hemoglobin depletion reveal glioblastoma biomarkers

Peripheral blood is gaining prominence as a noninvasive alternative to tissue biopsy to develop biomarkers for glioblastoma (GBM); however, widely utilized blood-based biomarkers in clinical settings have not yet been identified due to the lack of a robust detection approach. Here, we describe the application of globin reduction in RNA sequencing of whole blood (i.e., WBGR) and perform transcriptomic analysis to identify GBM-associated transcriptomic changes. By using WBGR, we improved the detection sensitivity of informatic reads and identified differential gene expression in GBM blood. By analyzing tumor tissues, we identified transcriptomic traits of GBM blood. Further functional enrichment analyses retained the most changed genes in GBM. Subsequent validation elicited a 10-gene panel covering mRNA, long noncoding RNA, and microRNA (i.e., GBM-Dx panel) that has translational potential to aid in the early detection or clinical management of GBM. Here, we report an integrated approach, WBGR, with comprehensive analytic capacity for blood-based marker identification.

Whole transcriptome profiling of liquid biopsies from tumour xenografted mouse models enables specific monitoring of tumour-derived extracellular RNA

While cell-free DNA (cfDNA) is widely being investigated, free circulating RNA (extracellular RNA, exRNA) has the potential to improve cancer therapy response monitoring and detection due to its dynamic nature. However, it remains unclear in which blood subcompartment tumour-derived exRNAs primarily reside. We developed a host-xenograft deconvolution framework, exRNAxeno, with mapping strategies to either a combined human-mouse reference genome or both species genomes in parallel, applicable to exRNA sequencing data from liquid biopsies of human xenograft mouse models. The tool enables to distinguish (human) tumoural RNA from (murine) host RNA, to specifically analyse tumour-derived exRNA. We applied the combined pipeline to total exRNA sequencing data from 95 blood-derived liquid biopsy samples from 30 mice, xenografted with 11 different tumours. Tumoural exRNA concentrations are not determined by plasma platelet levels, while host exRNA concentrations increase with platelet content. Furthermore, a large variability in exRNA abundance and transcript content across individual mice is observed. The tumoural gene detectability in plasma is largely correlated with the RNA expression levels in the tumour tissue or cell line. These findings unravel new aspects of tumour-derived exRNA biology in xenograft models and open new avenues to further investigate the role of exRNA in cancer.

Liquid Biopsy, an Everchanging Balance between Clinical Utility and Emerging Technologies

To date, tissue biopsy still represents the mainstay for tumor diagnosis and molecular characterization [...].

The Role of Vti1a in Biological Functions and Its Possible Role in Nervous System Disorders

Vesicle transport through interaction with t-SNAREs 1A (Vti1a), a member of the N-ethylmaleimide-sensitive factor attachment protein receptor protein family, is involved in cell signaling as a vesicular protein and mediates vesicle trafficking. Vti1a appears to have specific roles in neurons, primarily by regulating upstream neurosecretory events that mediate exocytotic proteins and the availability of secretory organelles, as well as regulating spontaneous synaptic transmission and postsynaptic efficacy to control neurosecretion. Vti1a also has essential roles in neural development, autophagy, and unconventional extracellular transport of neurons. Studies have shown that Vti1a dysfunction plays critical roles in pathological mechanisms of Hepatic encephalopathy by influencing spontaneous neurotransmission. It also may have an unknown role in amyotrophic lateral sclerosis. A VTI1A variant is associated with the risk of glioma, and the fusion product of the VTI1A gene and the adjacent TCF7L2 gene is involved in glioma development. This review summarizes Vti1a functions in neurons and highlights the role of Vti1a in the several nervous system disorders.

Preoperative Diagnosis and Molecular Characterization of Gliomas With Liquid Biopsy and Radiogenomics

Gliomas are a heterogenous group of central nervous system tumors with different outcomes and different therapeutic needs. Glioblastoma, the most common subtype in adults, has a very poor prognosis and disabling consequences. The World Health Organization (WHO) classification specifies that the typing and grading of gliomas should include molecular markers. The molecular characterization of gliomas has implications for prognosis, treatment planning, and prediction of treatment response. At present, gliomas are diagnosed via tumor resection or biopsy, which are always invasive and frequently risky methods. In recent years, however, substantial advances have been made in developing different methods for the molecular characterization of tumors through the analysis of products shed in body fluids. Known as liquid biopsies, these analyses can potentially provide diagnostic and prognostic information, guidance on choice of treatment, and real-time information on tumor status. In addition, magnetic resonance imaging (MRI) is another good source of tumor data; radiomics and radiogenomics can link the imaging phenotypes to gene expression patterns and provide insights to tumor biology and underlying molecular signatures. Machine and deep learning and computational techniques can also use quantitative imaging features to non-invasively detect genetic mutations. The key molecular information obtained with liquid biopsies and radiogenomics can be useful not only in the diagnosis of gliomas but can also help predict response to specific treatments and provide guidelines for personalized medicine. In this article, we review the available data on the molecular characterization of gliomas using the non-invasive methods of liquid biopsy and MRI and suggest that these tools could be used in the future for the preoperative diagnosis of gliomas.

Extracellular Vesicle-Based Method for Detecting MYCN Amplification Status of Pediatric Neuroblastoma

Simple Summary

MYCN gene amplification, the strongest prognostic marker of aggressive neuroblastoma, is detected on invasive biopsy tissues. This study aimed to establish a less invasive method to detect MYCN status based on MYCN mRNA contents in large extracellular vesicles or microvesicles. MYCN mRNA-containing microvesicles were detectable in three distinct MYCN-amplified neuroblastoma cell lines but absent in three neuroblastoma cells with MYCN-non-amplification. The feasibility of this EV-based workflow was successfully demonstrated by using the simulated samples (prepared by pulsing neuroblastoma MVs into the normal human serum) and bone marrow plasma specimens obtained from nine patients at various disease stages. Taken together, this study established the novel EV-based method for detecting MYCN status in pediatric neuroblastoma.

Abstract

MYCN amplification is the strongest predictor of high-risk neuroblastoma (NB). The standard procedure to detect MYCN status requires invasive procedures. Extracellular vesicles (EVs) contain molecular signatures of originated cells, present in biofluids, and serve as an invaluable source for cancer liquid biopsies. This study aimed to establish an EV-based method to detect the MYCN status of NB. Two EV subtypes, i.e., microvesicles (MVs) and exosomes, were sequentially isolated from the culture supernatant by step-wise centrifugation, ultrafiltration, and size-exclusion chromatography. Quantitative RT-PCR was performed to detect MYCN mRNA. As a result, MYCN mRNA was detectable in the MVs, but not exosomes, of MYCN-amplified NB cells. MYCN mRNA-containing MVs (MYCN-MV) were successfully detected in three distinct MYCN-amplified NB cell lines but absent in three MYCN non-amplification cells. The simulated samples were prepared by pulsing MVs into human serum. MYCN–MV detection in the simulated samples showed a less interfering effect from the human blood matrix. Validation using clinical specimens (2 mL bone marrow plasma) obtained from patients at various disease stages showed a promising result. Five out of six specimens of MYCN-amplified patients showed positive results, while there were no false positives in four plasma samples of the MYCN non-amplification group. This study communicated a novel EV-based method for detecting the MYCN status of pediatric NB based on MYCN mRNA contents in MVs. Future studies should be pursued in a prospective cohort to determine its true diagnostic performance.

Liquid biopsy: early and accurate diagnosis of brain tumor

Noninvasive examination is an emerging area in the field of neuro-oncology. Liquid biopsy captures the landscape of genomic alterations of brain tumors and revolutionizes the traditional diagnosis approaches. Rapidly changing sequencing technologies and more affordable prices put the screws on more application of liquid biopsy in clinical settings. In the past few years, extensive application of liquid biopsy has been seen throughout the whole diagnosis and treatment process of brain tumors, including early and accurate detection, characterization and dynamic monitoring. Here, we summarized and compared the most advanced techniques and target molecules or macrostructures related to brain tumor liquid biopsy. We further reviewed and emphasized recent progression in different clinical settings for brain tumors in blood and CSF. The preferred protocol, potential novel biomarkers and future development are discussed in the last part.

Dysregulated Alanine as a Potential Predictive Marker of Glioma-An Insight from Untargeted HRMAS-NMR and Machine Learning Data

Metabolic alterations play a crucial role in glioma development and progression and can be detected even before the appearance of the fatal phenotype. We have compared the circulating metabolic fingerprints of glioma patients versus healthy controls, for the first time, in a quest to identify a panel of small, dysregulated metabolites with potential to serve as a predictive and/or diagnostic marker in the clinical settings. High-resolution magic angle spinning nuclear magnetic resonance spectroscopy (HRMAS-NMR) was used for untargeted metabolomics and data acquisition followed by a machine learning (ML) approach for the analyses of large metabolic datasets. Cross-validation of ML predicted NMR spectral features was done by statistical methods (Wilcoxon-test) using JMP-pro16 software. Alanine was identified as the most critical metabolite with potential to detect glioma with precision of 1.0, recall of 0.96, and F1 measure of 0.98. The top 10 metabolites identified for glioma detection included alanine, glutamine, valine, methionine, N-acetylaspartate (NAA), γ-aminobutyric acid (GABA), serine, α-glucose, lactate, and arginine. We achieved 100% accuracy for the detection of glioma using ML algorithms, extra tree classifier, and random forest, and 98% accuracy with logistic regression. Classification of glioma in low and high grades was done with 86% accuracy using logistic regression model, and with 83% and 79% accuracy using extra tree classifier and random forest, respectively. The predictive accuracy of our ML model is superior to any of the previously reported algorithms, used in tissue- or liquid biopsy-based metabolic studies. The identified top metabolites can be targeted to develop early diagnostic methods as well as to plan personalized treatment strategies.