Using genomics to guide treatment for glioblastoma

Molecular Insights and Prognosis Associated With RBM8A in Glioblastoma

Background: Glioblastoma (GBM) is the most invasive brain tumors, and it is associated with high rates of recurrence and mortality. The purpose of this study was to investigate the expression of RBM8A in GBM and the potential influence of its expression on the disease.

Methods: Levels of RBM8A mRNA in GBM patients and controls were examined in The Cancer Genome Atlas (TCGA), GSE16011 and GSE90604 databases. GBM samples in TCGA were divided into RBM8A^high and RBM8A^low groups. Differentially expressed genes (DEGs) between GBM patients and controls were identified, as were DEGs between RBM8A^high and RBM8A^low groups. DEGs common to both of these comparisons were analyzed for coexpression and regression analyses. In addition, we identified potential effects of RBM8A on competing endogenous RNAs, immune cell infiltration, methylation modifications, and somatic mutations.

Results: RBM8A is expressed at significantly higher levels in GBM than control samples, and its level correlates with tumor purity. We identified a total of 488 mRNAs that differed between GBM and controls as well as between RBM8A^high and RBM8A^low groups, which enrichment analysis revealed to be associated mainly with neuroblast proliferation, and T cell immune responses. We identified 174 mRNAs that gave areas under the receiver operating characteristic curve >0.7 among coexpression module genes, of which 13 were significantly associated with overall survival of GBM patients. We integrated 11 candidate mRNAs through LASSO algorithm, then nomogram, risk score, and decision curve analyses were analyzed. We found that RBM8A may compete with DLEU1 for binding to miR-128-1-5p, and aberrant RBM8A expression was associations with tumor infiltration by immune cells. Some mRNAs associated with GBM prognosis also appear to be methylated or mutated.

Conclusions: Our study strongly links RBM8A expression to GBM pathobiology and patient prognosis. The candidate mRNAs identified here may lead to therapeutic targets against the disease.

High STK40 Expression as an Independent Prognostic Biomarker and Correlated with Immune Infiltrates in Low-Grade Gliomas

Background

Expression of STK40 is observed in some cancer types, while its role in low-grade gliomas (LGG) is unclear. The present study aimed to demonstrate the relationship between STK40 and LGG based on The Cancer Genome Atlas (TCGA) database and bioinformatics analysis.

Methods

Kruskal-Wallis test, Wilcoxon sign-rank test, and logistic regression were used to evaluate the relationship between clinicopathological features and STK40 expression. Kaplan-Meier method and Cox regression analysis were used to evaluate prognostic factors. Gene set enrichment analysis (GSEA) and immuno-infiltration analysis were used to determine the significant involvement of STK40 in function.

Results

High STK40 expression in LGG was associated with WHO grade (P<0.001), IDH status (P<0.001), primary therapy outcome (P=0.027), 1p/19q codeletion (P<0.001) and histological type (P<0.001). High STK40 expression predicted a poorer overall survival (OS) (HR: 3.07; 95% CI: 2.09-4.51; P<0.001), progression-free survival (PFS) (HR:2.11; 95% CI: 1.59-2.81; P<0.001) and disease specific survival (DSS) (HR: 3.27; 95% CI: 2.17-4.92; P<0.001). STK40 expression (HR: 2.284; 95% CI: 1.125-4.637; P=0.022) was independently correlated with OS in LGG patients. GSEA demonstrated that pathways including cell cycle mitotic, neutrophil degranulation, signaling by Rho GTPases, signaling by interleukins, M phase, PI3K-Akt signaling pathway and naba secreted factors were differentially enriched in STK40 high expression phenotype. Immune infiltration analysis showed that STK40 expression was correlated with some types of immune infiltrating cells.

Conclusion

STK40 expression was significantly correlated with poor survival and immune infiltration in LGG, and it may be a promising prognostic biomarker in LGG.

Brain Tumor Biobank Development for Precision Medicine: Role of the Neurosurgeon

Neuro-oncology biobanks are critical for the implementation of a precision medicine program. In this perspective, we review our first year experience of a brain tumor biobank with integrated next generation sequencing. From our experience, we describe the critical role of the neurosurgeon in diagnosis, research, and precision medicine efforts. In the first year of implementation of the biobank, 117 patients (Female: 62; Male: 55) had 125 brain tumor surgeries. 75% of patients had tumors biobanked, and 16% were of minority race/ethnicity. Tumors biobanked were as follows: diffuse gliomas (45%), brain metastases (29%), meningioma (21%), and other (5%). Among biobanked patients, 100% also had next generation sequencing. Eleven patients qualified for targeted therapy based on identification of actionable gene mutations. One patient with a hereditary cancer predisposition syndrome was also identified. An iterative quality improvement process was implemented to streamline the workflow between the operating room, pathology, and the research laboratory. Dedicated tumor bank personnel in the department of neurosurgery greatly improved standard operating procedure. Intraoperative selection and processing of tumor tissue by the neurosurgeon was integral to increasing success with cell culture assays. Currently, our institutional protocol integrates standard histopathological diagnosis, next generation sequencing, and functional assays on surgical specimens to develop precision medicine protocols for our patients. This perspective reviews the critical role of neurosurgeons in brain tumor biobank implementation and success as well as future directions for enhancing precision medicine efforts.

Expression and prognostic characteristics of m5 C regulators in low-grade glioma

Glioma is the most common intracranial malignant tumour. A clear diagnosis and molecular targeted therapy are of great significance for improving the survival time and quality of life of patients with low‐grade glioma. 5‐methylcytosine methylation is one of the ways of RNA modification, but there are limited studies on the role of m⁵C methylation of low‐grade glioma. Single‐nucleotide variant, RNA expression matrix and corresponding clinical data of low‐grade glioma came from public database. The single‐nucleotide variant and expression of m⁵C regulators were estimated. A prognostic model based on m⁵C regulators was constructed by Cox regression. Potential functions of these molecules were assessed by gene set enrichment analysis. DNMT3A mutation was the most frequent among the m⁵C regulators in low‐grade glioma. NSUN3, TET2, TRDMT1, ALYREF, DNMT3B, DNMT1, NOP2 and NSUN2 were up‐regulated. One prognostic model was constructed which had a strong predictive power for the overall survival of low‐grade glioma. We studied the expression and prognostic characteristics of m⁵C regulators in low‐grade glioma, supplied biomarkers for the diagnosis and prognosis and provided the foundation for the study of the pathogenesis of low‐grade glioma.

Molecular characteristics of diffuse lower grade gliomas: what neurosurgeons need to know

The importance of genomic information in intrinsic brain tumors is highlighted in the World Health Organization (WHO) 2016 classification of gliomas, which now incorporates both phenotype and genotype data to assign a diagnosis. By using genetic markers to both categorize tumors and advise patients on prognosis, this classification system has minimized the risk of tissue sampling error, improved diagnostic accuracy, and reduced inter-rater variability. In the neurosurgical community, it is critical to understand the role genetics plays in tumor biology, what certain mutations mean for the patient's prognosis and adjuvant treatment, and how to interpret the results of sequencing data that are generated following tumor resection. In this review, we examine the critical role of genetics for diagnosis and prognosis and highlight the importance of tumor genetics for neurosurgeons caring for patients with diffuse lower grade gliomas.

Next Generation Sequencing and Machine Learning Technologies Are Painting the Epigenetic Portrait of Glioblastoma

Even with a rare occurrence of only 1.35% of cancer cases in the United States of America, brain tumors are considered as one of the most lethal malignancies. The most aggressive and invasive type of brain tumor, glioblastoma, accounts for 60–70% of all gliomas and presents with life expectancy of only 12–18 months. Despite trimodal treatment and advances in diagnostic and therapeutic methods, there are no significant changes in patient outcome. Our understanding of glioblastoma was significantly improved with the introduction of next generation sequencing technologies. This led to the identification of different genetic and molecular subtypes, which greatly improve glioblastoma diagnosis. Still, because of the poor life expectancy, novel diagnostic, and treatment methods are broadly explored. Epigenetic modifications like methylation and changes in histone acetylation are such examples. Recently, in addition to genetic and molecular characteristics, epigenetic profiling of glioblastomas is also used for sample classification. Further advancement of next generation sequencing technologies is expected to identify in detail the epigenetic signature of glioblastoma that can open up new therapeutic opportunities for glioblastoma patients. This should be complemented with the use of computational power i.e., machine and deep learning algorithms for objective diagnostics and design of individualized therapies. Using a combination of phenotypic, genotypic, and epigenetic parameters in glioblastoma diagnostics will bring us closer to precision medicine where therapies will be tailored to suit the genetic profile and epigenetic signature of the tumor, which will grant longer life expectancy and better quality of life. Still, a number of obstacles including potential bias, availability of data for minorities in heterogeneous populations, data protection, and validation and independent testing of the learning algorithms have to be overcome on the way.

Imaging of intratumoral heterogeneity in high-grade glioma

High-grade glioma (HGG), and particularly Glioblastoma (GBM), can exhibit pronounced intratumoral heterogeneity that confounds clinical diagnosis and management. While conventional contrast-enhanced MRI lacks the capability to resolve this heterogeneity, advanced MRI techniques and PET imaging offer a spectrum of physiologic and biophysical image features to improve the specificity of imaging diagnoses. Published studies have shown how integrating these advanced techniques can help better define histologically distinct targets for surgical and radiation treatment planning, and help evaluate the regional heterogeneity of tumor recurrence and response assessment following standard adjuvant therapy. Application of texture analysis and machine learning (ML) algorithms has also enabled the emerging field of radiogenomics, which can spatially resolve the regional and genetically distinct subpopulations that coexist within a single GBM tumor. This review focuses on the latest advances in neuro-oncologic imaging and their clinical applications for the assessment of intratumoral heterogeneity.

Imaging signatures of glioblastoma molecular characteristics: A radiogenomics review

Over the past few decades, the advent and development of genomic assessment methods and computational approaches have raised the hopes for identifying therapeutic targets that may aid in the treatment of glioblastoma. However, the targeted therapies have barely been successful in their effort to cure glioblastoma patients, leaving them with a grim prognosis. Glioblastoma exhibits high heterogeneity, both spatially and temporally. The existence of different genetic subpopulations in glioblastoma allows this tumor to adapt itself to environmental forces. Therefore, patients with glioblastoma respond poorly to the prescribed therapies, as treatments are directed towards the whole tumor and not to the specific genetic subregions. Genomic alterations within the tumor develop distinct radiographic phenotypes. In this regard, MRI plays a key role in characterizing molecular signatures of glioblastoma, based on regional variations and phenotypic presentation of the tumor. Radiogenomics has emerged as a (relatively) new field of research to explore the connections between genetic alterations and imaging features. Radiogenomics offers numerous advantages, including noninvasive and global assessment of the tumor and its response to therapies. In this review, we summarize the potential role of radiogenomic techniques to stratify patients according to their specific tumor characteristics with the goal of designing patient-specific therapies. Level of Evidence: 5 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;52:54-69.