Integrated multi-omics analysis of oligodendroglial tumours identifies three subgroups of 1p/19q co-deleted gliomas

Adolescents and Young Adults With Cancer: CNS Tumors

Tumors of CNS are common in adolescents and young adults (AYAs). As the second leading cause of cancer-related death, CNS tumors in AYAs require improved clinical management. In this review, we discussed the current diagnostic approaches and recommended management strategies for malignant tumors in adult-type (IDH-mutant gliomas) and pediatric-type gliomas (pediatric high-grade gliomas), ependymoma and medulloblastoma, which commonly occur in AYAs. The impact of advanced molecular diagnostic approaches on the understanding of tumor biology of AYA CNS tumors is emphasized. To enhance participation in clinical trials, which poses a unique challenge in AYAs with CNS tumors, we propose encouraging referrals to neuro-oncology specialty care and improving collaboration between oncologists who care for both pediatric and adult patients. This will ensure better representation of AYA patients in research studies. Finally, we discussed the importance of considering neurocognitive and psychological function in AYAs with CNS tumor.

BCAS1 defines a heterogeneous cell population in diffuse gliomas

Oligodendrocyte precursor markers have become of great interest to identify new diagnostic and therapeutic targets for diffuse gliomas, since state-of-the-art studies point towards immature oligodendrocytes as a possible source of gliomagenesis. Brain enriched myelin associated protein 1 (BCAS1) is a novel marker of immature oligodendrocytes and was proposed to contribute to tumorigenesis in non-central nervous system tumors. However, BCAS1 role in diffuse glioma is still underexplored. This study analyzes the expression of BCAS1 in different tumor samples from patients with diffuse gliomas (17 oligodendrogliomas; 8 astrocytomas; 60 glioblastomas) and uncovers the molecular and ultrastructural features of BCAS1+ cells by immunostaining and electron microscopy. Our results show that BCAS1+ cells exhibit stellate or spherical morphology with similar ultrastructural features. Stellate and spherical cells were detected as isolated cells in all studied gliomas. Nevertheless, only stellate cells were found to be proliferative and formed tightly packed nodules with a highly proliferative rate in oligodendrogliomas. Our findings provide a comprehensive characterization of the BCAS1+ cell population within diffuse gliomas. The observed proliferative capacity and distribution of BCAS1+ stellate cells, particularly in oligodendrogliomas, highlight BCAS1 as an interesting marker, warranting further investigation into its role in tumor malignancy.

Alternative lengthening of telomeres is seen in a proportion of oligodendrogliomas and is associated with a worse prognosis

Oligodendrogliomas are known to be mutated for TERTp, and in this report, we evaluated 112 IDH-mutant, 1p19q codeleted oligodendrogliomas for ALT by FISH, and FISH for copy number changes of CDKN2A, MYC, PDGFRA, EGFR, chromosomes +7/10 and TERT-rearrangement. Enigmatically, 35.7% of cases were ALT-positive in spite of the vast majority of them being TERTp-mutant. ALT was associated with a shorter PFS (p=0.009) and remained an independent prognosticator in multivariate analysis. ALT was also associated with MYC amplification. ALT-positive cases were further examined with targeted sequencing. No genes were found to be of prognostic significance in this group.

Hypoxanthine phosphoribosyl transferase 1 metabolizes temozolomide to activate AMPK for driving chemoresistance of glioblastomas

Temozolomide (TMZ) is a standard treatment for glioblastoma (GBM) patients. However, TMZ has moderate therapeutic effects due to chemoresistance of GBM cells through less clarified mechanisms. Here, we demonstrate that TMZ-derived 5-aminoimidazole-4-carboxamide (AICA) is converted to AICA ribosyl-5-phosphate (AICAR) in GBM cells. This conversion is catalyzed by hypoxanthine phosphoribosyl transferase 1 (HPRT1), which is highly expressed in human GBMs. As the bona fide activator of AMP-activated protein kinase (AMPK), TMZ-derived AICAR activates AMPK to phosphorylate threonine 52 (T52) of RRM1, the catalytic subunit of ribonucleotide reductase (RNR), leading to RNR activation and increased production of dNTPs to fuel the repairment of TMZ-induced-DNA damage. RRM1 T52A expression, genetic interruption of HPRT1-mediated AICAR production, or administration of 6-mercaptopurine (6-MP), a clinically approved inhibitor of HPRT1, blocks TMZ-induced AMPK activation and sensitizes brain tumor cells to TMZ treatment in mice. In addition, HPRT1 expression levels are positively correlated with poor prognosis in GBM patients who received TMZ treatment. These results uncover a critical bifunctional role of TMZ in GBM treatment that leads to chemoresistance. Our findings underscore the potential of combined administration of clinically available 6-MP to overcome TMZ chemoresistance and improve GBM treatment.

Integrated machine learning survival framework develops a prognostic model based on inter-crosstalk definition of mitochondrial function and cell death patterns in a large multicenter cohort for lower-grade glioma

BACKGROUND

Lower-grade glioma (LGG) is a highly heterogeneous disease that presents challenges in accurately predicting patient prognosis. Mitochondria play a central role in the energy metabolism of eukaryotic cells and can influence cell death mechanisms, which are critical in tumorigenesis and progression. However, the prognostic significance of the interplay between mitochondrial function and cell death in LGG requires further investigation.

METHODS

We employed a robust computational framework to investigate the relationship between mitochondrial function and 18 cell death patterns in a cohort of 1467 LGG patients from six multicenter cohorts worldwide. A total of 10 commonly used machine learning algorithms were collected and subsequently combined into 101 unique combinations. Ultimately, we devised the mitochondria-associated programmed cell death index (mtPCDI) using machine learning models that exhibited optimal performance.

RESULTS

The mtPCDI, generated by combining 18 highly influential genes, demonstrated strong predictive performance for prognosis in LGG patients. Biologically, mtPCDI exhibited a significant correlation with immune and metabolic signatures. The high mtPCDI group exhibited enriched metabolic pathways and a heightened immune activity profile. Of particular importance, our mtPCDI maintains its status as the most potent prognostic indicator even following adjustment for potential confounding factors, surpassing established clinical models in predictive strength.

CONCLUSION

Our utilization of a robust machine learning framework highlights the significant potential of mtPCDI in providing personalized risk assessment and tailored recommendations for metabolic and immunotherapy interventions for individuals diagnosed with LGG. Of particular significance, the signature features highly influential genes that present further prospects for future investigations into the role of PCD within mitochondrial function.

A Multigene Signature Associated with Progression-Free Survival after Treatment for IDH Mutant and 1p/19q Codeleted Oligodendrogliomas

BACKGROUND

IDH mutant and 1p/19q codeleted oligodendrogliomas are the gliomas associated with the best prognosis. However, despite their sensitivity to treatment, patient survival remains heterogeneous. We aimed to identify gene expressions associated with response to treatment from a national cohort of patients with oligodendrogliomas, all treated with radiotherapy +/- chemotherapy.

METHODS

We extracted total RNA from frozen tumor samples and investigated enriched pathways using KEGG and Reactome databases. We applied a stability selection approach based on subsampling combined with the lasso-pcvl algorithm to identify genes associated with progression-free survival and calculate a risk score.

RESULTS

We included 68 patients with oligodendrogliomas treated with radiotherapy +/- chemotherapy. After filtering, 1697 genes were obtained, including 134 associated with progression-free survival: 35 with a better prognosis and 99 with a poorer one. Eight genes (ST3GAL6, QPCT, NQO1, EPHX1, CST3, S100A8, CHI3L1, and OSBPL3) whose risk score remained statistically significant after adjustment for prognostic factors in multivariate analysis were selected in more than 60% of cases were associated with shorter progression-free survival.

CONCLUSIONS

We found an eight-gene signature associated with a higher risk of rapid relapse after treatment in patients with oligodendrogliomas. This finding could help clinicians identify patients who need more intensive treatment.

Multi-Omics Profiling for Health

The world has witnessed a steady rise in both non-infectious and infectious chronic diseases, prompting a cross-disciplinary approach to understand and treating disease. Current medical care focuses on treating people after they become patients rather than preventing illness, leading to high costs in treating chronic and late-stage diseases. Additionally, a "one-size-fits all" approach to health care does not take into account individual differences in genetics, environment, or lifestyle factors, decreasing the number of people benefiting from interventions. Rapid advances in omics technologies and progress in computational capabilities have led to the development of multi-omics deep phenotyping, which profiles the interaction of multiple levels of biology over time and empowers precision health approaches. This review highlights current and emerging multi-omics modalities for precision health and discusses applications in the following areas: genetic variation, cardio-metabolic diseases, cancer, infectious diseases, organ transplantation, pregnancy, and longevity/aging. We will briefly discuss the potential of multi-omics approaches in disentangling host-microbe and host-environmental interactions. We will touch on emerging areas of electronic health record and clinical imaging integration with muti-omics for precision health. Finally, we will briefly discuss the challenges in the clinical implementation of multi-omics and its future prospects.

Epigenetic Regulation in Primary CNS Tumors: An Opportunity to Bridge Old and New WHO Classifications

Originally approved in 1979, a specific grading classification for central nervous system (CNS) tumors was devised by the World Health Organization (WHO) in an effort to guide cancer treatment and better understand prognosis. These "blue books" have since undergone several iterations based on tumor location, advancements in histopathology, and most recently, diagnostic molecular pathology in its fifth edition. As new research methods have evolved to elucidate complex molecular mechanisms of tumorigenesis, a need to update and integrate these findings into the WHO grading scheme has become apparent. Epigenetic tools represent an area of burgeoning interest that encompasses all non-Mendelian inherited genetic features affecting gene expression, including but not limited to chromatin remodeling complexes, DNA methylation, and histone regulating enzymes. The SWItch/Sucrose non-fermenting (SWI/SNF) chromatin remodeling complex is the largest mammalian family of chromatin remodeling proteins and is estimated to be altered in 20-25% of all human malignancies; however, the ways in which it contributes to tumorigenesis are not fully understood. We recently discovered that CNS tumors with SWI/SNF mutations have revealed an oncogenic role for endogenous retroviruses (ERVs), remnants of exogenous retroviruses that integrated into the germline and are inherited like Mendelian genes, several of which retain open reading frames for proteins whose expression putatively contributes to tumor formation. Herein, we analyzed the latest WHO classification scheme for all CNS tumors with documented SWI/SNF mutations and/or aberrant ERV expression, and we summarize this information to highlight potential research opportunities that could be integrated into the grading scheme to better delineate diagnostic criteria and therapeutic targets.

Omics approaches to understanding the efficacy and safety of disease-modifying treatments in multiple sclerosis

From the perspective of precision medicine, the challenge for the future is to improve the accuracy of diagnosis, prognosis, and prediction of therapeutic responses through the identification of biomarkers. In this framework, the omics sciences (genomics, transcriptomics, proteomics, and metabolomics) and their combined use represent innovative approaches for the exploration of the complexity and heterogeneity of multiple sclerosis (MS). This review examines the evidence currently available on the application of omics sciences to MS, analyses the methods, their limitations, the samples used, and their characteristics, with a particular focus on biomarkers associated with the disease state, exposure to disease-modifying treatments (DMTs), and drug efficacies and safety profiles.

Immunological profiles of human oligodendrogliomas define two distinct molecular subtypes

BACKGROUND

Human oligodendroglioma presents as a heterogeneous disease, primarily characterized by the isocitrate dehydrogenase (IDH) mutation and 1p/19q co-deletion. Therapy development for this tumor is hindered by incomplete knowledge of somatic driving alterations and suboptimal disease classification. We herein aim to identify intrinsic molecular subtypes through integrated analysis of transcriptome, genome and methylome.

METHODS

137 oligodendroglioma patients from the Cancer Genome Atlas (TCGA) dataset were collected for unsupervised clustering analysis of immune gene expression profiles and comparative analysis of genome and methylome. Two independent datasets containing 218 patients were used for validation.

FINDINGS

We identified and independently validated two reproducible subtypes associated with distinct molecular characteristics and clinical outcomes. The proliferative subtype, named Oligo1, was characterized by more tumors of CNS WHO grade 3, as well as worse prognosis compared to the Oligo2 subtype. Besides the clinicopathologic features, Oligo1 exhibited enrichment of cell proliferation, regulation of cell cycle and Wnt signaling pathways, and significantly altered genes, such as EGFR, NOTCH1 and MET. In contrast, Oligo2, with favorable outcome, presented increased activation of immune response and metabolic process. Higher T cell/APC co-inhibition and inhibitory checkpoint levels were observed in Oligo2 tumors. Finally, multivariable analysis revealed our classification was an independent prognostic factor in oligodendrogliomas, and the robustness of these molecular subgroups was verified in the validation cohorts.

INTERPRETATION

This study provides further insights into patient stratification as well as presents opportunities for therapeutic development in human oligodendrogliomas.

FUNDING

The funders are listed in the Acknowledgement.

Network reconstruction for trans acting genetic loci using multi-omics data and prior information

BACKGROUND

Molecular measurements of the genome, the transcriptome, and the epigenome, often termed multi-omics data, provide an in-depth view on biological systems and their integration is crucial for gaining insights in complex regulatory processes. These data can be used to explain disease related genetic variants by linking them to intermediate molecular traits (quantitative trait loci, QTL). Molecular networks regulating cellular processes leave footprints in QTL results as so-called trans-QTL hotspots. Reconstructing these networks is a complex endeavor and use of biological prior information can improve network inference. However, previous efforts were limited in the types of priors used or have only been applied to model systems. In this study, we reconstruct the regulatory networks underlying trans-QTL hotspots using human cohort data and data-driven prior information.

METHODS

We devised a new strategy to integrate QTL with human population scale multi-omics data. State-of-the art network inference methods including BDgraph and glasso were applied to these data. Comprehensive prior information to guide network inference was manually curated from large-scale biological databases. The inference approach was extensively benchmarked using simulated data and cross-cohort replication analyses. Best performing methods were subsequently applied to real-world human cohort data.

RESULTS

Our benchmarks showed that prior-based strategies outperform methods without prior information in simulated data and show better replication across datasets. Application of our approach to human cohort data highlighted two novel regulatory networks related to schizophrenia and lean body mass for which we generated novel functional hypotheses.

CONCLUSIONS

We demonstrate that existing biological knowledge can improve the integrative analysis of networks underlying trans associations and generate novel hypotheses about regulatory mechanisms.

A novel chemokine-based signature for prediction of prognosis and therapeutic response in glioma

AIMS

Gliomas are the primary malignant brain tumor and characterized as the striking cellular heterogeneity and intricate tumor microenvironment (TME), where chemokines regulate immune cell trafficking by shaping local networks. This study aimed to construct a chemokine-based gene signature to evaluate the prognosis and therapeutic response in glioma.

METHODS

In this study, 1024 patients (699 from TCGA and 325 from CGGA database) with clinicopathological information and mRNA sequencing data were enrolled. A chemokine gene signature was constructed by combining LASSO and SVM-RFE algorithm. GO, KEGG, and GSVA analyses were performed for function annotations of the chemokine signature. Candidate mRNAs were subsequently verified through qRT-PCR in an independent cohort including 28 glioma samples. Then, through immunohistochemical staining (IHC), we detected the expression of immunosuppressive markers and explore the role of this gene signature in immunotherapy for glioma. Lastly, the Genomics of Drug Sensitivity in Cancer (GDSC) were leveraged to predict the potential drug related to the gene signature in glioma.

RESULTS

A constructed chemokine gene signature was significantly associated with poorer survival, especially in glioblastoma, IDH wildtype. It also played an independent prognostic factor in both datasets. Moreover, biological function annotations of the predictive signature indicated the gene signature was positively associated with immune-relevant pathways, and the immunosuppressive protein expressions (PD-L1, IBA1, TMEM119, CD68, CSF1R, and TGFB1) were enriched in the high-risk group. In an immunotherapy of glioblastoma cohort, we confirmed the chemokine signature showed a good predictor for patients' response. Lastly, we predicted twelve potential agents for glioma patients with higher riskscore.

CONCLUSION

In all, our results highlighted a potential 4-chemokine signature for predicting prognosis in glioma and reflected the intricate immune landscape in glioma. It also threw light on integrating tailored risk stratification with precision therapy for glioblastoma.

Using AI-Based Evolutionary Algorithms to Elucidate Adult Brain Tumor (Glioma) Etiology Associated with IDH1 for Therapeutic Target Identification

Adult brain tumors (glioma) represent a cancer of unmet need where standard-of-care is non-curative; thus, new therapies are urgently needed. It is unclear whether isocitrate dehydrogenases (IDH1/2) when not mutated have any role in gliomagenesis or tumor growth. Nevertheless, IDH1 is overexpressed in glioblastoma (GBM), which could impact upon cellular metabolism and epigenetic reprogramming. This study characterizes IDH1 expression and associated genes and pathways. A novel biomarker discovery pipeline using artificial intelligence (evolutionary algorithms) was employed to analyze IDH-wildtype adult gliomas from the TCGA LGG-GBM cohort. Ninety genes whose expression correlated with IDH1 expression were identified from: (1) All gliomas, (2) primary GBM, and (3) recurrent GBM tumors. Genes were overrepresented in ubiquitin-mediated proteolysis, focal adhesion, mTOR signaling, and pyruvate metabolism pathways. Other non-enriched pathways included O-glycan biosynthesis, notch signaling, and signaling regulating stem cell pluripotency (PCGF3). Potential prognostic (TSPYL2, JAKMIP1, CIT, TMTC1) and two diagnostic (MINK1, PLEKHM3) biomarkers were downregulated in GBM. Their gene expression and methylation were negatively and positively correlated with IDH1 expression, respectively. Two diagnostic biomarkers (BZW1, RCF2) showed the opposite trend. Prognostic genes were not impacted by high frequencies of molecular alterations and only one (TMTC1) could be validated in another cohort. Genes with mechanistic links to IDH1 were involved in brain neuronal development, cell proliferation, cytokinesis, and O-mannosylation as well as tumor suppression and anaplerosis. Results highlight metabolic vulnerabilities and therapeutic targets for use in future clinical trials.

Comprehensive analysis of epigenetics regulation, prognostic and the correlation with immune infiltrates of GPX7 in adult gliomas

Gliomas are the most commonly occurring malignant brain tumor characterized by an immunosuppressive microenvironment accompanied by profound epigenetic changes, thus influencing the prognosis. Glutathione peroxidase 7 (GPX7) is essential for regulating reactive oxygen species homeostasis under oxidative stress. However, little is known about the function of GPX7 in gliomas. In this study, we hypothesized that GPX7 methylation status could influence biological functions and local immune responses that ultimately impact prognosis in adult gliomas. We conducted an integrated bioinformatics analysis mining GPX7 DNA methylation status, transcriptional and survival data of glioma patients. We discovered that GPX7 was remarkably increased in glioma tissues and cell lines, and was associated with poor prognosis. This upregulation was significantly linked to clinicopathological and molecular features, besides being expressed in a cell cycle-dependent manner. Our results consistently demonstrated that upregulation of GPX7 is tightly modulated by epigenetic processes, which also impacted the overall survival of patients with low-grade gliomas (LGG). Based on the analysis of biological functions, we found that GPX7 might be involved in immune mechanisms involving both innate and adaptive immunity, type I interferon production and regulation of synaptic transmission in LGG, whereas in GBM, it is mainly related to metabolic regulation of mitochondrial dynamics. We also found that GPX7 strongly correlates with immune cell infiltration and diverse immune cell markers, suggesting its role in tumor-specific immune response and in regulating the migration of immune cell types to the tumor microenvironment. Combining these multiple data, we provided the first evidence regarding the epigenetic-mediated regulatory mechanisms underlying GPX7 activation in gliomas. Furthermore, our study brings key insights into the significant effect of GPX7 in modulating both immune molecules and in immune cell infiltration in the microenvironment of gliomas, which might impact the patient outcome, opening up future opportunities to regulate the local immune response.

A Qualitative Signature to Identify TERT Promoter Mutant High-Risk Tumors in Low-Grade Gliomas

Background: Telomerase reverse transcriptase promoter (TERT-p) mutation has been frequently found, but associated with contrary prognosis, in both low-grade gliomas and glioblastomas. For the low-grade gliomas (Grades II-III), TERT-p mutant patients have a better prognosis than the wildtype patients, whereas for the GBMs (Grade IV), TERT-p mutation is related to a poor prognosis. We hypothesize that there exist high-risk patients in LGGs who share GBM-like molecular features, including TERT-p mutation, and need more intensive treatment than other LGGs. A molecular signature is needed to identify these high-risk patients for an accurate and timely treatment.

Methods: Using the within-sample relative expression orderings of gene pairs, we identified the gene pairs with significantly stable REOs, respectively, in both the TERT-p mutant LGGs and GBMs but with opposite directions in the two groups. These reversely stable gene pairs were used as the molecular signature to stratify the LGGs into high-risk and low-risk groups.

Results: A signature consisting of 21 gene pairs was developed, which can classify LGGs into two groups with significantly different overall survival. The high-risk group has a similar genetic mutation profile and a similar survival profile as GBMs, and these high-risk tumors may progress to a more malignant state.

Conclusion: The 21 gene-pair signature based on REOs is capable of identifying high-risk patients in LGGs and guiding the clinical choice for appropriate and timely intervention.

Alternations and Applications of the Structural and Functional Connectome in Gliomas: A Mini-Review

In the central nervous system, gliomas are the most common, but complex primary tumors. Genome-based molecular and clinical studies have revealed different classifications and subtypes of gliomas. Neuroradiological approaches have non-invasively provided a macroscopic view for surgical resection and therapeutic effects. The connectome is a structural map of a physical object, the brain, which raises issues of spatial scale and definition, and it is calculated through diffusion magnetic resonance imaging (MRI) and functional MRI. In this study, we reviewed the basic principles and attributes of the structural and functional connectome, followed by the alternations of connectomes and their influences on glioma. To extend the applications of connectome, we demonstrated that a series of multi-center projects still need to be conducted to systemically investigate the connectome and the structural–functional coupling of glioma. Additionally, the brain–computer interface based on accurate connectome could provide more precise structural and functional data, which are significant for surgery and postoperative recovery. Besides, integrating the data from different sources, including connectome and other omics information, and their processing with artificial intelligence, together with validated biological and clinical findings will be significant for the development of a personalized surgical strategy.

The Role of Artificial Intelligence in Early Cancer Diagnosis

Improving the proportion of patients diagnosed with early-stage cancer is a key priority of the World Health Organisation. In many tumour groups, screening programmes have led to improvements in survival, but patient selection and risk stratification are key challenges. In addition, there are concerns about limited diagnostic workforces, particularly in light of the COVID-19 pandemic, placing a strain on pathology and radiology services. In this review, we discuss how artificial intelligence algorithms could assist clinicians in (1) screening asymptomatic patients at risk of cancer, (2) investigating and triaging symptomatic patients, and (3) more effectively diagnosing cancer recurrence. We provide an overview of the main artificial intelligence approaches, including historical models such as logistic regression, as well as deep learning and neural networks, and highlight their early diagnosis applications. Many data types are suitable for computational analysis, including electronic healthcare records, diagnostic images, pathology slides and peripheral blood, and we provide examples of how these data can be utilised to diagnose cancer. We also discuss the potential clinical implications for artificial intelligence algorithms, including an overview of models currently used in clinical practice. Finally, we discuss the potential limitations and pitfalls, including ethical concerns, resource demands, data security and reporting standards.

Characteristics of Anaplastic Oligodendrogliomas Short-Term Survivors: A POLA Network Study

Background

Anaplastic oligodendrogliomas IDH-mutant and 1p/19q codeleted (AO) occasionally have a poor outcome. Herein we aimed at analyzing their characteristics.

Methods

We retrospectively analyzed the characteristics of 44 AO patients with a cancer-specific survival <5 years (short-term survivors, STS) and compared them with those of 146 AO patients with a survival ≥5 years (classical survivors, CS) included in the POLA network.

Results

Compared to CS, STS were older (P = .0001), less frequently presented with isolated seizures (P < .0001), more frequently presented with cognitive dysfunction (P < .0001), had larger tumors (P = .= .003), a higher proliferative index (P = .= .0003), and a higher number of chromosomal arm abnormalities (P = .= .02). Regarding treatment, STS less frequently underwent a surgical resection than CS (P = .= .0001) and were more frequently treated with chemotherapy alone (P = .= .009) or with radiotherapy plus temozolomide (P = .= .05). Characteristics independently associated with STS in multivariate analysis were cognitive dysfunction, a number of mitosis > 8, and the absence of tumor resection. Based on cognitive dysfunction, type of surgery, and number of mitosis, patients could be classified into groups of standard (18%) and high (62%) risk of <5 year survival.

Conclusion

The present study suggests that although STS poor outcome appears to largely result from a more advanced disease at diagnosis, surgical resection may be particularly important in this population.

Challenges of imaging interpretation to predict oligodendroglioma grade: a report from the Neuro-Oncology Branch

Background: To illustrate challenges of imaging interpretation in patients with oligodendroglioma seen at a referral center and evaluate interrater reliability. Methods: Two neuro-oncologists reviewed diagnostic preradiation MRIs of oligodendroglioma patients; interrater reliability was calculated with the kappa coefficient (k). A neuroradiologist measured presurgical apparent diffusion coefficient (ADC), if available. Results: Extensive enhancement was noted in four of 58 patients, k = 0.7; necrosis in seven of 58, k = 0.61; calcification in seven of 17, k = 1.0; diffusion restriction in two of 39 patients, k = 1.0 (all only in grade 3). ADC values with receiver operator characteristic analysis for area under the curve were 0.473, not significantly different from the null hypothesis (p = 0.14). Conclusions: Extensive enhancement, necrosis and calcification correlated with grade 3 oligodendroglioma in our sample. However, interrater variability is an important limitation when assessing radiographic features, supporting the need for standardization of imaging protocols and their interpretation.

MicroRNA Interactome Multiomics Characterization for Cancer Research and Personalized Medicine: An Expert Review

MicroRNAs (miRNAs) that are mutually modulated by their interacting partners (interactome) are being increasingly noted for their significant role in pathogenesis and treatment of various human cancers. Recently, miRNA interactome dissected with multiomics approaches has been the subject of focus since individual tools or methods failed to provide the necessary comprehensive clues on the complete interactome. Even though single-omics technologies such as proteomics can uncover part of the interactome, the biological and clinical understanding still remain incomplete. In this study, we present an expert review of studies involving multiomics approaches to identification of miRNA interactome and its application in mechanistic characterization, classification, and therapeutic target identification in a variety of cancers, and with a focus on proteomics. We also discuss individual or multiple miRNA-based interactome identification in various pathological conditions of relevance to clinical medicine. Various new single-omics methods that can be integrated into multiomics cancer research and the computational approaches to analyze and predict miRNA interactome are also highlighted in this review. In all, we contextulize the power of multiomics approaches and the importance of the miRNA interactome to achieve the vision and practice of predictive, preventive, and personalized medicine in cancer research and clinical oncology.

CODEL: phase III study of RT, RT + TMZ, or TMZ for newly diagnosed 1p/19q codeleted oligodendroglioma. Analysis from the initial study design

BACKGROUND

We report the analysis involving patients treated on the initial CODEL design.

METHODS

Adults (>18) with newly diagnosed 1p/19q World Health Organization (WHO) grade III oligodendroglioma were randomized to radiotherapy (RT; 5940 centigray ) alone (arm A); RT with concomitant and adjuvant temozolomide (TMZ) (arm B); or TMZ alone (arm C). Primary endpoint was overall survival (OS), arm A versus B. Secondary comparisons were performed for OS and progression-free survival (PFS), comparing pooled RT arms versus TMZ-alone arm.

RESULTS

Thirty-six patients were randomized equally. At median follow-up of 7.5 years, 83.3% (10/12) TMZ-alone patients progressed, versus 37.5% (9/24) on the RT arms. PFS was significantly shorter in TMZ-alone patients compared with RT patients (hazard ratio [HR] = 3.12; 95% CI: 1.26, 7.69; P = 0.014). Death from disease progression occurred in 3/12 (25%) of TMZ-alone patients and 4/24 (16.7%) on the RT arms. OS did not statistically differ between arms (comparison underpowered). After adjustment for isocitrate dehydrogenase (IDH) status (mutated/wildtype) in a Cox regression model utilizing IDH and RT treatment status as covariables (arm C vs pooled arms A + B), PFS remained shorter for patients not receiving RT (HR = 3.33; 95% CI: 1.31, 8.45; P = 0.011), but not OS ((HR = 2.78; 95% CI: 0.58, 13.22, P = 0.20). Grade 3+ adverse events occurred in 25%, 42%, and 33% of patients (arms A, B, and C). There were no differences between arms in neurocognitive decline comparing baseline to 3 months.

CONCLUSIONS

TMZ-alone patients experienced significantly shorter PFS than patients treated on the RT arms. The ongoing CODEL trial has been redesigned to compare RT + PCV versus RT + TMZ.

A New Era of Neuro-Oncology Research Pioneered by Multi-Omics Analysis and Machine Learning

Although the incidence of central nervous system (CNS) cancers is not high, it significantly reduces a patient’s quality of life and results in high mortality rates. A low incidence also means a low number of cases, which in turn means a low amount of information. To compensate, researchers have tried to increase the amount of information available from a single test using high-throughput technologies. This approach, referred to as single-omics analysis, has only been partially successful as one type of data may not be able to appropriately describe all the characteristics of a tumor. It is presently unclear what type of data can describe a particular clinical situation. One way to solve this problem is to use multi-omics data. When using many types of data, a selected data type or a combination of them may effectively resolve a clinical question. Hence, we conducted a comprehensive survey of papers in the field of neuro-oncology that used multi-omics data for analysis and found that most of the papers utilized machine learning techniques. This fact shows that it is useful to utilize machine learning techniques in multi-omics analysis. In this review, we discuss the current status of multi-omics analysis in the field of neuro-oncology and the importance of using machine learning techniques.

Multifaceted microglia - key players in primary brain tumour heterogeneity

Microglia are the resident innate immune cells of the immune-privileged CNS and, as such, represent the first line of defence against tissue injury and infection. Given their location, microglia are undoubtedly the first immune cells to encounter a developing primary brain tumour. Our knowledge of these cells is therefore important to consider in the context of such neoplasms. As the heterogeneous nature of the most aggressive primary brain tumours is thought to underlie their poor prognosis, this Review places a special emphasis on the heterogeneity of the tumour-associated microglia and macrophage populations present in primary brain tumours. Where available, specific information on microglial heterogeneity in various types and subtypes of brain tumour is included. Emerging evidence that highlights the importance of considering the heterogeneity of both the tumour and of microglial populations in providing improved treatment outcomes for patients is also discussed.

Aberrations of Genomic Imprinting in Glioblastoma Formation

In human glioblastoma (GBM), the presence of a small population of cells with stem cell characteristics, the glioma stem cells (GSCs), has been described. These cells have GBM potential and are responsible for the origin of the tumors. However, whether GSCs originate from normal neural stem cells (NSCs) as a consequence of genetic and epigenetic changes and/or dedifferentiation from somatic cells remains to be investigated. Genomic imprinting is an epigenetic marking process that causes genes to be expressed depending on their parental origin. The dysregulation of the imprinting pattern or the loss of genomic imprinting (LOI) have been described in different tumors including GBM, being one of the earliest and most common events that occurs in human cancers. Here we have gathered the current knowledge of the role of imprinted genes in normal NSCs function and how the imprinting process is altered in human GBM. We also review the changes at particular imprinted loci that might be involved in the development of the tumor. Understanding the mechanistic similarities in the regulation of genomic imprinting between normal NSCs and GBM cells will be helpful to identify molecular players that might be involved in the development of human GBM.

Per-sample standardization and asymmetric winsorization lead to accurate clustering of RNA-seq expression profiles

MOTIVATION

Data transformations are an important step in the analysis of RNA-seq data. Nonetheless, the impact of transformation on the outcome of unsupervised clustering procedures is still unclear.

RESULTS

Here, we present an Asymmetric Winsorization per Sample Transformation (AWST), which is robust to data perturbations and removes the need for selecting the most informative genes prior to sample clustering. Our procedure leads to robust and biologically meaningful clusters both in bulk and in single-cell applications.

AVAILABILITY

The AWST method is available at https://github.com/drisso/awst. The code to reproduce the analyses is available at https://github.com/drisso/awst\_analysis.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Beyond IDH-Mutation: Emerging Molecular Diagnostic and Prognostic Features in Adult Diffuse Gliomas

Diffuse gliomas are among the most common adult central nervous system tumors with an annual incidence of more than 16,000 cases in the United States. Until very recently, the diagnosis of these tumors was based solely on morphologic features, however, with the publication of the WHO Classification of Tumours of the Central Nervous System, revised 4th edition in 2016, certain molecular features are now included in the official diagnostic and grading system. One of the most significant of these changes has been the division of adult astrocytomas into IDH-wildtype and IDH-mutant categories in addition to histologic grade as part of the main-line diagnosis, although a great deal of heterogeneity in the clinical outcome still remains to be explained within these categories. Since then, numerous groups have been working to identify additional biomarkers and prognostic factors in diffuse gliomas to help further stratify these tumors in hopes of producing a more complete grading system, as well as understanding the underlying biology that results in differing outcomes. The field of neuro-oncology is currently in the midst of a "molecular revolution" in which increasing emphasis is being placed on genetic and epigenetic features driving current diagnostic, prognostic, and predictive considerations. In this review, we focus on recent advances in adult diffuse glioma biomarkers and prognostic factors and summarize the state of the field.

Cystathionine as a marker for 1p/19q codeleted gliomas by in vivo magnetic resonance spectroscopy

BACKGROUND

Codeletion of chromosome arms 1p and 19q (1p/19q codeletion) highly benefits diagnosis and prognosis in gliomas. In this study, we investigated the effect of 1p/19q codeletion on cancer cell metabolism and evaluated possible metabolic targets for tailored therapies.

METHODS

We combined in vivo 1H (proton) magnetic resonance spectroscopy (MRS) measurements in human gliomas with the analysis of a series of standard amino acids by liquid chromatography-mass spectroscopy (LC-MS) in human glioma biopsies. Sixty-five subjects with low-grade glioma were included in the study: 31 underwent the MRI/MRS examination, 47 brain tumor tissue samples were analyzed with LC-MS, and 33 samples were analyzed for gene expression with quantitative PCR. Additionally, we performed metabolic tracer experiments in cell models with 1p deletion.

RESULTS

We report the first in vivo detection of cystathionine by MRS in 1p/19q codeleted gliomas. Selective accumulation of cystathionine was observed in codeleted gliomas in vivo, in brain tissue samples, as well as in cells harboring heterozygous deletions for serine- and cystathionine-pathway genes located on 1p: phosphoglycerate dehydrogenase (PHGDH) and cystathionine gamma-lyase (CTH). Quantitative PCR analyses showed 40-50% lower expression of both PHGDH and CTH in 1p/19q codeleted gliomas compared with their non-codeleted counterparts.

CONCLUSIONS

Our results provide strong evidence of a selective vulnerability of codeleted gliomas to serine and glutathione depletion and point to cystathionine as a possible noninvasive marker of treatment response.

Systematically characterize the clinical and biological significances of 1p19q genes in 1p/19q non-codeletion glioma

1p/19q codeletion, which leads to the abnormal expression of 1p19q genes in oligodendroglioma, is associated with chemosensitivity and favorable prognosis. Here, we aimed to explore the clinical implications of 1p19q gene expression in 1p/19q non-codel gliomas. We analyzed expression of 1p19q genes in 668 1p/19q non-codel gliomas obtained from The Cancer Genome Atlas (n = 447) and the Chinese Glioma Genome Atlas (n = 221) for training and validation, respectively. The expression of 1p19q genes was significantly correlated with the clinicopathological features and overall survival of 1p/19q non-codel gliomas. Then, we derived a risk signature of 25 selected 1p19q genes that not only had prognosis value in total 1p/19q non-codel gliomas but also had prognosis value in stratified gliomas. The prognosis value of the risk signature was superior than known clinicopathological features in 1p/19q non-codel gliomas and was also highly associated with the following features: loss of CDKN2A/B copy number in mutant-IDH-astrocytoma; telomerase reverse transcriptase (TERT) promoter mutation, combined chromosome 7 gain/chromosome 10 loss and epidermal growth factor receptor amplification in wild-type-IDH-astrocytoma; classical and mesenchymal subtypes in glioblastoma. Furthermore, genes enriched in the biological processes of cell division, extracellular matrix, angiogenesis significantly correlated to the signature risk score, and this is also supported by the immunohistochemistry and cell biology experiments. In conclusion, the expression profile of 1p19q genes is highly associated with the malignancy and prognosis of 1p/19q non-codel gliomas. A 25-1p19q-gene signature has powerfully predictive value for both malignant molecular pathological features and prognosis across distinct subgroups of 1p/19q non-codel gliomas.

Ki-67 and MCM6 labeling indices are correlated with overall survival in anaplastic oligodendroglioma, IDH1-mutant and 1p/19q-codeleted: a multicenter study from the French POLA network

Anaplastic oligodendroglioma (AO), IDH-mutant and 1p/19q codeleted (IDHmut+/1p19qcodel), is a high-grade glioma with only limited prognostic markers. The primary objective of this study was to evaluate, by immunohistochemistry, the prognostic value of two proliferation markers, MCM6 and Ki-67, in a large series of IDHmut+/1p19qcodel AO included in the POLA ("Prise en charge des Oligodendrogliomes Anaplasiques") French national multicenter network. We additionally examined the transcriptome obtained from this series to understand the functional pathways dysregulated with the mRNA overexpression of these two markers. The labeling indices (LI) of MCM6 and Ki-67 were obtained via computer-assisted color image analyses on immunostained AO tissues of the cohort (n = 220). Furthermore, a subgroup of AO (n = 68/220) was used to perform transcriptomic analyses. A high LI of either MCM6 (≥50%) or Ki-67 (≥15%) correlated with shorter overall survival, both in univariate (P = 0.013 and P = 0.004, respectively) and multivariate analyses (P = 0.027; multivariate Cox model including age, mitotic index, MCM6 and Ki-67). MCM6 and Ki-67 LI also correlated with overall survival in an additional retrospective cohort of 30 grade II IDHmut+/1p19qcodel oligodendrogliomas. The prognostic value of MCM6 mRNA level was confirmed in The Cancer Genome Atlas (TCGA) IDHmut+/1p19qcodel gliomas. The transcriptomic approach revealed that high transcriptional expressions of MCM6 and MKI67 were both linked positively with cell cycle progression, DNA replication, mitosis, pro-neural phenotype as well as neurogenesis, and negatively with microglial cell activation, immune response, positive regulation of myelination, oligodendrocyte development, beta-amyloid binding and postsynaptic specialization. In conclusion, the overexpression of MCM6 and/or Ki-67 is independently associated to shorter overall survival in IDHmut+/1p19qcodel AO. These two easy-to-use and cost-effective markers could thus be used concurrently in routine pathology practice. Additionally, the transcriptomic analyses showed that AO with high proliferation index have down-regulated immune response and lower microglial cells activation, and bears pro-neural phenotype.

RNA processing genes characterize RNA splicing and further stratify lower-grade glioma

BACKGROUND

Aberrant expression of RNA processing genes may drive the alterative RNA profile in lower-grade gliomas (LGGs). Thus, we aimed to further stratify LGGs based on the expression of RNA processing genes.

METHODS

This study included 446 LGGs from The Cancer Genome Atlas (TCGA, training set) and 171 LGGs from the Chinese Glioma Genome Atlas (CGGA, validation set). The least absolute shrinkage and selection operator (LASSO) Cox regression algorithm was conducted to develop a risk-signature. The receiver operating characteristic (ROC) curves and Kaplan-Meier curves were used to study the prognosis value of the risk-signature.

RESULTS

Among the tested 784 RNA processing genes, 276 were significantly correlated with the OS of LGGs. Further LASSO Cox regression identified a 19-gene risk-signature, whose risk score was also an independently prognosis factor (P<0.0001, multiplex Cox regression) in the validation dataset. The signature had better prognostic value than the traditional factors "age", "grade" and "WHO 2016 classification" for 3- and 5-year survival both two datasets (AUCs > 85%). Importantly, the risk-signature could further stratify the survival of LGGs in specific subgroups of WHO 2016 classification. Furthermore, alternative splicing events for genes such as EGFR and FGFR were found to be associated with the risk score. mRNA expression levels for genes, which participated in cell proliferation and other processes, were significantly correlated to the risk score.

CONCLUSIONS

Our results highlight the role of RNA processing genes for further stratifying the survival of patients with LGGs and provide insight into the alternative splicing events underlying this role.

ADAR2/miR-589-3p axis controls glioblastoma cell migration/invasion

Recent studies have reported the emerging role of microRNAs (miRNAs) in human cancers. We systematically characterized miRNA expression and editing in the human brain, which displays the highest number of A-to-I RNA editing sites among human tissues, and in de novo glioblastoma brain cancer. We identified 299 miRNAs altered in their expression and 24 miRNAs differently edited in human brain compared to glioblastoma tissues. We focused on the editing site within the miR-589–3p seed. MiR-589–3p is a unique miRNA almost fully edited (∼100%) in normal brain and with a consistent editing decrease in glioblastoma. The edited version of miR-589–3p inhibits glioblastoma cell proliferation, migration and invasion, while the unedited version boosts cell proliferation and motility/invasion, thus being a potential cancer-promoting factor. We demonstrated that the editing of this miRNA is mediated by ADAR2, and retargets miR-589–3p from the tumor-suppressor PCDH9 to ADAM12, which codes for the metalloproteinase 12 promoting glioblastoma invasion. Overall, our study dissects the role of a unique brain-specific editing site within miR-589–3p, with important anticancer features, and highlights the importance of RNA editing as an essential player not only for diversifying the genomic message but also for correcting not-tolerable/critical genomic coding sites.

RCC2 promotes proliferation and radio-resistance in glioblastoma via activating transcription of DNMT1

Regulator of chromosome condensation 2 (RCC2) is a regulator of cell-cycle progression linked in multiple cancers to pro-tumorigenic phenomena including promotion of tumor growth, tumor metastases and poorer patient prognoses. However, the role of RCC2 in GBM remains under-investigated. Here, we sought to determine the relevance of RCC2 in GBM, as well as its roles in GBM development, progression and prognosis. Initial clinical evaluation determined significant RCC2 enrichment in GBM when compared to normal brain tissue, and elevated expression was closely associated with a poorer prognosis in glioma patients. Via shRNA inhibition, we determined that RCC2 is essential to tumor proliferation and tumorigenicity in vitro and in vivo. Additionally, RCC2 was determined to promote radioresistance of GBM tumor cells. Investigation of the underlying mechanisms implicated DNA mismatch repair, JAK-STAT pathway and activated transcription of DNA methyltransferase 1 (DNMT1). For validation, pharmacologic inhibition via administration of a DNMT1 inhibitor demonstrated attenuated GBM tumor growth both in vitro and in vivo. Collectively, this study determined a novel therapeutic target for GBM in the form of RCC2, which plays a pivotal role in GBM proliferation and radio-resistance via regulation of DNMT1 expression in a p-STAT3 dependent manner.

Molecular classification of adult gliomas: recent advances and future perspectives

PURPOSE OF REVIEW

This review summarizes recent advances in the molecular classification of adult gliomas.

RECENT FINDINGS

According to the 2016 WHO classification, five main molecular subgroups of adult diffuse gliomas can be distinguished based on the 1p/19q codeletion, isocitrate dehydrogenase (IDH), and histone H3.3 mutation status. In the future, this classification may be further refined based on the integration of additional biomarkers, in particular CDKN2A/B homozygous deletion in IDH-mutant astrocytomas, TERT promoter mutations, EGFR amplification, chromosome 7 gain and chromosome 10 loss in IDH-wildtype astrocytomas, and FGFR1 mutations in midline gliomas. Histone H3.3 G34R/V defines a distinct subgroup of hemispheric IDH-wildtype high-grade gliomas occurring in young patients and FGFR gene fusions characterize a subgroup of IDH-wildtype glioblastomas that could benefit from specific treatment approaches. RNA sequencing may identify targetable gene fusions in circumscribed gliomas lacking classical BRAF alterations. In chordoid gliomas, recurrent PRKCA mutations could serve as a new diagnostic marker. Among comprehensive molecular analysis methods, DNA methylation profiling appears as a particularly powerful approach to identify new molecular subgroups of gliomas and to classify difficult cases.

SUMMARY

The classification of adult gliomas may be improved by the integration of additional biomarkers and/or by comprehensive molecular analysis, in particular DNA methylation profiling. The most relevant approach, however, remains to be established.

Utility of Integrated Analysis of Pharmacogenomics and Pharmacometabolomics in Early Phase Clinical Trial: A Case Study of a New Molecular Entity

In this report, we present a case study of how pharmacogenomics and pharmacometabolomics can be useful to characterize safety and pharmacokinetic profiles in early phase new drug development clinical trials. During conducting a first-in-human trial for a new molecular entity, we were able to determine the mechanism of dichotomized variability in plasma drug concentrations, which appeared closely related to adverse drug reactions (ADRs) through integrated omics analysis. The pharmacogenomics screening was performed from whole blood samples using the Affymetrix DMET (Drug-Metabolizing Enzymes and Transporters) Plus microarray, and confirmation of genetic variants was performed using real-time polymerase chain reaction. Metabolomics profiling was performed from plasma samples using liquid chromatography coupled with quadrupole time-of-flight mass spectrometry. A GSTM1 null polymorphism was identified in pharmacogenomics test and the drug concentrations was higher in GSTM1 null subjects than GSTM1 functional subjects. The apparent drug clearance was 13-fold lower in GSTM1 null subjects than GSTM1 functional subjects (p < 0.001). By metabolomics analysis, we identified that the study drug was metabolized by cysteinylglycine conjugation in GSTM functional subjects but those not in GSTM1 null subjects. The incidence rate and the severity of ADRs were higher in the GSTM1 null subjects than the GSTM1 functional subjects. Through the integrated omics analysis, we could understand the mechanism of inter-individual variability in drug exposure and in adverse response. In conclusion, integrated multi-omics analysis can be useful for elucidating the various characteristics of new drug candidates in early phase clinical trials.

Oscillatory brain activity associates with neuroligin-3 expression and predicts progression free survival in patients with diffuse glioma

Introduction

Diffuse gliomas have local and global effects on neurophysiological brain functioning, which are often seen as ‘passive’ consequences of the tumor. However, seminal preclinical work has shown a prominent role for neuronal activity in glioma growth: mediated by neuroligin-3 (NLGN3), increased neuronal activity causes faster glioma growth. It is unclear whether the same holds true in patients. Here, we investigate whether lower levels of oscillatory brain activity relate to lower NLGN3 expression and predict longer progression free survival (PFS) in diffuse glioma patients.

Methods

Twenty-four newly diagnosed patients with diffuse glioma underwent magnetoencephalography and subsequent tumor resection. Oscillatory brain activity was approximated by calculating broadband power (0.5–48 Hz) of the magnetoencephalography. NLGN3 expression in glioma tissue was semi-quantitatively assessed by immunohistochemistry. Peritumor and global oscillatory brain activity was then compared between different levels of NLGN3 expression with Kruskal–Wallis tests. Cox proportional hazards analyses were performed to estimate the predictive value of oscillatory brain activity for PFS.

Results

Patients with low expression of NLGN3 had lower levels of global oscillatory brain activity than patients with higher NLGN3 expression (P < 0.001). Moreover, lower peritumor (hazard ratio 2.17, P = 0.008) and global oscillatory brain activity (hazard ratio 2.10, P = 0.008) predicted longer PFS.

Conclusions

Lower levels of peritumor and global oscillatory brain activity are related to lower NLGN3 expression and longer PFS, corroborating preclinical research. This study highlights the important interplay between macroscopically measured brain activity and glioma progression, and may lead to new therapeutic interventions in diffuse glioma patients.

Electronic supplementary material

The online version of this article (10.1007/s11060-018-2967-5) contains supplementary material, which is available to authorized users.

Machine Learning for Better Prognostic Stratification and Driver Gene Identification Using Somatic Copy Number Variations in Anaplastic Oligodendroglioma

BACKGROUND

1p/19q-codeleted anaplastic gliomas have variable clinical behavior. We have recently shown that the common 9p21.3 allelic loss is an independent prognostic factor in this tumor type. The aim of this study is to identify less frequent genomic copy number variations (CNVs) with clinical importance that may shed light on molecular oncogenesis of this tumor type.

MATERIALS AND METHODS

A cohort of 197 patients with anaplastic oligodendroglioma was collected as part of the French POLA network. Clinical, pathological, and molecular information was recorded. CNV analysis was performed using single-nucleotide polymorphism arrays. Computational biology and feature selection based on the random forests method were used to identify CNV events associated with overall survival and other clinical-pathological variables.

RESULTS

Recurrent chromosomal events were identified in chromosomes 4, 9, and 11. Forty-six focal amplification events and 22 focal deletion events were identified. Twenty-four focal CNV areas were associated with survival, and five of them were significantly associated with survival after multivariable analysis. Nine out of 24 CNV events were validated using an external cohort of The Cancer Genome Atlas. Five of the validated events contain a cancer-related gene or microRNA: CDKN2A deletion, SS18L1 amplification, RHOA/MIR191 copy-neutral loss of heterozygosity, FGFR3 amplification, and ARNT amplification. The CNV profile contributes to better survival prediction compared with clinical-based risk assessment.

CONCLUSION

Several recurrent CNV events, detected in anaplastic oligodendroglioma, enable better survival prediction. More importantly, they help in identifying potential genes for understanding oncogenesis and for personalized therapy.

IMPLICATIONS FOR PRACTICE

Genomic analysis of 197 anaplastic oligodendroglioma tumors reveals recurrent somatic copy number variation areas that may help in understanding oncogenesis and target identification for precision medicine. A machine learning multivariable model built using this genomic information enables better survival prediction.

Integrated Omics: Tools, Advances, and Future Approaches

With the rapid adoption of high-throughput omic approaches to analyze biological samples such as genomics, transcriptomics, proteomics, and metabolomics, each analysis can generate tera- to peta-byte sized data files on a daily basis. These data file sizes, together with differences in nomenclature among these data types, make the integration of these multi-dimensional omics data into biologically meaningful context challenging. Variously named as integrated omics, multi-omics, poly-omics, trans-omics, pan-omics, or shortened to just 'omics', the challenges include differences in data cleaning, normalization, biomolecule identification, data dimensionality reduction, biological contextualization, statistical validation, data storage and handling, sharing, and data archiving. The ultimate goal is towards the holistic realization of a 'systems biology' understanding of the biological question in hand. Commonly used approaches in these efforts are currently limited by the 3 i's - integration, interpretation, and insights. Post integration, these very large datasets aim to yield unprecedented views of cellular systems at exquisite resolution for transformative insights into processes, events, and diseases through various computational and informatics frameworks. With the continued reduction in costs and processing time for sample analyses, and increasing types of omics datasets generated such as glycomics, lipidomics, microbiomics, and phenomics, an increasing number of scientists in this interdisciplinary domain of bioinformatics face these challenges. We discuss recent approaches, existing tools, and potential caveats in the integration of omics datasets for development of standardized analytical pipelines that could be adopted by the global omics research community.

Reduced expression of DNA repair genes and chemosensitivity in 1p19q codeleted lower-grade gliomas

BACKGROUND

Lower-grade gliomas (LGGs, defined as WHO grades II and III) with 1p19q codeletion have increased chemosensitivity when compared to LGGs without 1p19q codeletion, but the mechanism is currently unknown.

METHODS

RNAseq data from 515 LGG patients in the Cancer Genome Atlas (TCGA) were analyzed to compare the effect of expression of the 9 DNA repair genes located on chromosome arms 1p and 19q on progression free survival (PFS) and overall survival (OS) between patients who received chemotherapy and those who did not. Chemosensitivity of cells with DNA repair genes knocked down was tested using MTS cell proliferation assay in HS683 cell line and U251 cell line.

RESULTS

The expression of 9 DNA repair genes on 1p and 19q was significantly lower in 1p19q-codeleted tumors (n = 175) than in tumors without the codeletion (n = 337) (p < 0.001). In LGG patients who received chemotherapy, lower expression of LIG1, POLD1, PNKP, RAD54L and MUTYH was associated with longer PFS and OS. This difference between chemotherapy and non-chemotherapy groups in the association of gene expression with survival was not observed in non-DNA repair genes located on chromosome arms 1p and 19q. MTS assays showed that knockdown of DNA repair genes LIG1, POLD1, PNKP, RAD54L and MUTYH significantly inhibited recovery in response to temozolomide when compared with control group (p < 0.001).

CONCLUSIONS

Our results suggest that reduced expression of DNA repair genes on chromosome arms 1p and 19q may account for the increased chemosensitivity of LGGs with 1p19q codeletion.

IDH-mutated astrocytomas with 19q-loss constitute a subgroup that confers better prognosis

IDH‐mutant gliomas are classified into astrocytic or oligodendroglial tumors by 1p/19q status in the WHO 2016 classification, with the latter presenting with characteristic morphology and better prognosis in general. However, the morphological and genetic features within each category are varied, and there might be distinguishable subtypes. We analyzed 170 WHO grade II‐IV gliomas resected in our institution. 1p/19q status was analyzed by microsatellite analysis, and genetic mutations were analyzed by next‐generation sequencing and Sanger sequencing. For validation, the Brain Lower Grade Glioma dataset of The Cancer Genome Atlas was analyzed. Of the 42 grade III IDH‐mutated gliomas, 12 were 1p‐intact/19q‐intact (anaplastic astrocytomas [AA]), 7 were 1p‐intact/19q‐loss (AA), and 23 showed 1p/19q‐codeletion (anaplastic oligodendrogliomas). Of the 88 IDH‐wild type glioblastomas (GBMs), 14 showed 1p‐intact/19q‐loss status. All of the seven 1p‐intact/19q‐loss AAs harbored TP53 mutation, but no TERT promotor mutation. All 19q‐loss AAs had regions presenting oligodendroglioma‐like morphology, and were associated with significantly longer overall survival compared to 19q‐intact AAs (P = .001). This tendency was observed in The Cancer Genome Atlas Lower Grade Glioma dataset. In contrast, there was no difference in overall survival between the 19q‐loss GBM and 19q‐intact GBM (P = .4). In a case of 19q‐loss AA, both oligodendroglial morphology and 19q‐loss disappeared after recurrence, possibly indicating correlation between 19q‐loss and oligodendroglial morphology. We showed that there was a subgroup, although small, of IDH‐mutated astrocytomas harboring 19q‐loss that present oligodendroglial morphology, and also were associated with significantly better prognosis compared to other 19q‐intact astrocytomas.

Network-based analysis of oligodendrogliomas predicts novel cancer gene candidates within the region of the 1p/19q co-deletion

Oligodendrogliomas are primary human brain tumors with a characteristic 1p/19q co-deletion of important prognostic relevance, but little is known about the pathology of this chromosomal mutation. We developed a network-based approach to identify novel cancer gene candidates in the region of the 1p/19q co-deletion. Gene regulatory networks were learned from gene expression and copy number data of 178 oligodendrogliomas and further used to quantify putative impacts of differentially expressed genes of the 1p/19q region on cancer-relevant pathways. We predicted 8 genes with strong impact on signaling pathways and 14 genes with strong impact on metabolic pathways widespread across the region of the 1p/19 co-deletion. Many of these candidates (e.g. ELTD1, SDHB, SEPW1, SLC17A7, SZRD1, THAP3, ZBTB17) are likely to push, whereas others (e.g. CAP1, HBXIP, KLK6, PARK7, PTAFR) might counteract oligodendroglioma development. For example, ELTD1, a functionally validated glioblastoma oncogene located on 1p, was overexpressed. Further, the known glioblastoma tumor suppressor SLC17A7 located on 19q was underexpressed. Moreover, known epigenetic alterations triggered by mutated SDHB in paragangliomas suggest that underexpressed SDHB in oligodendrogliomas may support and possibly enhance the epigenetic reprogramming induced by the IDH-mutation. We further analyzed rarely observed deletions and duplications of chromosomal arms within oligodendroglioma subcohorts identifying putative oncogenes and tumor suppressors that possibly influence the development of oligodendroglioma subgroups. Our in-depth computational study contributes to a better understanding of the pathology of the 1p/19q co-deletion and other chromosomal arm mutations. This might open opportunities for functional validations and new therapeutic strategies.

Incorporating Advances in Molecular Pathology Into Brain Tumor Diagnostics

Recent advances in molecular pathology have reshaped the practice of brain tumor diagnostics. The classification of gliomas has been restructured with the discovery of isocitrate dehydrogenase (IDH) 1/2 mutations in the vast majority of lower grade infiltrating gliomas and secondary glioblastomas (GBM), with IDH-mutant astrocytomas further characterized by TP53 and ATRX mutations. Whole-arm 1p/19q codeletion in conjunction with IDH mutations now define oligodendrogliomas, which are also enriched for CIC, FUBP1, PI3K, NOTCH1, and TERT-p mutations. IDH-wild-type (wt) infiltrating astrocytomas are mostly primary GBMs and are characterized by EGFR, PTEN, TP53, NF1, RB1, PDGFRA, and CDKN2A/B alterations, TERT-p mutations, and characteristic copy number alterations including gains of chromosome 7 and losses of 10. Other clinically and genetically distinct infiltrating astrocytomas include the aggressive H3K27M-mutant midline gliomas, and smaller subsets that occur in the setting of NF1 or have BRAF V600E mutations. Low-grade pediatric gliomas are both genetically and biologically distinct from their adult counterparts and often harbor a single driver event often involving BRAF, FGFR1, or MYB/MYBL1 genes. Large scale genomic and epigenomic analyses have identified distinct subgroups of ependymomas tightly linked to tumor location and clinical behavior. The diagnosis of embryonal neoplasms also integrates molecular testing: (I) 4 molecularly defined, biologically distinct subtypes of medulloblastomas are now recognized; (II) 3 histologic entities have now been reclassified under a diagnosis of "embryonal tumor with multilayered rosettes (ETMR), C19MC-altered"; and (III) atypical teratoid/rhabdoid tumors (AT/RT) now require SMARCB1 (INI1) or SMARCA4 (BRG1) alterations for their diagnosis. We discuss the practical use of contemporary biomarkers for an integrative diagnosis of central nervous system neoplasia.

Somatostatin receptor 2A in gliomas: Association with oligodendrogliomas and favourable outcome

Somatostatin receptor subtype 2A (SSTR2A) is a potential therapeutic target in gliomas. Data on SSTR2A expression in different glioma entities, however, is particularly conflicting. Our objective was to characterize SSTR2A status and explore its impact on survival in gliomas classified according to the specific molecular signatures of the updated WHO classification. In total, 184 glioma samples were retrospectively analyzed for SSTR2A expression using immunohistochemistry with monoclonal antibody UMB-1. Double staining with CD68 was used to exclude microglia and macrophages from analyses. SSTR2A staining intensity and its localization in tumor cells was evaluated and correlated with glioma entities and survival. Diagnoses included 101 glioblastomas (93 isocitrate dehydrogenase (IDH) -wildtype, 3 IDH-mutant, 5 not otherwise specified (NOS)), 60 astrocytomas (22 IDH-wildtype, 37 IDH-mutant, 1 NOS), and 23 oligodendrogliomas (19 IDH-mutant and 1p/19q-codeleted, 4 NOS). SSTR2A expression significantly associated with oligodendrogliomas (79% SSTR2A positive) compared to IDH-mutant or IDH-wildtype astrocytomas (27% and 23% SSTR2A positive, respectively), and especially glioblastomas of which only 13% were SSTR2A positive (p < 0.001, Fisher's exact test). The staining pattern in glioblastomas was patchy whereas more homogeneous membranous and cytoplasmic staining was detected in oligodendrogliomas. Positive SSTR2A was related to longer overall survival in grade II and III gliomas (HR 2.7, CI 1.2-5.8, p = 0.013). In conclusion, SSTR2A expression is infrequent in astrocytomas and negative in the majority of glioblastomas where it is of no prognostic significance. In contrast, oligodendrogliomas show intense membranous and cytoplasmic SSTR2A expression, which carries potential diagnostic, prognostic, and therapeutic value.

Comparative analysis of histologically classified oligodendrogliomas reveals characteristic molecular differences between subgroups

Background

Molecular data of histologically classified oligodendrogliomas are available offering the possibility to stratify these human brain tumors into clinically relevant molecular subtypes.

Methods

Gene copy number, mutation, and expression data of 193 histologically classified oligodendrogliomas from The Cancer Genome Atlas (TCGA) were analyzed by well-established computational approaches (unsupervised clustering, statistical testing, network inference).

Results

We applied hierarchical clustering to tumor gene copy number profiles and revealed three molecular subgroups within histologically classified oligodendrogliomas. We further screened these subgroups for molecular glioma markers (1p/19q co-deletion, IDH mutation, gain of chromosome 7 and loss of chromosome 10) and found that our subgroups largely resemble known molecular glioma subtypes. We excluded glioblastoma-like tumors (7a10d subgroup) and derived a gene expression signature distinguishing histologically classified oligodendrogliomas with concurrent 1p/19q co-deletion and IDH mutation (1p/19q subgroup) from those with predominant IDH mutation alone (IDHme subgroup). Interestingly, many signature genes were part of signaling pathways involved in the regulation of cell proliferation, differentiation, migration, and cell-cell contacts. We further learned a gene regulatory network associated with the gene expression signature revealing novel putative major regulators with functions in cytoskeleton remodeling (e.g. APBB1IP, VAV1, ARPC1B), apoptosis (CCNL2, CREB3L1), and neural development (e.g. MYTIL, SCRT1, MEF2C) potentially contributing to the manifestation of differences between both subgroups. Moreover, we revealed characteristic expression differences of several HOX and SOX transcription factors suggesting the activity of different glioma stemness programs in both subgroups.

Conclusions

We show that gene copy number profiles alone are sufficient to derive molecular subgroups of histologically classified oligodendrogliomas that are well-embedded into general glioma classification schemes. Moreover, our revealed novel putative major regulators and characteristic stemness signatures indicate that different developmental programs might be active in these subgroups, providing a basis for future studies.

Electronic supplementary material

The online version of this article (10.1186/s12885-018-4251-7) contains supplementary material, which is available to authorized users.

ATP binding cassette (ABC) transporters: expression and clinical value in glioblastoma

ATP-binding cassette transporters (ABC transporters) regulate traffic of multiple compounds, including chemotherapeutic agents, through biological membranes. They are expressed by multiple cell types and have been implicated in the drug resistance of some cancer cells. Despite significant research in ABC transporters in the context of many diseases, little is known about their expression and clinical value in glioblastoma (GBM). We analyzed expression of 49 ABC transporters in both commercial and patient-derived GBM cell lines as well as from 51 human GBM tumor biopsies. Using The Cancer Genome Atlas (TCGA) cohort as a training dataset and our cohort as a validation dataset, we also investigated the prognostic value of these ABC transporters in newly diagnosed GBM patients, treated with the standard of care. In contrast to commercial GBM cell lines, GBM-patient derived cell lines (PDCL), grown as neurospheres in a serum-free medium, express ABC transporters similarly to parental tumors. Serum appeared to slightly increase resistance to temozolomide correlating with a tendency for an increased expression of ABCB1. Some differences were observed mainly due to expression of ABC transporters by microenvironmental cells. Together, our data suggest that the efficacy of chemotherapeutic agents may be misestimated in vitro if they are the targets of efflux pumps whose expression can be modulated by serum. Interestingly, several ABC transporters have prognostic value in the TCGA dataset. In our cohort of 51 GBM patients treated with radiation therapy with concurrent and adjuvant temozolomide, ABCA13 overexpression is associated with a decreased progression free survival in univariate (p < 0.01) and multivariate analyses including MGMT promoter methylation (p = 0.05) suggesting reduced sensitivity to temozolomide in ABCA13 overexpressing GBM. Expression of ABC transporters is: (i) detected in GBM and microenvironmental cells and (ii) better reproduced in GBM-PDCL. ABCA13 expression is an independent prognostic factor in newly diagnosed GBM patients. Further prospective studies are warranted to investigate whether ABCA13 expression can be used to further personalize treatments for GBM.

Impact of 9p deletion and p16, Cyclin D1, and Myc hyperexpression on the outcome of anaplastic oligodendrogliomas

Objective

To study the presence of 9p deletion and p16, cyclin D1 and Myc expression and their respective diagnostic and prognostic interest in oligodendrogliomas.

Methods

We analyzed a retrospective series of 40 consecutive anaplastic oligodendrogliomas (OIII) from a single institution and compared them to a control series of 10 low grade oligodendrogliomas (OII). Automated FISH analysis of chromosome 9p status and immunohistochemistry for p16, cyclin D1 and Myc was performed for all cases and correlated with clinical and histological data, event free survival (EFS) and overall survival (OS).

Results

Chromosome 9p deletion was observed in 55% of OIII (22/40) but not in OII. Deletion was highly correlated to EFS (median = 29 versus 53 months, p<0.0001) and OS (median = 48 versus 83 months, p<0.0001) in both the total cohort and the OIII population. In 9p non-deleted oligodendrogliomas, p16 hyperexpression correlated with a shorter OS (p = 0.02 in OII and p = 0.0001 in OIII) whereas lack of p16 expression was correlated to a shorter EFS and OS in 9p deleted OIII (p = 0.001 and p = 0.0002 respectively). Expression of Cyclin D1 was significantly higher in OIII (median expression 45% versus 14% for OII, p = 0.0006) and was correlated with MIB-1 expression (p<0.0001), vascular proliferation (p = 0.002), tumor necrosis (p = 0.04) and a shorter EFS in the total cohort (p = 0.05). Hyperexpression of Myc was correlated to grade (median expression 27% in OII versus 35% in OIII, p = 0.03), and to a shorter EFS in 9p non-deleted OIII (p = 0.01).

Conclusion

Chromosome 9p deletion identifies a subset of OIII with significantly worse prognosis. The combination of 9p status and p16 expression level identifies two distinct OIII populations with divergent prognosis. Hyperexpression of Bcl1 and Myc appears highly linked to anaplasia but the prognostic value is unclear and should be investigated further.

The Updated World Health Organization Glioma Classification: Cellular and Molecular Origins of Adult Infiltrating Gliomas

CONTEXT

- In the recently updated World Health Organization (WHO) classification of central nervous system tumors, our concept of infiltrating gliomas as a molecular dichotomy between oligodendroglial and astrocytic tumors has been codified. Advances in animal models of glioma and a wealth of sophisticated molecular analyses of human glioma tissue have led to a greater understanding of some of the biologic underpinnings of gliomagenesis.

OBJECTIVE

- To review our understanding of gliomagenesis in the setting of the recently updated WHO classification of central nervous system tumors. Topics addressed include a summary of an updated diagnostic schema for infiltrating gliomas, the crucial importance of isocitrate dehydrogenase mutations, candidate cells of origin for gliomas, environmental and other posited contributing factors to gliomagenesis, and the possible role of chromatin topology in setting the stage for gliomagenesis.

DATA SOURCES

- We conducted a primary literature search using PubMed.

CONCLUSIONS

- With multidimensional molecular data sets spanning increasingly larger numbers of patients with infiltrating gliomas, our understanding of the disease at the point of surgical resection has improved dramatically and this understanding is reflected in the updated WHO classification. Animal models have demonstrated a diversity of candidates for glioma cells of origin, but crucial questions remain, including the role of neural stem cells, more differentiated progenitor cells, and glioma stem cells. At this stage the increase in data generated from human samples will hopefully inform the creation of newer animal models that will recapitulate more accurately the diversity of gliomas and provide novel insights into the biologic mechanisms underlying tumor initiation and progression.