A gene expression signature predicts recurrence-free survival in meningioma

Development of an MRI-Based Comprehensive Model Fusing Clinical, Radiomics and Deep Learning Models for Preoperative Histological Stratification in Intracranial Solitary Fibrous Tumor

BACKGROUND

Accurate preoperative histological stratification (HS) of intracranial solitary fibrous tumors (ISFTs) can help predict patient outcomes and develop personalized treatment plans. However, the role of a comprehensive model based on clinical, radiomics and deep learning (CRDL) features in preoperative HS of ISFT remains unclear.

PURPOSE

To investigate the feasibility of a CRDL model based on magnetic resonance imaging (MRI) in preoperative HS in ISFT.

STUDY TYPE

Retrospective.

POPULATION

Three hundred and ninety-eight patients from Beijing Tiantan Hospital, Capital Medical University (primary training cohort) and 49 patients from Lanzhou University Second Hospital (external validation cohort) with ISFT based on histopathological findings (237 World Health Organization [WHO] tumor grade 1 or 2, and 210 WHO tumor grade 3).

FIELD STRENGTH/SEQUENCE

3.0 T/T1-weighted imaging (T1) by using spin echo sequence, T2-weighted imaging (T2) by using fast spin echo sequence, and T1-weighted contrast-enhanced imaging (T1C) by using two-dimensional fast spin echo sequence.

ASSESSMENT

Area under the receiver operating characteristic curve (AUC) was used to assess the performance of the CRDL model and a clinical model (CM) in preoperative HS in the external validation cohort. The decision curve analysis (DCA) was used to evaluate the clinical net benefit provided by the CRDL model.

STATISTICAL TESTS

Cohen's kappa, intra-/inter-class correlation coefficients (ICCs), Chi-square test, Fisher's exact test, Student's t-test, AUC, DCA, calibration curves, DeLong test. A P value <0.05 was considered statistically significant.

RESULTS

The CRDL model had significantly better discrimination ability than the CM (AUC [95% confidence interval, CI]: 0.895 [0.807-0.912] vs. 0.810 [0.745-0.874], respectively) in the external validation cohort. The CRDL model can provide a clinical net benefit for preoperative HS at a threshold probability >20%.

DATA CONCLUSION

The proposed CRDL model holds promise for preoperative HS in ISFT, which is important for predicting patient outcomes and developing personalized treatment plans.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY: Stage 2.

New insights into the role of tetraspanin 6, 7, and 8 in physiology and pathology

BACKGROUND

The tetraspanin (TSPAN) family comprises 33 membrane receptors involved in various physiological processes in humans. Tetrasapanins are surface proteins expressed in cells of various organisms. They are localised to the cell membrane by four transmembrane domains (TM4SF). These domains bind several cell surface receptors and signalling proteins to tetraspanin-enriched lipid microdomains (TERM or TEM). Tetraspanins play a critical role in anchoring many proteins. They also act as a scaffold for cell signalling proteins.

AIM

To summarise how tetraspanins 6, 7 and 8 contribute to the carcinogenesis process in different types of cancer.

METHODS

To provide a comprehensive review of the role of tetraspanins 6, 7 and 8 in cancer biology, we conducted a thorough search in PubMed, Embase and performed manual search of reference list to collect and extract data.

DISCUSSION

The assembly of tetraspanins covers an area of approximately 100-400 nm. Tetraspanins are involved in various biological processes such as membrane fusion, aggregation, proliferation, adhesion, cell migration and differentiation. They can also regulate integrins, cell surface receptors and signalling molecules. Tetraspanins form direct bonds with proteins and other members of the tetraspanin family, forming a hierarchical network of interactions and are thought to be involved in cell and membrane compartmentalisation. Tetraspanins have been implicated in cancer progression and have been shown to have multiple binding partners and to promote cancer progression and metastasis. Clinical studies have documented a correlation between the level of tetraspanin expression and the prediction of cancer progression, including breast and lung cancer.

CONCLUSIONS

Tetraspanins are understudied in almost all cell types and their functions are not clearly defined. Fortunately, it has been possible to identify the basic mechanisms underlying the biological role of these proteins. Therefore, the purpose of this review is to describe the roles of tetraspanins 6, 7 and 8.

The Evolving Classification of Meningiomas: Integration of Molecular Discoveries to Inform Patient Care

Meningioma classification and treatment have evolved over the past eight decades. Since Bailey, Cushing, and Eisenhart's description of meningiomas in the 1920s and 1930s, there have been continual advances in clinical stratification by histopathology, radiography and, most recently, molecular profiling, to improve prognostication and predict response to therapy. Precise and accurate classification is essential to optimizing management for patients with meningioma, which involves surveillance imaging, surgery, primary or adjuvant radiotherapy, and consideration for clinical trials. Currently, the World Health Organization (WHO) grade, extent of resection (EOR), and patient characteristics are used to guide management. While these have demonstrated reliability, a substantial number of seemingly benign lesions recur, suggesting opportunities for improvement of risk stratification. Furthermore, the role of adjuvant radiotherapy for grade 1 and 2 meningioma remains controversial. Over the last decade, numerous studies investigating the molecular drivers of clinical aggressiveness have been reported, with the identification of molecular markers that carry clinical implications as well as biomarkers of radiotherapy response. Here, we review the historical context of current practices, highlight recent molecular discoveries, and discuss the challenges of translating these findings into clinical practice.

Detection of diagnostic and prognostic methylation-based signatures in liquid biopsy specimens from patients with meningiomas

Recurrence of meningiomas is unpredictable by current invasive methods based on surgically removed specimens. Identification of patients likely to recur using noninvasive approaches could inform treatment strategy, whether intervention or monitoring. In this study, we analyze the DNA methylation levels in blood (serum and plasma) and tissue samples from 155 meningioma patients, compared to other central nervous system tumor and non-tumor entities. We discover DNA methylation markers unique to meningiomas and use artificial intelligence to create accurate and universal models for identifying and predicting meningioma recurrence, using either blood or tissue samples. Here we show that liquid biopsy is a potential noninvasive and reliable tool for diagnosing and predicting outcomes in meningioma patients. This approach can improve personalized management strategies for these patients.

Update on the Diagnosis and Management of Meningiomas

This series of five articles (one original article and four reviews) focuses on the most recent and interesting research studies on the biomolecular and radiological diagnosis and the surgical and medical management of meningiomas [...].

Radiotherapy and radiosurgery for meningiomas

Meningiomas comprise a histologically and clinically diverse set of tumors arising from the meningothelial lining of the central nervous system. In the past decade, remarkable progress has been made in deciphering the biology of these common neoplasms. Nevertheless, effective systemic or molecular therapies for meningiomas remain elusive and are active areas of preclinical and clinical investigation. Thus, standard treatment modalities for meningiomas are limited to maximal safe resection, radiotherapy, or radiosurgery. This review examines the history, clinical rationale, and future directions of radiotherapy and radiosurgery as integral and effective treatments for meningiomas.

Hypermitotic meningiomas harbor DNA methylation subgroups with distinct biological and clinical features

BACKGROUND

Meningiomas, the most common primary intracranial tumors, can be separated into 3 DNA methylation groups with distinct biological drivers, clinical outcomes, and therapeutic vulnerabilities. Alternative meningioma grouping schemes using copy number variants, gene expression profiles, somatic short variants, or integrated molecular models have been proposed. These data suggest meningioma DNA methylation groups may harbor subgroups unifying contrasting theories of meningioma biology.

METHODS

A total of 565 meningioma DNA methylation profiles from patients with comprehensive clinical follow-up at independent discovery (n = 200) or validation (n = 365) institutions were reanalyzed and classified into Merlin-intact, Immune-enriched, or Hypermitotic DNA methylation groups. RNA sequencing from the discovery (n = 200) or validation (n = 302) cohort were analyzed in the context of DNA methylation groups to identify subgroups. Biological features and clinical outcomes were analyzed across meningioma grouping schemes.

RESULTS

RNA sequencing revealed differential enrichment of FOXM1 target genes across two subgroups of Hypermitotic meningiomas. Differential expression and ontology analyses showed the subgroup of Hypermitotic meningiomas without FOXM1 target gene enrichment was distinguished by gene expression programs driving macromolecular metabolism. Analysis of genetic, epigenetic, gene expression, or cellular features revealed Hypermitotic meningioma subgroups were concordant with Proliferative or Hypermetabolic meningiomas, which were previously reported alongside Merlin-intact and Immune-enriched tumors using an integrated molecular model. The addition of DNA methylation subgroups to clinical models refined the prediction of postoperative outcomes compared to the addition of DNA methylation groups.

CONCLUSIONS

Meningiomas can be separated into three DNA methylation groups and Hypermitotic meningiomas can be subdivided into Proliferative and Hypermetabolic subgroups, each with distinct biological and clinical features.

Molecular classification and grading of meningioma

PURPOSE

Meningiomas are the most common primary intracranial tumor in older adults (Ostrom et al. in Neuro Oncol 21(Suppl 5):v1-v100, 2019). Treatment is largely driven by, in addition to patient characteristics and extent of resection/Simpson grade, the World Health Organization (WHO) grading of meningiomas. The current grading scheme, based predominantly on histologic features and only limited molecular characterization of these tumors (WHO Classification of Tumours Editorial Board, in: Central nervous system tumours, International Agency for Research on Cancer, Lyon, 2021), (Mirian et al. in J Neurol Neurosurg Psychiatry 91(4):379-387, 2020), does not consistently reflect the biologic behavior of meningiomas. This leads to both under-treatment and over-treatment of patients, and hence, suboptimal outcomes (Rogers et al. in Neuro Oncol 18(4):565-574). The goal of this review is to synthesize studies to date investigating molecular features of meningiomas as they relate to patient outcomes, in order to clarify best practices in assessing and, therefore, treating meningiomas.

METHODS

The available literature of genomic landscape and molecular features of in meningioma was screened using PubMed.

RESULTS

Greater understanding of meningiomas is reached by integrating histopathology, mutational analysis, DNA copy number changes, DNA methylation profiles, and potentially additional modalities to fully capture the clinical and biologic heterogeneity of these tumors.

CONCLUSION

Diagnosis and classification of meningioma is best accomplished using a combination of histopathology with genomic and epigenomic factors. Future classification schemes may benefit from such an integrated approach.

Expressions of Progesterone Receptor of Orbital Meningiomas in Indonesia

OBJECTIVE

Visual disturbances that can heal after a complete resection of orbital meningiomas are only about 2.9%. Grading and expression of the progesterone receptor (PR) in orbital meningiomas, according to World Health Organization (WHO) is a useful predictive value of recurrence in the treatment management of orbital meningiomas. This study aims to determine the relationship of PR expression on the grading of orbital meningiomas as tumour prognostic factors.

METHODS

This cross-sectional observational analysis observed 44 orbital meningioma in Cicendo Eye Hospital Bandung and Hasan Sadikin Hospital between 2017-2020. We performed of mRNA PR with RT-qPCR technique and calculation with the 2∆∆Ct formula. Statistical analysis used the Kruskal-Wallis Test, followed by the Mann-Whitney post hoc test with p<0.005.

RESULTS

Relative expression of mRNA PR in meningioma orbita grade I to grade III decreased significantly the expression of relative mRNA PR at grade I, II, III of 21.69±44.35, 20.39±26.30 and 1.25±0.85, with Kruskal-Wallis test, p =0.007. Mann Whitney's test results showed relative mRNA PR expression between grades I and II not different (p = 0.055), relative expression mRNA PR between grades I and III differed significantly (p = 0.024), and relative expression mRNA PR between grades I and III was not different (p = 0.638).

CONCLUSION

mRNA PR expression is viable for prognostic value, predicting recurrence and implementing more effective management of subsequent therapy, it must be combined with other markers to determine the nature of the orbital meningioma.

Matched Paired Primary and Recurrent Meningiomas Points to Cell-Death Program Contributions to Genomic and Epigenomic Instability along Tumor Progression

Meningioma (MN) is an important cause of disability, and predictive tools for estimating the risk of recurrence are still scarce. The need for objective and cost-effective techniques addressed to this purpose is well known. In this study, we present methylation-specific multiplex ligation-dependent probe amplification (MS-MLPA) as a friendly method for deepening the understanding of the mechanisms underlying meningioma progression. A large follow-up allowed us to obtain 50 samples, which included the primary tumor of 20 patients in which half of them are suffering one recurrence and the other half are suffering more than one. We histologically characterized the samples and performed MS-MLPA assays validated by FISH to assess their copy number alterations (CNA) and epigenetic status. Interestingly, we determined the increase in tumor instability with higher values of CNA during the progression accompanied by an increase in epigenetic damage. We also found a loss of HIC1 and the hypermethylation of CDKN2B and PTEN as independent prognostic markers. Comparison between grade 1 and higher primary MN's self-evolution pointed to a central role of GSTP1 in the first stages of the disease. Finally, a high rate of alterations in genes that are related to apoptosis and autophagy, such as DAPK1, PARK2, BCL2, FHIT, or VHL, underlines an important influence on cell-death programs through different pathways.

Glutamine anaplerosis is required for amino acid biosynthesis in human meningiomas

BACKGROUND

We postulate that meningiomas undergo distinct metabolic reprogramming in tumorigenesis and unraveling their metabolic phenotypes provide new therapeutic insights. Glutamine catabolism is key to the growth and proliferation of tumors. Here, we investigated the metabolomics of freshly resected meningiomas and glutamine metabolism in patient-derived meningioma cells.

METHODS

1H NMR spectroscopy of tumor tissues from meningioma patients was used to differentiate the metabolite profiles of grade-I and grade-II meningiomas. Glutamine metabolism was examined using 13C/15N glutamine tracer, in 5 patient-derived meningioma cells.

RESULTS

Alanine, lactate, glutamate, glutamine, and glycine were predominantly elevated only in grade-II meningiomas by 74%, 76%, 35%, 75%, and 33%, respectively, with alanine and glutamine levels being statistically significant (P ≤ .02). 13C/15N glutamine tracer experiments revealed that both grade-I and -II meningiomas actively metabolize glutamine to generate various key carbon intermediates including alanine and proline that are necessary for the tumor growth. Also, it is shown that glutaminase (GLS1) inhibitor, CB-839 is highly effective in downregulating glutamine metabolism and decreasing proliferation in meningioma cells.

CONCLUSION

Alanine and glutamine/glutamate are mainly elevated in grade-II meningiomas. Grade-I meningiomas possess relatively higher glutamine metabolism providing carbon/nitrogen for the biosynthesis of key nonessential amino acids. GLS1 inhibitor (CB-839) is very effective in downregulating glutamine metabolic pathways in grade-I meningiomas leading to decreased cellular proliferation.

A Magnetic Resonance Imaging-Based Radiomic Model for the Noninvasive Preoperative Differentiation Between Transitional and Atypical Meningiomas

Preoperative distinction between transitional meningioma and atypical meningioma would aid the selection of appropriate surgical techniques, as well as the prognosis prediction. Here, we aimed to differentiate between these two tumors using radiomic signatures based on preoperative, contrast-enhanced T1-weighted and T2-weighted magnetic resonance imaging. A total of 141 transitional meningioma and 101 atypical meningioma cases between January 2014 and December 2018 with a histopathologically confirmed diagnosis were retrospectively reviewed. All patients underwent magnetic resonance imaging before surgery. For each patient, 1227 radiomic features were extracted from contrast-enhanced T1-weighted and T2-weighted images each. Least absolute shrinkage and selection operator regression analysis was performed to select the most informative features of different modalities. Subsequently, stepwise multivariate logistic regression was chosen to further select strongly correlated features and build classification models that can distinguish transitional from atypical meningioma. The diagnostic abilities were evaluated by receiver operating characteristic analysis. Furthermore, a nomogram was built by incorporating clinical characteristics, radiological features, and radiomic signatures, and decision curve analysis was used to validate the clinical usefulness of the nomogram. Sex, tumor shape, brain invasion, and four radiomic features differed significantly between transitional meningioma and atypical meningioma. The clinicoradiomic model derived by fusing the above features resulted in the best discrimination ability, with areas under the curves of 0.809 (95% confidence interval, 0.743-0.874) and 0.795 (95% confidence interval, 0.692-0.899) and sensitivity values of 74.0% and 71.4% in the training and validation cohorts, respectively. The clinicoradiomic model demonstrated good performance for the differentiation between transitional and atypical meningioma. It is a quantitative tool that can potentially aid the selection of surgical techniques and the prognosis prediction and can thus be applied in patients with these two meningioma subtypes.

Consensus core clinical data elements for meningiomas

BACKGROUND

With increasing molecular analyses of meningiomas, there is a need to harmonize language used to capture clinical data across centers to ensure that molecular alterations are appropriately linked to clinical variables of interest. Here the International Consortium on Meningiomas presents a set of core and supplemental meningioma-specific Common Data Elements (CDEs) to facilitate comparative and pooled analyses.

METHODS

The generation of CDEs followed the four-phase process similar to other National Institute of Neurological Disorders and Stroke (NINDS) CDE projects: discovery, internal validation, external validation, and distribution.

RESULTS

The CDEs were organized into patient- and tumor-level modules. In total, 17 core CDEs (10 patient-level and 7-tumour-level) as well as 14 supplemental CDEs (7 patient-level and 7 tumour-level) were defined and described. These CDEs are now made publicly available for dissemination and adoption.

CONCLUSIONS

CDEs provide a framework for discussion in the neuro-oncology community that will facilitate data sharing for collaborative research projects and aid in developing a common language for comparative and pooled analyses. The meningioma-specific CDEs presented here are intended to be dynamic parameters that evolve with time and The Consortium welcomes international feedback for further refinement and implementation of these CDEs.

The Prognostic Value of Methylation Signatures and NF2 Mutations in Atypical Meningiomas

Simple Summary

The WHO 2016 classification of human meningiomas is debated due to the subjective evaluation of the histopathological diagnostics and grading. However, meningioma classification based on genome-wide DNA methylation profiling has become useful in classification of these tumors by being a better prognostic tool. The current pilot study was designed to test out genome-wide DNA methylation profiling on atypical meningiomas as these tumors have a highly variable risk of recurrence. Although we found that it had diagnostic value, further refinements on the methylation profile procedure are required. With this study we aim to motivate and impact researchers to continue to work and debate towards an improved meningioma classification including molecular and genetic biomarkers, which will benefit patients with such diagnoses.

Abstract

Background: Due to the solely subjective histopathological assessment, the WHO 2016 classification of human meningiomas is subject to interobserver variation. Consequently, the need for more reliable and objective markers are highly needed. The aim of this pilot study was to apply genome-wide DNA methylation analysis on a series of atypical meningiomas to evaluate the practical utility of this approach, examine whether prognostic subclasses are achieved and investigate whether there is an association between the methylation subclasses with poor prognosis and time to recurrence. NF1/2 mutation analyses were also performed to explore the prognostic value of such mutations in these atypical meningiomas. Methods: Twenty intracranial WHO grade II atypical meningiomas from adult patients were included. They consisted of 10 cases with recurrence (group I), and 10 cases without recurrence (group II). The formalin-fixed and paraffin-embedded tissues underwent standardized genome-wide DNA methylation analysis, and the profiles were matched with the reference library and tumor classifier from Heidelberg. NF1/2 somatic mutation analyses were performed using the CNSv1panel from Düsseldorf. Results: Eighteen out of 20 cases matched to the meningioma class using the common brain tumor classifier (v11b4). Four of these cases matched to a methylation subclass related to a prognostic subgroup based on a cut-off of 0.9. NF2 mutations were detected in 55% of cases across both groups, and the most prominent copy number alterations were chromosomal losses of 22q, 1p and 14q. No significant NF1 mutations were identified. Conclusions: Genome-wide DNA methylation profiling represents a useful tool in the diagnostics of meningiomas, however, methodological adjustments need to be addressed.

MPscore: A Novel Predictive and Prognostic Scoring for Progressive Meningioma

Simple Summary

Subtyping for meningioma is urgently required to stratify the patients with high risks of recurrence and progression due to the intertumoral heterogeneity in meningioma. Here, we performed a consensus clustering of 179 meningiomas and identified progressive subtype (subtype 3) based the transcriptome profiles. Loss of chromosome 1q along with Neurofibromin 2 (NF2) mutation or loss of chromosome 22p is exclusively presented in subtype 3 meningioma. DNA methylation analyses of meningioma subtypes also suggested hypermethylation was observed in subtype 3 meningioma. Our findings identified low expression of Alkaline Phosphatase (ALPL) is the most significant feature in progressive subtype of meningioma. We constructed and validated a meningioma progression score (MPscore) to characterize the progressive phenotype in meningioma. The predictive accuracy has also been validated in three independent cohorts. Therefore, MPscore can be potentially useful for meningioma recurrence prediction and stratification.

Abstract

Meningioma is the most common tumor in central nervous system (CNS). Although most cases of meningioma are benign (WHO grade I) and curable by surgical resection, a few tumors remain diagnostically and therapeutically challenging due to the frequent recurrence and progression. The heterogeneity of meningioma revealed by DNA methylation profiling suggests the demand of subtyping for meningioma. Therefore, we performed a clustering analyses to characterize the progressive features of meningioma and constructed a meningioma progression score to predict the risk of the recurrence. A total of 179 meningioma transcriptome from RNA sequencing was included for progression subtype clustering. Four biologically distinct subtypes (subtype 1, subtype 2, subtype 3 and subtype 4) were identified. Copy number alternation and genomewide DNA methylation of each subtype was also characterized. Immune cell infiltration was examined by the microenvironment cell populations counter. All anaplastic meningiomas (7/7) and most atypical meningiomas (24/32) are enriched in subtype 3 while no WHO II or III meningioma presents in subtype 1, suggesting subtype 3 meningioma is a progressive subtype. Stemness index and immune response are also heterogeneous across four subtypes. Monocytic lineage is the most immune cell type in all meningiomas, except for subtype 1. CD8 positive T cells are predominantly observed in subtype 3. To extend the clinical utility of progressive meningioma subtyping, we constructed the meningioma progression score (MPscore) by the signature genes in subtype 3. The predictive accuracy and prognostic capacity of MPscore has also been validated in three independent cohort. Our study uncovers four biologically distinct subtypes in meningioma and the MPscore is potentially helpful in the recurrence risk prediction and response to treatments stratification in meningioma.

A Prognostic Gene-Expression Signature and Risk Score for Meningioma Recurrence After Resection

BACKGROUND

Prognostic markers for meningioma are needed to risk-stratify patients and guide postoperative surveillance and adjuvant therapy.

OBJECTIVE

To identify a prognostic gene signature for meningioma recurrence and mortality after resection using targeted gene-expression analysis.

METHODS

Targeted gene-expression analysis was used to interrogate a discovery cohort of 96 meningiomas and an independent validation cohort of 56 meningiomas with comprehensive clinical follow-up data from separate institutions. Bioinformatic analysis was used to identify prognostic genes and generate a gene-signature risk score between 0 and 1 for local recurrence.

RESULTS

We identified a 36-gene signature of meningioma recurrence after resection that achieved an area under the curve of 0.86 in identifying tumors at risk for adverse clinical outcomes. The gene-signature risk score compared favorably to World Health Organization (WHO) grade in stratifying cases by local freedom from recurrence (LFFR, P < .001 vs .09, log-rank test), shorter time to failure (TTF, F-test, P < .0001), and overall survival (OS, P < .0001 vs .07) and was independently associated with worse LFFR (relative risk [RR] 1.56, 95% CI 1.30-1.90) and OS (RR 1.32, 95% CI 1.07-1.64), after adjusting for clinical covariates. When tested on an independent validation cohort, the gene-signature risk score remained associated with shorter TTF (F-test, P = .002), compared favorably to WHO grade in stratifying cases by OS (P = .003 vs P = .10), and was significantly associated with worse OS (RR 1.86, 95% CI 1.19-2.88) on multivariate analysis.

CONCLUSION

The prognostic meningioma gene-expression signature and risk score presented may be useful for identifying patients at risk for recurrence.

Radiomic features of magnetic resonance images as novel preoperative predictive factors of bone invasion in meningiomas

PURPOSE

Bone invasion in meningiomas is a prognostic determinant, and a priori knowledge may alter surgical techniques. Here, we aim to predict bone invasion in meningiomas using radiomic signatures based on preoperative, contrast-enhanced T1-weighted (T1C) and T2-weighted (T2) magnetic resonance imaging (MRI).

METHODS

In this retrospective study, 490 patients diagnosed with meningiomas, including WHO grade I (448cases), grade II (38cases), and grade III (4cases), were enrolled and 213 out of 490 cases (43.5 %) had bone invasion. The patients were randomly divided into training (n = 343) and test (n = 147) datasets at a 7:3 ratio. For each patient, 1227 radiomic features were extracted from T1C and T2, respectively. Spearman's correlation and least absolute shrinkage and selection operator (LASSO) regression analyses were performed to select the most informative features. Subsequently, a 5-fold cross-validation was used to compare the performance of different classification algorithms, and logistic regression was chosen to predict the risk of bone invasion.

RESULTS

Eight radiomic features were selected from T1C and T2 respectively, and three models were built using radiomic features. The radiomic models derived from T1C alone or a combination of T1C and T2 had the best performance in predicting risk of bone invasion, with areas under the curve in the training dataset of 0.714 [95 % CI, 0.660-0.768] and 0.722 [95 % CI, 0.668-0.776] and in the test datasets of 0.715 [95 % CI, 0.632-0.798] and 0.713 [95 % CI, 0.628-0.798], respectively.

CONCLUSIONS

The radiomic model may aid clinicians with preoperative prediction of bone invasion by meningiomas, which can help in predicting prognosis and devising surgical strategies.

Molecular and translational advances in meningiomas

Meningiomas are the most common primary intracranial neoplasm. The current World Health Organization (WHO) classification categorizes meningiomas based on histopathological features, but emerging molecular data demonstrate the importance of genomic and epigenomic factors in the clinical behavior of these tumors. Treatment options for symptomatic meningiomas are limited to surgical resection where possible and adjuvant radiation therapy for tumors with concerning histopathological features or recurrent disease. At present, alternative adjuvant treatment options are not available in part due to limited historical biological analysis and clinical trial investigation on meningiomas. With advances in molecular and genomic techniques in the last decade, we have witnessed a surge of interest in understanding the genomic and epigenomic landscape of meningiomas. The field is now at the stage to adopt this molecular knowledge to refine meningioma classification and introduce molecular algorithms that can guide prediction and therapeutics for this tumor type. Animal models that recapitulate meningiomas faithfully are in critical need to test new therapeutics to facilitate rapid-cycle translation to clinical trials. Here we review the most up-to-date knowledge of molecular alterations that provide insight into meningioma behavior and are ready for application to clinical trial investigation, and highlight the landscape of available preclinical models in meningiomas.

A radiomics model for preoperative prediction of brain invasion in meningioma non-invasively based on MRI: A multicentre study

Background

Prediction of brain invasion pre-operatively rather than postoperatively would contribute to the selection of surgical techniques, predicting meningioma grading and prognosis. Here, we aimed to predict the risk of brain invasion in meningioma pre-operatively using a nomogram by incorporating radiomic and clinical features.

Methods

In this case-control study, 1728 patients from Beijing Tiantan Hospital (training cohort: n = 1070) and Lanzhou University Second Hospital (external validation cohort: n = 658) were diagnosed with meningiomas by histopathology. Radiomic features were extracted from the T1-weighted post-contrast and T2-weighted magnetic resonance imaging. The least absolute shrinkage and selection operator was used to select the most informative features of different modalities. The support vector machine algorithm was used to predict the risk of brain invasion. Furthermore, a nomogram was constructed by incorporating radiomics signature and clinical risk factors, and decision curve analysis was used to validate the clinical usefulness of the nomogram.

Findings

Sixteen features were significantly correlated with brain invasion. The clinicoradiomic model derived from the fusing MRI sequences and sex resulted in the best discrimination ability for risk prediction of brain invasion, with areas under the curves (AUCs) of 0•857 (95% CI, 0•831–0•887) and 0•819 (95% CI, 0•775–0•863) and sensitivities of 72•8% and 90•1% in the training and validation cohorts, respectively.

Interpretation

Our clinicoradiomic model showed good performance and high sensitivity for risk prediction of brain invasion in meningioma, and can be applied in patients with meningiomas.

Funding

This work was supported by the 10.13039/501100001809National Natural Science Foundation of China (81772006, 81922040); the 10.13039/501100004739Youth Innovation Promotion Association CAS (grant numbers 2019136); special fund project for doctoral training program of 10.13039/100012899Lanzhou University Second Hospital (grant numbers YJS-BD-33).

Molecular description of ANGPT2 associated colorectal carcinoma

Angiopoietin-2 (ANGPT2) is a prognostic factor in metastatic colorectal cancer (CRC). Nevertheless, it remains to be elucidated which molecular characteristics make up the ANGPT2-related poor-prognosis CRC subset. Public transcriptomic datasets were collected from Gene Expression Omnibus GEO and with the TCGAbiolinks R-package for the TCGA. After appropriate normalization, differential expression analysis was performed using Benjamini and Hochberg method for false discovery rate. Plasma from two prospective clinical trials were used to investigate the clinical impact of ANGPT2-related biomarkers. In the 935 samples included in four annotated platforms (GPL) and derived from localized CRC, ANGPT2^hi expression conferred a worst overall survival (HR = 1.20; p = 0.02). CRC stage, ANGPT2^hi expression but not Consortium Molecular Subtype (CMS) predict overall survival in multivariate analysis. ANGPT2 expression was not correlated with a specific CMS nor to RAS, RAF, MSI, p53, CIN, CIMP genomic alterations. Gene expression analysis revealed that ANGPT2^hi CRC subset is characterized by angiogenesis-related gene expression, presence of myeloid cells, stromal organization and resistance to chemotherapy. A prognostic model was proposed using seric levels of ANGPT2, STC1 and CD138 in 97 mCRC patients. Our results provide evidence that ANGPT2 is a prognostic factor in localized CRC and defined a specific CRC subset with potential clinical implementation.

Differentially Expressed MicroRNAs in Radioresistant and Radiosensitive Atypical Meningioma: A Clinical Study in Chinese Patients

Background: For atypical meningiomas (AMs), the combination of gross total resection (GTR) and adjuvant radiotherapy (ART) is still a controversial therapeutic strategy to improve prognosis. This study analyzed the factors influencing the prognosis on AM patients treated with GTR + ART by investigating both clinical characteristics and the change in microRNA (miRNA) expression.

Materials and Methods: Adult AM patients who were admitted to the Tiantan hospital from 2008 to 2015 and underwent GTR + ART were included. Patients who suffered recurrence within 3 years after operation were considered radioresistant, while the others were considered radiosensitive. Clinical characterizations were compared between these two groups. The microRNA (miRNA) expression was detected via miRNA microarray in 10 patients, five from the radiosensitive group and from the radioresistant group.

Results: A total of 55 cases were included in this study. No significant difference was found in the clinical characteristics (gender, age, tumor location, tumor size, peritumoral brain edema, and Ki-67 index) between radiosensitive and radioresistant patients. We found seven significantly upregulated miRNAs (miR-4286, miR-4695-5p, miR-6732-5p, miR-6855-5p, miR-7977, miR-6765-3p, miR-6787-5p) and seven significantly downregulated miRNAs (miR-1275, miR-30c-1-3p, miR-4449, miR-4539, miR-4684-3p, miR-6129, miR-6891-5p) in patients resistant to radiotherapy. The differentially expressed miRNAs were enriched mostly in the fatty acid metabolic pathways (hsa00061, hsa01212) and transforming growth factor beta signaling pathway (hsa04350).

Conclusion: For AM patients treated with GTR + ART, the changes in miRNA expression discovered in this study may be a potential predictor of individual sensitivity to adjuvant radiotherapy. Further research is needed regarding the predictive power and mechanism by which these miRNAs influence prognosis.

Are convexity meningiomas all the same? A clinico-radiological analysis of surgically treated eloquent areas convexity meningiomas

BACKGROUND

Convexity meningiomas are considered low-risk tumors, with high possibility of cure and low risk of relapse after resection. Very few studies have investigated meningiomas located in or around highly eloquent regions (namely perirolandic and perisylvian fissures). This study aimed to determine the differences in preoperative characteristics and postoperative outcomes between convexity meningiomas at eloquent area and non-eloquent areas.

METHODS

Retrospective study on patients who underwent surgical resection for convexity meningioma. Patients were divided into eloquent and non-eloquent area. Statistical analysis was made comparing preoperative and postoperative data of both groups.

RESULTS

The study included a total of 117 patients: 80 with eloquent area tumor and 37 with non- eloquent area tumor. Statistically significant differences were detected between the groups in preoperative KPS (93 ± 10 in eloquent vs. 97 ± 6 in non-eloquent; p = .008) and in large-caliber vein involvement (76.3% in cases vs. 16.2% in controls; p < .001). Postoperatively, patients with eloquent area tumors showed initial deterioration in neurological status followed by recovery; final outcomes were comparable to that of patients with non-eloquent area tumors. However, patients with eloquent area meningiomas had higher propensity to suffer from seizures postoperatively. Postoperative complications and long-term outcomes were not significantly different between the two groups.

CONCLUSIONS

Patients with eloquent areas convexity meningiomas do not appear to have higher surgical risk. Neurological status is more likely to worsen immediately after surgery but long-term recovery is satisfactory. Seizure control after surgery appears to be poorer in patients with perirolandic meningioma.

Leveraging the Role of the Metastatic Associated Protein Anterior Gradient Homologue 2 in Unfolded Protein Degradation: A Novel Therapeutic Biomarker for Cancer

Effective diagnostic, prognostic and therapeutic biomarkers can help in tracking disease progress, predict patients’ survival, and considerably affect the drive for successful clinical management. The present review aims to determine how the metastatic-linked protein anterior gradient homologue 2 (AGR2) operates to affect cancer progression, and to identify associated potential diagnostic, prognostic and therapeutic biomarkers, particularly in central nervous system (CNS) tumors. Studies that show a high expression level of AGR2, and associate the protein expression with the resilience to chemotherapeutic treatments or with poor cancer survival, are reported. The primary protein structures of the seven variants of AGR2, including their functional domains, are summarized. Based on experiments in various biological models, this review shows an orchestra of multiple molecules that regulate AGR2 expression, including a feedback loop with p53. The AGR2-associated molecular functions and pathways including genomic integrity, proliferation, apoptosis, angiogenesis, adhesion, migration, stemness, and inflammation, are detailed. In addition, the mechanisms that can enable the rampant oncogenic effects of AGR2 are clarified. The different strategies used to therapeutically target AGR2-positive cancer cells are evaluated in light of the current evidence. Moreover, novel associated pathways and clinically relevant deregulated genes in AGR2 high CNS tumors are identified using a meta-analysis approach.

Semaphorin 3C as a Therapeutic Target in Prostate and Other Cancers

The semaphorins represent a large family of signaling molecules with crucial roles in neuronal and cardiac development. While normal semaphorin function pertains largely to development, their involvement in malignancy is becoming increasingly evident. One member, Semaphorin 3C (SEMA3C), has been shown to drive a number of oncogenic programs, correlate inversely with cancer prognosis, and promote the progression of multiple different cancer types. This report surveys the body of knowledge surrounding SEMA3C as a therapeutic target in cancer. In particular, we summarize SEMA3C's role as an autocrine andromedin in prostate cancer growth and survival and provide an overview of other cancer types that SEMA3C has been implicated in including pancreas, brain, breast, and stomach. We also propose molecular strategies that could potentially be deployed against SEMA3C as anticancer agents such as biologics, small molecules, monoclonal antibodies and antisense oligonucleotides. Finally, we discuss important considerations for the inhibition of SEMA3C as a cancer therapeutic agent.