Meta-gene markers predict meningioma recurrence with high accuracy

Abstract

Meningiomas, the most common adult brain tumors, recur in up to half of cases. This requires timely intervention and therefore accurate risk assessment of recurrence is essential. Our current practice relies heavily on histological grade and extent of surgical excision to predict meningioma recurrence. However, prediction accuracy can be as poor as 50% for low or intermediate grade tumors which constitute the majority of cases. Moreover, attempts to find molecular markers to predict their recurrence have been impeded by low or heterogenous genetic signal. We therefore sought to apply systems-biology approaches to transcriptomic data to better predict meningioma recurrence. We apply gene co-expression networks to a cohort of 252 adult patients from the publicly available genetic repository Gene Expression Omnibus. Resultant gene clusters (“modules”) were represented by the first principle component of their expression, and their ability to predict recurrence assessed with a logistic regression model. External validation was done using two independent samples: one merged microarray-based cohort with a total of 108 patients and one RNA-seq-based cohort with 145 patients, using the same modules. We used the bioinformatics database Enrichr to examine the gene ontology associations and driver transcription factors of each module. Using gene co-expression analysis, we were able predict tumor recurrence with high accuracy using a single module which mapped to cell cycle-related processes (AUC of 0.81 ± 0.09 and 0.77 ± 0.10 in external validation using microarray and RNA-seq data, respectively). This module remained predictive when controlling for WHO grade in all cohorts, and was associated with several cancer-associated transcription factors which may serve as novel therapeutic targets for patients with this disease. With the easy accessibility of gene panels in healthcare diagnostics, our results offer a basis for routine molecular testing in meningioma management and propose potential therapeutic targets for future research.

BAP1-deficient meningioma presenting with trabecular architecture and cytokeratin expression: a report of two cases and review of the literature

“…Unlike other brain tumours such as gliomas that now require specific molecular alterations for their diagnosis, the WHO classification of meningiomas still relies heavily on histopathological features such as tumour cytoarchitecture and mitotic counts that are subject to pathologists’ subjective interpretation. However, recent developments from methylomics,4 5 genomics6 and transcriptomics7 and most recently from our group with integration of multiple data sets8 have been shown to outperform histopathology alone in predicting meningioma outcomes. Additionally, several adverse molecular prognostic factors have been identified: TERT promotor mutation, homozygous deletion (HD) of CDKN2A/B , and loss of H3K27me3 are known to be associated with aggressive biology and early recurrence 9 .…”

MicroRNAs miR-16 and miR-519 control meningioma cell proliferation via overlapping transcriptomic programs shared with the RNA-binding protein HuR

“…On another transcriptomic dataset [GSE74385 ( 9 )], the miR-mimics signature again classified samples in two groups. WHO grade 1 (13/16) and non-recurrent (16/20) meningioma were over-represented in the first, the second regrouping higher grades (29/37) and recurrent tumors (14/16), with decreased EP300 (P = 1.13e-04) and increased FOXM1 (P = 6.62e-07) levels, previously reported in recurrent meningiomas, as down- and upregulated, respectively ( 11 ).…”

“…Genes UP in RB were associated to mRNA processing and transport (all FDR < 0.05). Moreover, we checked EP300 level, which was reported to be a solid marker of meningioma recurrence, independently of WHO grade ( 11 ). Consistent differences were observed between the two subgroups (P = 2.75e-10; Figure 5C ) for this mRNA, along with other markers associated with aggressiveness/proliferation, also outside the 208-gene list ( HIF1A , RB1 , BAD or IDH1 ; all FDR < 1e-14).…”

“…In this regard, the restricted 208-gene signature deserves attention as many of these genes ( Table S8 ) may emerge as precious additions to the meningioma biomarker repertoire. Moreover, many targets genes and signatures proposed by the transcriptomics (HuR, HIF1A and hypoxia, EP300) have already been functionally validated or meta-analytically cross-validated in previous works ( 11 , 43 , 61 ). In a previous study on HuR in meningioma patients and following knockdown in the same meningioma cell lines, we already correlated mRNA and protein levels, as well as HuR cellular localization and post-translational modifications.…”

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

“…2b, c ). In addition to the noninvasive application of p-MeLB, its accuracy is comparable to or even surpasses other individual benchmark methods evaluated in surgical specimens, such as Ki-67/MIB1 immunoexpression (AUC: 87.7%) 44 , transcriptome-base markers (AUC: 0.81) 45 , 46 or composite scores involving multiple risk factors (AUC: 0.849) 47 with or without consideration for imaging features (AUC: 0.75-0.78) 48 .…”