Dataset Information

Recurrence/Regrowth in Grade I Meningioma: How to Predict?

ABSTRACT: The HLA-G and HLA-E molecules, Ki67, progesterone (PR), estrogen (ER) and androgen receptors (AR), p53, COX-2, and HER2 were studied to assess whether the biological behavior of grade I meningiomas is related to their expression. Tissue samples from 96 patients with grade I intracranial meningiomas were analyzed by immunohistochemistry on tissue microarray blocks (TMA) using antibodies specific for HLA-G, HLA-E, Ki67, PR, ER, AR, p53, COX-2, and HER2. Meningiomas were classified as small (?2 cm, 1.0%), medium (>2 and ?4 cm, 32.3%), and large (>4 cm, 66.7%). Tumor size was not related to recurrence/regrowth (p = 0.486), but was significantly correlated with peritumoral edema (p = 0.031) and intratumoral calcifications (p = 0.018). Recurrent meningiomas were observed in 14.6% of cases. Immunostaining for each marker was: HLA-G 100%; HLA-E 95.6%; PR 62%; ER 2.1%; AR 6.5%; p53 92.6%; COX-2 100%; HER2 0%; Ki67, mean 2.61 ± 2.29%, median 2.1%. Primary and recurrent meningiomas showed no significant relation with HLA-E and hormone receptors (p > 0.05), except for Ki67, where a higher median was observed in recurrent tumors than in primary (p = 0.014). The larger the tumor, the more severe the peritumoral edema, and the greater the presence of calcifications. Ki67 appears to be a good biomarker of recurrence/regrowth in grade I meningiomas.

SUBMITTER: de Carvalho GTC

PROVIDER: S-EPMC7438949 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Recurrence/Regrowth in Grade I Meningioma: How to Predict?

de Carvalho Gervásio Teles Cardoso GTC da Silva-Martins Warley Carvalho WC de Magalhães Kênia Cristina Soares Fonseca KCSF Nunes Cristiana Buzelin CB Soares Aleida Nazareth AN Tafuri Luciene Simões de Assis LSA Simões Renata Toscano RT

Frontiers in oncology 20200804

The HLA-G and HLA-E molecules, Ki67, progesterone (PR), estrogen (ER) and androgen receptors (AR), p53, COX-2, and HER2 were studied to assess whether the biological behavior of grade I meningiomas is related to their expression. Tissue samples from 96 patients with grade I intracranial meningiomas were analyzed by immunohistochemistry on tissue microarray blocks (TMA) using antibodies specific for HLA-G, HLA-E, Ki67, PR, ER, AR, p53, COX-2, and HER2. Meningiomas were classified as small (≤2 cm, ...[more]

PMID: 32903787

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Project description: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.

Project description:BackgroundWe and others have identified mutually exclusive molecular subgroups of meningiomas; however, the implications of this classification for clinical prognostication remain unclear. Integrated genomic and epigenomic analyses implicate unique oncogenic processes associated with each subgroup, suggesting the potential for divergent clinical courses. The aim of this study was to understand the associated clinical outcomes of each subgroup, as this could optimize treatment for patients.MethodsWe analyzed outcome data for 469 meningiomas of known molecular subgroup, including extent of resection, postoperative radiation, surveillance imaging, and time to recurrence, when applicable. Statistical relationships between outcome variables and subgroup were assessed. Features previously associated with recurrence were further investigated after stratification by subgroup. We used Kaplan-Meier analyses to compare progression-free survival, and identified factors significantly associated with recurrence using Cox proportional hazards modeling.ResultsMeningioma molecular subgroups exhibited divergent clinical courses at 2 years of follow-up, with several aggressive subgroups (NF2, PI3K, HH, tumor necrosis factor receptor-associated factor 7 [TRAF7]) recurring at an average rate of 22 times higher than others (KLF4, POLR2A, SMARCB1). PI3K-activated tumors recurred earlier than other subgroups but had intermediate long-term outcome. Among low-grade tumors, HH and TRAF7 meningiomas exhibited elevated recurrence compared with other subgroups. Recurrence of NF2 tumors was associated with male sex, high grade, and elevated Ki-67. Multivariate analysis identified molecular subgroup as an independent predictor of recurrence, along with grade and previous recurrence.ConclusionWe describe distinct clinical outcomes and recurrence rates associated with meningioma molecular subgroups. Our findings emphasize the importance of genomic characterization to guide postoperative management decisions for meningiomas.

Dataset Information

Recurrence/Regrowth in Grade I Meningioma: How to Predict?

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Recurrence/Regrowth in Grade I Meningioma: How to Predict?

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