Value of KI-67/MIB-1 labeling index and simpson grading system to predict the recurrence of who grade I intracranial meningiomas compared to who grade II

Recurrence of atypical and anaplastic intracranial Meningiomas: A meta-analysis of risk factors

BACKGROUND

The predictive role of multiple risk factors for intracranial atypical and anaplastic meningioma recurrence is convoluted. This meta-analysis assessed the predictive value of selected factors for recurrence in these Meningiomas.

METHODS

Studies encompassing risk factor data including gross total resection (GTR), subtotal resection (STR), post-op radiotherapy, Ki-67 % index >3 %, and location were searched for in PubMed, Embase, and Web of Science, and thereafter analyzed using robust Bayesian meta-analysis.

RESULTS

Eighteen observational studies involving 1589 patients met inclusion criteria for analysis. GTR was identified as a good prognostic factor for recurrence (OR = 0.212; 95 % CI (-1.972, -1.002); heterogeneity BF=0.702), and STR had a significantly higher risk of recurrence (OR = 4.43; 95 % CI 0.658-2.011; heterogeneity BF=0.724). Post-operative radiotherapy did not statistically significantly affect the recurrence process (OR = 1.02; 95 % CI (-1.848, 0.626); heterogeneity (BF=1.034)). Ki67 % index >3 % had an augmented chance of recurrence (OR = 2.38; 95 % CI (-0.220, 2.355); heterogeneity (BF=1.162)). A meta-regression analysis showed that WHO grade III Meningiomas had a higher chance of recurring than grade II Meningiomas.

CONCLUSION

Among the selected factors, STR and Ki67 % index > 3 % were associated with a higher risk of recurrence, with post-operative radiotherapy making no difference. GTR appeared to inversely impact recurrence. Compared to grade II, grade III Meningiomas had higher odds of recurring.

Biomarkers for prognosis of meningioma patients: A systematic review and meta-analysis

Meningioma is the most common primary brain tumor and many studies have evaluated numerous biomarkers for their prognostic value, often with inconsistent results. Currently, no reliable biomarkers are available to predict the survival, recurrence, and progression of meningioma patients in clinical practice. This study aims to evaluate the prognostic value of immunohistochemistry-based (IHC) biomarkers of meningioma patients. A systematic literature search was conducted up to November 2023 on PubMed, CENTRAL, CINAHL Plus, and Scopus databases. Two authors independently reviewed the identified relevant studies, extracted data, and assessed the risk of bias of the studies included. Meta-analyses were performed with the hazard ratio (HR) and 95% confidence interval (CI) of overall survival (OS), recurrence-free survival (RFS), and progression-free survival (PFS). The risk of bias in the included studies was evaluated using the Quality in Prognosis Studies (QUIPS) tool. A total of 100 studies with 16,745 patients were included in this review. As the promising markers to predict OS of meningioma patients, Ki-67/MIB-1 (HR = 1.03, 95%CI 1.02 to 1.05) was identified to associate with poor prognosis of the patients. Overexpression of cyclin A (HR = 4.91, 95%CI 1.38 to 17.44), topoisomerase II α (TOP2A) (HR = 4.90, 95%CI 2.96 to 8.12), p53 (HR = 2.40, 95%CI 1.73 to 3.34), vascular endothelial growth factor (VEGF) (HR = 1.61, 95%CI 1.36 to 1.90), and Ki-67 (HR = 1.33, 95%CI 1.21 to 1.46), were identified also as unfavorable prognostic biomarkers for poor RFS of meningioma patients. Conversely, positive progesterone receptor (PR) and p21 staining were associated with longer RFS and are considered biomarkers of favorable prognosis of meningioma patients (HR = 0.60, 95% CI 0.41 to 0.88 and HR = 1.89, 95%CI 1.11 to 3.20). Additionally, high expression of Ki-67 was identified as a prognosis biomarker for poor PFS of meningioma patients (HR = 1.02, 95%CI 1.00 to 1.04). Although only in single studies, KPNA2, CDK6, Cox-2, MCM7 and PCNA are proposed as additional markers with high expression that are related with poor prognosis of meningioma patients. In conclusion, the results of the meta-analysis demonstrated that PR, cyclin A, TOP2A, p21, p53, VEGF and Ki-67 are either positively or negatively associated with survival of meningioma patients and might be useful biomarkers to assess the prognosis.

The evaluation of tumorigenicity and characterization of colonies in a soft agar colony formation assay using polymerase chain reaction

In regenerative medicine, the tumorigenic potency of cells in cellular therapy products (CTPs) is a major concern for their application to patients. This study presents a method-the soft agar colony formation assay using polymerase chain reaction (PCR)-to evaluate tumorigenicity. MRC-5 cells, contaminated with HeLa cells, were cultured for up to 4 weeks in soft agar medium. Cell-proliferation-related mRNAs, Ki-67 and cyclin B, could be detected in 0.01% of HeLa cells after 5 days of culture, whereas cyclin-dependent kinase 1 (CDK1) could be detected after 2 weeks. On the other hand, CDK2, proliferating cell nuclear antigen (PCNA), and minichromosome maintenance protein 7 (MCM7) were not useful to detect HeLa cells even after 4 weeks of culture. The cancer stem cell (CSC) markers, aldehyde dehydrogenase 1 (ALDH1) and CD133 in 0.01% of HeLa cells, could be detected 2 and 4 weeks after culture, respectively. However, another CSC marker CD44 was not useful because its expression was also detected in MRC-5 cells alone. This study suggests that the application of the PCR method to the soft agar colony formation assay could evaluate not only the tumorigenic potency in the short-term but also characterize the colonies, eventually improving the safety of CTPs.

Association between preoperative hematologic markers and aggressive behavior in meningiomas

INTRODUCTION

Meningiomas have varying degrees of aggressive behavior. Some systemic hematologic makers are associated with malignancy, but their value in predicting aggressive meningioma behavior is not fully understood.

OBJECTIVE

To evaluate the association between preoperative markers such as neutrophil-lymphocyte ratio (NLR), neutrophil-monocyte ratio (NMR), monocyte-lymphocyte ratio (MLR), platelet-lymphocyte ratio (PLR), and prognostic nutritional index (PNI), and diagnostic and prognostic factors including WHO grade, proliferation index, presence of edema on preoperative MRI, and tumor recurrence.

METHODS

A retrospective review of patients treated between 2000 and 2019 with a preoperative complete blood count (CBC) differential lab draw before intracranial meningioma resection was conducted. All preoperative steroid dosages were converted to dexamethasone equivalents. Primary outcomes included presence/absence of perilesional edema, WHO grade, Ki-67/MIB-index, and recurrence. Univariate and multivariable regression analyses were conducted.

RESULTS

A total of 209 meningioma patients were included. Of these, 143 (68 %) were WHO grade I, 61 (29 %) grade II and 5 (2 %) were grade III. Recurrence was reported in 19 (9.1 %) tumors. No hematologic markers were associated with recurrence. In separate multivariable logistic analyses, no biomarkers were associated with perilesional edema or WHO grade. MLR was associated with higher MIB-index (p = 0.018, OR 6.57, 95 % CI 1.37-30.91).

CONCLUSION

Most hematologic markers were not associated with meningioma invasiveness, grade, proliferative index, or aggressiveness. Preoperative MLR was associated with high proliferation index in patients undergoing surgery for intracranial meningioma. Higher MLR could be a surrogate for meningioma proliferation and has potential to be used as an adjunct for risk-stratifying meningiomas.

A High MCM6 Proliferative Index in Atypical Meningioma Is Associated with Shorter Progression Free and Overall Survivals

The aim of this study was to evaluate the prognostic value of MCM6, in comparison with Ki-67, in two series of grade 1 and 2 meningiomas, and to evaluate its correlation with methylation classes. The first cohort included 100 benign (grade 1, World Health Organization 2021) meningiomas, and the second 69 atypical meningiomas (grade 2). Immunohistochemical Ki-67 and MCM6 labeling indices (LI) were evaluated independently by two observers. Among the atypical meningiomas, 33 cases were also studied by genome-wide DNA methylation. In grade 2 meningiomas, but not grade 1, both Ki-67 and MCM6 LIs were correlated with PFS (p = 0.004 and p = 0.005, respectively; Cox univariate analyses). Additionally, MCM6 was correlated with overall survival only in univariate analysis. In a multivariate model, including mitotic index, Ki-67, MCM6, age, sex, and the quality of surgical resection, only MCM6 was correlated with PFS (p = 0.046). Additionally, we found a significant correlation between PTEN loss and high MCM6 or Ki-67 LIs. Although no correlation was found with the methylation classes and subtypes returned by the meningioma algorithm MNGv2.4., MCM6 LI was significantly correlated with the methylation of 2 MCM6 gene body loci. In conclusion, MCM6 is a relevant prognostic marker in atypical meningiomas. This reproducible and easy-to-use marker allows the identification of a highly aggressive subtype of proliferative meningiomas, characterized notably by frequent PTEN losses, which was previously reported to be sensitive to histone deacetylase inhibitors.

An integrative non-invasive malignant brain tumors classification and Ki-67 labeling index prediction pipeline with radiomics approach

BACKGROUND

The histological sub-classes of brain tumors and the Ki-67 labeling index (LI) of tumor cells are major factors in the diagnosis, prognosis, and treatment management of patients. Many existing studies primarily focused on the classification of two classes of brain tumors and the Ki-67LI of gliomas. This study aimed to develop a preoperative non-invasive radiomics pipeline based on multiparametric-MRI to classify-three types of brain tumors, glioblastoma (GBM), metastasis (MET) and primary central nervous system lymphoma (PCNSL), and to predict their corresponding Ki-67LI.

METHODS

In this retrospective study, 153 patients with malignant brain tumors were involved. The radiomics features were extracted from three types of MRI (T1-weighted imaging (T1WI), fluid-attenuated inversion recovery (FLAIR), and contrast-enhanced T1-weighted imaging (CE-T1WI)) with three masks (tumor core, edema, and whole tumor masks) and selected by a combination of Pearson correlation coefficient (CORR), LASSO, and Max-Relevance and Min-Redundancy (mRMR) filters. The performance of six classifiers was compared and the top three performing classifiers were used to construct the ensemble learning model (ELM). The proposed ELM was evaluated in the training dataset (108 patients) by 5-fold cross-validation and in the test dataset (45 patients) by hold-out. The accuracy (ACC), sensitivity (SEN), specificity (SPE), F1-Score, and the area under the receiver operating characteristic curve (AUC) indicators evaluated the performance of the models.

RESULTS

The best feature sets and ELM with the optimal performance were selected to construct the tri-categorized brain tumor aided diagnosis model (training dataset AUC: 0.96 (95% CI: 0.93, 0.99); test dataset AUC: 0.93) and Ki-67LI prediction model (training dataset AUC: 0.96 (95% CI: 0.94, 0.98); test dataset AUC: 0.91). The CE-T1WI was the best single modality for all classifiers. Meanwhile, the whole tumor was the most vital mask for the tumor classification and the tumor core was the most vital mask for the Ki-67LI prediction.

CONCLUSION

The developed radiomics models led to the precise preoperative classification of GBM, MET, and PCNSL and the prediction of Ki-67LI, which could be utilized in clinical practice for the treatment planning for brain tumors.