MRI features as a helpful tool to predict the molecular subgroups of medulloblastoma: state of the art

Colafati, Giovanna Stefania; Voicu, Ioan Paul; Carducci, Chiara; Miele, Evelina; Carai, Andrea; Loreto, Simona Di; Marrazzo, Antonio; Cacchione, Antonella; Cecinati, Valerio; Tornesello, Assunta; Mastronuzzi, Angela

doi:10.1177/1756286418775375

Cited by 32 publications

(19 citation statements)

References 67 publications

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“…The risk stratification for medulloblastoma was suggested long ago without incorporating genetic findings (Packer et al, 2003). Magnetic resonance (MR) imaging-based signatures were promising in predicting molecular subgroups (Colafati et al, 2018) but have not been used in predicting patient prognosis. Although gene signatures were investigated in several studies, a major focus was on patient preselection for targeted therapy (Shou et al, 2015) or molecular classification (Corno et al, 2012;Chen et al, 2013).…”

Section: Discussionmentioning

confidence: 99%

Construction and Validation of a 13-Gene Signature for Prognosis Prediction in Medulloblastoma

et al. 2020

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Background: Recent studies have identified several molecular subgroups of medulloblastoma associated with distinct clinical outcomes; however, no robust gene signature has been established for prognosis prediction. Our objective was to construct a robust gene signature-based model to predict the prognosis of patients with medulloblastoma. Methods: Expression data of medulloblastomas were acquired from the Gene Expression Omnibus (GSE85217, n = 763; GSE37418, n = 76). To identify genes associated with overall survival (OS), we performed univariate survival analysis and least absolute shrinkage and selection operator (LASSO) Cox regression. A risk score model was constructed based on selected genes and was validated using multiple datasets. Differentially expressed genes (DEGs) between the risk groups were identified. Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Ontology (GO), and protein-protein interaction (PPI) analyses were performed. Network modules and hub genes were identified using Cytoscape. Furthermore, tumor microenvironment (TME) was evaluated using ESTIMATE algorithm. Tumor-infiltrating immune cells (TIICs) were inferred using CIBERSORTx. Results: A 13-gene model was constructed and validated. Patients classified as high-risk group had significantly worse OS than those as low-risk group (Training set: p < 0.0001; Validation set 1: p < 0.0001; Validation set 2: p = 0.00052). The area under the curve (AUC) of the receiver operating characteristic (ROC) analysis indicated a good performance in predicting 1-, 3-, and 5-year OS in all datasets. Multivariate analysis integrating clinical factors demonstrated that the risk score was an independent predictor for the OS (validation set 1: p = 0.001, validation set 2: p = 0.004). We then identified 265 DEGs between risk groups and PPI analysis predicted modules that were highly related to central nervous system and embryonic development. The risk score was significantly correlated with programmed deathligand 1 (PD-L1) expression (p < 0.001), as well as immune score (p = 0.035), stromal score (p = 0.010), and tumor purity (p = 0.010) in Group 4 medulloblastomas. Correlations between the 13-gene signature and the TIICs in Sonic hedgehog and Group 4 medulloblastomas were revealed.

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Section: Discussionmentioning

confidence: 99%

Construction and Validation of a 13-Gene Signature for Prognosis Prediction in Medulloblastoma

et al. 2020

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“…The authors concluded that although maximal safe resection should remain the standard of care, aggressive neuro-surgical removal of small residual portions of medulloblastoma should not be attempted, particularly when the anticipated morbidity is high. Several different studies discussed herein, some of which were summarized in a review previously suggest that MRI features can be a helpful and promising tool for early identification of molecular subgrouping in medulloblastoma with potential to influence therapy [41] . The availability of robust and reliable imagingbased predictors of molecular subgrouping could aid neuro-surgical decision-making.…”

Section: Practical and Clinical Implicationsmentioning

confidence: 99%

Radiogenomics of medulloblastoma: imaging surrogates of molecular biology

Dasgupta¹,

Gupta²

2018

JTGG

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Medulloblastoma is a heterogeneous disease comprising four molecular subgroups-wingless (WNT), sonic hedge hog (SHH), group 3, and group 4, with distinct developmental origins, unique transcriptional profiles, diverse phenotypes, and varying clinical outcomes. Magnetic resonance imaging (MRI) is the preferred first-line imaging modality in the diagnosis and staging of suspected brain tumors including medulloblastoma. It is being increasingly recognized that imaging features reflect underlying disease biology that can serve as independent predictive and prognostic biomarkers. Radiogenomics is an emerging field of research that aims to define relationships between non-invasive imaging features (radio-phenotypes) and genomic data/molecular markers (molecular phenotypes). Recent studies have reported encouraging data regarding imaging genomics of medulloblastoma with certain MRI features correlating with specific molecular subgroups. These include lateralized cerebellar location for SHH-subgroup; cerebellopontine angle location for WNT-subgroup; and inferior location with dilation of superior recess of the IVth ventricle for group 4 tumors. Minimal enhancement of primary tumor and ependymal metastases (infundibular/suprasellar) with mismatching pattern is a specific feature of group 4 medulloblastoma. A 5-metabolite signature profile on magnetic resonance spectroscopy reliably differentiates SHH-subgroup from non-WNT/non-SHH medulloblastoma. SHH-specific binary nomogram (location on horizontal and vertical axis, relationship with dorsal brainstem, pattern of contrast-enhancement, and peri-tumoral edema as discriminating imaging features) is associated with excellent predictive accuracy, followed by group 4-specific nomogram, with suboptimal accuracy of WNT and group 3-specific nomograms. The advent of deep machine-learning techniques and convoluted artificial neural networks should provide unique opportunities to further improve the accuracy of such radiogenomic correlation and prediction.

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“…As a first diagnostic procedure, a cranial CT-Scan is often performed. In the case of medulloblastoma patients, the tumor mass typically appears hyperdense on the CT scan [ 10 , 11 ]. Further diagnostic exams should be carried out after the initial proof of a cerebellar tumorous mass.…”

Section: Diagnostic and Stagingmentioning

confidence: 99%

Medulloblastoma in adults – reviewing the literature from a surgeon’s point of view

Eibl

Hammer

Yakubov

et al. 2021

Aging

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Medulloblastoma is a common primary brain tumor in children but it is a rare cancer in adult patients. We reviewed the literature, searching PubMed for articles on this rare tumor entity, with a focus on tumor biology, advanced neurosurgical opportunities for safe tumor resection, and multimodal treatment options. Adult medulloblastoma occurs at a rate of 0.6 per one million people per year. There is a slight disparity between male and female patients, and patients with a fair skin tone are more likely to have a medulloblastoma. Patients present with cerebellar signs and signs of elevated intracranial pressure. Diagnostic efforts should consist of cerebral MRI and MRI of the spinal axis. Cerebrospinal fluid should be investigated to look for tumor dissemination. Medulloblastoma tumors can be classified as classic, desmoplastic, anaplastic, and large cell, according to the WHO tumor classification. Molecular subgroups include WNT, SHH, group 3, and group 4 tumors. Further molecular analyses suggest that there are several subgroups within the four existing subgroups, with significant differences in patient age, frequency of metastatic spread, and patient survival. As molecular markers have started to play an increasing role in determining treatment strategies and prognosis, their importance has increased rapidly. Treatment options include microsurgical tumor resection and radiotherapy and, in addition, chemotherapy that respects the tumor biology of individual patients offers targeted therapeutic approaches. For neurosurgeons, intraoperative imaging and tumor fluorescence may improve resection rates. Disseminated disease, residual tumor after surgery, lower radiation dose, and low Karnofsky performance status are all suggestive of a poor outcome. Extraneural spread occurs only in very few cases. The reported 5-year-survival rates range between 60% and 80% for all adult medulloblastoma patients.

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MRI features as a helpful tool to predict the molecular subgroups of medulloblastoma: state of the art

Cited by 32 publications

References 67 publications

Construction and Validation of a 13-Gene Signature for Prognosis Prediction in Medulloblastoma

Construction and Validation of a 13-Gene Signature for Prognosis Prediction in Medulloblastoma

Radiogenomics of medulloblastoma: imaging surrogates of molecular biology

Medulloblastoma in adults – reviewing the literature from a surgeon’s point of view

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