Metabolism pathway-based subtyping in endometrial cancer: An integrated study by multi-omics analysis and machine learning algorithms.

Endometrial cancer (EC), the second most common malignancy in the female reproductive system, has garnered increasing attention for its genomic heterogeneity, but understanding of its metabolic characteristics is still poor. We explored metabolic dysfunctions in EC through a comprehensive multi-omics analysis (RNA-seq datasets from The Cancer Genome Atlas [TCGA], Cancer Cell Line Encyclopedia [CCLE], and GEO datasets; the Clinical Proteomic Tumor Analysis Consortium [CPTAC] proteomics; CCLE metabolomics) to develop useful molecular targets for precision therapy. Unsupervised consensus clustering was performed to categorize EC patients into three metabolism-pathway-based subgroups (MPSs). These MPS subgroups had distinct clinical prognoses, transcriptomic and genomic alterations, immune microenvironment landscape, and unique patterns of chemotherapy sensitivity. Moreover, the MPS2 subgroup had a better response to immunotherapy. Finally, three machine learning algorithms (LASSO, random forest, and stepwise multivariate Cox regression) were used for developing a prognostic metagene signature based on metabolic molecules. Thus, a 13-hub gene-based classifier was constructed to predict patients' MPS subtypes, offering a more accessible and practical approach. This metabolism-based classification system can enhance prognostic predictions and guide clinical strategies for immunotherapy and metabolism-targeted therapy in EC.

Evolution of neurosurgical advances and nuances in medulloblastoma therapy.

Medulloblastoma, the most common malignant brain tumor in children, presents a complex treatment challenge due to its propensity for infiltrative growth within the posterior fossa and its potential attachment to critical anatomical structures. Central to the management of medulloblastoma is the surgical resection of the tumor, which is a key determinant of patient prognosis. However, the extent of surgical resection (EOR), ranging from gross total resection (GTR) to subtotal resection (STR) or even biopsy, has been the subject of extensive debate and investigation within the medical community. Today, the impact of neurosurgical EOR on the prognosis of medulloblastoma patients remains a complex and evolving area of investigation. The conflicting findings in the literature, the challenges posed by critical surrounding anatomical structures, the potential for surgical complications and neurologic morbidity, and the nuanced interactions with molecular subgroups all contribute to the complexity of this issue. As the field continues to advance, the imperative to strike a delicate balance between maximizing resection and preserving quality of life remains central to the management of medulloblastoma patients.

Biological heterogeneity in diffuse large B-cell lymphoma.

Diffuse large B-cell lymphoma (DLBCL) is heterogeneous both in clinical outcomes and the underlying disease biology. Over the last 2 decades, several different approaches for dissecting biological heterogeneity have emerged. Gene expression profiling (GEP) stratifies DLBCL into 3 broad groups (ABC, GCB, and DZsig/MHG), each with parallels to different normal mature B cell developmental states and prognostic implications. More recently, several different genomic approaches have been developed to categorize DLBCL based on the co-occurrence of tumor somatic mutations, identifying more granular biologically unified subgroups that complement GEP-based approaches. We review the molecular approaches and clinical evidence supporting the stratification of DLBCL patients based on tumor biology. By offering a platform for subtype-guided therapy, these divisions remain a promising avenue for improving patient outcomes, especially in subgroups with inferior outcomes with current standard-of-care therapy.

Successful Implementation of Image-Guided Pencil-Beam Scanning Proton Therapy in Medulloblastomas.

Medulloblastoma is the most common malignant brain tumour in children, while much rarer in adults. Although the prognosis and outcomes have greatly improved in the era of modern multidisciplinary management, long-term treatment-induced toxicities are common. Craniospinal irradiation followed by a boost to the primary and metastatic tumour sites forms the backbone of treatment. Proton therapy has been endorsed over conventional photon-based radiotherapy due to its superior dosimetric advantages and subsequently lower incidence and severity of toxicities. We report here our experience from South-East Asia's first proton therapy centre of treating 40 patients with medulloblastoma (38 children and adolescents, 2 adults) who received image-guided, intensity-modulated proton therapy with pencil-beam scanning between 2019 and 2023, with a focus on dosimetry, acute toxicities, and early survival outcomes. All patients could complete the planned course of proton therapy, with mostly mild acute toxicities that were manageable on an outpatient basis. Haematological toxicity was not dose-limiting and did not prolong the overall treatment time. Preliminary data on early outcomes including overall survival and disease-free survival are encouraging, although a longer follow-up and data on long-term toxicities are needed.

Opportunities and Advances in Radiomics and Radiogenomics for Pediatric Medulloblastoma Tumors.

Recent advances in artificial intelligence have greatly impacted the field of medical imaging and vastly improved the development of computational algorithms for data analysis. In the field of pediatric neuro-oncology, radiomics, the process of obtaining high-dimensional data from radiographic images, has been recently utilized in applications including survival prognostication, molecular classification, and tumor type classification. Similarly, radiogenomics, or the integration of radiomic and genomic data, has allowed for building comprehensive computational models to better understand disease etiology. While there exist excellent review articles on radiomics and radiogenomic pipelines and their applications in adult solid tumors, in this review article, we specifically review these computational approaches in the context of pediatric medulloblastoma tumors. Based on our systematic literature research via PubMed and Google Scholar, we provide a detailed summary of a total of 15 articles that have utilized radiomic and radiogenomic analysis for survival prognostication, tumor segmentation, and molecular subgroup classification in the context of pediatric medulloblastoma. Lastly, we shed light on the current challenges with the existing approaches as well as future directions and opportunities with using these computational radiomic and radiogenomic approaches for pediatric medulloblastoma tumors.

Identification of molecular subgroups in osteomyelitis induced by staphylococcus aureus infection through gene expression profiles.

BACKGROUND: Staphylococcus aureus (S. aureus) infection-induced osteomyelitis (OM) is an inflammatory bone disease accompanied by persistent bone destruction, and the treatment is challenging because of its tendency to recur. Present study was aimed to explore the molecular subgroups of S. aureus infection-induced OM and to deepen the mechanistic understanding for molecularly targeted treatment of OM. METHODS: Integration of 164 OM samples and 60 healthy samples from three datasets of the Gene Expression Omnibus (GEO) database. OM patients were classified into different molecular subgroups based on unsupervised algorithms and correlations of clinical characteristics between subgroups were analyzed. Next, The CIBERSORT algorithm was used to evaluate the proportion of immune cell infiltration in different OM subgroups. Weighted gene co-expression analysis (WGCNA) was used to identify different gene modules and explore the relationship with clinical characteristics, and further annotated OM subgroups and gene modules by the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. RESULTS: Two subgroups with excellent consistency were identified in this study, subgroup and hospital length of stay were independent predictors of OM. Compared with subgroup I, OM patients in subgroup II had longer hospital length of stay and more severe disease. Meanwhile, the infiltration proportions of monocytes and macrophages M0 were higher in patients of OM subgroup II. Finally, combined with the characteristics of the KEGG enrichment modules, the expression of osteoclast differentiation-related genes such as CTSK was upregulated in OM subgroup II, which may be closely associated with more severe OM patients. CONCLUSION: The current study showed that OM subgroup II had longer hospital length of stay and more severe disease, the osteoclast differentiation pathway and the main target CTSK contribute to our deeper understanding for the molecular mechanisms associated with S. aureus infection-induced OM, and the construction of molecular subgroups suggested the necessity for different subgroups of patients to receive individualized treatment.

Bioinformatics analysis based on DNA methylation data identified in lung adenocarcinoma subgroups with different immune characteristics and clinical outcomes.

Background: DNA methylation can be used to predict clinical outcomes and improve the classification of tumors. The present study aimed to develop a new lung adenocarcinoma (LUAD) classification system according to the immune cell gene-related methylation sites and to reveal the survival outcomes, clinical characteristics, immune cell infiltration, stem cell characteristics, and genomic variations of each molecular subgroup. Methods: The DNA methylation sites of LUAD samples collected from The Cancer Genome Atlas (TCGA) database were analyzed, and the prognosis-related differential methylation sites (DMS) were screened. Consistent clustering of the samples was conducted using ConsensusClusterPlus, and the classification results were verified by principal component analysis (PCA). The survival and clinical results, immune cell infiltration, stemness, DNA mutation, and copy number variation (CNV) of each molecular subgroup were analyzed. Results: A total of 40 DMS were obtained by difference and univariate COX analyses, and the TCGA LUAD samples were divided into three subgroups: cluster 1 (C1), cluster 2 (C2), and cluster 3 (C3). Among these subgroups, the overall survival (OS) of C3 was significantly higher than that of C1 and C2. Compared with C1 and C3, C2 had the lowest innate immune cell and adaptive immune cell infiltration scores; the lowest stromal score, immune score, and iconic immune checkpoint expression; and the highest expression of messenger RNA (mRNA) expression-based stemness indices (mRNAsi), DNA methylation-based stemness index (mDNAsi), and tumor mutational burden (TMB). Conclusions: In this study, we proposed a LUAD typing system based on DMS, which was closely related to the survival, clinical features, immune characteristics, and genomic variations of LUAD, and may contribute to the development of personalized therapy for new specific subtypes.

Erratum: Novel MRI deformation-heterogeneity radiomic features are associated with molecular subgroups and overall survival in pediatric medulloblastoma: Preliminary findings from a multi-institutional study.

[This corrects the article DOI: 10.3389/fonc.2022.915143.].

Relationship between molecular markers and lymphadenectomy and lymphovascular space invasion in endometrial cancer.

PURPOSE: Relationship between pathologic parameters, surgical parameters, or lymph node status with oncologic outcomes is not fully elucidated in endometrial cancer (EC). We want to investigate the molecular classification of uterine cancer in the Turkish population and its relationship between lymphadenectomy and lymph node metastasis. METHODS: In this study, 100 patients' clinical and pathologic data diagnosed with EC were analyzed. Pathologic and molecular parameters were investigated and compared them with clinical parameters. RESULTS: According to the molecular analysis, 16 patients (16%) had p53 mutation, 3 patients (3%) were classified as POLE mutant group, 38 (38%) patients in the MSI group, and the remaining 43 patients (43%) into the no specific mutation profile (NSMP) group. Lymph node metastasis rate was significantly higher in copy number high (CNH) group compared to the others. In the CNH group, 29 of 437 (6.6%) dissected lymph nodes had metastasis. The median OS was the highest in the POLE group (72 months) and lowest in the CNH group (36 months). CONCLUSION: Endometrial cancer patients showed significantly different overall and disease-free survival according to the molecular subtypes and it was consistent with the literature, Lymph node metastasis risk was the highest in CNH group. MSI status is important for the lymph node metastasis risk but not all abnormalities, especially PMS2 and MLH1 expression changes showed the highest risk.

Overexpression of the orphan nuclear receptor NR2F6 is associated with improved survival across molecular subgroups in endometrial cancer patients.

INTRODUCTION: NR2F6 (nuclear receptor subfamily 2 group F member 6, also called Ear-2) is known to be an orphan nuclear receptor that has been characterized as an intracellular immune checkpoint in effector T cells and, therefore, may control tumor development and growth. The prognostic impact of NR2F6 in endometrial cancers is evaluated in this study. MATERIALS AND METHODS: Expression analysis of NR2F6 in 142 endometrial cancer patients was performed by immunohistochemistry of primary paraffin­embedded tumor samples. Staining intensity of positive tumor cells was automatically assessed semi-quantitatively, and results were correlated with clinicopathological characteristics and survival. RESULTS: Forty five of 116 evaluable samples (38.8%) showed an overexpression of NR2F6. This leads to an improvement of the overall survival (OS) and progression-free survival (PFS). In NR2F6-positive patients, the estimated mean OS was 156.9 months (95% confidence interval (CI) 143.1-170.7) compared to 106.2 months in NR2F6-negative patients (95% CI 86.2-126.3; p = 0.022). The estimated PFS differed by 63 months (152 months (95% CI 135.7-168.4) vs. 88.3 months (95% CI 68.5-108.0), p = 0.002). Furthermore, we found significant associations between NR2F6 positivity, MMR status, and PD1 status. A multivariate analysis suggests NR2F6 to be an independent factor influencing the OS (p = 0.03). CONCLUSION: In this study, we could demonstrate that there is a longer progression-free and overall survival for NR2F6-positive patients with endometrial cancer. We conclude that NR2F6 might play an essential role in endometrial cancers. Further studies are required to validate its prognostic impact.

Integrated genomic analysis defines molecular subgroups in dilated cardiomyopathy and identifies novel biomarkers based on machine learning methods.

Aim: As the most common cardiomyopathy, dilated cardiomyopathy (DCM) often leads to progressive heart failure and sudden cardiac death. This study was designed to investigate the molecular subgroups of DCM. Methods: Three datasets of DCM were downloaded from GEO database (GSE17800, GSE79962 and GSE3585). After log2-transformation and background correction with "limma" package in R software, the three datasets were merged into a metadata cohort. The consensus clustering was conducted by the "Consensus Cluster Plus" package to uncover the molecular subgroups of DCM. Moreover, clinical characteristics of different molecular subgroups were compared in detail. We also adopted Weighted gene co-expression network analysis (WGCNA) analysis based on subgroup-specific signatures of gene expression profiles to further explore the specific gene modules of each molecular subgroup and its biological function. Two machine learning methods of LASSO regression algorithm and SVM-RFE algorithm was used to screen out the genetic biomarkers, of which the discriminative ability of molecular subgroups was evaluated by receiver operating characteristic (ROC) curve. Results: Based on the gene expression profiles, heart tissue samples from patients with DCM were clustered into three molecular subgroups. No statistical difference was found in age, body mass index (BMI) and left ventricular internal diameter at end-diastole (LVIDD) among three molecular subgroups. However, the results of left ventricular ejection fraction (LVEF) statistics showed that patients from subgroup 2 had a worse condition than the other group. We found that some of the gene modules (pink, black and grey) in WGCNA analysis were significantly related to cardiac function, and each molecular subgroup had its specific gene modules functions in modulating occurrence and progression of DCM. LASSO regression algorithm and SVM-RFE algorithm was used to further screen out genetic biomarkers of molecular subgroup 2, including TCEAL4, ISG15, RWDD1, ALG5, MRPL20, JTB and LITAF. The results of ROC curves showed that all of the genetic biomarkers had favorable discriminative effectiveness. Conclusion: Patients from different molecular subgroups have their unique gene expression patterns and different clinical characteristics. More personalized treatment under the guidance of gene expression patterns should be realized.

Clinical Prognostic Implications of Wnt Hub Genes Expression in Medulloblastoma.

Medulloblastoma is the most common type of pediatric malignant primary brain tumor, and about one-third of patients die due to disease recurrence and most survivors suffer from long-term side effects. MB is clinically, genetically, and epigenetically heterogeneous and subdivided into at least four molecular subgroups: WNT, SHH, Group 3, and Group 4. We evaluated common differentially expressed genes between a Brazilian RNA-seq GSE181293 dataset and microarray GSE85217 dataset cohort of pediatric MB samples using bioinformatics methodology in order to identify hub genes of the molecular subgroups based on PPI network construction, survival and functional analysis. The main finding was the identification of five hub genes from the WNT subgroup that are tumor suppressors, and whose lower expression is related to a worse prognosis for MB patients. Furthermore, the common genes correlated with the five tumor suppressors participate in important pathways and processes for tumor initiation and progression, as well as development and differentiation, and some of them control cell stemness and pluripotency. These genes have not yet been studied within the context of MB, representing new important elements for investigation in the search for therapeutic targets, prognostic markers or for understanding of MB biology.

Clinical and Molecular Features in Medulloblastomas Subtypes in Children in a Cohort in Taiwan.

Medulloblastoma (MB) was classified into four molecular subgroups: WNT, SHH, group 3, and group 4. In 2017, 12 subtypes within 4 subgroups and 8 subtypes within non-WNT/non-SHH subgroups according to the differences of clinical features and biology were announced. In this study, we aimed to identify the heterogeneity of molecular features for discovering subtype specific factors linked to diagnosis and prognosis. We retrieved 70 MBs in children to perform RNA sequencing and a DNA methylation array in Taiwan. Integrated with clinical annotations, we achieved classification of 12 subtypes of pediatric MBs in our cohort series with reference to the other reported series. We analyzed the correlation of cell type enrichment in SHH MBs and found that M2 macrophages were enriched in SHH ß, which related to good outcomes of SHH MBs. The high infiltration of M2 macrophages may be an indicator of a favorable prognosis and therapeutic target for SHH MBs. Furthermore, C11orf95-RELA fusion was observed to be associated with recurrence and a poor prognosis. These results will contribute to the establishment of a molecular diagnosis linked to prognostic indicators of relevance and help to promote molecular-based risk stratified treatment for MBs in children.

Predicting IDH subtype of grade 4 astrocytoma and glioblastoma from tumor radiomic patterns extracted from multiparametric magnetic resonance images using a machine learning approach.

Background and purpose: Semantic imaging features have been used for molecular subclassification of high-grade gliomas. Radiomics-based prediction of molecular subgroups has the potential to strategize and individualize therapy. Using MRI texture features, we propose to distinguish between IDH wild type and IDH mutant type high grade gliomas. Methods: Between 2013 and 2020, 100 patients were retrospectively analyzed for the radiomics study. Immunohistochemistry of the pathological specimen was used to initially identify patients for the IDH mutant/wild phenotype and was then confirmed by Sanger's sequencing. Image texture analysis was performed on contrast-enhanced T1 (T1C) and T2 weighted (T2W) MR images. Manual segmentation was performed on MR image slices followed by single-slice multiple sampling image augmentation. Both whole tumor multislice segmentation and single-slice multiple sampling approaches were used to arrive at the best model. Radiomic features were extracted, which included first-order features, second-order (GLCM-Grey level co-occurrence matrix), and shape features. Feature enrichment was done using LASSO (Least Absolute Shrinkage and Selection Operator) regression, followed by radiomic classification using Support Vector Machine (SVM) and a 10-fold cross-validation strategy for model development. The area under the Receiver Operator Characteristic (ROC) curve and predictive accuracy were used as diagnostic metrics to evaluate the model to classify IDH mutant and wild-type subgroups. Results: Multislice analysis resulted in a better model compared to the single-slice multiple-sampling approach. A total of 164 MR-based texture features were extracted, out of which LASSO regression identified 14 distinctive GLCM features for the endpoint, which were used for further model development. The best model was achieved by using combined T1C and T2W MR images using a Quadratic Support Vector Machine Classifier and a 10-fold internal cross-validation approach, which demonstrated a predictive accuracy of 89% with an AUC of 0.89 for each IDH mutant and IDH wild subgroup. Conclusion: A machine learning classifier of radiomic features extracted from multiparametric MRI images (T1C and T2w) provides important diagnostic information for the non-invasive prediction of the IDH mutant or wild-type phenotype of high-grade gliomas and may have potential use in either escalating or de-escalating adjuvant therapy for gliomas or for using targeted agents in the future.

ROS1 genomic rearrangements are rare actionable drivers in microsatellite stable colorectal cancer.

c-Ros oncogene 1, receptor tyrosine kinase (ROS1) genomic rearrangements have been reported previously in rare cases of colorectal cancer (CRC), yet little is known about the frequency, molecular characteristics, and therapeutic vulnerabilities of ROS1-driven CRC. We analyzed a clinical dataset of 40 589 patients with CRC for ROS1 genomic rearrangements and their associated genomic characteristics (Foundation Medicine, Inc [FMI]). We moreover report the disease course and treatment response of an index patient with ROS1-rearranged metastatic CRC. ROS1 genomic rearrangements were identified in 34 (0.08%) CRC samples. GOPC-ROS1 was the most common ROS1 fusion identified (11 samples), followed by TTC28-ROS1 (3 samples). Four novel 5' gene partners of ROS1 were identified (MCM9, SRPK1, EPHA6, P4HA1). Contrary to previous reports on fusion-positive CRC, ROS1-rearrangements were found exclusively in microsatellite stable (MSS) CRCs. KRAS mutations were significantly less abundant in ROS1-rearranged vs ROS1 wild type cases. The index patient presented with chemotherapy-refractory metastatic right-sided colon cancer harboring GOPC-ROS1. Molecularly targeted treatment with crizotinib induced a rapid and sustained partial response. After 15 months on crizotinib disseminated tumor progression occurred and KRAS Q61H emerged in tissue and liquid biopsies. ROS1 rearrangements define a small, yet therapeutically actionable molecular subgroup of MSS CRC. In summary, the high prevalence of GOPC-ROS1 and noncanonical ROS1 fusions pose diagnostic challenges. We advocate NGS-based comprehensive molecular profiling of MSS CRCs that are wild type for RAS and BRAF and patient enrollment in precision trials.

Consensus clustering of gene expression profiles in peripheral blood of acute ischemic stroke patients.

Acute ischemic stroke (AIS) is a primary cause of mortality and morbidity worldwide. Currently, no clinically approved immune intervention is available for AIS treatment, partly due to the lack of relevant patient classification based on the peripheral immunity status of patients with AIS. In this study, we adopted the consensus clustering approach to classify patients with AIS into molecular subgroups based on the transcriptomic profiles of peripheral blood, and we identified three distinct AIS molecular subgroups and 8 modules in each subgroup by the weighted gene co-expression network analysis. Remarkably, the pre-ranked gene set enrichment analysis revealed that the co-expression modules with subgroup I-specific signature genes significantly overlapped with the differentially expressed genes in AIS patients with hemorrhagic transformation (HT). With respect to subgroup II, exclusively male patients with decreased proteasome activity were identified. Intriguingly, the majority of subgroup III was composed of female patients who showed a comparatively lower level of AIS-induced immunosuppression (AIIS). In addition, we discovered a non-linear relationship between female age and subgroup-specific gene expression, suggesting a gender- and age-dependent alteration of peripheral immunity. Taken together, our novel AIS classification approach could facilitate immunomodulatory therapies, including the administration of gender-specific therapeutics, and attenuation of the risk of HT and AIIS after ischemic stroke.

Medulloblastoma in the Modern Era: Review of Contemporary Trials, Molecular Advances, and Updates in Management.

Critical discoveries over the past two decades have transformed our understanding of medulloblastoma from a single entity into a clinically and biologically heterogeneous disease composed of at least four molecularly distinct subgroups with prognostically and therapeutically relevant genomic signatures. Contemporary clinical trials also have provided valuable insight guiding appropriate treatment strategies. Despite therapeutic and biological advances, medulloblastoma patients across the age spectrum experience tumor- and treatment-related morbidity and mortality. Using an updated risk stratification approach integrating both clinical and molecular features, ongoing research seeks to (1) cautiously reduce therapy and mitigate toxicity in low-average risk patients, and (2) thoughtfully intensify treatment with incorporation of novel, biologically guided agents for patients with high-risk disease. Herein, we review important historical and contemporary studies, discuss management updates, and summarize current knowledge of the biological landscape across unique pediatric, infant, young adult, and relapsed medulloblastoma populations.

Telomerase Reverse Transcriptase Promoter Mutations in A Cohort Of Adult Gliomas - Clinicopathological Correlates.

Background: Introduction: Gliomas were previously classified histologically, although now the latest WHO classification incorporates several molecular markers to classify these. Detection of TERT promoter mutations is assuming increased importance due to its relevance to prognostication. Objective: : The aim of this study was to determine the frequency of TERT promoter mutations, association of TERT promoter mutations with other molecular alterations and to assess the role of TERT promoter mutations in overall survival and progression-free survival in relation to histological and molecular glioma subtypes. Materials and Methods: This study analyzed a cohort of 107 adult patients with diffuse gliomas, WHO grades II and III and glioblastoma, by immunohistochemistry for IDH and ATRX mutations, FISH for 1p/19q co-deletions and PCR sequencing for TERT promoter mutation. Further, five glioma molecular sub-groups were derived using three molecular alteration and included the sub-groups with: i) IDH mutations only, ii) IDH and TERT mutations only, iii) IDH and 1p/19q co-deletion only, iv) Triple negative, and v) Triple positive. Results: IDH mutations and 1p/19q co-deletions were individually and significantly associated with an improved progression free (P = 0.001 and P = 0.002, respectively) and overall survival (P = 0.000 and P = 0.005, respectively) in the present cohort of gliomas. TERT promoter mutations occurred frequently in anaplastic oligodendrogliomas (94%), oligodendrogliomas (87.5%) and glioblastomas (54%). Sub-division into molecular sub-groups showed that the triple-positive tumors carried the best prognosis, followed by IDH only, triple negative and finally the TERT mutation only tumors (P < 0.000). Conclusion: : This indicates that sub-classification using these molecular markers separates tumors into prognostically relevant categories.

Single nCounter assay for prediction of MYCN amplification and molecular classification of medulloblastomas: a multicentric study.

PURPOSE: Medulloblastoma is the most frequent pediatric malignant brain tumor, and is divided into four main subgroups: WNT, SHH, group 3, and group 4. MYCN amplification is an important medulloblastoma prognostic biomarker. We aimed to molecular classify and predict MYCN amplification in a single assay. METHODS: It was included 209 medulloblastomas from 205 patients (Brazil, Argentina, and Portugal), divided into training (n = 50) and validation (n = 159) sets. A nCounter assay was carried out using a custom panel for molecular classification, with additional genes, including MYCN. nSolver 4.0 software and the R environment were used for profiling and MYCN mRNA analysis. MYCN amplification by FISH was performed in 64 cases. RESULTS: The 205 medulloblastomas were classified in SHH (44.9%), WNT (15.6%), group 3 (18.1%) and group 4 (21.4%). In the training set, MYCN amplification was detected in three SHH medulloblastomas by FISH, which showed significantly higher MYCN mRNA counts than non-FISH amplified cases, and a cutoff for MYCN amplification was established ([Formula: see text] + 4σ = 11,124.3). Applying this threshold value in the validation set, we identified MYCN mRNA counts above the cutoff in three cases, which were FISH validated. CONCLUSION: We successfully stratified medulloblastoma molecular subgroups and predicted MYCN amplification using a single nCounter assay without the requirement of additional biological tissue, costs, or bench time.

Magnetic Resonance Imaging in the Contemporary Management of Medulloblastoma: Current and Emerging Applications.

Medulloblastoma, the most common malignant primary brain tumor in children, is now considered to comprise of four distinct molecular subgroups-wingless (WNT), sonic hedgehog (SHH), Group 3, and Group 4 medulloblastoma, each associated with distinct developmental origins, unique transcriptional profiles, diverse phenotypes, and variable clinical behavior. Due to its exquisite anatomic resolution, multiparametric nature, and ability to image the entire craniospinal axis, magnetic resonance imaging (MRI) is the preferred and recommended first-line imaging modality for suspected brain tumors including medulloblastoma. Preoperative MRI can reliably differentiate medulloblastoma from other common childhood posterior fossa masses such as ependymoma, pilocytic astrocytoma, and brainstem glioma. On T1-weighted images, medulloblastoma is generally iso- to hypointense, while on T2-weighted images, the densely packed cellular component of the tumor is significantly hypointense and displays restricted diffusion on diffusion-weighted imaging. Following intravenous gadolinium, medulloblastoma shows significant but variable and heterogeneous contrast enhancement. Given the propensity of neuraxial spread in medulloblastoma, sagittal fat-suppressed T1-postcontrast spinal MRI is recommended to rule out leptomeningeal metastases for accurate staging. Following neurosurgical excision, postoperative MRI done within 24-48 h confirms the extent of resection, accurately quantifying residual tumor burden imperative for risk assignment. Post-treatment MRI is needed to assess response and effectiveness of adjuvant radiotherapy and systemic chemotherapy. After completion of planned therapy, surveillance MRI is recommended periodically on follow-up for early detection of recurrence for timely institution of salvage therapy, as well as for monitoring treatment-related late complications. Recent studies suggest that preoperative MRI can reliably identify SHH and Group 4 medulloblastoma but has suboptimal predictive accuracy for WNT and Group 3 tumors. In this review, we focus on the role of MRI in the diagnosis, staging, and quantifying residual disease; post-treatment response assessment; and periodic surveillance, and provide a brief summary on radiogenomics in the contemporary management of medulloblastoma.