Classifying Medulloblastoma Subgroups Based on Small, Clinically Achievable Gene Sets

Singular Value Decomposition-Based Penalized Multinomial Regression for Classifying Imbalanced Medulloblastoma Subgroups Using Methylation Data

Medulloblastoma (MB) is a molecularly heterogeneous brain malignancy with large differences in clinical presentation. According to genomic studies, there are at least four distinct molecular subgroups of MB: sonic hedgehog (SHH), wingless/INT (WNT), Group 3, and Group 4. The treatment and outcomes depend on appropriate classification. It is difficult for the classification algorithms to identify these subgroups from an imbalanced MB genomic data set, where the distribution of samples among the MB subgroups may not be equal. To overcome this problem, we used singular value decomposition (SVD) and group lasso techniques to find DNA methylation probe features that maximize the separation between the different imbalanced MB subgroups. We used multinomial regression as a classification method to classify the four different molecular subgroups of MB using the reduced DNA methylation data. Coordinate descent is used to solve our loss function associated with the group lasso, which promotes sparsity. By using SVD, we were able to reduce the 321,174 probe features to just 200 features. Less than 40 features were successfully selected after applying the group lasso, which we then used as predictors for our classification models. Our proposed method achieved an average overall accuracy of 99% based on fivefold cross-validation technique. Our approach produces improved classification performance compared with the state-of-the-art methods for classifying MB molecular subgroups.

Transcriptome analysis stratifies second-generation non-WNT/non-SHH medulloblastoma subgroups into clinically tractable subtypes

Medulloblastoma (MB), one of the most common malignant pediatric brain tumor, is a heterogenous disease comprised of four distinct molecular groups (WNT, SHH, Group 3, Group 4). Each of these groups can be further subdivided into second-generation MB (SGS MB) molecular subgroups, each with distinct genetic and clinical characteristics. For instance, non-WNT/non-SHH MB (Group 3/4) can be subdivided molecularly into eight distinct and clinically relevant tumor subgroups. A further molecular stratification/summarization of these SGS MB would allow for the assignment of patients to risk-associated treatment protocols. Here, we performed DNA- and RNA-based analysis of 574 non-WNT/non-SHH MB and analyzed the clinical significance of various molecular patterns within the entire cohort and the eight SGS MB, with the aim to develop an optimal risk stratification of these tumors. Multigene analysis disclosed several survival-associated genes highly specific for each molecular subgroup within this non-WNT/non-SHH MB cohort with minimal inter-subgroup overlap. These subgroup-specific and prognostically relevant genes were associated with pathways that could underlie SGS MB clinical-molecular diversity and tumor-driving mechanisms. By combining survival-associated genes within each SGS MB, distinct metagene sets being appropriate for their optimal risk stratification were identified. Defined subgroup-specific metagene sets were independent variables in the multivariate models generated for each SGS MB and their prognostic value was confirmed in a completely non-overlapping validation cohort of non-WNT/non-SHH MB (n = 377). In summary, the current results indicate that the integration of transcriptome data in risk stratification models may improve outcome prediction for each non-WNT/non-SHH SGS MB. Identified subgroup-specific gene expression signatures could be relevant for clinical implementation and survival-associated metagene sets could be adopted for further SGS MB risk stratification. Future studies should aim at validating the prognostic role of these transcriptome-based SGS MB subtypes in prospective clinical trials.

Case report: Somatic mutations in microtubule dynamics-associated genes in patients with WNT-medulloblastoma tumors

Medulloblastoma (MB) is the most common pediatric brain tumor which accounts for about 20% of all pediatric brain tumors and 63% of intracranial embryonal tumors. MB is considered to arise from precursor cell populations present during an early brain development. Most cases (~70%) of MB occur at the age of 1-4 and 5-9, but are also infrequently found in adults. Total annual frequency of pediatric tumors is about 5 cases per 1 million children. WNT-subtype of MB is characterized by a high probability of remission, with a long-term survival rate of about 90%. However, in some rare cases there may be increased metastatic activity, which dramatically reduces the likelihood of a favorable outcome. Here we report two cases of MB with a histological pattern consistent with desmoplastic/nodular (DP) and classic MB, and genetically classified as WNT-MB. Both cases showed putative causal somatic protein truncating mutations identified in microtubule-associated genes: ARID2, TUBB4A, and ANK3.

Molecular subgrouping of medulloblastoma in pediatric population using the NanoString assay and comparison with immunohistochemistry methods

PURPOSE

Molecular subgrouping of medulloblastoma has become important due to its impact on risk group stratification. Immunohistochemistry (IHC) has been widely used but it has innate limitations. The NanoString assay has been proposed as an alternative method. This study aims to present the characteristics of medulloblastoma subgrouped by the NanoString assay and to compare the subgrouping results with the IHC method.

METHODS

Pediatric patients with histological diagnosis of medulloblastoma who underwent surgery from 2007 to 2021 were included. Clinical characteristics, pathological findings were reviewed. Molecular subgrouping was performed by IHC and by NanoString nCounter Elements TagSets assay. Test for concordance between two methods was made.

RESULTS

Among a total of 101 patients analyzed, subgrouping using the NanoString assay resulted in 14 (13.8%) WNT, 20 (19.8%) SHH, 18 (17.8%) Group 3, and 39 (38.6%) Group 4 subgroup cases. Survival analysis revealed the following from best to worse prognosis: WNT, Group 4, SHH, and Group 3. In SHH subgroup the large cell/anaplastic histology was present in 30% of cases. Seventy-one cases were analyzed for concordance between NanoString and IHC. Cohen's kappa value indicated moderate agreement but identification of Groups 3 and 4 with IHC using NPR3 and KCNA1 markers exhibited poor results.

CONCLUSIONS

The NanoString assay of Korean medulloblastoma patients revealed a more aggressive clinical course in the SHH subgroup which may be explained by a higher proportion of large cell/anaplastic histology being present in this subgroup. IHC did not distinguish Group 3 or 4 accurately. The NanoString assay may represent a good alternative method for practical use in the clinical field.

RBM24 in the Post-Transcriptional Regulation of Cancer Progression: Anti-Tumor or Pro-Tumor Activity?

Simple Summary

RBM24 is a highly conserved RNA-binding protein that plays critical roles in the post-transcriptional regulation of gene expression for initiating cell differentiation during embryonic development and for maintaining tissue homeostasis in adult life. Evidence is now accumulating that it is frequently dysregulated across human cancers. Importantly, RBM24 may act as a tumor suppressor or as an oncogene in a context- or background-dependent manner. Its activity can be regulated by protein–protein interactions and post-translational modifications, making it a potential therapeutic target for cancer treatment. However, molecular mechanisms underlying its function in tumor growth and metastasis remain elusive. Further investigation will be necessary to better understand how its post-transcriptional regulatory activity is controlled and how it is implicated in tumor progression. This review provides a comprehensive analysis of recent findings on the implication of RBM24 in cancer and proposes future research directions to delve more deeply into the mechanisms underlying its tumor-suppressive function or oncogenic activity.

Abstract

RNA-binding proteins are critical post-transcriptional regulators of gene expression. They are implicated in a wide range of physiological and pathological processes by modulating nearly every aspect of RNA metabolisms. Alterations in their expression and function disrupt tissue homeostasis and lead to the occurrence of various cancers. RBM24 is a highly conserved protein that binds to a large spectrum of target mRNAs and regulates many post-transcriptional events ranging from pre-mRNA splicing to mRNA stability, polyadenylation and translation. Studies using different animal models indicate that it plays an essential role in promoting cellular differentiation during organogenesis and tissue regeneration. Evidence is also accumulating that its dysregulation frequently occurs across human cancers. In several tissues, RBM24 clearly functions as a tumor suppressor, which is consistent with its inhibitory potential on cell proliferation. However, upregulation of RBM24 in other cancers appears to promote tumor growth. There is a possibility that RBM24 displays both anti-tumor and pro-tumor activities, which may be regulated in part through differential interactions with its protein partners and by its post-translational modifications. This makes it a potential biomarker for diagnosis and prognosis, as well as a therapeutic target for cancer treatment. The challenge remains to determine the post-transcriptional mechanisms by which RBM24 modulates gene expression and tumor progression in a context- or background-dependent manner. This review discusses recent findings on the potential function of RBM24 in tumorigenesis and provides future directions for better understanding its regulatory role in cancer cells.

Gene expression profiling of Group 3 medulloblastomas defines a clinically tractable stratification based on KIRREL2 expression

Medulloblastomas (MB) molecularly designated as Group 3 (Grp 3) MB represent a more clinically aggressive tumor variant which, as a group, displays heterogeneous molecular characteristics and disease outcomes. Reliable risk stratification of Grp 3 MB would allow for appropriate assignment of patients to aggressive treatment protocols and, vice versa, for sparing adverse effects of high-dose radio-chemotherapy in patients with standard or low-risk tumors. Here we performed RNA-based analysis on an international cohort of 179 molecularly designated Grp 3 MB treated with HIT protocols. We analyzed the clinical significance of differentially expressed genes, thereby developing optimal prognostic subdivision of this MB molecular group. We compared the transcriptome profiles of two Grp 3 MB subsets with various outcomes (76 died within the first 60 months vs. 103 survived this period) and identified 224 differentially expressed genes (DEG) between these two clinical groups (Limma R algorithm, adjusted p-value < 0.05). We selected the top six DEG overexpressed in the unfavorable cohort for further survival analysis and found that expression of all six genes strongly correlated with poor outcomes. However, only high expression of KIRREL2 was identified as an independent molecular prognostic indicator of poor patients' survival. Based on clinical and molecular patterns, four risk categories were outlined for Grp 3 MB patients: i. low-risk: M0-1/MYC non-amplified/KIRREL2 low (n = 48; 5-year OS-95%); ii. standard-risk: M0-1/MYC non-amplified/KIRREL2 high or M2-3/MYC non-amplified/KIRREL2 low (n = 65; 5-year OS-70%); iii. high-risk: M2-3/MYC non-amplified/KIRREL2 high (n = 36; 5-year OS-30%); iv. very high risk-all MYC amplified tumors (n = 30; 5-year OS-0%). Cross-validated survival models incorporating KIRREL2 expression with clinical features allowed for the reclassification of up to 50% of Grp 3 MB patients into a more appropriate risk category. Finally, KIRREL2 immunopositivity was also identified as a predictive indicator of Grp 3 MB poor survival, thus suggesting its application as a possible prognostic marker in routine clinical settings. Our results indicate that integration of KIRREL2 expression in risk stratification models may improve Grp 3 MB outcome prediction. Therefore, simple gene and/or protein expression analyses for this molecular marker could be easily adopted for Grp 3 MB prognostication and may help in assigning patients to optimal therapeutic approaches in prospective clinical trials.