Bioinformatic Strategies for the Genomic and Epigenomic Characterization of Brain Tumors

Mechanosensitive brain tumor cells construct blood-tumor barrier to mask chemosensitivity

Major obstacles in brain cancer treatment include the blood-tumor barrier (BTB), which limits the access of most therapeutic agents, and quiescent tumor cells, which resist conventional chemotherapy. Here, we show that Sox2⁺ tumor cells project cellular processes to ensheathe capillaries in mouse medulloblastoma (MB), a process that depends on the mechanosensitive ion channel Piezo2. MB develops a tissue stiffness gradient as a function of distance to capillaries. Sox2⁺ tumor cells perceive substrate stiffness to sustain local intracellular calcium, actomyosin tension, and adhesion to promote cellular process growth and cell surface sequestration of β-catenin. Piezo2 knockout reverses WNT/β-catenin signaling states between Sox2⁺ tumor cells and endothelial cells, compromises the BTB, reduces the quiescence of Sox2⁺ tumor cells, and markedly enhances the MB response to chemotherapy. Our study reveals that mechanosensitive tumor cells construct the BTB to mask tumor chemosensitivity. Targeting Piezo2 addresses the BTB and tumor quiescence properties that underlie treatment failures in brain cancer.

Identification of Let-7 miRNA Activity as a Prognostic Biomarker of SHH Medulloblastoma

Medulloblastoma (MB) is the most common pediatric embryonal brain tumor. The current consensus classifies MB into four molecular subgroups: sonic hedgehog-activated (SHH), wingless-activated (WNT), Group 3, and Group 4. MYCN and let-7 play a critical role in MB. Thus, we inferred the activity of miRNAs in MB by using the ActMiR procedure. SHH-MB has higher MYCN expression than the other subgroups. We showed that high MYCN expression with high let-7 activity is significantly associated with worse overall survival, and this association was validated in an independent MB dataset. Altogether, our results suggest that let-7 activity and MYCN can further categorize heterogeneous SHH tumors into more and less-favorable prognostic subtypes, which provide critical information for personalizing treatment options for SHH-MB. Comparing the expression differences between the two SHH-MB prognostic subtypes with compound perturbation profiles, we identified FGFR inhibitors as one potential treatment option for SHH-MB patients with the less-favorable prognostic subtype.

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

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 death-ligand 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.

Conclusion: Our study constructed and validated a robust 13-gene signature model estimating the prognosis of medulloblastoma patients. We also revealed genes and pathways that may be related to the development and prognosis of medulloblastoma, which might provide candidate targets for future investigation.