Project description:Epigenetics tightly regulates gene expression during brain development, which ensemble distinct cell types and form complicated functional brain organ. DNA methylation is an important mark which undergo dramatically changes during brain development. The disturb of this process will lead to various brain tumors. To study the concordant DNA methylation changes during normal brain development, we sequenced DNA methylome of pediatric brain tissues from autopsy with various ages. We systematically compared the DNA methylome of pediatric brain and adult brain and identified candidate DMRs that contribute to normal brain development. This comprehensive analysis will provide important epigenetic reference for human brain development which will be a valuable data to study the epigenetic mechanism of pediatric brain tumor.
Project description:Despite in-depth knowledge of the molecular features and oncogenic drivers associated with adult and pediatric brain tumors, identifying effective targeted therapies for these cancers remains challenging. To identify novel gene dependencies in adult and pediatric brain tumor isolates, we integrated data from functional genomic lethality screens in primary brain tumor isolates with machine learning network models from lethality screens performed in >900 cancer cell lines. Integrated network models revealed molecular and phenotypic features that predict candidate genetic dependencies in multiple brain tumor types, including: atypical teratoid rhabdoid tumors, diffuse intrinsic pontine gliomas, ependymomas, medulloblastomas, and glioblastomas (primary and recurrent). Some examples of dependencies and predictors include: ADAR and MX1 protein expression; EFR3A and low EFR3B expression; FBXO42 and spindle assembly checkpoint activation; FGFR1 and high FGF2 expression; and SMARCC2 in SMARCB1 mutated ATRT tumors. In general, the results demonstrate that large functional genetic data sets can be leveraged to identify, validate, and categorize gene dependencies and their associated biomarkers in primary tumor isolates. The results also highlight some of the challenges and limitations of this approach.
Project description:Tumors of the central nervous system are the most common solid neoplasia during human childhood, representing one of the leading causes of cancer-related mortality. Tumors that originate from astrocyte cells (astrocytoma) in the brain are the most frequently found. According to their histological and pathological features, these tumors are classified into four categories. However, recently an extra layer of molecular classification of the tumorigenesis-associated genes IDH1/2 and H3F3A has been incorporated into the classification guidelines. While mutations in H3F3A are exclusively found in a subtype of pediatric astrocytoma grade IV, mutations in IDH1/2 are very rare in children younger than 14 years old. The transcriptomic profiles of astrocytoma in adults and children have been extensively studied however focusing on the study of the transcriptomic profile of the different grades of astrocytoma (including the additional layer of molecular classification) in pediatric populations are scarce. Therefore, we have profiled the transcriptomic landscape of the four grades of pediatric astrocytoma by RNA sequencing.
Project description:With the increasing use of immunotherapy in cancer, understanding the tumor immune microenvironment (TIME) has become increasingly important. There has been great success in treatment of some pediatric high-grade gliomas with immune checkpoint inhibition, however overall, the immune microenvironment in pediatric brain tumors remains poorly understood. Accordingly, we developed a clinical immune-oncology gene expression assay and used it to profile a diverse range of pediatric brain tumors with detailed clinical and molecular annotation, including a total of 1379 cases. Our findings demonstrate the critical importance of understanding the TIME to guide the prognosis and management of pediatric brain tumors. In pediatric low-grade gliomas we identified distinct patterns of immune activation with prognostic significance in BRAF V600E mutant tumors. In pediatric high-grade gliomas, we observed elevated inflammation and T cell infiltrates in tumors that have historically been considered immune cold, as well as genomic correlates of inflammation levels. In our cohort of mismatch repair deficient high-grade gliomas treated with immune checkpoint inhibition, we found that high tumor inflammation signature was a significant predictor of treatment response. Furthermore, we demonstrated the potential for multimodal biomarkers to improve the treatment stratification of these patients. Importantly, while overall patterns of immune activation were observed for histologically and genomically defined tumor types, there was nonetheless significant variability within each diagnostic entity, indicating that the immune response must be evaluated as an independent feature. In sum, in addition to the histology and molecular profile, this work establishes the importance of assessing and reporting on the tumor immune microenvironment as an essential axis of cancer diagnosis in the era of personalized medicine.
Project description:Pediatric high-grade gliomas (pHGGs), including glioblastoma multiforme (GBM) and diffuse intrinsic pontine glioma (DIPG), are highly morbid childhood brain tumors. Even with treatment, overall survival is poor, making pHGG the number one cause of cancer death in children. Up to 80% of DIPGs harbor a somatic missense mutation in genes encoding Histone H3 proteins. To investigate whether this H3K27M mutation is associated with distinct chromatin structure that alters transcription regulation, we generated the first high-resolution Hi-C maps of pHGG cell lines and tumor tissue. By integrating transcriptome (RNA-Seq), enhancer landscape (ChIP-Seq), genome structure (Hi-C), and chromatin accessibility (ATAC-Seq) datasets from H3K27M mutant and wild-type specimens, we identified tumor specific enhancers and regulatory networks for known oncogenes. In addition, we identified distinct genomic structural variations that lead to enhancer hijacking and gene co-amplification, including A2M, JAG2, FLRT1.