This cohort study evaluates the feasibility and utility of incorporating comparative gene expression information into the precision medicine framework for difficult-to-treat pediatric and young adult patients with cancer.
Motivation
Knowledge graphs (KG) are being adopted in industry, commerce, and academia. Biomedical KG present a challenge due to the complexity, size, and heterogeneity of the underlying information.
Results
In this work we present the Scalable Precision Medicine Open Knowledge Engine (SPOKE), a biomedical KG connecting millions of concepts via semantically meaningful relationships. SPOKE contains 27 million nodes of 21 different types and 53 million edges of 55 types downloaded from 41 databases. The graph is built on the framework of 11 ontologies that maintain its structure, enable mappings, and facilitate navigation. SPOKE is built weekly by python scripts which download each resource, check for integrity and completeness, and then create a “parent table” of nodes and edges. Graph queries are translated by a REST API and users can submit searches directly via an API or a graphical user interface. Conclusions/Significance: SPOKE enables the integration of seemingly disparate information to support precision medicine efforts.
Availability
The SPOKE neighborhood explorer is available at https://spoke.rbvi.ucsf.edu.
Supplementary information
Supplementary data are available at Bioinformatics online.
Although increasingly recognized as critical to genomic research, genomic data sharing is hindered by an absence of standards regarding timing, patient privacy, use agreement standards, and data characterization and quality. Only after months of identifying, permissioning for use, committing to terms restricting use and sharing, downloading, and assessing quality, is it possible to know whether or not a dataset can be used. In this paper, we evaluate the barriers to data sharing based on the Treehouse experience and offer recommendations for use agreement standards, data characterization and metadata standardization to enhance data sharing and outcomes for all pediatric cancer patients.
As part of the risk management plan for human system risks at the US National Aeronautics and Space Administration (NASA), the NASA Human Systems Risk Board uses causal diagrams (in the form of directed, acyclic graphs, or DAGs) to communicate the complex web of events that leads from exposure to the spaceflight environment to performance and health outcomes. However, the use of DAGs in this way is relatively new at NASA, and thus far, no method has been articulated for testing their veracity using empirical data. In this paper, we demonstrate a set of procedures for doing so, using (a) a DAG related to the risk of bone fracture after exposure to spaceflight; and (b) four datasets originally generated to investigate this phenomenon in rodents. Tests of expected marginal correlation and conditional independencies derived from the DAG indicate that the rodent data largely agree with the structure of the diagram. Incongruencies between tests and the expected relationships in one of the datasets are likely explained by inadequate representation of a key DAG variable in the dataset. Future directions include greater tie-in with human data sources, including multiomics data, which may allow for more robust characterization and measurement of DAG variables.
Diffuse intrinsic pontine gliomas (DIPG) are incurable brain tumors with an aggressive onset. Apart from irradiation, there are currently no effective therapies available for patients with DIPG, who have a median survival time of less than one year. Most DIPG cells harbor mutations in genes encoding histone H3 (H3K27M) proteins, resulting in a global reduction of H3K27 trimethylation and activation of oncogenic signaling pathways. Here we show that the H3K27M mutations contribute to RAS pathway signaling, which is augmented by additional RAS activators including PDGFRA. H3K27M mutation led to increased expression of receptor tyrosine kinases (RTK). A RAS pathway functional screen identified ERK5, but not ERK1/2, as a RAS pathway effector important for DIPG growth. Suppression of ERK5 decreased DIPG cell proliferation and induced apoptosis in vitro and in vivo. In addition, depletion or inhibition of ERK5 significantly increased survival of mice intracranially engrafted with DIPG cells. Mechanistically, ERK5 directly stabilized the proto-oncogene MYC at the protein level. Collectively, our data demonstrate an underappreciated role of H3K27M in RAS activation and reveal novel therapeutic targets for treating DIPG tumors.
Significance:
These findings identify the H3K27M mutation as an enhancer of RAS activation in DIPG and ERK5 as a novel, immediately actionable molecular target.
Background
Diffuse midline gliomas with histone H3 K27M (H3K27M) mutations occur in early childhood and are marked by an invasive phenotype and global decrease in H3K27me3, an epigenetic mark that regulates differentiation and development. H3K27M mutation timing and effect on early embryonic brain development are not fully characterized.
Results
We analyzed multiple publicly available RNA sequencing datasets to identify differentially expressed genes between H3K27M and non-K27M pediatric gliomas. We found that genes involved in the epithelial-mesenchymal transition (EMT) were significantly overrepresented among differentially expressed genes. Overall, the expression of pre-EMT genes was increased in the H3K27M tumors as compared to non-K27M tumors, while the expression of post-EMT genes was decreased. We hypothesized that H3K27M may contribute to gliomagenesis by stalling an EMT required for early brain development, and evaluated this hypothesis by using another publicly available dataset of single-cell and bulk RNA sequencing data from developing cerebral organoids. This analysis revealed similarities between H3K27M tumors and pre-EMT normal brain cells. Finally, a previously published single-cell RNA sequencing dataset of H3K27M and non-K27M gliomas revealed subgroups of cells at different stages of EMT. In particular, H3.1K27M tumors resemble a later EMT stage compared to H3.3K27M tumors.
Conclusions
Our data analyses indicate that this mutation may be associated with a differentiation stall evident from the failure to proceed through the EMT-like developmental processes, and that H3K27M cells preferentially exist in a pre-EMT cell phenotype. This study demonstrates how novel biological insights could be derived from combined analysis of several previously published datasets, highlighting the importance of making genomic data available to the community in a timely manner.
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