Preclinical testing of the glycogen synthase kinase-3β inhibitor tideglusib for rhabdomyosarcoma

Rhabdomyosarcoma (RMS) is the most common childhood soft tissue sarcoma. RMS often arise from myogenic precursors and displays a poorly differentiated skeletal muscle phenotype most closely resembling regenerating muscle. GSK3β is a ubiquitously expressed serine-threonine kinase capable of repressing the terminal myogenic differentiation program in cardiac and skeletal muscle. Recent unbiased chemical screening efforts have prioritized GSK3β inhibitors as inducers of myodifferentiation in RMS, suggesting efficacy as single agents in suppressing growth and promoting self-renewal in zebrafish transgenic embryonal RMS (eRMS) models in vivo. In this study, we tested the irreversible GSK3β-inhibitor, tideglusib for in vivo efficacy in patient-derived xenograft models of both alveolar rhabdomyosarcoma (aRMS) and eRMS. Tideglusib had effective on-target pharmacodynamic efficacy, but as a single agent had no effect on tumor progression or myodifferentiation. These results suggest that as monotherapy, GSK3β inhibitors may not be a viable treatment for aRMS or eRMS

Advancing clinical cohort selection with genomics analysis on a distributed platform.

The affordability of next-generation genomic sequencing and the improvement of medical data management have contributed largely to the evolution of biological analysis from both a clinical and research perspective. Precision medicine is a response to these advancements that places individuals into better-defined subsets based on shared clinical and genetic features. The identification of personalized diagnosis and treatment options is dependent on the ability to draw insights from large-scale, multi-modal analysis of biomedical datasets. Driven by a real use case, we premise that platforms that support precision medicine analysis should maintain data in their optimal data stores, should support distributed storage and query mechanisms, and should scale as more samples are added to the system. We extended a genomics-based columnar data store, GenomicsDB, for ease of use within a distributed analytics platform for clinical and genomic data integration, known as the ODA framework. The framework supports interaction from an i2b2 plugin as well as a notebook environment. We show that the ODA framework exhibits worst-case linear scaling for array size (storage), import time (data construction), and query time for an increasing number of samples. We go on to show worst-case linear time for both import of clinical data and aggregate query execution time within a distributed environment. This work highlights the integration of a distributed genomic database with a distributed compute environment to support scalable and efficient precision medicine queries from a HIPAA-compliant, cohort system in a real-world setting. The ODA framework is currently deployed in production to support precision medicine exploration and analysis from clinicians and researchers at UCLA David Geffen School of Medicine

Genomic heterogeneity and clinical characterization of SARS-CoV-2 in Oregon

The first reported case of COVID-19 in the State of Oregon occurred in late February 2020, with subsequent outbreaks occurring in the populous Portland metro area but also with significant outbreaks in less-populous and rural areas. Here we report viral sequences from 188 patients across the hospitals and associated clinics in the Providence Health System in the State of Oregon dating back to the early days of the outbreak. We show a significant shift in dominant clade lineages over time in Oregon, with the rapid emergence and dominance of Spike D614G-positive variants. We also highlight significant diversity in SARS-CoV-2 sequences in Oregon, including a large number of rare mutations, indicative that these genomes could be utilized for outbreak tracing. Lastly, we show that SARS-CoV-2 genomic information may offer additional utility in combination with clinical covariates in the prediction of acute disease phenotypes

Defining the Extracellular Matrix of Rhabdomyosarcoma

Rhabdomyosarcoma (RMS) is the most common soft-tissue sarcoma of childhood with a propensity to metastasize. Current treatment for patients with RMS includes conventional systemic chemotherapy, radiation therapy, and surgical resection; nevertheless, little to no improvement in long term survival has been achieved in decades-underlining the need for target discovery and new therapeutic approaches to targeting tumor cells or the tumor microenvironment. To evaluate cross-species sarcoma extracellular matrix production, we have used murine models which feature knowledge of the myogenic cell-of-origin. With focus on the RMS/undifferentiated pleomorphic sarcoma (UPS) continuum, we have constructed tissue microarrays of 48 murine and four human sarcomas to analyze expression of seven different collagens, fibrillins, and collagen-modifying proteins, with cross-correlation to RNA deep sequencing. We have uncovered that RMS produces increased expression of type XVIII collagen alpha 1 (COL18A1), which is clinically associated with decreased long-term survival. We have also identified significantly increased RNA expression of COL4A1, FBN2, PLOD1, and PLOD2 in human RMS relative to normal skeletal muscle. These results complement recent studies investigating whether soft tissue sarcomas utilize collagens, fibrillins, and collagen-modifying enzymes to alter the structural integrity of surrounding host extracellular matrix/collagen quaternary structure resulting in improved ability to improve the ability to invade regionally and metastasize, for which therapeutic targeting is possible

Functional genomic analysis of epithelioid sarcoma reveals distinct proximal and distal subtype biology

BACKGROUND: Metastatic epithelioid sarcoma (EPS) remains a largely unmet clinical need in children, adolescents and young adults despite the advent of EZH2 inhibitor tazemetostat. METHODS: In order to realise consistently effective drug therapies, a functional genomics approach was used to identify key signalling pathway vulnerabilities in a spectrum of EPS patient samples. EPS biopsies/surgical resections and cell lines were studied by next-generation DNA exome and RNA deep sequencing, then EPS cell cultures were tested against a panel of chemical probes to discover signalling pathway targets with the most significant contributions to EPS tumour cell maintenance. RESULTS: Other biologically inspired functional interrogations of EPS cultures using gene knockdown or chemical probes demonstrated only limited to modest efficacy in vitro. However, our molecular studies uncovered distinguishing features (including retained dysfunctional SMARCB1 expression and elevated GLI3, FYN and CXCL12 expression) of distal, paediatric/young adult-associated EPS versus proximal, adult-associated EPS. CONCLUSIONS: Overall results highlight the complexity of the disease and a limited chemical space for therapeutic advancement. However, subtle differences between the two EPS subtypes highlight the biological disparities between younger and older EPS patients and emphasise the need to approach the two subtypes as molecularly and clinically distinct diseases