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Biology subjects

Lathara, M.

Publications and source records attributed to Lathara, M..

2 recordsLinked to original sources

Bromodomain 4 inhibition leads to MYCN downregulation in Wilms' tumor cells

Wilms tumor is the most common childhood kidney cancer. Two distinct histological subtypes of Wilms tumor have been described: tumors lacking anaplasia (the favorable subtype) and tumors displaying anaplastic features (the unfavorable subtype). Children with favorable disease generally have a very good prognosis, while those with anaplasia are oftentimes refractory to standard treatments and suffer poor outcomes. MYCN dysregulation has been associated with a number of pediatric cancers including the anaplastic subtype of Wilms tumor. In this context, we undertook a functional genomics approach to uncover novel therapeutic strategies for those patients with anaplastic Wilms tumor. Genomic analysis and in vitro experimentation demonstrate that Wilms tumor cell growth can be reduced by modulating MYCN overexpression via BRD4 inhibition. We observed a time dependent reduction of MYCN and MYC protein levels upon BRD4 inhibition in Wilms tumor cell lines which led to increased cell death and suppressed proliferation. We suggest that AZD5153, a novel dual-BRD4 inhibitor, can reduce MYCN levels and should be further explored for its therapeutic potential against Wilms tumor.

cancer biology↗

Advancing clinical cohort selection with genomics analysis on a distributed platform

BackgroundThe 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. ResultsWe 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. ConclusionsThis 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.

bioinformatics↗