Biology subjects

Davis, L. E.

Publications and source records attributed to Davis, L. E..

2 recordsLinked to original sources

bioRxiv2022

PRMT5 as a Novel Druggable Vulnerability for EWSR1-ATF1-driven Clear Cell Sarcoma

Clear cell sarcoma of soft tissue (CCSST) is an ultra-rare sarcoma with poor prognosis presently with no cure. It is characterized by a balanced t(12;22) (q13;q12) chromosomal translocation, resulting in a fusion of the Ewings sarcoma gene EWSR1 with activating transcription factor 1 (ATF1) to give an oncogene EWSR1-ATF1. Unlike normal ATF1, whose transcription activity is dependent on phosphorylation, EWSR1-ATF1 is constitutively active to drive ATF1-dependent gene transcription to cause tumorigenesis. No EWSR1-ATF1-targeted therapies have been identified due to the challenges in targeting intracellular transcription factors. To identify potential druggable targets for CCSST, we show that protein arginine methyltransferase 5 (PRMT5) is a novel enzyme in enhancing EWSR1-ATF1-mediated gene transcription to sustain CCSST cell proliferation. Genetic silencing of PRMT5 in CCSST cells resulted in severely impaired cell proliferation and EWSR1-ATF1-driven transcription. Furthermore, the clinical-stage PRMT5 inhibitor JNJ-64619178 potently and efficaciously inhibited CCSST cell growth in vitro and in vivo. These results provide new insights into PRMT5 as a transcription regulator and warrant JNJ-64619178 for further clinical development to treat CCSST patients.

cancer biology↗

bioRxiv2018

Probabilistic modeling of personalized drug combinations from integrated chemical screen and molecular data in sarcoma

Cancer patients with advanced disease exhaust available clinical regimens and lack actionable genomic medicine results, leaving a large patient population without effective treatments options when their disease inevitably progresses. To address the unmet clinical need for evidence-based therapy assignment when standard clinical approaches have failed, we have developed a probabilistic computational modeling approach which integrates sequencing data with functional assay data to develop patient-specific combination cancer treatments. This computational modeling approach addresses three major challenges in personalized cancer therapy, which we validate across multiple species via computationally-designed personalized synergistic drug combination predictions, identification of unifying therapeutic targets to overcome intra-tumor heterogeneity, and mitigation of cancer cell resistance and rewiring mechanisms. These proof-of-concept studies support the use of an integrative functional approach to personalized combination therapy prediction for the population of high-risk cancer patients lacking viable clinical options and without actionable DNA sequencing-based therapy.

bioinformatics↗