Dataset Information

Identification of hnRNP-A1 as a pharmacodynamic biomarker of type I PRMT inhibition in blood and tumor tissues.

ABSTRACT: Arginine methylation has been recognized as a post-translational modification with pleiotropic effects that span from regulation of transcription to metabolic processes that contribute to aberrant cell proliferation and tumorigenesis. This has brought significant attention to the development of therapeutic strategies aimed at blocking the activity of protein arginine methyltransferases (PRMTs), which catalyze the formation of various methylated arginine products on a wide variety of cellular substrates. GSK3368715 is a small molecule inhibitor of type I PRMTs currently in clinical development. Here, we evaluate the effect of type I PRMT inhibition on arginine methylation in normal human peripheral blood mononuclear cells and utilize a broad proteomic approach to identify type I PRMT substrates. This work identified heterogenous nuclear ribonucleoprotein A1 (hnRNP-A1) as a pharmacodynamic biomarker of type I PRMT inhibition. Utilizing targeted mass spectrometry (MS), methods were developed to detect and quantitate changes in methylation of specific arginine residues on hnRNP-A1. This resulted in the development and validation of novel MS and immune assays useful for the assessment of GSK3368715 induced pharmacodynamic effects in blood and tumors that can be applied to GSK3368715 clinical trials.

SUBMITTER: Noto PB

PROVIDER: S-EPMC7746746 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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Identification of hnRNP-A1 as a pharmacodynamic biomarker of type I PRMT inhibition in blood and tumor tissues.

Noto Paul B PB Sikorski Timothy W TW Zappacosta Francesca F Wagner Craig D CD Montes de Oca Rocio R Szapacs Matthew E ME Annan Roland S RS Liu Yan Y McHugh Charles F CF Mohammad Helai P HP Piccoli Steven P SP Creasy Caretha L CL

Scientific reports 20201217 1

Arginine methylation has been recognized as a post-translational modification with pleiotropic effects that span from regulation of transcription to metabolic processes that contribute to aberrant cell proliferation and tumorigenesis. This has brought significant attention to the development of therapeutic strategies aimed at blocking the activity of protein arginine methyltransferases (PRMTs), which catalyze the formation of various methylated arginine products on a wide variety of cellular sub ...[more]

PMID: 33335114

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Project description:BackgroundPharmacodynamic biomarkers are becoming increasingly valuable for assessing drug activity and target modulation in clinical trials. However, identifying quality biomarkers is challenging due to the increasing volume and heterogeneity of relevant data describing the biological networks that underlie disease mechanisms. A biological pathway network typically includes entities (e.g. genes, proteins and chemicals/drugs) as well as the relationships between these and is typically curated or mined from structured databases and textual co-occurrence data. We propose a hybrid Natural Language Processing and directed relationships-based network analysis approach using IBM Watson for Drug Discovery to rank all human genes and identify potential candidate biomarkers, requiring only an initial determination of a specific target-disease relationship.MethodsThrough natural language processing of scientific literature, Watson for Drug Discovery creates a network of semantic relationships between biological concepts such as genes, drugs, and diseases. Using Bruton's tyrosine kinase as a case study, Watson for Drug Discovery's automatically extracted relationship network was compared with a prominent manually curated physical interaction network. Additionally, potential biomarkers for Bruton's tyrosine kinase inhibition were predicted using a matrix factorization approach and subsequently compared with expert-generated biomarkers.ResultsWatson's natural language processing generated a relationship network matching 55 (86%) genes upstream of BTK and 98 (95%) genes downstream of Bruton's tyrosine kinase in a prominent manually curated physical interaction network. Matrix factorization analysis predicted 11 of 13 genes identified by Merck subject matter experts in the top 20% of Watson for Drug Discovery's 13,595 ranked genes, with 7 in the top 5%.ConclusionTaken together, these results suggest that Watson for Drug Discovery's automatic relationship network identifies the majority of upstream and downstream genes in biological pathway networks and can be used to help with the identification and prioritization of pharmacodynamic biomarker evaluation, accelerating the early phases of disease hypothesis generation.

Dataset Information

Identification of hnRNP-A1 as a pharmacodynamic biomarker of type I PRMT inhibition in blood and tumor tissues.

Publications

Identification of hnRNP-A1 as a pharmacodynamic biomarker of type I PRMT inhibition in blood and tumor tissues.

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