Biology subjects

Schurer, S. C.

Publications and source records attributed to Schurer, S. C..

5 recordsLinked to original sources

bioRxiv2024

Pan-Cancer Drug Sensitivity Prediction from Gene Expression using Deep Learning

Cancer is a group of complex diseases, with tumor heterogeneity, durable drug efficacy, emerging resistance, and host toxicity presenting major challenges to the development of effective cancer therapeutics. While traditionally used methods have remained limited in their capacity to overcome these challenges in cancer drug development, efforts have been made in recent years toward applying "big data" to cancer research and precision oncology. By curating, standardizing, and integrating data from various databases, we developed deep learning architectures that use perturbation and baseline transcriptional signatures to predict efficacious small molecule compounds and genetic dependencies in cancer. A series of internal validations followed by prospective validation in prostate cancer cell lines were performed to ensure consistent performance and model applicability. We report SensitivitySeq, a novel bioinformatics tool for prioritizing small molecule compounds and gene dependencies in silico to drive the development of targeted therapies for cancer. To the best of our knowledge, this is the first supervised deep learning approach, validated in vitro, to predict drug sensitivity using baseline cancer cell line gene expression alongside cell line-independent perturbation-response consensus signatures. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=180 SRC="FIGDIR/small/623715v1_ufig1.gif" ALT="Figure 1"> View larger version (73K): org.highwire.dtl.DTLVardef@9f48bcorg.highwire.dtl.DTLVardef@16056f7org.highwire.dtl.DTLVardef@1ed9095org.highwire.dtl.DTLVardef@1af3fce_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2024

The cyclin-G associated kinase (GAK) is a novel mitotic kinase and therapeutic target in diffuse large B-cell lymphoma

New drug targets are needed for diffuse large B-cell lymphoma (DLBCL), the most common lymphoma subtype, to enable enable development better treatments for patients not cured by standard care. We conducted a phenotypic screen of kinase inhibitors and identified the cyclin G-associated kinase (GAK) as a tumor-selective target. Though GAK is previously described primarily as a participant in membrane trafficking, we found its kinase activity is a key mitotic regulator in DLBCL. Inhibition caused G2/M-phase arrest, chromosome misalignment, and spindle distortion, effects absent in non-malignant controls. Transcriptomics data from clinical samples showed increased GAK expression associates with RB1 deficiency in DLBCL cases, suggesting dependency on GAK linked to retinoblastoma associated protein (RB) loss of function, a common DLBCL driver. RB-deficient DLBCL cells treated with a selective GAK tool compound showed complete arrest at G2/M, pronounced distortion of mitotic spindles, and widespread chromosomal damage. High-content live-cell imaging revealed onset of mitotic catastrophe in response to GAK inhibition, which was more rapid and severe in isogenic cells with RB1 deletion. No GAK-selective inhibitors suitable for clinical development are currently available, but several drugs approved or under development inhibit GAK activity even more potently than thier intended clinical targets. For instance, OTS167, developed against MELK for use in solid tumors, has particularly potent anti-GAK potency and has achieved single-agent tumor-burden reduction in vivo against a DLBCL patient-derived xenograft. GAK is therefore a novel mitotic kinase in DLBCL, linked to the common, undruggable RB loss of function biomarker, and suitable for rapid clinical translation through drug repurposing. SignificanceWe identify cyclin-G associated kinase (GAK) as a novel therapeutic vulnerability in diffuse large B-cell lymphoma. Clinical kinase inhibitors with GAK activity create an opportunity for rapid therapeutic translation through drug repurposing.

cancer biology↗

bioRxiv2022

Cellular And Molecular Effects Of Understudied Kinase Pregnancy Upregulated Non-Ubiquitous Calcium-Calmodulin Dependent Kinase (PNCK) In Renal Cell Carcinoma.

Renal Cell Carcinoma (RCC) is a uniformly fatal disease when advanced. While immunotherapy and tyrosine kinase inhibitor-based combinations are associated with improved progression-free and overall survival, the majority of patients eventually develop treatment resistance and succumb to progressive, refractory disease. This underscores the urgent need to identify novel, non-canonical RCC targets for drug development. Through a comprehensive pan-cancer, pan-kinome analysis of the Cancer Genome Atlas (TCGA), the understudied kinase, pregnancy upregulated non-ubiquitous calcium-calmodulin dependent kinase (PNCK) was identified as the most differentially overexpressed kinase in RCC. PNCK mRNA was significantly overexpressed in RCC tissues compared to adjacent normal tissue, and its overexpression correlated with tumor T-stage grade and poor disease specific survival in both clear cell and papillary RCCs. PNCK overexpression in VHL mutant and VHL wild type RCC cell lines was associated with increased CREB phosphorylation, as well as increased cell proliferation and cell cycle progression. PNCK down-regulation, conversely, was associated with inhibition of CREB phosphorylation, decreased cell proliferation, cell cycle arrest and increased apoptosis, with differential effects observed between VHL mutant and VHL wild type cell lines. Pathway analyses in PNCK knockdown cells showed significant down regulation of hypoxia and angiogenesis pathways, as well as modulation of pathways promoting cell cycle arrest and apoptosis. The above results demonstrate for the first time the biological role of PNCK, an understudied kinase, in renal cell carcinoma and validate PNCK as a potential novel target for drug development in this fatal disease.

molecular biology↗

bioRxiv2019

Connecting omics signatures of diseases, drugs, and mechanisms of actions with iLINCS

There are only a few platforms that integrate multiple omics data types, bioinformatics tools, and interfaces for integrative analyses and visualization that do not require programming skills. Among these, iLINCS is unique in scope and versatility of the data provided and the analytics facilitated. iLINCS (http://ilincs.org) is an integrative web-based platform for analysis of omics data and signatures of cellular perturbations. The platform facilitates analysis of user-submitted omics signatures of diseases and cellular perturbations in the context of a large compendium of pre-computed signatures (>200,000), as well as mining and re-analysis of the large collection of omics datasets (>12,000), pre-computed signatures, and their connections. Analytics workflows driven by user-friendly interfaces enable users with only conceptual understanding of the analysis strategy to execute sophisticated analyses of omics signatures, such as systems biology analyses and interpretation of signatures, mechanism of action analysis, and signature-driven drug repositioning. In summary, iLINCS workflows integrate vast omics data resources and a range of analytics and interactive visualization tools into a comprehensive platform for analysis of omics signatures.

bioinformatics↗

bioRxiv2017

Drug Target Ontology to Classify and Integrate Drug Discovery Data

BackgroundOne of the most successful approaches to develop new small molecule therapeutics has been to start from a validated druggable protein target. However, only a small subset of potentially druggable targets has attracted significant research and development resources. The Illuminating the Druggable Genome (IDG) project develops resources to catalyze the development of likely targetable, yet currently understudied prospective drug targets. A central component of the IDG program is a comprehensive knowledge resource of the druggable genome.\n\nResultsAs part of that effort, we have been developing a framework to integrate, navigate, and analyze drug discovery data based on formalized and standardized classifications and annotations of druggable protein targets, the Drug Target Ontology (DTO). DTO was constructed by extensive curation and consolidation of various resources. DTO classifies the four major drug target protein families, GPCRs, kinases, ion channels and nuclear receptors, based on phylogenecity, function, target development level, disease association, tissue expression, chemical ligand and substrate characteristics, and target-family specific characteristics. The formal ontology was built using a new software tool to auto-generate most axioms from a database while also supporting manual knowledge acquisition. A modular, hierarchical implementation facilitates development and maintenance and makes use of various external ontologies, thus integrating the DTO into the ecosystem of biomedical ontologies. As a formal OWL-DL ontology, DTO contains asserted and inferred axioms. Modeling data from the Library of Integrated Network-based Cellular Signatures (LINCS) program illustrates the potential of DTO for contextual data integration and nuanced definition of important drug target characteristics. DTO has been implemented in the IDG user interface Portal, Pharos and the TIN-X explorer of protein target disease relationships.\n\nConclusionsDTO was built based on the need for a formal semantic model for druggable targets including various related information such as protein, gene, protein domain, protein structure, binding site, small molecule drug, mechanism of action, protein tissue localization, disease association, and many other types of information. DTO will further facilitate the otherwise challenging integration and formal linking to biological assays, phenotypes, disease models, drug poly-pharmacology, binding kinetics and many other processes, functions and qualities that are at the core of drug discovery. The first version of DTO is publically available via the website http://drugtargetontology.org/, Github (https://github.com/DrugTargetOntology/DTO), and the NCBO Bioportal (https://bioportal.bioontology.org/ontologies/DTO). The long-term goal of DTO is to provide such an integrative framework and to populate the ontology with this information as a community resource.

bioinformatics↗