Biology subjects

Stathias, V.

Publications and source records attributed to Stathias, V..

6 recordsLinked to original sources

bioRxiv2025

Drug and single-cell gene expression integration identifies sensitive and resistant glioblastoma cell populations

Glioblastoma (GBM) remains the most common and lethal adult malignant primary brain cancer with few treatment options. A significant issue hindering GBM therapeutic development is intratumor heterogeneity. GBM tumors contain neoplastic cells within a spectrum of different transcriptional states. Identifying effective therapeutics requires a platform that predicts the differential sensitivity and resistance of these states to various treatments. Here, we developed a novel framework, ISOSCELES (Inferred cell Sensitivity Operating on the integration of Single-Cell Expression and L1000 Expression Signatures), to quantify the cellular drug sensitivity and resistance landscape. Using single-cell RNA sequencing of newly diagnosed and recurrent GBM tumors, we identified compounds from the LINCS L1000 database with transcriptional response signatures selectively discordant with distinct GBM cell states. We validated the significance of these findings in vitro, ex vivo, and in vivo, and identified a novel combination of an OLIG2 inhibitor and Depatux-M for GBM. Our studies suggest that ISOSCELES identifies cell states sensitive and resistant to targeted therapies in GBM and that it can be applied to identify new synergistic combinations. HighlightsO_LIIntegration of GBM single-cell RNA sequencing data with L1000-derived drug response signatures facilitates clustering of tumor cells and small molecules on cell-drug connectivity. C_LIO_LICell-drug connectivity predicts the identities of drug-sensitive and resistant cell states. C_LIO_LIIn silico perturbation analysis using cell-drug connectivity predicts drug-induced changes in the cell-drug connectivity landscape in vivo. C_LIO_LIIn silico perturbation analysis to predict drug-induced changes in the tumor cell-drug connectivity landscape predicts drug combinations that synergize in vivo to extend survival. C_LI

cancer biology↗

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

Identification of targetable epigenetic vulnerabilities in uveal melanoma

Uveal melanoma (UM) is the most common primary intraocular malignancy in adults, with a strong predilection for hepatic metastasis, occurring in approximately 50% of cases. Metastatic UM remains highly resistant to therapy and is almost invariably fatal. The strongest genetic drivers of UM metastasis are loss-of-function mutations in tumor suppressor BAP1, an epigenetic regulator that serves as the ubiquitin hydrolase subunit of the polycomb repressive deubiquitinase (PR-DUB) complex, and a key player in global epigenetic regulation. Inactivation of BRCA Associated Protein 1 (BAP1) has been shown to induce widespread epigenetic alterations across multiple model systems. To identify novel therapeutic strategies, we investigated whether targeting the epigenome could reveal new vulnerabilities in UM. We performed high-throughput compound screening using a curated epigenetic inhibitor library and identified BET (bromodomain and extra-terminal domain) inhibition as a particularly promising approach. Interestingly, we observed significant heterogeneity in the efficacy of different BET inhibitors in UM. While previous clinical trials with two BET inhibitors have failed to show efficacy in UM, our findings highlight substantial differences in the potency of specific BET inhibitors for this malignancy. Notably, the BET inhibitor mivebresib (ABBV-075) significantly improved survival rates by 50% in a metastatic UM xenograft mouse model and completely prevented detectable metastases in the bones, spinal cord, and brain. Unexpectedly, RNA sequencing revealed a strong transcriptional overlap between BET inhibition and histone deacetylase (HDAC) inhibition--an approach currently under clinical evaluation for UM treatment. Both BET and HDAC inhibitors reversed gene expression signatures associated with high metastatic risk and induced a neuronal differentiation-like phenotype in UM cells. Together, our findings demonstrate that UM cells exhibit a distinct vulnerability to BET inhibition and establish BET inhibitors as promising candidates for further clinical evaluation for metastatic UM.

cancer biology↗

bioRxiv2020

The Clinical Kinase Index: Prioritizing Understudied Kinases as Targets for the Treatment of Cancer

The approval of the first kinase inhibitor, Gleevec, in 2001, ushered in a paradigm shift for oncological treatment--the use of genomic data for targeted, efficacious therapies. Since then, over 48 additional small molecule kinase inhibitors have been approved, solidifying the case for kinases as a highly druggable and attractive target class. Despite the established role deregulated kinase activity plays in cancer, only 8% of the entire kinome has been effectively "drugged". Moreover, a quarter of the 634 human kinases are vastly understudied. We have developed a comprehensive scoring system which utilizes differential gene expression, clinical and pathological parameters, overall survival and mutational hotspot analysis to rank and prioritize clinically-relevant kinase targets across 17 solid tumor cancers from The Cancer Genome Atlas (TCGA). Collectively, we report that dark kinases have potential clinical value as biomarkers or as new drug targets that warrant further study.

bioinformatics↗

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↗