Biology subjects

D'Antuono, M.

Publications and source records attributed to D'Antuono, M..

4 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

Identification of disease-specific vulnerability states at the single-cell level

Intratumor heterogeneity in glioblastoma (GBM) impedes successful treatment as it is not obvious which tumor cells should be targeted. Here, we posit that single-cell-resolution transcriptomic data can be integrated with loss-of-function screens to identify the most critical cells to target within a tumor. We parsed CRISPR screen data from the Dependency Map (DepMap) Consortium and identified a GBM Dependency Signature (GDS) - 168 genes that are essential for GBM cell viability in vitro. Through similarity scoring of GDS transcriptomic profiles in single-cell RNA-sequencing (scRNA-seq) data and iterative hierarchical clustering, we identify and report 3 single-cell vulnerability states (VS) characterized in 49 GBM tumors using both scRNA-seq and spatial transcriptomic data. These VS reflect single-cell gene dependencies and differ significantly in enrichment profiles and spatial distributions. Additionally, each VS is differently sensitive to cancer drugs, with VS2 solely responsive to temozolomide treatment. Importantly, the proportion of VS in each GBM tumor is variable, suggesting a means of stratifying patients in clinical trials. Collectively, we have developed a novel computational pipeline to identify unique vulnerability states in GBM and other cancers, which can be used to identify existing or novel drugs for incurable diseases.

cancer biology↗

bioRxiv2024

OrthologAL: A Shiny application for quality-aware humanization of non-human pre-clinical high-dimensional gene expression data

Single-cell and spatial transcriptomics provide unprecedented insight into the inner workings of disease. Pharmacotranscriptomic approaches are powerful tools that leverage gene expression data for drug repurposing and treatment discovery in many diseases. Multiple databases attempt to connect human cellular transcriptional responses to small molecules for use in transcriptome-based drug discovery efforts. However, pre-clinical research often requires in vivo experiments in non-human species, which makes capitalizing on such valuable resources difficult. To facilitate the application of pharmacotranscriptomic databases to pre-clinical research models and to facilitate human orthologous conversion of non-human transcriptomes, we introduce OrthologAL. OrthologAL leverages the BioMart database to access different gene sets from Ensembl, facilitating the interaction between these servers without needing user-generated code. Researchers can input their single-cell or other high-dimensional gene expression data from any species, and OrthologAL will output a human ortholog-converted dataset for download and use. To demonstrate the utility of this application, we characterized orthologous conversion in single-cell, single-nuclei, and spatial transcriptomic data derived from common pre-clinical models, including patient-derived orthotopic xenografts of medulloblastoma, and mouse and rat models of spinal cord injury. We show that OrthologAL can convert these data types efficiently to that of corresponding orthologs while preserving the dimensional architecture of the original non-human expression data. OrthologAL will be broadly useful for applying pre-clinical, high-dimensional transcriptomics data in functional small molecule predictions using existing human-annotated databases.

bioinformatics↗

bioRxiv2024

MAT2a and AHCY Inhibition Disrupts Antioxidant Metabolism and Reduces Glioblastoma Cell Survival

Glioblastoma (GBM) is a highly aggressive primary malignant adult brain tumor that inevitably recurs with a fatal prognosis. This is due in part to metabolic reprogramming that allows tumors to evade treatment. We therefore must uncover the pathways mediating these adaptations to develop novel and effective treatments. We searched for genes that are essential in GBM cells as measured by a whole-genome pan-cancer CRISPR screen available from DepMap and identified the methionine metabolism genes MAT2A and AHCY. We conducted genetic knockdown, evaluated mitochondrial respiration, and performed targeted metabolomics to study the function of these genes in GBM. We demonstrate that MAT2A or AHCY knockdown induces oxidative stress, hinders cellular respiration, and reduces the survival of GBM cells. Furthermore, selective MAT2a or AHCY inhibition reduces GBM cell viability, impairs oxidative metabolism, and changes the metabolic profile of these cells towards oxidative stress and cell death. Mechanistically, MAT2a or AHCY regulates spare respiratory capacity, the redox buffer cystathionine, lipid and amino acid metabolism, and prevents DNA damage in GBM cells. Our results point to the methionine metabolic pathway as a novel vulnerability point in GBM. SignificanceWe demonstrated that methionine metabolism maintains antioxidant production to facilitate pro-tumorigenic ROS signaling and GBM tumor cell survival. Importantly, targeting this pathway in GBM can potentially reduce tumor growth and improve survival in patients.

cancer biology↗