Biology subjects

Ivan, M. E.

Publications and source records attributed to Ivan, M. E..

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

Targeting ZNF638 activates antiviral immune responses and potentiates immune checkpoint inhibition in glioblastoma

Viral mimicry refers to the activation of innate anti-viral immune responses due to the induction of endogenous retroelement (RE) expression. Viral mimicry has been previously described to augment anti-tumor immune responses and sensitize solid tumors to immunotherapy including colorectal cancer, melanoma, and clear renal cell carcinoma. Here, we found that targeting a novel, master epigenetic regulator, Zinc Finger Protein 638 (ZNF638), induces viral mimicry in glioblastoma (GBM) preclinical models and potentiates immune checkpoint inhibition (ICI). ZNF638 recruits the HUSH complex, which precipitates repressive H3K9me3 marks on endogenous REs. In GBM, ZNF638 is associated with marked locoregional immunosuppressive transcriptional signatures, reduced endogenous RE expression and poor immune cell infiltration (CD8+ T-cells, dendritic cells). ZNF638 knockdown decreased H3K9-trimethylation, increased cytosolic dsRNA and activated intracellular dsRNA-signaling cascades (RIG-I, MDA5 and IRF3). Furthermore, ZNF638 knockdown upregulated antiviral immune programs and significantly increased PD-L1 immune checkpoint expression in patient-derived GBM neurospheres and diverse murine models. Importantly, targeting ZNF638 sensitized mice to ICI in syngeneic murine orthotopic models through innate interferon signaling. This response was recapitulated in recurrent GBM (rGBM) samples with radiographic responses to checkpoint inhibition with widely increased expression of dsRNA, PD-L1 and perivascular CD8 cell infiltration, suggesting dsRNA-signaling may mediate response to immunotherapy. Finally, we showed that low ZNF638 expression was a biomarker of clinical response to ICI and improved survival in rGBM patients and melanoma patients. Our findings suggest that ZNF638 could serve as a target to potentiate immunotherapy in gliomas.

cancer biology↗

bioRxiv2020

Multiplex-inhibitor bead mass spectrometry reveals differential kinome signatures in newly diagnosed and recurrent glioblastoma

Glioblastoma (GBM) is the most common and aggressive adult brain tumor. Despite years of research, clinical trials have not improved the outcome for GBM. Standard of care for newly diagnosed GBM includes surgical resection, followed by radiation and chemotherapy. Tumor recurrence is inevitable and since most patients are not candidates for a second surgical resection, there is an urgent need to identify resistance mechanisms that arise in recurrent GBM. We postulated that examining the differences of activated kinases between newly diagnosed and recurrent GBM may provide insight to resistance mechanisms. To map the kinome landscape of newly diagnosed (nGBM) and recurrent GBM (rGBM) patient derived xenograft tumors, we used Multiplexed Inhibitor Beads and Mass Spectrometry (MIB-MS). We performed pathway analysis of kinases that differed in MIB-binding between nGBM and rGBM to identify kinase-driven signaling pathways. We also analyzed transcriptional profiles to determine the overlap in signaling pathways seen using proteomics or transcriptomics. Using MIB-MS kinome profiling, we found key differences in kinase-driven signaling pathways that may account for the increase in aggressive behavior seen in recurrent GBM. This included a shift in pathways driving cell invasion and proliferation, as well as upregulation of signaling pathways that drive GBM stem-cell like cell differentiation. Analysis of RNA-sequencing showed no statistically significant differences between enriched gene ontologies in nGBM and rGBM, demonstrating the importance of MIB-MS kinome profiling. Collectively, these studies suggest that kinome profiling may inform future clinical trials for kinase inhibitors in GBM.

cancer biology↗