Biology subjects

Burgenske, D.

Publications and source records attributed to Burgenske, D..

3 recordsLinked to original sources

bioRxiv2026

In Vivo Selection of anti-glioblastoma DNA aptamer-drug conjugates in an orthotopic patient-derived xenograft model

Glioblastoma (GBM) is an aggressive, high-grade glioma with a near-universally fatal prognosis. Therapeutic failure is often attributed to the highly selective blood brain barrier (BBB), the diffuse infiltrative nature of the tumor, and the marked intratumoral heterogeneity of GBM. Although antibody drug conjugates ADCs have shown promise for high grade gliomas such as GBM, efficacy is limited by ADC size. Aptamers--short, synthetic, single-stranded DNA or RNA molecules--can be [~]6-fold lower in molecular weight than IgG antibodies and have the potential to cross the intact BBB. Unlike other nucleic acid-based therapies, aptamer function arises from three-dimensional shape rather than genetic coding. Here we aim to replace the targeting component of the ADC paradigm with a DNA aptamer, thus creating an aptamer-drug conjugate (ApDC). We employed in vivo SELEX using an orthotopic patient-derived xenograft (PDX) GBM mouse model and a vast ([~]100 trillion 80-mer sequences) ApDC library. We report the results from this first in vivo ApDC selection of its kind. We characterize target tissue binding ex vivo, cell association, biodistribution, and pharmacokinetics from this selection. This study exemplifies an unbiased approach to a problem that rational design has yet to overcome, offering a new direction for GBM therapeutic development.

cancer biology↗

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

Uncovering the signaling networks of disseminated glioblastoma cells in vivo with INSIGHT

Dysregulation of intracellular signaling networks underpins cancer. However, a systems-level elucidation of how signaling networks within distinct cell subpopulations drive cancer progression in vivo has been unattainable due to technical limitations. We developed INSIGHT (INvestigating SIGnaling network of specific cell subpopulation in Heterogeneous Tissue), a new platform technology combining fluorescence-activated cell sorting with ultra-sensitive mass spectrometry to enable phosphoproteomic characterization of rare and discrete cell subpopulations from fixed tissues. We demonstrated the broad utility of INSIGHT by analyzing the oligodendroglial cell-specific signaling network in the mouse brain. We then applied INSIGHT to investigate the rare, disseminated tumor cell subpopulation in glioblastoma patient-derived xenograft models. INSIGHT uncovered a global rewiring of signaling networks with tumor cell dissemination, marked by a transition from proliferation-associated signaling in the primary tumor cells to signaling associated with postsynapse, neuronal migration, and ion homeostasis in disseminated tumor cells. We reveal interconnections between signaling circuitries within the networks, with numerous proteins, including GluA2, exhibiting altered phosphorylation without protein expression changes, emphasizing the role of post-translational modifications in glioblastoma dissemination. We validated key phosphorylation changes and inferred differentially active kinases with tumor spread to offer new systems-level insights into glioblastoma dissemination mechanisms in vivo. INSIGHT is generally applicable to a wide range of biological systems without genetic engineering and provides quantitative phosphorylation and protein expression data for selected cell subpopulations from heterogeneous tissues.

systems biology↗