Jann Nagina Sarkaria

This study shows that CT-179, an OLIG2 inhibitor, reduces glioblastoma growth and enhances chemoradiotherapy efficacy in preclinical models.

This study explores optimizing aptamer-drug conjugates for GBM treatment by focusing on internalization and tumor homing through in vivo SELEX, but faces challenges with tumor specificity.

The paper introduces INSIGHT, a platform for phosphoproteomic analysis of rare cell subpopulations, and applies it to study signaling changes in disseminated glioblastoma cells in vivo.

This study develops and tests DNA aptamer-drug conjugates (ApDCs) for glioblastoma treatment using an in vivo SELEX approach in a patient-derived xenograft model.

This study uses cell-laden microgels to model GBM tumor microenvironment heterogeneity and screen TMZ resistance in patient-derived xenografts.

This study shows that restoring STAG2 in STAG2-mutant cancer cells rapidly reduces global chromatin loop size, with limited transcriptional changes, including induction of EFEMP1.

This study uses 3D hydrogel models to investigate how extracellular matrix, hypoxia, and neuronal cells affect glioblastoma radiosensitivity, identifying potential strategies to improve treatment.

The paper integrates single-cell RNA sequencing data with drug response signatures to identify drug-sensitive and resistant glioblastoma cell states and predict synergistic drug combinations.

This study uses a multi-omics approach to analyze early TMZ responses in GBM cells, identifying TMZ-associated peptide antigens and immune-related changes.

The paper identifies non-mutational p53 functional deficiency in TP53 wild-type tumors using SVM models, revealing significant functional impairment and clinical implications.

massNet uses deep learning to classify spatial mass spectrometry data without preprocessing, achieving higher accuracy and faster speed than classical methods for tumor delineation in mouse brain tissue.

The paper introduces msiPL, a deep learning model for analyzing MSI data, enabling efficient non-linear visualization and identification of biologically relevant patterns.

The paper quantifies tyrosine phosphorylation in FFPE tissues to reveal patient-specific signaling networks and identifies oncogenic kinases in cancer tumors.

This study shows that purine metabolism affects DNA repair and therapy resistance in glioblastoma, suggesting that targeting purine synthesis could improve treatment outcomes.

The paper identifies four molecularly distinct glioblastoma subtypes using single-cell protein-activity analysis and shows drug sensitivity is state-dependent.