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Arslan, J.

Publications and source records attributed to Arslan, J..

2 recordsLinked to original sources

A Catalytically Inactive Protein Kinase C alpha Mutation Drives Chordoid Glioma by Pathway Rewiring

Abstract SummaryChordoid glioma (ChG) is a rare, low-grade brain tumor characterized by a novel recurrent point mutation, D463H, in the kinase domain of protein kinase C alpha (PKC). The mutation is invariably an Asp to His substitution, suggesting it endows a unique function beyond catalytic inactivation associated with other cancer-associated PKC mutations. Here we use in vitro and in cellulo activity assays to show that PKCD463H is catalytically inactive, functions as a dominant-negative mutant to suppress endogenous PKC and uniquely rewires the cellular interactome. Specifically, phosphoproteomic, proximity labeling, and co-immunoprecipitation mass-spectrometry data from cells overexpressing PKCD463H identify altered phosphorylation of substrates and binding to multiple proteins involved in cell-cell junctions compared to WT enzyme. Lastly, single nuclei RNAseq reveals that ChG derives from specialized tanycytes. Our data suggest that this disease-defining, fully penetrant mutation promotes neomorphic non-catalytic scaffolding to impair cell junction function.

molecular biology↗

Deciphering Oxygen Distribution and Hypoxia Profiles in the Tumor Microenvironment: A Data-Driven Mechanistic Modeling Approach

The distribution of hypoxia within tissues plays a critical role in tumor diagnosis and prognosis. Recognizing the significance of tumor oxygenation and hypoxia gradients, we introduce mathematical frameworks grounded in mechanistic modeling approaches for their quantitative assessment within a tumor microenvironment. Our approach provides a non-invasive method to measure and predict hypoxia using known blood vasculature. Formulating a reaction-diffusion model for oxygen distribution, we apply it to derive the corresponding hypoxia profile. The modeling and simulations successfully replicate the observed inter- and intra-tumor heterogeneity in experimentally obtained hypoxia profiles across various tumor tissues (breast, ovarian, and pancreatic) in our dataset. Employing a data-driven approach, we propose a method to deduce partial differential equation (PDE) models with spatially dependent parameters, enabling us to comprehend the variability of hypoxia profiles within a tissue. The versatility of our framework lies not only in capturing diverse and dynamic behaviors of tumor oxygenation but also in categorizing states of vascularization. These categories are distinguished based on the dynamics of oxygen molecules, as identified by the model parameters.

cancer biology↗