Dataset Information

Astrogliosis Releases Pro-Oncogenic Chitinase 3-Like 1 Causing MAPK Signaling in Glioblastoma.

ABSTRACT: Although reactive astrocytes constitute a major component of the cellular environment in glioblastoma, their function and crosstalk to other components of the environment is still poorly understood. Gene expression analysis of purified astrocytes from both the tumor core and non-infiltrated cortex reveals a tumor-related up-regulation of Chitinase 3-like 1 (CHI3L1), a cytokine which is related to inflammation, extracellular tissue remodeling, and fibrosis. Further, we established and validated a co-culture model to investigate the impact of reactive astrocytes within the tumor microenvironment. Here we show that reactive astrocytes promote a subtype-shift of glioblastoma towards the mesenchymal phenotype, driving mitogen-activated protein kinases (MAPK) signaling as well as increased proliferation and migration. In addition, we demonstrate that MAPK signaling is directly caused by a CHI3L1-IL13RA2 co-binding, which leads to increased downstream MAPK and AKT signaling. This novel microenvironmental crosstalk highlights the crucial role of non-neoplastic cells in malignant brain tumors and opens up new perspectives for targeted therapies in glioblastoma.

SUBMITTER: Wurm J

PROVIDER: S-EPMC6826948 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

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Astrogliosis Releases Pro-Oncogenic Chitinase 3-Like 1 Causing MAPK Signaling in Glioblastoma.

Wurm Julian J Behringer Simon P SP Ravi Vidhya M VM Joseph Kevin K Neidert Nicolas N Maier Julian P JP Doria-Medina Roberto R Follo Marie M Delev Daniel D Pfeifer Dietmar D Beck Jürgen J Sankowski Roman R Schnell Oliver O Heiland Dieter H DH

Cancers 20190926 10

Although reactive astrocytes constitute a major component of the cellular environment in glioblastoma, their function and crosstalk to other components of the environment is still poorly understood. Gene expression analysis of purified astrocytes from both the tumor core and non-infiltrated cortex reveals a tumor-related up-regulation of Chitinase 3-like 1 (CHI3L1), a cytokine which is related to inflammation, extracellular tissue remodeling, and fibrosis. Further, we established and validated a ...[more]

PMID: 31561550

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Project description:BackgroundGlioblastoma (GBM) is a highly aggressive brain tumor associated with a poor patient prognosis. The survival rate remains low despite standard therapies, highlighting the urgent need for novel treatment strategies. Advanced imaging techniques, particularly magnetic resonance imaging (MRI), are crucial in assessing GBM. Disruptions in various oncogenic signaling pathways, such as Receptor Tyrosine Kinase (RTK)-Ras-Extracellular signal-regulated kinase (ERK) signaling, Phosphoinositide 3- Kinases (PI3Ks), tumor protein p53 (TP53), and Neurogenic locus notch homolog protein (NOTCH), contribute to the development of different tumor types, each exhibiting distinct morphological and phenotypic features that can be observed at a microscopic level. However, identifying genetic abnormalities for targeted therapy often requires invasive procedures, prompting exploration into non-invasive approaches like radiogenomics. This study explores the utility of radiogenomics and machine learning (ML) in predicting these oncogenic signaling pathways in GBM patients.MethodsWe collected post-operative MRI scans (T1w, T1c, FLAIR, T2w) from the BRATS-19 dataset, including scans from patients with both GBM and LGG, linked to genetic and clinical data via TCGA and CPTAC. Signaling pathway data was manually extracted from cBioPortal. Radiomic features were extracted from four MRI modalities using PyRadiomics. Dimensionality reduction and feature selection were applied and Data imbalance was addressed with SMOTE. Five ML models were trained to predict signaling pathways, with Grid Search optimizing hyperparameters and 5-fold cross-validation ensuring unbiased performance. Each model's performance was evaluated using various metrics on test data.ResultsOur results showed a positive association between most signaling pathways and the radiomic features derived from MRI scans. The best models achieved high AUC scores, namely 0.7 for RTK-RAS, 0.8 for PI3K, 0.75 for TP53, and 0.4 for NOTCH, and therefore, demonstrated the potential of ML models in accurately predicting oncogenic signaling pathways from radiomic features, thereby informing personalized therapeutic approaches and improving patient outcomes.ConclusionWe present a novel approach for the non-invasive prediction of deregulation in oncogenic signaling pathways in glioblastoma (GBM) by integrating radiogenomic data with machine learning models. This research contributes to advancing precision medicine in GBM management, highlighting the importance of integrating radiomics with genomic data to understand tumor behavior and treatment response better.

Project description:BackgroundAngiogenesis is important in physiological and pathological conditions, as blood vessels provide nutrients and oxygen needed for tissue growth and survival. Therefore, targeting angiogenesis is a prominent strategy in both tissue engineering and cancer treatment. However, not all of the approaches to promote or inhibit angiogenesis lead to successful outcomes. Angiogenesis-based therapies primarily target pro-angiogenic factors such as vascular endothelial growth factor-A (VEGF) or fibroblast growth factor (FGF) in isolation. However, pre-clinical and clinical evidence shows these therapies often have limited effects. To improve therapeutic strategies, including targeting FGF and VEGF in combination, we need a quantitative understanding of the how the promoters combine to stimulate angiogenesis.ResultsIn this study, we trained and validated a detailed mathematical model to quantitatively characterize the crosstalk of FGF and VEGF intracellular signaling. This signaling is initiated by FGF binding to the FGF receptor 1 (FGFR1) and heparan sulfate glycosaminoglycans (HSGAGs) or VEGF binding to VEGF receptor 2 (VEGFR2) to promote downstream signaling. The model focuses on FGF- and VEGF-induced mitogen-activated protein kinase (MAPK) signaling and phosphorylation of extracellular regulated kinase (ERK), which promotes cell proliferation. We apply the model to predict the dynamics of phosphorylated ERK (pERK) in response to the stimulation by FGF and VEGF individually and in combination. The model predicts that FGF and VEGF have differential effects on pERK. Additionally, since VEGFR2 upregulation has been observed in pathological conditions, we apply the model to investigate the effects of VEGFR2 density and trafficking parameters. The model predictions show that these parameters significantly influence the response to VEGF stimulation.ConclusionsThe model agrees with experimental data and is a framework to synthesize and quantitatively explain experimental studies. Ultimately, the model provides mechanistic insight into FGF and VEGF interactions needed to identify potential targets for pro- or anti-angiogenic therapies.

Dataset Information

Astrogliosis Releases Pro-Oncogenic Chitinase 3-Like 1 Causing MAPK Signaling in Glioblastoma.

Publications

Astrogliosis Releases Pro-Oncogenic Chitinase 3-Like 1 Causing MAPK Signaling in Glioblastoma.

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