Dataset Information

Image-based personalization of computational models for predicting response of high-grade glioma to chemoradiation.

ABSTRACT: High-grade gliomas are an aggressive and invasive malignancy which are susceptible to treatment resistance due to heterogeneity in intratumoral properties such as cell proliferation and density and perfusion. Non-invasive imaging approaches can measure these properties, which can then be used to calibrate patient-specific mathematical models of tumor growth and response. We employed multiparametric magnetic resonance imaging (MRI) to identify tumor extent (via contrast-enhanced T₁-weighted, and T₂-FLAIR) and capture intratumoral heterogeneity in cell density (via diffusion-weighted imaging) to calibrate a family of mathematical models of chemoradiation response in nine patients with unresected or partially resected disease. The calibrated model parameters were used to forecast spatially-mapped individual tumor response at future imaging visits. We then employed the Akaike information criteria to select the most parsimonious member from the family, a novel two-species model describing the enhancing and non-enhancing components of the tumor. Using this model, we achieved low error in predictions of the enhancing volume (median: - 2.5%, interquartile range: 10.0%) and a strong correlation in total cell count (Kendall correlation coefficient 0.79) at 3-months post-treatment. These preliminary results demonstrate the plausibility of using multiparametric MRI data to inform spatially-informative, biologically-based predictive models of tumor response in the setting of clinical high-grade gliomas.

SUBMITTER: Hormuth DA

PROVIDER: S-EPMC8055874 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature

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Image-based personalization of computational models for predicting response of high-grade glioma to chemoradiation.

Hormuth David A DA Al Feghali Karine A KA Elliott Andrew M AM Yankeelov Thomas E TE Chung Caroline C

Scientific reports 20210419 1

High-grade gliomas are an aggressive and invasive malignancy which are susceptible to treatment resistance due to heterogeneity in intratumoral properties such as cell proliferation and density and perfusion. Non-invasive imaging approaches can measure these properties, which can then be used to calibrate patient-specific mathematical models of tumor growth and response. We employed multiparametric magnetic resonance imaging (MRI) to identify tumor extent (via contrast-enhanced T<sub>1</sub>-wei ...[more]

PMID: 33875739

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Project description:IntroductionLow-grade glioma (LGG) is a prevalent malignant tumor in the intracranial region. Despite the advancements in treatment methods for this malignancy over the past decade, significant challenges still persist in the form of drug resistance and tumor recurrence. The Notch signaling pathway plays essential roles in many physiological processes as well as in cancer development. However, the significance of the pathway and family genes in LGG are poorly understood.MethodsWe conducted gene expression profiling analysis using the TCGA dataset to investigate the gene set associated with the Notch signaling pathway. we have proposed a metric called "NotchScore" that quantifies the strength of the Notch signaling pathway and enables us to assess its significance in predicting prognosis and immune response in LGG. We downregulated JAG1 in low-grade gliomas to assess its influence on the proliferation and migration of these tumors. Ultimately, we determined the impact of the transcription factor VDR on the transcription of PDL1 through chip-seq data analysis.ResultsOur findings indicate that tumors with a higher NotchScore, exhibit poorer prognosis, potentially due to their ability to evade the anti-tumor effects of immune cells by expressing immune checkpoints. Among the genes involved in the Notch signaling pathway, JAG1 has emerged as the most representative in terms of capturing the characteristics of both NotchScore and Notch pathways. The experimental results demonstrate that silencing JAG1 yielded a significant decrease in tumor cell proliferation in LGG cell lines. Our study revealed mechanisms by which tumors evade the immune system through the modulation of PDL1 transcription levels via the PI3K-Akt signaling pathway. Additionally, JAG1 potentially influences PDL1 in LGG by regulating the PI3K-Akt signaling pathway and the expression of the transcription factor VDR.DiscussionThese findings contribute to our understanding of immune evasion by tumors in LGG. The insights gained from this research may have implications for the development of therapeutic interventions for LGG.

Dataset Information

Image-based personalization of computational models for predicting response of high-grade glioma to chemoradiation.

Publications

Image-based personalization of computational models for predicting response of high-grade glioma to chemoradiation.

Similar Datasets

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