Emerging Roles of Wild-type and Mutant IDH1 in Growth, Metabolism and Therapeutics of Glioma

Garrett, Matthew C.; Fujii, Yuki; Osaka, Natsuki; Ito, Daisuke; Hirota, Yoshio; Sasaki, Atsuo T.

doi:10.36255/exonpublications.gliomas.2021.chapter4

Cited by 7 publications

(7 citation statements)

References 85 publications

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“…Mutations in the IDH enzyme worsen the prognosis of gliomas. It is therefore important to distinguish between the two types of glial tumor in order to tailor treatments and improve prognosis [27].…”

Section: Resultsmentioning

confidence: 99%

Benchmarking feature selection and feature extraction methods to improve the performances of machine-learning algorithms for patient classification using metabolomics biomedical data

Labory,

Njomgue-Fotso,

Bottini

2023

Preprint

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ObjectiveClassification tasks are an open challenge in the field of biomedicine. While several machine-learning techniques exist to accomplish this objective, several peculiarities associated with biomedical data, especially when it comes to omics measurements, prevent their use or good performance achievements. Omics approaches aim to understand a complex biological system through systematic analysis of its content at the molecular level. On the other hand, omics data are heterogeneous, sparse and affected by the classical “curse of dimensionality” problem, i.e. having much fewer observation samples (n) than omics features (p). Furthermore, a major problem with multi- omics data is the imbalance either at the class or feature level. The objective of this work is to study whether feature extraction and/or feature selection techniques can improve the performances of classification machine-learning algorithms on omics measurements.MethodsAmong all omics, metabolomics has emerged as a powerful tool in cancer research, facilitating a deeper understanding of the complex metabolic landscape associated with tumorigenesis and tumor progression. Thus, we selected three publicly available metabolomics datasets, and we applied several feature extraction techniques both linear and non-linear, coupled or not with feature selection methods, and evaluated the performances regarding patient classification in the different configurations for the three datasets.ResultsWe provide general workflow and guidelines on when to use those techniques depending on the characteristics of the data available. For the three datasets, we showed that applying feature selection based on biological previous knowledge improves the performances of the classifiers. Notebook used to perform all analysis are available at:https://github.com/Plant-Net/Metabolomic_project/.

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Section: Resultsmentioning

confidence: 99%

Benchmarking feature selection and feature extraction methods to improve the performances of machine-learning algorithms for patient classification using metabolomics biomedical data

Labory,

Njomgue-Fotso,

Bottini

2023

Preprint

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“…Glioblastomas with IDH-1 mutations account for 73% of tumors and are reported to have a slightly better prognosis due to their enhanced response to radiotherapy and temozolomide (26). Regardless, IDH-1 mutants and wildtype tumors have different metabolic profiles, suggesting that each type may have unique therapeutic vulnerabilities (27). One such potential vulnerability is seen in the IDH1 R132H mutational subtype, which has significantly improved prognosis versus other gliomas, and overexpresses microRNA mIR-128.…”

Section: Treatment Planningmentioning

confidence: 99%

MRI radiomics and potential applications to glioblastoma

Hooper

Ginat

2023

Front. Oncol.

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“…Ang(1-7) binds to Mas receptors (MasRs) [117,118]. ATI may also be cleaved by ACE2 to form Ang (1)(2)(3)(4)(5)(6)(7)(8)(9), which can be cleaved by ACE1 and is converted to Ang(1-7), which in addition to binding to MasRs, can also bind to AT2R with low affinity, and Mas-related-G protein coupled receptors (MrgDs) [119]. MrgDs are a recently discovered component of the RAS [102], and their role in the GB TME is yet to be defined.…”

Section: The Renin-angiotensin System and Convergent Signaling Pathways In Glioblastomamentioning

confidence: 99%

“…IDH-wild-type GB is more common, tends to arise de novo, and is generally more aggressive with a worse prognosis than IDH-mutant GB. By contrast, IDH-mutant GB is predominantly observed in secondary GB and is associated with a better prognosis [6]. The current standard treatment for GB involves maximal safe surgical resection with adjuvant chemotherapy and radiotherapy, known as the Stupp protocol [7].…”

Section: Introductionmentioning

confidence: 99%

The Renin–Angiotensin System in the Tumor Microenvironment of Glioblastoma

O'Rawe

Kilmister

Mantamadiotis

et al. 2021

Cancers

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Glioblastoma (GB) is an aggressive primary brain tumor. Despite intensive research over the past 50 years, little advance has been made to improve the poor outcome, with an overall median survival of 14.6 months following standard treatment. Local recurrence is inevitable due to the quiescent cancer stem cells (CSCs) in GB that co-express stemness-associated markers and components of the renin–angiotensin system (RAS). The dynamic and heterogeneous tumor microenvironment (TME) plays a fundamental role in tumor development, progression, invasiveness, and therapy resistance. There is increasing evidence showing the critical role of the RAS in the TME influencing CSCs via its upstream and downstream pathways. Drugs that alter the hallmarks of cancer by modulating the RAS present a potential new therapeutic alternative or adjunct to conventional treatment of GB. Cerebral and GB organoids may offer a cost-effective method for evaluating the efficacy of RAS-modulating drugs on GB. We review the nexus between the GB TME, CSC niche, and the RAS, and propose re-purposed RAS-modulating drugs as a potential therapeutic alternative or adjunct to current standard therapy for GB.

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Emerging Roles of Wild-type and Mutant IDH1 in Growth, Metabolism and Therapeutics of Glioma

Cited by 7 publications

References 85 publications

Benchmarking feature selection and feature extraction methods to improve the performances of machine-learning algorithms for patient classification using metabolomics biomedical data

Benchmarking feature selection and feature extraction methods to improve the performances of machine-learning algorithms for patient classification using metabolomics biomedical data

MRI radiomics and potential applications to glioblastoma

The Renin–Angiotensin System in the Tumor Microenvironment of Glioblastoma

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