Improved risk stratification in younger<i>IDH</i>wild-type glioblastoma patients by combining a 4-miRNA signature with<i>MGMT</i>promoter methylation status

Unger, Kristian; Fleischmann, Daniel; Ruf, Viktoria; Felsberg, Jörg; Piehlmaier, D.; Samaga, Daniel; Heß, Julia; Suresh, Marian Preetham; Mittelbronn, Michel; Lauber, Kirsten; Budach, Wilfried; Sabel, Michael; Rödel, Claus; Reifenberger, Guido; Herms, Jochen; Tonn, Jörg‐Christian; Zitzelsberger, Horst; Belka, Claus; Niyazi, Maximilian

doi:10.1093/noajnl/vdaa137

Cited by 3 publications

(2 citation statements)

References 44 publications

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“…The trial also includes several secondary endpoints, including treatment safety and tolerability of the treatment, PFS, quality of life assessment, and evaluation of cognitive function. In addition to these endpoints, there is a translational exploratory objective in the study to validate a 4-micro-ribunucleoid-acid (4-miRNA) signature-based risk subgroup classification [23] , [24] . This objective aims to investigate the potential of a specific 4-miRNA signature in predicting treatment response or prognosis.…”

Section: Introductionmentioning

confidence: 99%

Dosimetric feasibility analysis and presentation of an isotoxic dose-escalated radiation therapy concept for glioblastoma used in the PRIDE trial (NOA-28; ARO-2022-12)

Bodensohn,

Fleischmann,

Maier

et al. 2024

Clinical and Translational Radiation Oncology

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Section: Introductionmentioning

confidence: 99%

Dosimetric feasibility analysis and presentation of an isotoxic dose-escalated radiation therapy concept for glioblastoma used in the PRIDE trial (NOA-28; ARO-2022-12)

Bodensohn,

Fleischmann,

Maier

et al. 2024

Clinical and Translational Radiation Oncology

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“…To name but a few, Yuan et al suggested a 3-miRNA signature (hsa-miR-222, hsa-miR-302, hsa-miR-646) as a predictor of overall survival (OS), utilizing miRNA expression data for GBM patients from the Cancer Genome Atlas (TCGA) dataset [ 8 ]. A recently published 4-miRNA signature showed prognostic value, taking into account MGMT promoter methylation and age as cofactors, in IDH1/2 wild type GBM patients [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

A 3-miRNA Signature Enables Risk Stratification in Glioblastoma Multiforme Patients with Different Clinical Outcomes

et al. 2022

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Malignant gliomas constitute a complex disease phenotype that demands optimum decision-making as they are highly heterogeneous. Such inter-individual variability also renders optimum patient stratification extremely difficult. microRNA (hsa-miR-20a, hsa-miR-21, hsa-miR-21) expression levels were determined by RT-qPCR, upon FFPE tissue sample collection of glioblastoma multiforme patients (n = 37). In silico validation was then performed through discriminant analysis. Immunohistochemistry images from biopsy material were utilized by a hybrid deep learning system to further cross validate the distinctive capability of patient risk groups. Our standard-of-care treated patient cohort demonstrates no age- or sex- dependence. The expression values of the 3-miRNA signature between the low- (OS > 12 months) and high-risk (OS < 12 months) groups yield a p-value of <0.0001, enabling risk stratification. Risk stratification is validated by a. our random forest model that efficiently classifies (AUC = 97%) patients into two risk groups (low- vs. high-risk) by learning their 3-miRNA expression values, and b. our deep learning scheme, which recognizes those patterns that differentiate the images in question. Molecular-clinical correlations were drawn to classify low- (OS > 12 months) vs. high-risk (OS < 12 months) glioblastoma multiforme patients. Our 3-microRNA signature (hsa-miR-20a, hsa-miR-21, hsa-miR-10a) may further empower glioblastoma multiforme prognostic evaluation in clinical practice and enrich drug repurposing pipelines.

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Systematic in vitro analysis of therapy resistance in glioblastoma cell lines by integration of clonogenic survival data with multi-level molecular data

et al. 2023

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Despite intensive basic scientific, translational, and clinical efforts in the last decades, glioblastoma remains a devastating disease with a highly dismal prognosis. Apart from the implementation of temozolomide into the clinical routine, novel treatment approaches have largely failed, emphasizing the need for systematic examination of glioblastoma therapy resistance in order to identify major drivers and thus, potential vulnerabilities for therapeutic intervention. Recently, we provided proof-of-concept for the systematic identification of combined modality radiochemotherapy treatment vulnerabilities via integration of clonogenic survival data upon radio(chemo)therapy with low-density transcriptomic profiling data in a panel of established human glioblastoma cell lines. Here, we expand this approach to multiple molecular levels, including genomic copy number, spectral karyotyping, DNA methylation, and transcriptome data. Correlation of transcriptome data with inherent therapy resistance on the single gene level yielded several candidates that were so far underappreciated in this context and for which clinically approved drugs are readily available, such as the androgen receptor (AR). Gene set enrichment analyses confirmed these results, and identified additional gene sets, including reactive oxygen species detoxification, mammalian target of rapamycin complex 1 (MTORC1) signaling, and ferroptosis/autophagy-related regulatory circuits to be associated with inherent therapy resistance in glioblastoma cells. To identify pharmacologically accessible genes within those gene sets, leading edge analyses were performed yielding candidates with functions in thioredoxin/peroxiredoxin metabolism, glutathione synthesis, chaperoning of proteins, prolyl hydroxylation, proteasome function, and DNA synthesis/repair. Our study thus confirms previously nominated targets for mechanism-based multi-modal glioblastoma therapy, provides proof-of-concept for this workflow of multi-level data integration, and identifies novel candidates for which pharmacological inhibitors are readily available and whose targeting in combination with radio(chemo)therapy deserves further examination. In addition, our study also reveals that the presented workflow requires mRNA expression data, rather than genomic copy number or DNA methylation data, since no stringent correlation between these data levels could be observed. Finally, the data sets generated in the present study, including functional and multi-level molecular data of commonly used glioblastoma cell lines, represent a valuable toolbox for other researchers in the field of glioblastoma therapy resistance.

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Improved risk stratification in youngerIDHwild-type glioblastoma patients by combining a 4-miRNA signature withMGMTpromoter methylation status

Cited by 3 publications

References 44 publications

Dosimetric feasibility analysis and presentation of an isotoxic dose-escalated radiation therapy concept for glioblastoma used in the PRIDE trial (NOA-28; ARO-2022-12)

Dosimetric feasibility analysis and presentation of an isotoxic dose-escalated radiation therapy concept for glioblastoma used in the PRIDE trial (NOA-28; ARO-2022-12)

A 3-miRNA Signature Enables Risk Stratification in Glioblastoma Multiforme Patients with Different Clinical Outcomes

Systematic in vitro analysis of therapy resistance in glioblastoma cell lines by integration of clonogenic survival data with multi-level molecular data

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