Metabolic remodeling in glioblastoma: a longitudinal multi-omics study

Abstract

Monitoring tumor evolution and predicting survival using non-invasive liquid biopsy is an unmet need for glioblastoma patients. The era of proteomics and metabolomics blood analyzes, may help in this context. A case-control study was conducted. Patients were included in the GLIOPLAK trial (ClinicalTrials.gov Identifier: NCT02617745), a prospective bicentric study conducted between November 2015 and December 2022. Patients underwent biopsy alone and received radiotherapy and temozolomide. Blood samples were collected at three different time points: before and after concomitant radiochemotherapy, and at the time of tumor progression. Plasma samples from patients and controls were analyzed using metabolomics and proteomics, generating 371 omics features. Descriptive, differential, and predictive analyses were performed to assess the relationship between plasma omics feature levels and patient outcome. Diagnostic performance and longitudinal variations were also analyzed. The study included 67 subjects (34 patients and 33 controls). A significant differential expression of metabolites and proteins between patients and controls was observed. Predictive models using omics features showed high accuracy in distinguishing patients from controls. Longitudinal analysis revealed temporal variations in a few omics features including CD22, CXCL13, EGF, IL6, GZMH, KLK4, and TNFRSP6B. Survival analysis identified 77 omics features significantly associated with OS, with ERBB2 and ITGAV consistently linked to OS at all timepoints. Pathway analysis revealed dynamic oncogenic pathways involved in glioblastoma progression. This study provides insights into the potential of plasma omics features as biomarkers for glioblastoma diagnosis, progression and overall survival. Clinical implication should now be explored in dedicated prospective trials.

Keywords: Glioblastoma; Liquid biopsy; Mass spectrometry; Metabolomic; Proteomic.

Fig. 1

Overview of the study protocol. Plasma samples and clinical data were collected from 33 control samples and 34 unresected glioblastoma patients before, during treatment, and at disease progression. Plasma samples were analyzed using metabolomics and proteomics, generating 371 omics features. This data was then used to compare controls with diseased samples and build predictive models to assess the relationship between plasma omics feature levels and disease status, patient outcome, and progression-free survival

Fig. 2

Omics-based differential analysis. A Overview of the differential expression analysis of proteins and metabolites between patients versus controls at the three studied time (inclusion, W6 and progression). B Barplot showing the proportions of proteins and metabolites and their novelty compared with literature. C Volcano plot of the most differentially expressed proteins and metabolites between Patients and Controls at baseline (left), at progression (middle) and at W6 (right)

Fig. 3

Proteins and metabolites expression levels across samples. A Levels of omics features with two distinct profiles of between glioblastoma samples and controls. B Violin plots showing the omics feature levels across the different groups: controls, inclusion, week 6 (W6) and at progression

Fig. 4

A Overview of the differential expression analysis of proteins and metabolites between patients. B Barplot showing the proportions of proteins and metabolites and their novelty status regarding the literature. C Volcano plot highlighting the most differentially expressed proteins and metabolites between timepoints. D Violin plots of the most differentially expressed proteins between timepoints

Fig. 5

Machine learning analysis. A Overview of the diagnostic performance of the predictive model. Each tile shows the probability of accurately classifying patients and controls from the test set. B ROC curves of each omics feature and their combined model. C Violin plots of the most significative omics features included in the model

Fig. 6

Forest plot of the omics features associated with overall survival