Metabolic remodeling in glioblastoma: a longitudinal multi-omics study

被引:0
|
作者
Fontanilles, Maxime [1 ,2 ]
Heisbourg, Jean-David [3 ]
Daban, Arthur [2 ]
Di Fiore, Frederic [2 ]
Pepin, Louis-Ferdinand [4 ]
Marguet, Florent [5 ]
Langlois, Olivier [6 ]
Alexandru, Cristina [2 ]
Tennevet, Isabelle [2 ]
Ducatez, Franklin [3 ,7 ]
Pilon, Carine [3 ]
Plichet, Thomas [3 ]
Mokbel, Deborah [3 ]
Lesueur, Celine [3 ]
Bekri, Soumeya [3 ]
Tebani, Abdellah [3 ]
机构
[1] Normandie Univ, INSERM U1245, Canc & Brain Genom, IRON Grp,UNIROUEN, Rouen, France
[2] Canc Ctr Henri Becquerel, Dept Med Oncol, Rue Amiens, F-76038 Rouen, France
[3] Normandie Univ, Dept Metab Biochem, INSERM U1245, CHU Rouen,UNIROUEN, F-76000 Rouen, France
[4] Canc Ctr Henri Becquerel, Clin Res Unit, Rue Amiens, F-76038 Rouen, France
[5] Normandie Univ, Normandy Ctr Genom & Personalized Med, CHU Rouen, Dept Pathol,INSERM U1245,UNIROUEN, 1 Rue Germont, F-76031 Rouen, France
[6] CHU Rouen, Dept Neurosurg, F-76000 Rouen, France
[7] Normandie Univ, Dept Neonatal Pediat Intens Care & Neuropediat, INSERM U1245, CHU Rouen,UNIROUEN, F-76000 Rouen, France
来源
ACTA NEUROPATHOLOGICA COMMUNICATIONS | 2024年 / 12卷 / 01期
关键词
Glioblastoma; Proteomic; Metabolomic; Liquid biopsy; Mass spectrometry; TUMORS; ACID;
D O I
10.1186/s40478-024-01861-5
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
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. Circulating omic features distinguishes patients with glioblastoma from healthy subjects.Plasma proteomic features change over time in patients undergoing radiochemotherapy.Certain plasma proteins are correlated with survival.
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页数:13
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