胶质母细胞瘤中的代谢重塑：一项纵向多组学研究

Metabolic remodeling in glioblastoma: a longitudinal multi-omics study

Acta Neuropathologica Communications

影响因子:5.7

分区:医学1区 Top / 神经科学1区

发表日期:2024 Oct 12

作者: Maxime Fontanilles, Jean-David Heisbourg, Arthur Daban, Frederic Di Fiore, Louis-Ferdinand Pépin, Florent Marguet, Olivier Langlois, Cristina Alexandru, Isabelle Tennevet, Franklin Ducatez, Carine Pilon, Thomas Plichet, Déborah Mokbel, Céline Lesueur, Soumeya Bekri, Abdellah Tebani

DOI: 10.1186/s40478-024-01861-5

摘要

利用非侵入性液体活检监测肿瘤演变和预测存活率，是胶质母细胞瘤患者尚未满足的需求。蛋白质组学和代谢组学血液分析时代或可在此背景下提供帮助。进行了一项病例对照研究。患者被纳入GLIOPLAK试验（ClinicalTrials.gov编号：NCT02617745），该研究为一项前瞻性双中心研究，于2015年11月至2022年12月进行。患者接受活检并接受放疗和替莫唑胺治疗。血样在三个不同时间点采集：同步放化疗前后，以及肿瘤进展时。对患者和对照的血浆样本进行了代谢组学和蛋白质组学分析，获得了371个组学特征。进行了描述性、差异性和预测性分析，以评估血浆组学特征水平与患者结局之间的关系。还分析了诊断性能和纵向变化。研究共纳入67名受试者（34名患者和33名对照）。观察到患者与对照之间在代谢物和蛋白质的差异表达显著。利用组学特征建立的预测模型在区分患者和对照方面表现出高准确性。纵向分析揭示了包括CD22、CXCL13、EGF、IL6、GZMH、KLK4和TNFRSP6B在内的少数组学特征的时间变化。生存分析鉴定出77个与总生存期（OS）显著相关的组学特征，其中ERBB2和ITGAV在所有时间点均与OS持续相关。通路分析显示涉及胶质母细胞瘤进展的动态肿瘤发生通路。该研究提供了血浆组学特征作为胶质母细胞瘤诊断、进展和总生存潜在生物标志物的见解。应在专门的前瞻性试验中进一步探索其临床应用意义。

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.