Liquid biopsy-based identification of prognostic and immunotherapeutically relevant gene signatures in lower grade glioma

• Published in: Journal of big data 2023-12, Vol.10 (1), p.19-25, Article 19
• Main Authors: Wu, Changwu, Long, Wenyong, Qin, Chaoying, Wang, Xiangyu, Li, Yuzhe, Xiao, Kai, Li, Yue, Liu, Qing
• Format: Article
• Language: English
• Subjects: Big Data; Biomarkers; Biopsy; Blood circulation; Clinical significance; Communications Engineering; Computational Science and Engineering; Computer Science; Data Mining and Knowledge Discovery; Database Management; Feasibility; Immune system; Immunotherapy; Information Storage and Retrieval; Liquid biopsy; Lower grade glioma; Mathematical Applications in Computer Science; Networks; Peptides; Prognosis; Therapy; Tumor microenvironment; Tumors; Vaccines
• ISSN: 2196-1115; 2196-1115
• DOI: 10.1186/s40537-023-00686-8

Background Recent studies have shown that immunotherapies, including peptide vaccines, remain promising strategies for patients with lower grade glioma (LGG); however new biomarkers need to be developed to identify patients who may benefit from therapy. We aimed to investigate the feasibility of liquid biopsy-based gene signatures in predicting the prognosis of LGG patients, as well as the benefits of immunotherapy. Methods We evaluated the association between circulating immune cells and treatment response by analyzing peripheral blood mononuclear cell (PBMC) samples from LGG patients receiving peptide vaccine immunotherapy, identified response-related genes (RRGs), and constructed RRG-related Response Score. In addition, RRG-related RiskScore was constructed in LGG tumor samples based on RRGs; association analysis for RiskScore and characteristics of TME as well as patient prognosis were performed in two LGG tumor datasets. The predictive power of RiskScore for immunotherapy benefits was analyzed in an anti-PD-1 treatment cohort. Results This study demonstrated the importance of circulating immune cells, including monocytes, in the immunotherapeutic response and prognosis of patients with LGG. Overall, 43 significant RRGs were identified, and three clusters with different characteristics were identified in PBMC samples based on RRGs. The constructed RRG-related Response Score could identify patients who produced a complete response to peptide vaccine immunotherapy and could predict prognosis. Additionally, three subtypes were identified in LGG tumors based on RRGs, with subtype 2 being an immune “hot” phenotype suitable for immune checkpoint therapy. The constructed RRG-related RiskScore was significantly positively correlated with the level of tumor immune cell infiltration. Patients with high RiskScore had a worse prognosis and were more likely to respond to immune checkpoint therapy. The therapeutic advantage and clinical benefits of patients with a high RiskScore were confirmed in an anti-PD-1 treatment cohort. Conclusion This study confirmed the potential of liquid biopsy for individualized treatment selection in LGG patients and determined the feasibility of circulating immune cells as biomarkers for LGG. Scoring systems based on RRGs can predict the benefits of immunotherapy and prognosis in patients with LGG. This work would help to increase our understanding of the clinical significance of liquid biopsy and more effectively guide individualized