Identification of personalized neoantigen-based vaccines and immune subtype characteristic analysis of glioblastoma based on abnormal alternative splicing

The development of personalized neoantigen-based vaccines in cancer immunotherapy has shown promise. In this study, a large-scale bioinformatics analysis was performed to identify potential GBM-associated neoantigens based on abnormal alternative splicing, and then screen suitable patients for vaccination. Gene expression profiles and clinical information were collected from TCGA. We filtered the percent-spliced-in (PSI) spectrum of alternative splicing events in the dataset to identify abnormal alternative splicing events. MAF package was used to identify and analyse tumour mutation burden (TMB) in cancer samples. Tumour Immune Estimation Resource (TIMER) was used to calculate and visualize the infiltration of antigen presenting cells (APCs). In addition, consistent clustering algorithm utilized to identify immune subtypes of GBM. Five potential tumour neoantigens (LRP1, TCF12, DERL3, WIPI2, and TSHZ3) were identified in GBM by selecting genes both with abnormal alternative splicing (upregulated) and gene frameshift mutations, in which LRP1 was significantly associated with APCs. According to the expressions of five potential tumour neoantigens, 160 patients with GBM were divided into three immune subtypes. Patients in cluster3 exhibited good prognoses. Furthermore, the characteristics, including TMB, abnormal alternative splicing events, immune activity, immune cells proportion, and association with tumour biomarkers, were unique in each immune subtypes. The characteristics of cluster3 illustrated that cluster3 participants were more suitable candidates for vaccination. LRP1 was identified as a potential neoantigen for immunotherapy against GBM, and patients in cluster3 were more suitable for vaccination. Our findings provide important guidance for the development of novel neoantigens and therapeutic targets in patients with GBM.