Characterizing and forecasting neoantigens-resulting from MUC mutations in COAD

Area of Science:

• Oncology
• Immunology
• Bioinformatics

Background:

• Colon adenocarcinoma (COAD) immunotherapy faces challenges impacting treatment effectiveness.
• Neoantigens, due to their tumor specificity and immunogenicity, are crucial targets for cancer immunotherapy.
• Identifying MUC somatic mutations (MUCmut) as potential tumor antigens in COAD is a key research area.

Purpose of the Study:

• To identify and predict potential tumor antigens derived from MUC somatic mutations (MUCmut) in COAD.
• To evaluate the association between MUCmut and tumor characteristics, prognosis, and the tumor microenvironment.
• To predict high-confidence neopeptides from MUCmut for potential immunotherapy applications.

Main Methods:

• Utilized TCGA, TIMER2.0, and cBioPortal databases for association analysis in 2242 COAD patients.
• Employed TSNAdb and differential agretopicity index (DAI) to predict neopeptides from MUCmut in 531 COAD patients.
• Calculated DAI by comparing MUCmut peptide HLA binding affinity to wild-type peptides.

Main Results:

• MUC16, MUC17, MUC5B, MUC2, MUC4, and MUC6 showed high mutation frequencies.
• MUC16 and MUC4 mutations correlated with longer DFS and PFS; MUC13 and MUC20 with shorter OS.
• Mutant MUC family members were linked to higher TMB, MSI, lymphocyte infiltration, and immune checkpoint gene expression.
• Identified 452 SNVs, 57 SNVs, 1 Q-frame shift, and 157 INDELs of MUCmut peptides, with 10 high-confidence neopeptides predicted by DAI.

Conclusions:

• Mutant MUC family-derived neoantigens demonstrate significant immunogenicity and therapeutic potential.
• The combination of TSNAdb and DAI successfully identified novel MUCmut neoantigens.
• These identified MUCmut neoantigens represent promising targets for advancing COAD immunotherapy.