Background: Mutations commonly occur in cancer cells, arising neoantigen as potential targets for personalized immunotherapy of lung adenocarcinoma (LUAD). However, the substantial heterogeneity observed among individuals and distinct foci within the same patient presents significant challenges in formulating immunotherapy strategies. The aim of the work is to characterize the mutation pattern and identify neopeptides across different patients and diverse foci within the same patients with LUAD.
Methods: Seven lung adenocarcinoma samples and matched tissues/blood are collected from 4 patients with LUAD for whole exome sequencing, mutation signature analysis, HLA binding prediction and neoantigen screening. Dimeric HLA-A2 molecules were prepared by Bac-to-Bac baculovirus expression system to establish a T cell stimulation system based on HLA-A2-coated artificial antigen-presenting cells for the validation of immunogenic neopeptides.
Results: Similar mutation pattern with predominant missense mutation and high tumor mutation burden was observed across individuals with lung adenocarcinomas and between non-invasive and invasive foci. We screened and identified 3 consistent mutated genes among 100 top genes with highest mutation scores contributed across 4 patients, and 3 mutated peptides among 30 with highest HLA-A2 binding affinity distributed in at least 2 out of 4 foci in the same patient. Notably, LUAD-7-MT peptide encoded by NANOGNB demonstrated higher immunogenicity in promoting CD8+ T cells proliferation and IFN-γ secretion than the corresponding wildtype peptide.
Conclusions: This study provides an in-depth analysis of mutation characteristics of LUAD and establishes a neoantigen screening and validation system for identifying immunogenicity neopeptide across individual patients and diverse foci in the same patient with multifocal LUAD.
Keywords: NANOGNB; artificial antigen-presenting cells; immunogenicity neopeptide; multifocal lung adenocarcinoma; neopeptide screening.
Copyright © 2024 Wang, Jiang, Zhao, Wu, Xiong, Wu and Weng.