CELL INTELLIGENCE · 肿瘤细胞治疗研究

Neoantigens are ideal targets for cancer immunotherapy because they are expressed de novo in tumor tissue but not in healthy tissue and are therefore recognized as foreign by the immune system. Advances in next-generation sequencing and bioinformatics technologies have enabled the quick identification and prediction of tumor-specific neoantigens; however, only a small fraction of predicted neoantigens are immunogenic. To improve the predictability of immunogenic neoantigens, we developed the in silico neoantigen prediction workflows VACINUS pMHC and VACINUS TCR: VACINUS pMHC incorporates physical binding between peptides and MHCs (pMHCs), and VACINUS TCR integrates T cell reactivity to the pMHC complex through deep learning-based pairing with T cell receptors (TCRs) of putative tumor-reactive CD8 tumor-infiltrating lymphocytes (TILs). We then validated our neoantigen prediction workflows both in vitro and in vivo in patients with hepatocellular carcinoma (HCC) and in a B16F10 mouse melanoma model. The predictive abilities of VACINUS pMHC and VACINUS TCR were confirmed in a validation cohort of 8 patients with HCC. Of a total of 118 neoantigen candidates predicted by VACINUS pMHC , 48 peptides were ultimately selected using VACINUS TCR . In vitro validation revealed that among the 48 predicted neoantigen candidates, 13 peptides were immunogenic. Assessment of the antitumor efficacy of the candidate neoepitopes using a VACINUS TCR in vivo mouse model suggested that vaccination with the predicted neoepitopes induced neoantigen-specific T cell responses and enabled the trafficking of neoantigen-specific CD8 + T cell clones into the tumor tissue, leading to tumor suppression. This study showed that the prediction of immunogenic neoantigens can be improved by integrating a tumor-reactive TIL TCR-pMHC ternary complex.