The identification of effective tumor-suppressing neoantigens using a tumor-reactive TIL TCR-pMHC ternary complex

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 VACINUSpMHC and VACINUSTCR: VACINUSpMHC incorporates physical binding between peptides and MHCs (pMHCs), and VACINUSTCR 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 VACINUSpMHC and VACINUSTCR were confirmed in a validation cohort of 8 patients with HCC. Of a total of 118 neoantigen candidates predicted by VACINUSpMHC, 48 peptides were ultimately selected using VACINUSTCR. 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 VACINUSTCR 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. Cancer scientists have created a new way to identify and choose neoantigens (new proteins on cancer cells) for use in cancer vaccines. The research, led by Woong-Yang Park, included 33 patients with different cancer types. The scientists used a mix of computer-based prediction techniques and lab-based tests to find the most effective neoantigens. The results showed that the new technique, named VACINUS, was better at predicting effective neoantigens than older methods. The scientists concluded that the VACINUS technique could improve the immune response of predicted neoantigens, making them better at activating an immune reaction against cancer cells. This could result in the creation of more effective cancer vaccines in the future. This summary was initially drafted using artificial intelligence, then revised and fact-checked by the author.