BACKGROUND. Recent genomic and bioinformatic technological advances have made it possible to dissect the immune response to personalized neoantigens encoded by tumor-specific mutations. However, timely and efficient identification of neoantigens is still a major obstacle to personalized neoantigen-based cancer immunotherapy. METHODS. Two different pipelines of neoantigen identification were established in this study: (a) Clinical-grade targeted sequencing was performed in patients with refractory solid tumor, and mutant peptides with high variant allele frequency and predicted high HLA-binding affinity were synthesized de novo. (b) An inventory-shared neoantigen peptide library of common solid tumors was constructed, and patients’ hotspot mutations were matched to the neoantigen peptide library. The candidate neoepitopes were identified by recalling memory T cell responses in vitro. Subsequently, neoantigen-loaded dendritic cell vaccines and neoantigen-reactive T cells were generated for personalized immunotherapy in 6 patients. RESULTS. Immunogenic neoepitopes were recognized by autologous T cells in 3 of 4 patients who used the de novo synthesis mode and in 6 of 13 patients who used the shared neoantigen peptide library. A metastatic thymoma patient achieved a complete and durable response beyond 29 months after treatment. Immune-related partial response was observed in another patient with metastatic pancreatic cancer. The remaining 4 patients achieved prolonged stabilization of disease with a median progression-free survival of 8.6 months. CONCLUSION. The current study provides feasible pipelines for neoantigen identification. Implementing these strategies to individually tailor neoantigens could facilitate neoantigen-based translational immunotherapy research. TRIAL REGISTRATION. ChiCTR.org ChiCTR-OIC-16010092, ChiCTR-OIC-17011275, ChiCTR-OIC-17011913; ClinicalTrials.gov NCT03171220. FUNDING. This work was funded by grants from the National Key Research and Development Program of China (2017YFC1308900), the National Major Projects for “Major New Drugs Innovation and Development” (2018ZX09301048-003), the National Natural Science Foundation of China (81672367, 81572329, 81572601), and the Key Research and Development Program of Jiangsu Province (BE2017607).
Fangjun Chen, Zhengyun Zou, Juan Du, Shu Su, Jie Shao, Fanyan Meng, Ju Yang, Qiuping Xu, Naiqing Ding, Yang Yang, Qin Liu, Qin Wang, Zhichen Sun, Shujuan Zhou, Shiyao Du, Jia Wei, Baorui Liu
Usage data is cumulative from January 2020 through January 2021.
Usage information is collected from two different sources: this site (JCI) and Pubmed Central (PMC). JCI information (compiled daily) shows human readership based on methods we employ to screen out robotic usage. PMC information (aggregated monthly) is also similarly screened of robotic usage.
Various methods are used to distinguish robotic usage. For example, Google automatically scans articles to add to its search index and identifies itself as robotic; other services might not clearly identify themselves as robotic, or they are new or unknown as robotic. Because this activity can be misinterpreted as human readership, data may be re-processed periodically to reflect an improved understanding of robotic activity. Because of these factors, readers should consider usage information illustrative but subject to change.