Dataset Information

TruNeo: an integrated pipeline improves personalized true tumor neoantigen identification.

ABSTRACT:

Background

Neoantigen-based personal vaccines and adoptive T cell immunotherapy have shown high efficacy as a cancer treatment in clinical trials. Algorithms for the accurate prediction of neoantigens have played a pivotal role in such studies. Some existing bioinformatics methods, such as MHCflurry and NetMHCpan, identify neoantigens mainly through the prediction of peptide-MHC binding affinity. However, the predictive accuracy of immunogenicity of these methods has been shown to be low. Thus, a ranking algorithm to select highly immunogenic neoantigens of patients is needed urgently in research and clinical practice.

Results

We develop TruNeo, an integrated computational pipeline to identify and select highly immunogenic neoantigens based on multiple biological processes. The performance of TruNeo and other algorithms were compared based on data from published literature as well as raw data from a lung cancer patient. Recall rate of immunogenic ones among the top 10-ranked neoantigens were compared based on the published combined data set. Recall rate of TruNeo was 52.63%, which was 2.5 times higher than that predicted by MHCflurry (21.05%), and 2 times higher than NetMHCpan 4 (26.32%). Furthermore, the positive rate of top 10-ranked neoantigens for the lung cancer patient were compared, showing a 50% positive rate identified by TruNeo, which was 2.5 times higher than that predicted by MHCflurry (20%).

Conclusions

TruNeo, which considers multiple biological processes rather than peptide-MHC binding affinity prediction only, provides prioritization of candidate neoantigens with high immunogenicity for neoantigen-targeting personalized immunotherapies.

SUBMITTER: Tang Y

PROVIDER: S-EPMC7672179 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundRecent 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 one of the major obstacles to using personalized neoantigen-based cancer immunotherapy.MethodsTwo different pipelines of neoantigens identification were established in this study: (1) 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 de novo synthesized. (2) 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 six patients.ResultsImmunogenic neo-epitopes were recognized by autologous T cells in 3 of 4 patients who utilized the de novo synthesis mode and in 6 of 13 patients who performed shared neoantigen peptide library, respectively. 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 four patients achieved the prolonged stabilization of disease with a median PFS of 8.6 months.ConclusionsThe current study provided feasible pipelines for neoantigen identification. Implementing these strategies to individually tailor neoantigens could facilitate the neoantigen-based translational immunotherapy research.TRIAL REGSITRATION. ChiCTR.org ChiCTR-OIC-16010092, ChiCTR-OIC-17011275, ChiCTR-OIC-17011913; ClinicalTrials.gov NCT03171220.FundingThis work was funded by grants from the National Key Research and Development Program of China (Grant No. 2017YFC1308900), the National Major Projects for "Major New Drugs Innovation and Development" (Grant No.2018ZX09301048-003), the National Natural Science Foundation of China (Grant No. 81672367, 81572329, 81572601), and the Key Research and Development Program of Jiangsu Province (No. BE2017607).