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Personalized adoptive immunotherapy for patients with EBV-associated tumors and complications: Evaluation of novel naturally processed and presented EBV-derived T-cell epitopes.

ABSTRACT: Morbidity and mortality of immunocompromised patients are increased by primary infection with or reactivation of Epstein-Barr virus (EBV), possibly triggering EBV+ post-transplant lymphoproliferative disease (PTLD). Adoptive transfer of EBV-specific cytotoxic T cells (EBV-CTLs) promises a non-toxic immunotherapy to effectively prevent or treat these complications. To improve immunotherapy and immunomonitoring this study aimed at identifying and evaluating naturally processed and presented HLA-A*03:01-restricted EBV-CTL epitopes as immunodominant targets. More than 15000 peptides were sequenced from EBV-immortalized B cells transduced with soluble HLA-A*03:01, sorted using different epitope prediction tools and eleven candidates were preselected. T2 and Flex-T peptide-binding and dissociation assays confirmed the stability of peptide-MHC complexes. Their immunogenicity and clinical relevance were evaluated by assessing the frequencies and functionality of EBV-CTLs in healthy donors (n > 10) and EBV+ PTLD-patients (n = 5) by multimer staining, Eli- and FluoroSpot assays. All eleven peptides elicited EBV-CTL responses in the donors. Their clinical applicability was determined by small-scale T-cell enrichment using Cytokine Secretion Assay and immunophenotyping. Mixtures of these peptides when added to the EBV Consensus pool revealed enhanced stimulation and enrichment efficacy. These EBV-specific epitopes broadening the repertoire of known targets will improve manufacturing of clinically applicable EBV-CTLs and monitoring of EBV-specific T-cell responses in patients.

SUBMITTER: Bieling M

PROVIDER: S-EPMC5797009 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:A number of machine learning-based predictors have been developed for identifying immunogenic T-cell epitopes based on major histocompatibility complex (MHC) class I and II binding affinities. Rationally selecting the most appropriate tool has been complicated by the evolving training data and machine learning methods. Despite the recent advances made in generating high-quality MHC-eluted, naturally processed ligandome, the reliability of new predictors on these epitopes has yet to be evaluated. This study reports the latest benchmarking on an extensive set of MHC-binding predictors by using newly available, untested data of both synthetic and naturally processed epitopes. 32 human leukocyte antigen (HLA) class I and 24 HLA class II alleles are included in the blind test set. Artificial neural network (ANN)-based approaches demonstrated better performance than regression-based machine learning and structural modeling. Among the 18 predictors benchmarked, ANN-based mhcflurry and nn_align perform the best for MHC class I 9-mer and class II 15-mer predictions, respectively, on binding/non-binding classification (Area Under Curves = 0.911). NetMHCpan4 also demonstrated comparable predictive power. Our customization of mhcflurry to a pan-HLA predictor has achieved similar accuracy to NetMHCpan. The overall accuracy of these methods are comparable between 9-mer and 10-mer testing data. However, the top methods deliver low correlations between the predicted versus the experimental affinities for strong MHC binders. When used on naturally processed MHC-ligands, tools that have been trained on elution data (NetMHCpan4 and MixMHCpred) shows better accuracy than pure binding affinity predictor. The variability of false prediction rate is considerable among HLA types and datasets. Finally, structure-based predictor of Rosetta FlexPepDock is less optimal compared to the machine learning approaches. With our benchmarking of MHC-binding and MHC-elution predictors using a comprehensive metrics, a unbiased view for establishing best practice of T-cell epitope predictions is presented, facilitating future development of methods in immunogenomics.

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).

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Personalized adoptive immunotherapy for patients with EBV-associated tumors and complications: Evaluation of novel naturally processed and presented EBV-derived T-cell epitopes.

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