Detection of neoantigen-specific T cells following a personalized vaccine in a patient with glioblastoma.
ABSTRACT: Neoantigens represent promising targets for personalized cancer vaccine strategies. However, the feasibility of this approach in lower mutational burden tumors like glioblastoma (GBM) remains unknown. We have previously reported the use of an immunogenomics pipeline to identify candidate neoantigens in preclinical models of GBM. Here, we report the application of the same immunogenomics pipeline to identify candidate neoantigens and guide screening for neoantigen-specific T cell responses in a patient with GBM treated with a personalized synthetic long peptide vaccine following autologous tumor lysate DC vaccination. Following vaccination, reactivity to three HLA class I- and five HLA class II-restricted candidate neoantigens were detected by IFN-γ ELISPOT in peripheral blood. A similar pattern of reactivity was observed among isolated post-treatment tumor-infiltrating lymphocytes. Genomic analysis of pre- and post-treatment GBM reflected clonal remodeling. These data demonstrate the feasibility and translational potential of a therapeutic neoantigen-based vaccine approach in patients with primary CNS tumors.
Project description:<h4>Background</h4>Personalized cancer vaccines based on neoantigens have reached the clinical trial stage in melanoma. Different vaccination protocols showed efficacy in preclinical models without a clear indication of the quality and the number of neoantigens required for an effective cancer vaccine.<h4>Methods</h4>In an effort to develop potent and efficacious neoantigen-based vaccines, we have developed different neoantigen minigene (NAM) vaccine vectors to determine the rules for a successful neoantigen cancer vaccine (NCV) delivered by plasmid DNA and electroporation. Immune responses were analyzed at the level of single neoantigen by flow cytometry and correlated with tumor growth. Adoptive T cell transfer, from HLA-2.1.1 mice, was used to demonstrate the efficacy of the NCV pipeline against human-derived tumors.<h4>Results</h4>In agreement with previous bodies of evidence, immunogenicity was driven by predicted affinity. A strong poly-functional and poly-specific immune response was observed with high affinity neoantigens. However, only a high poly-specific vaccine vector was able to completely protect mice from subsequent tumor challenge. More importantly, this pipeline - from the selection of neoantigens to vaccine design - applied to a new model of patient derived tumor xenograft resulted in therapeutic treatment.<h4>Conclusions</h4>These results suggest a feasible strategy for a neoantigen cancer vaccine that is simple and applicable for clinical developments.
Project description:Background:Personalized cancer vaccines based on tumor-derived neoantigens have shown strong and long-lasting antitumor effect in patients with some solid tumors. However, whether neoantigens identified from primary lesions could represent their metastatic lesions, and consequently the effect of vaccine therapy remained unknown. Methods:To investigate whether neoantigens identified from primary tumors are similar to their matched metastases in lung cancer, we identified 79 samples from 24 cases. All of samples were collected before any systemic therapy. Major criteria for neoantigen identification included: derived from tumor-specific mutations, fold change >10 comparing to germline expression level, high predicted human leukocyte antigen (HLA) binding affinity and peptide of 9-11 amino acids in length. Results:We found a wide range of tumor neoantigen burden in both primaries and metastases. The counts, overall distribution pattern and predicted HLA binding affinity of neoantigens were similar between primaries and metastases. However, only 20% of shared neoantigens (presented in both primaries and metastases) was observed, which were mainly derived from single nucleotide variants (SNVs) and fusions. A variety of corresponding HLA alleles were observed and 50.0% of cases were HLA-C*06:02. Finally, we observed the neoantigen intrametastases homogeneity in patients with sole brain metastases. Conclusions:Neoantigen landscape in terms of the number, type and predicted HLA binding affinity was similar between primaries and metastases, but the percentage of shared neoantigens is only modest, suggesting vaccine development based solely on primary tumor neoantigen may not offer optimal therapeutic outcome, and shared neoantigen needs to be seriously considered.
Project description:This paper describes the sequencing protocol and computational pipeline for the PGV-001 personalized vaccine trial. PGV-001 is a therapeutic peptide vaccine targeting neoantigens identified from patient tumor samples. Peptides are selected by a computational pipeline that identifies mutations from tumor/normal exome sequencing and ranks mutant sequences by a combination of predicted Class I MHC affinity and abundance estimated from tumor RNA. The personalized genomic vaccine (PGV) pipeline is modular and consists of independently usable tools and software libraries. We hope that the functionality of these tools may extend beyond the specifics of the PGV-001 trial and enable other research groups in their own neoantigen investigations.
Project description:Neoantigens can function as actual antigens to facilitate tumor rejection, which play a crucial role in cancer immunology and immunotherapy. Emerging evidence revealed that neoantigens can be used to develop personalized, cancer-specific vaccines. To date, large numbers of immunogenomic peptides have been computationally predicted to be potential neoantigens. However, experimental validation remains the gold standard for potential clinical application. Experimentally validated neoantigens are rare and mostly appear scattered among scientific papers and various databases. Here, we constructed dbPepNeo, a specific database for human leukocyte antigen class I (HLA-I) binding neoantigen peptides based on mass spectrometry (MS) validation or immunoassay in human tumors. According to the verification methods of these neoantigens, the collection of peptides was classified as 295 high confidence, 247 medium confidence and 407?794 low confidence neoantigens, respectively. This can serve as a valuable resource to aid further screening for effective neoantigens, optimize a neoantigen prediction pipeline and study T-cell receptor (TCR) recognition. Three applications of dbPepNeo are shown. In summary, this work resulted in a platform to promote the screening and confirmation of potential neoantigens in cancer immunotherapy. Database URL: www.biostatistics.online/dbPepNeo/.
Project description:<h4>Background</h4>Cancer neoantigens are expressed only in cancer cells and presented on the tumor cell surface in complex with major histocompatibility complex (MHC) class I proteins for recognition by cytotoxic T cells. Accurate and rapid identification of neoantigens play a pivotal role in cancer immunotherapy. Although several in silico tools for neoantigen prediction have been presented, limitations of these tools exist.<h4>Results</h4>We developed pTuneos, a computational pipeline for prioritizing tumor neoantigens from next-generation sequencing data. We tested the performance of pTuneos on the melanoma cancer vaccine cohort data and tumor-infiltrating lymphocyte (TIL)-recognized neopeptide data. pTuneos is able to predict the MHC presentation and T cell recognition ability of the candidate neoantigens, and the actual immunogenicity of single-nucleotide variant (SNV)-based neopeptides considering their natural processing and presentation, surpassing the existing tools with a comprehensive and quantitative benchmark of their neoantigen prioritization performance and running time. pTuneos was further tested on The Cancer Genome Atlas (TCGA) cohort data as well as the melanoma and non-small cell lung cancer (NSCLC) cohort data undergoing checkpoint blockade immunotherapy. The overall neoantigen immunogenicity score proposed by pTuneos is demonstrated to be a powerful and pan-cancer marker for survival prediction compared to traditional well-established biomarkers.<h4>Conclusions</h4>In summary, pTuneos provides the state-of-the-art one-stop and user-friendly solution for prioritizing SNV-based candidate neoepitopes, which could help to advance research on next-generation cancer immunotherapies and personalized cancer vaccines. pTuneos is available at https://github.com/bm2-lab/pTuneos , with a Docker version for quick deployment at https://cloud.docker.com/u/bm2lab/repository/docker/bm2lab/ptuneos .
Project description: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 one of the major obstacles to using personalized neoantigen-based cancer immunotherapy. METHODS:Two 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. RESULTS:Immunogenic 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. CONCLUSIONS:The 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. FUNDING:This 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).
Project description:Head and neck squamous cell carcinomas (HNSCC) are an ideal immunotherapy target due to their high mutation burden and frequent infiltration with lymphocytes. Preclinical models to investigate targeted and combination therapies as well as defining biomarkers to guide treatment represent an important need in the field. Immunogenomics approaches have illuminated the role of mutation-derived tumor neoantigens as potential biomarkers of response to checkpoint blockade as well as representing therapeutic vaccines. Here, we aimed to define a platform for checkpoint and other immunotherapy studies using syngeneic HNSCC cell line models (MOC2 and MOC22), and evaluated the association between mutation burden, predicted neoantigen landscape, infiltrating T cell populations and responsiveness of tumors to anti-PD1 therapy. We defined dramatic hematopoietic cell transcriptomic alterations in the MOC22 anti-PD1 responsive model in both tumor and draining lymph nodes. Using a cancer immunogenomics pipeline and validation with ELISPOT and tetramer analysis, we identified the H-2Kb-restricted ICAM1P315L (mICAM1) as a neoantigen in MOC22. Finally, we demonstrated that mICAM1 vaccination was able to protect against MOC22 tumor development defining mICAM1 as a bona fide neoantigen. Together these data define a pre-clinical HNSCC model system that provides a foundation for future investigations into combination and novel therapeutics.
Project description:Glioblastoma (GBM) remains a significant cause of cancer-related mortality in pediatric and adult patients with limited treatment options. Immunotherapy represents a promising new therapeutic approach in many solid and hematologic malignancies, including GBM, although only a subset of patients responds clinically. Thus, current efforts are focused on identifying patients most likely to benefit from immune-based therapies. The cancer immunogenomics approach identifies candidate neoantigens from genomics information and represents a potentially exciting new space in precision neuro-oncology. In this review, we discuss the role of neoantigens in GBM both as predictive biomarkers and as targets of immunotherapy.