Heterogeneity of neoantigen landscape between primary lesions and their matched metastases in lung cancer.
ABSTRACT: 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:<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:T lymphocytes against tumor-specific mutated neoantigens can induce tumor regression. Also, the size of the immunogenic cancer mutanome is supposed to correlate with the clinical efficacy of checkpoint inhibition. Herein, we studied the susceptibility of tumor cell lines from lymph node metastases occurring in a melanoma patient over several years towards blood-derived, neoantigen-specific CD8+ T cells. In contrast to a cell line established during early stage III disease, all cell lines generated at later time points from stage IV metastases exhibited partial or complete loss of HLA class I expression. Whole exome and transcriptome sequencing of the four tumor lines and a germline control were applied to identify expressed somatic single nucleotide substitutions (SNS), insertions and deletions (indels). Candidate peptides encoded by these variants and predicted to bind to the patient's HLA class I alleles were synthesized and tested for recognition by autologous mixed lymphocyte-tumor cell cultures (MLTCs). Peptides from four mutated proteins, HERPUD1G161S, INSIG1S238F, MMS22LS437F and PRDM10S1050F, were recognized by MLTC responders and MLTC-derived T cell clones restricted by HLA-A*24:02 or HLA-B*15:01. Intracellular peptide processing was verified with transfectants. All four neoantigens could only be targeted on the cell line generated during early stage III disease. HLA loss variants of any kind were uniformly resistant. These findings corroborate that, although neoantigens represent attractive therapeutic targets, they also contribute to the process of cancer immunoediting as a serious limitation to specific T cell immunotherapy.
Project description:<h4>Objective</h4>To develop a neoantigen-targeted personalized cancer treatment for non-small cell lung cancer (NSCLC), neoantigens were obtained from collected human lung cancer samples, and the utility of neoantigen and neoantigen-reactive T cells (NRTs) was assessed.<h4>Methods</h4>Tumor specimens from three patients with NSCLC were obtained and analyzed by whole-exome sequencing, and neoantigens were predicted accordingly. Dendritic cells and T lymphocytes were isolated, NRTs were elicited and IFN-γ ELISPOT tests were conducted. HLA-A2.1/K<sup>b</sup> transgenic mice were immunized with peptides from HLA-A*02:01<sup>+</sup>patient with high immunogenicity, and NRTs were subjected to IFN-γ, IL-2 and TNF-α ELISPOT as well as time-resolved fluorescence assay for cytotoxicity assays to verify the immunogenicity <i>in vitro</i>. The HLA-A*02:01<sup>+</sup>lung cancer cell line was transfected with minigene and inoculated into the flanks of C57BL/6<sup>nu/nu</sup> mice and the NRTs induced by the immunogenic polypeptides from autologous HLA-A2.1/K<sup>b</sup> transgenic mice were adoptively transfused to verify their immunogenicity <i>in vivo</i>.<h4>Results</h4>Multiple putative mutation-associated neoantigens with strong affinity for HLA were selected from each patient. Immunogenic neoantigen were identified in all three NSCLC patients, the potency of ACAD8-T105I, BCAR1-G23V and PLCG1-M425L as effective neoantigen to active T cells in suppressing tumor growth was further proven both <i>in vitro</i> and <i>in vivo</i> using HLA-A2.1/Kb transgenic mice and tumor-bearing mouse models.<h4>Conclusion</h4>Neoantigens with strong immunogenicity can be screened from NSCLC patients through the whole-exome sequencing of patient specimens and machine-learning-based neoantigen predictions. NRTs shown efficient antitumor responses in transgenic mice and tumor-bearing mouse models. Our results indicate that the development of neoantigen-based personalized immunotherapies in NSCLC is possible.<h4>Precis</h4>Neoantigens with strong immunogenicity were screened from NSCLC patients. This research provides evidence suggesting that neoantigen-based therapy might serve as feasible treatment for NSCLC.
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:Neoantigens are now recognized drivers of the antitumor immune response. Recurrent neoantigens, shared among groups of patients, have thus become increasingly coveted therapeutic targets. Here, we report on the data-driven identification of a robustly presented, immunogenic neoantigen that is derived from the combination of HLA-A*01:01 and RAS.Q61K. Analysis of large patient cohorts indicated that this combination applies to 3% of patients with melanoma. Using HLA peptidomics, we were able to demonstrate robust endogenous presentation of the neoantigen in 10 tumor samples. We detected specific reactivity to the mutated peptide within tumor-infiltrating lymphocytes (TILs) from 2 unrelated patients, thus confirming its natural immunogenicity. We further investigated the neoantigen-specific clones and their T cell receptors (TCRs) via a combination of TCR sequencing, TCR overexpression, functional assays, and single-cell transcriptomics. Our analysis revealed a diverse repertoire of neoantigen-specific clones with both intra- and interpatient TCR similarities. Moreover, 1 dominant clone proved to cross-react with the highly prevalent RAS.Q61R variant. Transcriptome analysis revealed a high association of TCR clones with specific T cell phenotypes in response to cognate melanoma, with neoantigen-specific cells showing an activated and dysfunctional phenotype. Identification of recurrent neoantigens and their reactive TCRs can promote "off-the-shelf" precision immunotherapies, alleviating limitations of personalized treatments.
Project description:T-cell recognition of somatic mutation-derived cancer neoepitopes can lead to tumor regression. Due to the difficulty to identify effective neoepitopes, constructing a database for sharing experimentally validated cancer neoantigens will be beneficial to precise cancer immunotherapy. Meanwhile, the routine neoepitope prediction <i>in silico</i> is important but laborious for clinical use. Here we present NEPdb, a database that contains more than 17,000 validated human immunogenic neoantigens and ineffective neoepitopes within human leukocyte antigens (HLAs) <i>via</i> curating published literature with our semi-automatic pipeline. Furthermore, NEPdb also provides pan-cancer level predicted HLA-I neoepitopes derived from 16,745 shared cancer somatic mutations, using state-of-the-art predictors. With a well-designed search engine and visualization modes, this database would enhance the efficiency of neoantigen-based cancer studies and treatments. NEPdb is freely available at http://nep.whu.edu.cn/.
Project description:The remarkable clinical efficacy of the immune checkpoint blockade therapies has motivated researchers to discover immunogenic epitopes and exploit them for personalized vaccines. Human leukocyte antigen (HLA)-binding peptides derived from processing and presentation of mutated proteins are one of the leading targets for T-cell recognition of cancer cells. Currently, most studies attempt to identify neoantigens based on predicted affinity to HLA molecules, but the performance of such prediction algorithms is rather poor for rare HLA class I alleles and for HLA class II. Direct identification of neoantigens by mass spectrometry (MS) is becoming feasible; however, it is not yet applicable to most patients and lacks sensitivity. In an attempt to capitalize on existing immunopeptidomics data and extract information that could complement HLA-binding prediction, we first compiled a large HLA class I and class II immunopeptidomics database across dozens of cell types and HLA allotypes and detected hotspots that are subsequences of proteins frequently presented. About 3% of the peptidome was detected in both class I and class II. Based on the gene ontology of their source proteins and the peptide's length, we propose that their processing may partake by the cellular class II presentation machinery. Our database captures the global nature of the in vivo peptidome averaged over many HLA alleles, and therefore, reflects the propensity of peptides to be presented on HLA complexes, which is complementary to the existing neoantigen prediction features such as binding affinity and stability or RNA abundance. We further introduce two immunopeptidomics MS-based features to guide prioritization of neoantigens: the number of peptides matching a protein in our database and the overlap of the predicted wild-type peptide with other peptides in our database. We show as a proof of concept that our immunopeptidomics MS-based features improved neoantigen prioritization by up to 50%. Overall, our work shows that, in addition to providing huge training data to improve the HLA binding prediction, immunopeptidomics also captures other aspects of the natural in vivo presentation that significantly improve prediction of clinically relevant neoantigens.
Project description:Tumor-specific mutations can generate neoantigens that drive CD8 T cell responses against cancer. Next-generation sequencing and computational methods have been successfully applied to identify mutations and predict neoantigens. However, only a small fraction of predicted neoantigens are immunogenic. Currently, predicted peptide binding affinity for MHC-I is often the major criterion for prioritizing neoantigens, although little progress has been made toward understanding the precise functional relationship between affinity and immunogenicity. We therefore systematically assessed the immunogenicity of peptides containing single amino acid mutations in mouse tumor models and divided them into two classes of immunogenic mutations. The first comprises mutations at a nonanchor residue, for which we find that the predicted absolute binding affinity is predictive of immunogenicity. The second involves mutations at an anchor residue; here, predicted relative affinity (compared with the WT counterpart) is a better predictor. Incorporating these features into an immunogenicity model significantly improves neoantigen ranking. Importantly, these properties of neoantigens are also predictive in human datasets, suggesting that they can be used to prioritize neoantigens for individualized neoantigen-specific immunotherapies.