Candidate Neoantigens Research Articles

TruNeo: an integrated pipeline improves personalized true tumor neoantigen identification

BackgroundNeoantigen-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.ResultsWe 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%).ConclusionsTruNeo, 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.

IMMU-49. PREDICTING PATIENT-SPECIFIC ANTIGENS FOR MEDULLOBLASTOMA IMMUNOTHERAPY

Abstract BACKGROUND Identification of tumor rejection antigens remains a major barrier to the development of effective immunotherapeutics and their application to pediatric brain tumors. To identify candidate antigen targets for medulloblastoma adoptive cellular therapy, we performed a comprehensive immunogenomic analysis of medulloblastoma transcription profiles and in silico antigen prediction across a broad array of antigen classes. We hypothesized that medulloblastomas are immunologically heterogeneous and express genes with limited normal tissue expression that may serve as targets for immunotherapy. METHODS Immunologic heterogeneity was assessed using several published algorithms and approaches implemented within the R programming language. Patient-specific HLA haplotypes were called via customized Optitype and Phlat algorithms. Patient-specific tumor associated antigens (TAA) were selected only if expressed >1 transcript per million (TPM) in tumor and the standardized expression across a human tissue database was below 1 TPM. Patient-specific HLA and TAA sequences were extracted from RNA-seq data for prediction with eight MHC class I and four MHC class II affinity algorithms. Only expressed mutations and personalized TAAs were used for antigenic epitope predictions. All epitopes were screened against a human reference proteomic library to guarantee that epitopes were not shared by other expressed isoforms or genes. Public mass-spec data was also screened for protein-level antigen expression. RESULTS Although absolute immune cell content is predicted to be low, immune gene-signature analysis revealed subgroup-specific differences. Antigen prediction analysis revealed most patients express few candidate neoantigen targets passing all filtering criteria. Importantly, cancer testis antigens as well as previously unappreciated neurodevelopmental antigens were found expressed across all medulloblastoma subgroups and most patients. Protein level antigen expression was confirmed for some predicted TAAs. CONCLUSION Medulloblastomas are immunologically cold yet subgroups have distinct immune cell gene-signatures. Using a custom antigen prediction pipeline, we identified potential tumor rejection antigens with important implications for development of medulloblastoma cellular therapies.

EPCO-22. IDENTIFYING NEOANTIGENS FOR A PERSONALIZED MUTATION-DERIVED GENOMIC VACCINE IN PATIENTS WITH NEWLY DIAGNOSED GLIOBLASTOMA

Abstract BACKGROUND Glioblastomas (GBM) are known for having a lower mutational burden than many other tumor types. Nevertheless, somatic variants that occur in GBM mutational processes may give rise to neoantigens, which can be targeted in a personalized genomic vaccine to elicit a patient-specific anti-tumor response. METHODS Normal and tumor DNA and tumor RNA are extracted from tumor specimens and PBMCs. Exome and RNA sequencing and HLA typing is performed for each patient. Neoantigens are identified using the OpenVax computational pipeline, which calls somatic variants in the DNA and prioritizes corresponding candidate neoantigens for vaccination based on tumor RNA expression and predicted MHC class I binding affinity for each of the patient’s HLA alleles. This work is the base for a phase I trial of personalized neoantigen vaccines in combination with Tumor Treating Fields for GBM (NCT03223103). RESULTS For each of the 9 patients enrolled in the trial, an average of 1005 somatic mutations were identified (range 299–2441), of which 118 were coding variants (range 52–198), 20 were coding and expressed in the tumor RNA (range 9–45), and 16 were coding, expressed and resulted in predicted MHC class I ligands (range 7–33). An average of 2.3% of all somatic variants identified in each tumor gave rise to predicted neoantigens. Sufficient numbers of neoantigens were identified in all tumor samples of the patients enrolled in the study. The overall somatic mutation landscape for the tumors revealed 4/9 PIK3R1 deletions, 2/9 IDH1 substitutions, as well as disruptions in PTEN, TP53, and ATRX, among others. CONCLUSIONS Identifying sufficient neoantigens for inclusion in the personalized genomic vaccine is computationally feasible despite a typically low GBM mutational burden. Driver and passenger mutations can be identified through the same computational pipeline utilized for other tumor types.

DeepAntigen: a novel method for neoantigen prioritization via 3D genome and deep sparse learning.

The mutations of cancers can encode the seeds of their own destruction, in the form of T-cell recognizable immunogenic peptides, also known as neoantigens. It is computationally challenging, however, to accurately prioritize the potential neoantigen candidates according to their ability of activating the T-cell immunoresponse, especially when the somatic mutations are abundant. Although a few neoantigen prioritization methods have been proposed to address this issue, advanced machine learning model that is specifically designed to tackle this problem is still lacking. Moreover, none of the existing methods considers the original DNA loci of the neoantigens in the perspective of 3D genome which may provide key information for inferring neoantigens' immunogenicity. In this study, we discovered that DNA loci of the immunopositive and immunonegative MHC-I neoantigens have distinct spatial distribution patterns across the genome. We therefore used the 3D genome information along with an ensemble pMHC-I coding strategy, and developed a group feature selection-based deep sparse neural network model (DNN-GFS) that is optimized for neoantigen prioritization. DNN-GFS demonstrated increased neoantigen prioritization power comparing to existing sequence-based approaches. We also developed a webserver named deepAntigen (http://yishi.sjtu.edu.cn/deepAntigen) that implements the DNN-GFS as well as other machine learning methods. We believe that this work provides a new perspective toward more accurate neoantigen prediction which eventually contribute to personalized cancer immunotherapy. Data and implementation are available on webserver: http://yishi.sjtu.edu.cn/deepAntigen. Supplementary data are available at Bioinformatics online.

Abstract 6610: To identify neoantigens relevant to anti-tumor activity is challenging even with NGS-based prediction combined with MHC ligandome profiling

Abstract Background T cells that target neoantigens, which are mutant peptides encoded by tumor-specific mutations in cancer cells, have a pivotal role in response to immunotherapy. Current affordable NGS and bioinformatics have facilitated neoantigen research. However, the prediction of neoantigens in silico analysis lacks actual proof that the predicted neoantigens are indeed relevant to anti-tumor immune response. In this study, immunogenicity and anti-tumor activity of in silico predicted neoepitopes of gastric cancer cell lines, YTN2 and YTN16, were evaluated in vitro and in vivo. Besides, MHC ligandome profiling was performed on whether predicted neoantigens were presented by cancer cells. Methods YTN2 and YTN16 are subclones established from a chemically induced murine gastric cancer (Cancer Sci. 2018;109:1480-1492). Whole-exome sequencing and microarray analysis were performed. Binding affinities of peptides with MHC-I were predicted using NetMHCpan. Peptides with IC50≤250 nM were considered as candidate neoantigen. Results Exome sequencing identified 3329 and 3347 missense mutations in YTN2 and YTN16 tumors, respectively. Most of the mutations (3007) were common in YTN2 and YTN16. Among them, 2356 and 2166 mutations were expressed in YTN2 and YTN16, respectively. Candidate neoantigen peptides were 263 and 250 in YTN2 and YTN16, respectively. Although we detected 75 neoantigen-peptide-specific T cell responses, only six neoantigens (Cdt1, Cers4, Nlrp1a, Scarb2, Vmn2r121, and Zfp106)-specific T cells could recognize YTN2 or YTN16 cell line and produced IFNγ. To evaluate in vivo anti-tumor effects of neoantigen-specific T cells, we expanded four neoantigens (Cdt1, Nlrp1a, Vmn2r121, and Zfp106)-specific CD8+ T cell lines and injected them into YTN16 tumor-bearing mice. Only Cdt1-specific T cells completely eradicated the tumor, and Nlrp1a-specific T cells partially inhibited tumor growth. However, Vmn2r121- or Zfp106-specific CD8+ T cell lines could not inhibit the tumor growth. MHC ligandome profiling identified 424 Db-binding peptides and 228 Kb-binding peptides in YTN2, while 317 Db-binding peptides and 228 Kb-binding peptides were detected in YTN16. Almost all MHC-binding peptides had wild type sequences. Only one mutated peptide, Cdt1, was detected in YTN2 but not YTN16. Although Cdt1-specific CD8+ T cells recognized both YTN2 and YTN16. Other neoantigens, Cers4, Nlrp1a, Scarb2, Vmn2r121, and Zfp106, were not detected by MHC ligandome profiling, though T cells specific to these neoantigens responded to cancer cells. Conclusion Currently available technologies anyhow identified neoantigens that are relevant to anti-tumor immune response. However, any single methodology is not sufficient to identify real neoantigens. Detection of neoantigen-specific T cells is still important. Citation Format: Koji Nagaoka, Takayuki Kanaseki, Changbo Sun, Akihiro Hosoi, Serina Tokita, Kazuhiro Maejima, Masashi Fujita, Hidewaki Nakagawa, Toshihiko Torigoe, Kazuhiro Kakimi. To identify neoantigens relevant to anti-tumor activity is challenging even with NGS-based prediction combined with MHC ligandome profiling [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 6610.

Abstract 4727: Molecular heterogeneity in glioblastoma multiforme influences variant clonality and neoantigen prediction accuracy

Abstract Gliomas originate from neuroglial progenitor cells in the brain or spinal cord and have an incidence of ~6 cases per 100,000 individuals each year in the United States. Approximately half of glioma cases present as an aggressive type of astrocytoma known as glioblastoma multiforme (GBM). Due to its location and diffuse morphology, GBM has only a 30% two-year survival rate and additional treatment strategies are needed. Clinical trials for GBM personalized neoantigen vaccines have recently reported neoantigen-specific T cell responses, but treatment efficacy remains low. We posit that this may be due to GBM's high molecular heterogeneity, which could complicate the identification of clonal neoantigens for vaccine design. To address this topic, we performed whole-exome sequencing (WES) and RNA-seq on 30 human brain tumors. This cohort contains 21 primary/recurrent GBMs and 19 brain metastases derived from breast cancer, non-small cell lung cancer, or melanoma. We integrated variant calls, RNA expression, and HLA typing data for input into pVACtools, a suite of tools for neoantigen prediction, to obtain candidate neoantigen sequences with IC50 < 500nM. Mutation and neoantigen burden varied widely between the two tumor types. The range of variant counts for GBM samples is 7-169 (median 81), while the brain metastases have a range of 158-1317 (median 274.5). In addition, the former's class I and II neoantigen counts range from 1-312 (median 14), while the latter's range from 3-1159 (median 23.5). The percentage of variants that created neoantigens per sample ranged from 0.0-54.0% (median 7.1%). These counts exclude a recurrent GBM with two distinct hypermutated subclones that contain missense mutations in MSH6, a canonical DNA damage repair gene. By comparing intratumoral heterogeneity levels, we found that GBM patients had a lower proportion of clonal variants and neoantigens than tumors that had metastasized to the brain. In addition, the proportion of variants and neoantigens missed by single sector sequencing was significantly higher in GBM compared to brain metastases, indicating substantial heterogeneity in this tumor type. Variation in immune cell infiltrate was assessed using transcriptome data, and while many tumors demonstrated cellular heterogeneity, there was no clear correlation between this metric and tumor type. We are performing TCR sequencing to further investigate the immune landscape heterogeneity by determining TCR clonality diversity per region. Overall, this analysis demonstrates that GBM contains cellular and molecular heterogeneity, which can complicate clonal neoantigen selection. Multi-sample sequencing as a standard for GBM genomic analysis could alleviate these inconsistencies to improve neoantigen vaccine design and increase treatment efficacy. Citation Format: Megan M. Richters, Maximilian Schaettler, Zach L. Skidmore, Cody A. Ramirez, Matthew C. Mosior, Obi L. Griffith, Malachi Griffith, Gavin P. Dunn. Molecular heterogeneity in glioblastoma multiforme influences variant clonality and neoantigen prediction accuracy [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 4727.

Abstract 4566: Performance of neoantigen prediction for the design of TG4050, a patient specific neoantigen cancer vaccine

Abstract The development of therapeutic cancer vaccines to immunize against tumor antigens constitutes a promising modality. Mutation associated antigens are considered major targets given their specificity to tumor cells. These mutations are specific to the patients and require a tailor-made vaccine targeting mutations identified in each tumor. Many mutations are identified in the tumoral genome in most patients, but only a small fraction (around 1%) is suitable as vaccine target. Herein, we report data documenting the prediction performance of the algorithm used for the design of TG4050, a clinical stage patient specific viral-based neoantigen vaccine. We have trained a set of independent machine learning algorithms to score each candidate neoantigen for several steps of the MHC antigen presentation pathway, including MHC binding, intracellular processing, similarity to self, and likelihood to elicit a T-cell response in peptide stimulated ELISPOT. Further, we have developed a novel graph neural network to combine all these scores to predict the likelihood that a neoantigen will elicit a T-cell response while also incorporating patient-specific factors, such as expression level and conservation of the mutation across different clones. To validate the system, we collected samples from 6 patients diagnosed with NSCLC, sequenced healthy and tumor tissue, identified mutations and ranked them using our algorithm; then, to evaluate immunogenicity, we focused our analysis on CD8+ T cell and measured the frequency of IFN γ+ cells against predicted peptides in autologous PBMC. Immunogenicity of peptides was assayed in 5 pools then deconvoluted against individual peptides. From 3339 to 4782 somatic variants were detected in tumor tissue samples. After applying technical filtering, removing synonymous mutations, and filtering on transcript expression we detected a median of 281 (192-471) expressed tumor mutations resulting in a median of 2767 candidate class I epitopes (1769 - 4573). The model resulted in high accuracy allowing us to identify peptides with pre-existing ex vivo immunogenic responses in 5 out of 6 patients. Immunogenicity of peptide pools was correlated with ranking by the algorithm. Immunogenicity of the 6 top ranking individual epitopes in each patient showed a median of 5 (2-6) immunogenic peptides resulting in a 77% of true positive rate (TP). It should be noted that when no response was detected, it cannot be excluded that a response could be primed by a vaccine. In a similar setting, the netMHC 4.0 algorithm yielded a TP of 30% and only identified 39% of positive calls of our algorithm. We demonstrate that the prediction algorithm is accurate in identifying immunogenic cancer mutations even among a large set of candidates. Ongoing TG4050 clinical studies (NCT03839524 and NCT04183166) will allow further validation of the antitumor activity of the elicited immune response. Citation Format: Brandon Malone, Caroline Tosch, Benoit Grellier, Kousuke Onoue, Timo Sztyler, Karola Rittner, Yoshiko Yamashita, Eric Quemeneur, Kaidre Bendjama. Performance of neoantigen prediction for the design of TG4050, a patient specific neoantigen cancer vaccine [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 4566.

A novel neoantigen discovery approach based on chromatin high order conformation

BackgroundHigh-throughput sequencing technology has yielded reliable and ultra-fast sequencing for DNA and RNA. For tumor cells of cancer patients, when combining the results of DNA and RNA sequencing, one can identify potential neoantigens that stimulate the immune response of the T cell. However, when the somatic mutations are abundant, it is computationally challenging to efficiently prioritize the identified neoantigen candidates according to their ability of activating the T cell immuno-response.MethodsNumerous prioritization or prediction approaches have been proposed to address this issue but none of them considers the original DNA loci of the neoantigens from the perspective of 3D genome. Based on our previous discoveries, we propose to investigate the distribution of neoantigens with different immunogenicity abilities in 3D genome and propose to adopt this important information into neoantigen prediction.ResultsWe retrospect the DNA origins of the immuno-positive and immuno-negative neoantigens in the context of 3D genome and discovered that DNA loci of the immuno-positive neoantigens and immuno-negative neoantigens have very different distribution pattern. Specifically, comparing to the background 3D genome, DNA loci of the immuno-positive neoantigens tend to locate at specific regions in the 3D genome. We thus used this information into neoantigen prediction and demonstrated the effectiveness of this approach.ConclusionWe believe that the 3D genome information will help to increase the precision of neoantigen prioritization and discovery and eventually benefit precision and personalized medicine in cancer immunotherapy.

ASNEO: Identification of personalized alternative splicing based neoantigens with RNA-seq.

Cancer neoantigens have shown great potential in immunotherapy, while current software focuses on identifying neoantigens which are derived from SNVs, indels or gene fusions. Alternative splicing widely occurs in tumor samples and it has been proven to contribute to the generation of candidate neoantigens. Here we present ASNEO, which is an integrated computational pipeline for the identification of personalized Alternative Splicing based NEOantigens with RNA-seq. Our analyses showed that ASNEO could identify neopeptides which are presented by MHC I complex through mass spectrometry data validation. When ASNEO was applied to two immunotherapy-treated cohorts, we found that alternative splicing based neopeptides generally have a higher immune score than that of somatic neopeptides and alternative splicing based neopeptides could be a marker to predict patient survival pattern. Our identification of alternative splicing derived neopeptides would contribute to a more complete understanding of the tumor immune landscape. Prediction of patient-specific alternative splicing neopeptides has the potential to contribute to the development of personalized cancer vaccines.

Immuno-phenotyping of tumor-specific CD8 T cells using high-dimensional mass cytometry

Abstract There is strong evidence that tumor-specific T cells play a central role in tumor rejection. immunoSCAPE leverages the high-dimensional immune profiling capabilities of CyTOF for the identification of antigen-specific T cells to support the development of immunotherapy strategies. By applying CyTOF in conjunction with combinatorial peptide-MHC tetramer staining and high-performance dimensional analysis tools, we broadly and sensitively map T cell reactivity against MHC-class I epitopes while concurrently performing in-depth characterization of these rare antigen-specific T cells. Here, we have applied our unique capabilities to detect and characterize circulating neoantigen-specific T cells in lung carcinoma patient undergoing atezolizumab treatment. Out of 782 neoantigen candidates, T cell reactivity against 13 unique neoantigens was detected within patients who responded to atezolizumab treatment, while T cells within non-responder patients recognized 7 neoantigens. Additionally, neoantigen-specific CD8 T cells from treatment responders exhibit a differentiated effector phenotype similar to CMV or EBV-specific T cells, whereas neoantigen-specific CD8 T cells from non-responders display a memory-like phenotype, suggesting that the properties of tumor-reactive T cells may be associated with clinical response to anti-PD-L1 treatment. By providing insight into the nature and function of tumor-specific T cells, immunoSCAPE’s unique high-dimensional immune profiling platform is a valuable tool to guide the development of novel immunotherapy strategies through assessment of drug biological activity, definition of mechanism of action, and identification of biomarkers of patient response.

ProGeo-neo: a customized proteogenomic workflow for neoantigen prediction and selection

BackgroundNeoantigens can be differentially recognized by T cell receptor (TCR) as these sequences are derived from mutant proteins and are unique to the tumor. The discovery of neoantigens is the first key step for tumor-specific antigen (TSA) based immunotherapy. Based on high-throughput tumor genomic analysis, each missense mutation can potentially give rise to multiple neopeptides, resulting in a vast total number, but only a small percentage of these peptides may achieve immune-dominant status with a given major histocompatibility complex (MHC) class I allele. Specific identification of immunogenic candidate neoantigens is consequently a major challenge.Currently almost all neoantigen prediction tools are based on genomics data.ResultsHere we report the construction of proteogenomics prediction of neoantigen (ProGeo-neo) pipeline, which incorporates the following modules: mining tumor specific antigens from next-generation sequencing genomic and mRNA expression data, predicting the binding mutant peptides to class I MHC molecules by latest netMHCpan (v.4.0), verifying MHC-peptides by MaxQuant with mass spectrometry proteomics data searched against customized protein database, and checking potential immunogenicity of T-cell-recognization by additional screening methods. ProGeo-neo pipeline achieves proteogenomics strategy and the neopeptides identified were of much higher quality as compared to those identified using genomic data only.ConclusionsThe pipeline was constructed based on the genomics and proteomics data of Jurkat leukemia cell line but is generally applicable to other solid cancer research. With massively parallel sequencing and proteomics profiling increasing, this proteogenomics workflow should be useful for neoantigen oriented research and immunotherapy.

Abstract A8: Immuno-phenotyping of tumor-specific CD8 T cells using high-dimensional mass cytometry

Abstract There is strong evidence that tumor-specific T cells play a central role in tumor rejection. However, few tools can simultaneously detect rare antigen-specific T cells and deeply characterize their function and phenotype. immunoSCAPE leverages the high-dimensional immune profiling capabilities of mass cytometry combined with a unique technology for the identification of antigen-specific T cells to support the development of immunotherapy strategies. By applying cytometry by time of flight in conjunction with combinatorial peptide-MHC tetramer staining and high-performance dimensional analysis tools, we broadly and sensitively map T-cell reactivity against MHC-class I epitopes while concurrently performing in-depth characterization of these rare antigen-specific T cells. Here, we have applied our unique capabilities to detect and characterize circulating neoantigen-specific T cells in lung carcinoma patients undergoing atezolizumab treatment. Out of 782 neoantigen candidates, T-cell reactivity against 13 unique neoantigens was detected within patients who responded to atezolizumab treatment, while T cells within nonresponder patients recognized 7 neoantigens. Additionally, neoantigen-specific CD8 T cells from treatment responders exhibit a differentiated effector phenotype similar to CMV or EBV-specific T cells, whereas neoantigen-specific CD8 T cells from nonresponders display a memory-like phenotype, suggesting that the properties of tumor-reactive T cells may be associated with clinical response to anti-PD-L1 treatment. By providing insight into the nature and function of tumor-specific T cells, immunoSCAPE’s unique high-dimensional immune profiling platform is a valuable tool to guide the development of novel immunotherapy strategies through assessment of drug biologic activity, definition of mechanism of action, and identification of biomarkers of patient response. Citation Format: David Roumanes, Michael Fehlings, Mahesh Yadav, Alessandra Nardin. Immuno-phenotyping of tumor-specific CD8 T cells using high-dimensional mass cytometry [abstract]. In: Proceedings of the AACR Special Conference on Tumor Immunology and Immunotherapy; 2019 Nov 17-20; Boston, MA. Philadelphia (PA): AACR; Cancer Immunol Res 2020;8(3 Suppl):Abstract nr A8.

Neoleukin-2 enhances anti-tumour immunity downstream of peptide vaccination targeted by an anti-MHC class II VHH.

Cancer-specific mutations can lead to peptides of unique sequence presented on MHC class I to CD8 T cells. These neoantigens can be potent tumour-rejection antigens, appear to be the driving force behind responsiveness to anti-CTLA-4 and anti-PD1/L1-based therapies and have been used to develop personalized vaccines. The platform for delivering neoantigen-based vaccines has varied, and further optimization of both platform and adjuvant will be necessary to achieve scalable vaccine products that are therapeutically effective at a reasonable cost. Here, we developed a platform for testing potential CD8 T cell tumour vaccine candidates. We used a high-affinity alpaca-derived VHH against MHC class II to deliver peptides to professional antigen-presenting cells. We show in vitro and in vivo that peptides derived from the model antigen ovalbumin are better able to activate naive ovalbumin-specific CD8 T cells when conjugated to an MHC class II-specific VHH when compared with an irrelevant control VHH. We then used the VHH-peptide platform to evaluate a panel of candidate neoantigens in vivo in a mouse model of pancreatic cancer. None of the candidate neoantigens tested led to protection from tumour challenge; however, we were able to show vaccine-induced CD8 T cell responses to a melanoma self-antigen that was augmented by combination therapy with the synthetic cytokine mimetic Neo2/15.

Identification of neoantigens and development of antigen-specific immunotherapy

Cancer cells harboring somatic mutations give rise to neoantigens, which are immunologically foreign in nature to be distinguished from itself, showing high immunogenicity and, thus, induce specific T-cell responses against cancer. Therefore, neoantigens are expected to be promising targets for anti-cancer immunotherapy. The general methods used to identify candidate neoantigens are as follows: (1) non-synonymous mutations are identified by whole exome and RNA sequencing; (2) neoantigens from the mutations are predicted based on in silico MHC ligand prediction algorithm; (3) specific T-cell responses toward the candidate neoantigens are verified using tumor infiltrating T cells or peripheral blood mononuclear cells. In hematological malignancy, several neoantigens have been identified as an important treatment target. In contrast with solid malignancies, the occurrence of frameshift mutations and fusion genes producing neoantigens are high. A shared neoantigen derived from frameshift mutation of nucleophosmin I, which is often observed in acute myeloid leukemia, was reported to induce specific immune responses in vitro and in vivo. We should examine neoantigens as possible target of novel immunotherapy despite several issues to be addressed for clinical application.

Personalized neoantigen-pulsed dendritic cell vaccines show superior immunogenicity to neoantigen-adjuvant vaccines in mouse tumor models

Development of personalized cancer vaccines based on neoantigens has become a new direction in cancer immunotherapy. Two forms of cancer vaccines have been widely studied: tumor-associated antigen (including proteins, peptides, or tumor lysates)-pulsed dendritic cell (DC) vaccines and protein- or peptide-adjuvant vaccines. However, different immune modalities may produce different therapeutic effects and immune responses when the same antigen is used. Therefore, it is necessary to choose a more effective neoantigen vaccination method. In this study, we compared the differences in immune and anti-tumor effects between neoantigen-pulsed DC vaccines and neoantigen-adjuvant vaccines using murine lung carcinoma (LL2) candidate neoantigens. The enzyme-linked immunospot (ELISPOT) assay showed that 4/6 of the neoantigen-adjuvant vaccines and 6/6 of the neoantigen-pulsed DC vaccines induced strong T-cell immune responses. Also, 2/6 of the neoantigen-adjuvant vaccines and 5/6 of the neoantigen-pulsed DC vaccines exhibited potent anti-tumor effects. The results indicated that the neoantigen-pulsed DC vaccines were superior to the neoantigen-adjuvant vaccines in both activating immune responses and inhibiting tumor growth. Our fundings provide an experimental basis for the selection of immune modalities for the use of neoantigens in individualized tumor immunotherapies.

IMMU-09. COMBINATION IMMUNE TREATMENT OF A HIGHLY AGGRESSIVE ORTHOTOPIC MURINE GLIOBLASTOMA WITH CHECKPOINT BLOCKADE AND MULTI-VALENT NEOANTIGEN VACCINATION

Abstract Recent success using immunotherapies to treat a growing range of cancer types has reinvigorated efforts to identify effective immune-based treatments for glioblastoma (GBM). However, the limited clinical benefit observed in GBM patients treated with checkpoint blockade immunotherapy (CBI) has indicated a need for more rigorous approaches to augment host-immune recognition and killing of GBM. Of the myriad barriers to effective brain tumor immunotherapy, one potentially critical consideration is that a more systematic approach to tumor-specific antigen identification and targeting is necessary to potently stimulate and direct T cells to treat GBM. Therefore, in this study we set out to identify endogenous tumor neoantigens in the anti-PD-L1 resistant murine glioblastoma, CT2A, and assess the efficacy of neoantigen vaccination in combination with anti-PD-L1 treatment. In order to identify candidate CT2A neoantigens, we employed DNA whole exome sequencing, RNA sequencing, and in silico neoantigen prediction analyses. High-affinity H-2Kb and H-2Db candidate CT2A neoantigens were screened for immunogenicity by IFN-γ ELISPOT assays. Of the 30 top-ranking neoantigen candidates, 13 demonstrated immunogenic CD8+ T cell responses in the spleens of mice vaccinated with mutant peptides. Assessing for endogenous reactivity, we identified neoantigen specific CD8+ T cell responses in the intracranial TIL and draining lymph nodes to two H2-Kb restricted, Epb4H471L and Pomgnt1R497L, and one H2-Db restricted neoantigen, Plin2G332R. Survival studies showed that therapeutic neoantigen vaccination with Epb4H471L, Pomgnt1R497L, and Plin2G332R, in combination with anti-PD-L1 checkpoint blockade was superior to either neoantigen vaccination or anti-PD-L1 therapy alone. Thus, by applying a cancer immunogenomics approach to identify endogenous neoantigen reactivity in an aggressive and treatment resistant murine glioblastoma, we provide a preclinical framework to investigate the effects of multi-modality immunotherapeutic interventions in anti-glioma immunity.

Pre-treatment tumor neo-antigen responses in draining lymph nodes are infrequent but predict checkpoint blockade therapy outcome

ABSTRACT Immune checkpoint blockade (ICPB) is a powerfully effective cancer therapy in some patients. Tumor neo-antigens are likely main targets for attack but it is not clear which and how many tumor mutations in individual cancers are actually antigenic, with or without ICPB therapy and their role as neo-antigen vaccines or as predictors of ICPB responses. To examine this, we interrogated the immune response to tumor neo-antigens in a murine model in which the tumor is induced by a natural human carcinogen (i.e. asbestos) and mimics its human counterpart (i.e. mesothelioma). We identified and screened 33 candidate neo-antigens, and found T cell responses against one candidate in tumor-bearing animals, mutant UQCRC2. Interestingly, we found a high degree of inter-animal variation in the magnitude of neo-antigen responses in otherwise identical mice. ICPB therapy with Cytotoxic T-lymphocyte-associated protein (CTLA-4) and α-glucocorticoid-induced TNFR family related gene (GITR) in doses that induced tumor regression, increased the magnitude of responses and unmasked functional T cell responses against another neo-antigen, UNC45a. Importantly, the magnitude of the pre-treatment draining lymph node (dLN) response to UNC45a closely corresponded to ICPB therapy outcomes. Surprisingly however, boosting pre-treatment UNC45a-specific T cell numbers did not improve response rates to ICPB. These observations suggest a novel biomarker approach to the clinical prediction of ICPB response and have important implications for the development of neo-antigen vaccines.

Identification of a CD8+ T-cell response to a predicted neoantigen in malignant mesothelioma

ABSTRACT Neoantigens present unique and specific targets for personalized cancer immunotherapy strategies. Given the low mutational burden yet immunotherapy responsiveness of malignant mesothelioma (MM) when compared to other carcinogen-induced malignancies, identifying candidate neoantigens and T cells that recognize them has been a challenge. We used pleural effusions to gain access to MM tumor cells as well as immune cells in order to characterize the tumor-immune interface in MM. We characterized the landscape of potential neoantigens from SNVs identified in 27 MM patients and performed whole transcriptome sequencing of cell populations from 18 patient-matched pleural effusions. IFNγ ELISpot was performed to detect a CD8+ T cell responses to predicted neoantigens in one patient. We detected a median of 68 (range 7–258) predicted neoantigens across the samples. Wild-type non-binding to mutant binding predicted neoantigens increased risk of death in a model adjusting for age, sex, smoking status, histology and treatment (HR: 33.22, CI: 2.55–433.02, p = .007). Gene expression analysis indicated a dynamic immune environment within the pleural effusions. TCR clonotypes increased with predicted neoantigen burden. A strong activated CD8+ T-cell response was identified for a predicted neoantigen produced by a spontaneous mutation in the ROBO3 gene. Despite the challenges associated with the identification of bonafide neoantigens, there is growing evidence that these molecular changes can provide an actionable target for personalized therapeutics in difficult to treat cancers. Our findings support the existence of candidate neoantigens in MM despite the low mutation burden of the tumor, and may present improved treatment opportunities for patients.

PTuneos: prioritizing tumor neoantigens from next-generation sequencing data

BackgroundCancer 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.ResultsWe 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.ConclusionsIn 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.

The Landscape of Tumor Fusion Neoantigens: A Pan-Cancer Analysis.

SummaryCompared with SNV&indel-based neoantigens, fusion-based neoantigens are not well characterized. In the present study, we performed a comprehensive analysis of the landscape of tumor fusion neoantigens in cancer and proposed a score scheme to quantitatively assess their immunogenic potentials. By analyzing three large-scale tumor datasets, we demonstrated that (1) the tumor fusion candidate neoantigen burden is not related to the immunotherapy outcome; (2) fusion neoantigens tend to have notably higher immunogenic potentials than SNV&indel-based candidate neoantigens, making them better candidates for cancer vaccines; (3) fusion candidate neoantigens distribute sparsely between individual patients. Although several recurrent candidate neoantigens exist, they usually have extremely low immunogenic potentials, suggesting that vaccination-based cancer immunotherapy must be personalized; (4) compared with fusion mutations involving tumor passenger genes, fusion mutations involving oncogenic genes have remarkably low immunogenic potentials, indicating that they undergo selection pressure during tumorigenesis.