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BackgroundMutation-derived neoantigens are critical targets for tumor rejection in cancer immunotherapy, and better tools for neoepitope identification and prediction are needed to improve neoepitope targeting strategies. Computational tools have enabled the identification of patient-specific neoantigen candidates from sequencing data, but limited data availability has hindered their capacity to predict which of the many neoepitopes will most likely give rise to T cell recognition. MethodTo address this, we make use of experimentally validated T cell recognition towards 17,500 neoepitope candidates, with 467 being T cell recognized, across 70 cancer patients undergoing immunotherapy. ResultsWe evaluated 27 neoepitope characteristics, and created a random forest model, IMPROVE, to predict neoepitope immunogenicity. The presence of hydrophobic and aromatic residues in the peptide binding core were the most important features for predicting neoepitope immunogenicity.ConclusionOverall, IMPROVE was found to significantly advance the identification of neoepitopes compared to other current methods.

Abstract Recent findings have positioned tumor mutation-derived neoepitopes as attractive targets for cancer immunotherapy. Cancer vaccines that deliver neoepitopes via various vaccine formulations have demonstrated promising preliminary results in patients and animal models. In the presented work, we assessed the ability of plasmid DNA to confer neoepitope immunogenicity and anti-tumor effect in two murine syngeneic cancer models. We demonstrated that neoepitope DNA vaccination led to anti-tumor immunity in the CT26 and B16F10 tumor models, with the long-lasting presence of neoepitope-specific T-cell responses in blood, spleen, and tumors after immunization. We further observed that engagement of both the CD4+ and CD8+ T cell compartments was essential to hamper tumor growth. Additionally, combination therapy with immune checkpoint inhibition provided an additive effect, superior to either monotherapy. DNA vaccination offers a versatile platform that allows the encoding of multiple neoepitopes in a single formulation and is thus a feasible strategy for personalized immunotherapy via neoepitope vaccination.

Checkpoint inhibition (CPI) therapy and adoptive cell therapy with autologous tumor-infiltrating lymphocytes (TIL-based ACT) are the two most effective immunotherapies for the treatment of metastatic melanoma. While CPI has been the dominating therapy in the past decade, TIL-based ACT is beneficial for individuals even after progression on previous immunotherapies. Given that notable differences in response have been made when used as a subsequent treatment, we investigated how the qualities of TILs changed when the ex vivo microenvironment of intact tumor fragments were modulated with checkpoint inhibitors targeting programmed death receptor 1 (PD-1) and cytotoxic T-lymphocyte-associated protein 4 (CTLA-4). Initially, we show that unmodified TILs from CPI-resistant individuals can be produced, are overwhelmingly terminally differentiated, and are capable of responding to tumor. We then investigate these properties in ex vivo checkpoint modulated TILs finding that that they retain these qualities. Lastly, we confirmed the specificity of the TILs to the highest responding tumor antigens, and identified this reactivity resides largely in CD39+CD69+ terminally differentiated populations. Overall, we found that anti-PD-1 will alter the proliferative capacity while anti-CTLA4 will influence breadth of antigen specificity.

BackgroundThe B-cell lymphoma-extra-large (Bcl-XL) protein plays an important role in cancer cells’ resistance to apoptosis. Pre-clinical studies have shown that vaccination with Bcl-XL-derived peptides can induce tumor-specific T cell responses that may lead to the elimination of cancer cells. Furthermore, pre-clinical studies of the novel adjuvant CAF®09b have shown that intraperitoneal (IP) injections of this adjuvant can improve the activation of the immune system. In this study, patients with hormone-sensitive prostate cancer (PC) received a vaccine consisting of Bcl-XL-peptide with CAF®09b as an adjuvant. The primary aim was to evaluate the tolerability and safety of IP and intramuscular (IM) administration, determine the optimal route of administration, and characterize vaccine immunogenicity.Patients and methodsTwenty patients were included. A total of six vaccinations were scheduled: in Group A (IM to IP injections), ten patients received three vaccines IM biweekly; after a three-week pause, patients then received three vaccines IP biweekly. In Group B (IP to IM injections), ten patients received IP vaccines first, followed by IM under a similar vaccination schedule. Safety was assessed by logging and evaluating adverse events (AE) according to Common Terminology Criteria for Adverse Events (CTCAE v. 4.0). Vaccines-induced immune responses were analyzed by Enzyme-Linked Immunospot and flow cytometry.ResultsNo serious AEs were reported. Although an increase in T cell response against the Bcl-XL-peptide was found in all patients, a larger proportion of patients in group B demonstrated earlier and stronger immune responses to the vaccine compared to patients in group A. Further, we demonstrated vaccine-induced immunity towards patient-specific CD4, and CD8 T cell epitopes embedded in Bcl-XL-peptide and an increase in CD4 and CD8 T cell activation markers CD107a and CD137 following vaccination. At a median follow-up of 21 months, no patients had experienced clinically significant disease progression.ConclusionThe Bcl-XL-peptide-CAF®09b vaccination was feasible and safe in patients with l hormone-sensitive PC. In addition, the vaccine was immunogenic and able to elicit CD4 and CD8 T cell responses with initial IP administration eliciting early and high levels of vaccine-specific responses in a higher number og patients.Clinical trial registrationhttps://clinicaltrials.gov, identifier NCT03412786.

Patients with hematological malignancies are prioritized for COVID-19 vaccine due to their high risk for severe SARS-CoV-2 infection-related disease and mortality. To understand T cell immunity, its long-term persistence, and its correlation with antibody response, we evaluated the BNT162b2 COVID-19 mRNA vaccine-specific immune response in chronic lymphocytic leukemia (CLL) and myeloid dysplastic syndrome (MDS) patients. Longitudinal analysis of CD8+ T cells using DNA-barcoded peptide-MHC multimers covering the full SARS-CoV-2 Spike-protein (415 peptides) showed vaccine-specific T cell activation and persistence of memory T cells up to six months post-vaccination. Surprisingly, a higher frequency of vaccine-induced antigen-specific CD8+ T cells was observed in the patient group compared to a healthy donor group. Furthermore, and importantly, immunization with the second booster dose significantly increased the frequency of antigen-specific CD8+ T cells as well as the total number of T cell specificities. Altogether 59 BNT162b2 mRNA vaccine-derived immunogenic responses were identified, of which 23 established long-term CD8+ T cell memory response with a strong immunodominance for NYNYLYRLF (HLA-A24:02) and YLQPRTFLL (HLA-A02:01) epitopes. In summary, we mapped the vaccine-induced antigen-specific CD8+ T cells and showed a booster-specific activation and enrichment of memory T cells that could be important for long-term disease protection in this patient group.

IntroductionDifferentially polarized macrophages, especially YM1+ and MHCII+ macrophages, play an important role in asthma development. The origin of these polarized macrophages has not been elucidated yet. We therefore aimed to investigate how proliferation, monocyte recruitment, and/or switching of polarization states contribute to this specific pool of polarized interstitial and alveolar macrophages during development of house dust mite (HDM)-induced allergic lung inflammation in mice.MethodsMale and female mice were first treated intranasally with PKH26 to label lung-resident macrophages and were then exposed to either HDM or phosphate-buffered saline (PBS) for two weeks. Different myeloid immune cell types were quantified in lung tissue and blood using flow cytometry.ResultsWe found that macrophage polarization only starts up in the second week of HDM exposures. Before this happened, unpolarized alveolar and interstitial macrophages transiently increased in HDM-exposed mice. This transient increase was mostly local proliferation of alveolar macrophages, while interstitial macrophages also contained unlabeled macrophages suggesting monocyte contribution. After two weeks of exposures, the number of interstitial and alveolar macrophages was similar between HDM and PBS-exposed mice, but the distribution of polarization states was remarkably different. HDM-exposed mice selectively developed YM1+ alveolar macrophages and MHCII-hi interstitial macrophages while nonpolarized macrophages were lost compared to PBS-exposed mice. DiscussionIn this HDM model we have shown that development of a polarized macrophage pool during allergic inflammation is first dependent on proliferation of nonpolarized tissue-resident macrophages with some help of infiltrating unlabeled cells, presumably circulating monocytes. These nonpolarized macrophages then acquire their polarized phenotype by upregulating YM1 on alveolar macrophages and MHCII on interstitial macrophages. This novel information will help us to better understand the role of macrophages in asthma and designing therapeutic strategies targeting macrophage functions.

The majority of neoantigens arise from unique mutations that are not shared between individual patients, making neoantigen-directed immunotherapy a fully personalized treatment approach. Novel technical advances in next-generation sequencing of tumor samples and artificial intelligence (AI) allow fast and systematic prediction of tumor neoantigens. This study investigates feasibility, safety, immunity, and anti-tumor potential of the personalized peptide-based neoantigen vaccine, EVX-01, including the novel CD8+ T-cell inducing adjuvant, CAF®09b, in patients with metastatic melanoma (NTC03715985). The AI platform PIONEERTM was used for identification of tumor-derived neoantigens to be included in a peptide-based personalized therapeutic cancer vaccine. EVX-01 immunotherapy consisted of 6 administrations with 5–10 PIONEERTM-predicted neoantigens as synthetic peptides combined with the novel liposome-based Cationic Adjuvant Formulation 09b (CAF®09b) to strengthen T-cell responses. EVX-01 was combined with immune checkpoint inhibitors to augment the activity of EVX-01-induced immune responses. The primary endpoint was safety, exploratory endpoints included feasibility, immunologic and objective responses. This interim analysis reports the results from the first dose-level cohort of five patients. We documented a short vaccine manufacturing time of 48–55 days which enabled the initiation of EVX-01 treatment within 60 days from baseline biopsy. No severe adverse events were observed. EVX-01 elicited long-lasting EVX-01-specific T-cell responses in all patients. Competitive manufacturing time was demonstrated. EVX-01 was shown to be safe and able to elicit immune responses targeting tumor neoantigens with encouraging early indications of a clinical and meaningful antitumor efficacy, warranting further study.

Immune checkpoint blockade (ICB) is standard-of-care for patients with metastatic melanoma. It may re-invigorate T cells recognizing tumors, and several tumor antigens have been identified as potential targets. However, little is known about the dynamics of tumor antigen-specific T cells in the circulation, which might provide valuable information on ICB responses in a minimally invasive manner. Here, we investigated individual signatures composed of up to 167 different melanoma-associated epitope (MAE)-specific CD8+ T cells in the blood of stage IV melanoma patients before and during anti-PD-1 treatment, using a peptide-loaded multimer-based high-throughput approach. Additionally, checkpoint receptor expression patterns on T cell subsets and frequencies of myeloid-derived suppressor cells and regulatory T cells were quantified by flow cytometry. Regression analysis using the MAE-specific CD8+ T cell populations was applied to identify those that correlated with overall survival (OS). The abundance of MAE-specific CD8+ T cell populations, as well as their dynamics under therapy, varied between patients. Those with a dominant increase of these T cell populations during PD-1 ICB had a longer OS and progression-free survival than those with decreasing or balanced signatures. Patients with a dominantly increased MAE-specific CD8+ T cell signature also exhibited an increase in TIM-3+ and LAG-3+ T cells. From these results, we created a model predicting improved/reduced OS by combining data on dynamics of the three most informative MAE-specific CD8+ T cell populations. Our results provide insights into the dynamics of circulating MAE-specific CD8+ T cell populations during ICB, and should contribute to a better understanding of biomarkers of response and anti-cancer mechanisms.

T cells play a crucial role in controlling and driving the immune response with their ability to discriminate peptides derived from healthy as well as pathogenic proteins. In this review, we focus on the currently available computational tools for epitope prediction, with a particular focus on tools aimed at identifying neoepitopes, i.e. cancer-specific peptides and their potential for use in immunotherapy for cancer treatment. This review will cover how these tools work, what kind of data they use, as well as pros and cons in their respective applications.

Mutation-derived neoantigens are important targets for T cell-mediated reactivity toward tumors and, due to their unique tumor expression, an attractive target for immunotherapy. Neoepitope-specific T cells have been detected across a number of solid cancers with high mutational burden tumors, but neoepitopes have been mostly selected from single nucleotide variations (SNVs), and little focus has been given to neoepitopes derived from in-frame and frameshift indels, which might be equally important and potentially highly immunogenic. Clear cell renal cell carcinomas (ccRCCs) are medium-range mutational burden tumors with a high pan-cancer proportion of frameshift mutations. In this study, the mutational landscape of tumors from six RCC patients was analyzed by whole-exome sequencing (WES) of DNA from tumor fragments (TFs), autologous tumor cell lines (TCLs), and tumor-infiltrating lymphocytes (TILs, germline reference). Neopeptides were predicted using MuPeXI, and patient-specific peptide–MHC (pMHC) libraries were created for all neopeptides with a rank score < 2 for binding to the patient's HLAs. T cell recognition toward neoepitopes in TILs was evaluated using the high-throughput technology of DNA barcode-labeled pMHC multimers. The patient-specific libraries consisted of, on average, 258 putative neopeptides (range, 103–397, n = 6). In four patients, WES was performed on two different sources (TF and TCL), whereas in two patients, WES was performed only on TF. Most of the peptides were predicted from both sources. However, a fraction was predicted from one source only. Among the total predicted neopeptides, 16% were derived from frameshift indels. T cell recognition of 52 neoepitopes was detected across all patients (range, 4–18, n = 6) and spanning two to five HLA restrictions per patient. On average, 21% of the recognized neoepitopes were derived from frameshift indels (range, 0–43%, n = 6). Thus, frameshift indels are equally represented in the pool of immunogenic neoepitopes as SNV-derived neoepitopes. This suggests the importance of a broad neopeptide prediction strategy covering multiple sources of tumor material, and including different genetic alterations. This study, for the first time, describes the T cell recognition of frameshift-derived neoepitopes in RCC and determines their immunogenic profile.

Advances within cancer immunotherapy have fueled a paradigm shift in cancer treatment, resulting in increasing numbers of cancer types benefitting from novel treatment options. Despite originally being considered an immunologically silent malignancy, recent studies encourage the research of breast cancer immunogenicity to evaluate immunotherapy as a treatment strategy. However, the epitope landscape in breast cancer is minimally described, limiting the options for antigen-specific, targeted strategies. Aromatase, never in mitosis A-related kinase 3 (NEK3), protein inhibitor of activated STAT3 (PIAS3), and prolactin are known as upregulated proteins in breast cancer. In the present study, these four proteins are identified as novel T cell targets in breast cancer. From the four proteins, 147 peptides were determined to bind HLA-A*0201 and -B*0702 using a combined in silico/in vitro affinity screening. T cell recognition of all 147 peptide-HLA-A*0201/-B*0702 combinations was assessed through the use of a novel high-throughput method utilizing DNA barcode labeled multimers. T cell recognition of sequences within all four proteins was demonstrated in peripheral blood of patients, and significantly more T cell responses were detected in patients compared to healthy donors for both HLA-A*0201 and -B*0702. Notably, several of the identified responses were directed toward peptides, with a predicted low or intermediate binding affinity. This demonstrates the importance of including low-affinity binders in the search for epitopes within shared tumor associated antigens (TAAs), as these might be less subject to immune tolerance mechanisms. The study presents four novel TAAs containing multiple possible targets for immunotherapy of breast cancer.

Personalization of cancer immunotherapies such as therapeutic vaccines and adoptive T-cell therapy may benefit from efficient identification and targeting of patient-specific neoepitopes. However, current neoepitope prediction methods based on sequencing and predictions of epitope processing and presentation result in a low rate of validation, suggesting that the determinants of peptide immunogenicity are not well understood. We gathered published data on human neopeptides originating from single amino acid substitutions for which T cell reactivity had been experimentally tested, including both immunogenic and non-immunogenic neopeptides. Out of 1,948 neopeptide-HLA (human leukocyte antigen) combinations from 13 publications, 53 were reported to elicit a T cell response. From these data, we found an enrichment for responses among peptides of length 9. Even though the peptides had been pre-selected based on presumed likelihood of being immunogenic, we found using NetMHCpan-4.0 that immunogenic neopeptides were predicted to bind significantly more strongly to HLA compared to non-immunogenic peptides. Investigation of the HLA binding strength of the immunogenic peptides revealed that the vast majority (96%) shared very strong predicted binding to HLA and that the binding strength was comparable to that observed for pathogen-derived epitopes. Finally, we found that neopeptide dissimilarity to self is a predictor of immunogenicity in situations where neo- and normal peptides share comparable predicted binding strength. In conclusion, these results suggest new strategies for prioritization of mutated peptides, but new data will be needed to confirm their value.

Manual analysis of flow cytometry data and subjective gate-border decisions taken by individuals continue to be a source of variation in the assessment of antigen-specific T cells when comparing data across laboratories, and also over time in individual labs. Therefore, strategies to provide automated analysis of major histocompatibility complex (MHC) multimer-binding T cells represent an attractive solution to decrease subjectivity and technical variation. The challenge of using an automated analysis approach is that MHC multimer-binding T cell populations are often rare and therefore difficult to detect. We used a highly heterogeneous dataset from a recent MHC multimer proficiency panel to assess if MHC multimer-binding CD8+ T cells could be analyzed with computational solutions currently available, and if such analyses would reduce the technical variation across different laboratories. We used three different methods, FLOw Clustering without K (FLOCK), Scalable Weighted Iterative Flow-clustering Technique (SWIFT), and ReFlow to analyze flow cytometry data files from 28 laboratories. Each laboratory screened for antigen-responsive T cell populations with frequency ranging from 0.01 to 1.5% of lymphocytes within samples from two donors. Experience from this analysis shows that all three programs can be used for the identification of high to intermediate frequency of MHC multimer-binding T cell populations, with results very similar to that of manual gating. For the less frequent populations (