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Background: The outbreak of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) resulted in the global COVID-19 pandemic. The urgency for an effective SARS-CoV-2 vaccine has led to the development of the first series of vaccines at unprecedented speed. The discovery of SARS-CoV-2 spike-glycoprotein mutants, however, and consequentially the potential to escape vaccine-induced protection and increased infectivity, demonstrates the persisting importance of monitoring SARS-CoV-2 mutations to enable early detection and tracking of genomic variants of concern. Results: We developed the CoVigator tool with three components: (1) a knowledge base that collects new SARS-CoV-2 genomic data, processes it and stores its results; (2) a comprehensive variant calling pipeline; (3) an interactive dashboard highlighting the most relevant findings. The knowledge base routinely downloads and processes virus genome assemblies or raw sequencing data from the COVID-19 Data Portal (C19DP) and the European Nucleotide Archive (ENA), respectively. The results of variant calling are visualized through the dashboard in the form of tables and customizable graphs, making it a versatile tool for tracking SARS-CoV-2 variants. We put a special emphasis on the identification of intrahost mutations and make available to the community what is, to the best of our knowledge, the largest dataset on SARS-CoV-2 intrahost mutations. In the spirit of open data, all CoVigator results are available for download. The CoVigator dashboard is accessible via covigator.tron-mainz.de. Conclusions: With increasing demand worldwide in genome surveillance for tracking the spread of SARS-CoV-2, CoVigator will be a valuable resource of an up-to-date list of mutations, which can be incorporated into global efforts.

Designed mutations increase the stimulatory strength of D-neopeptides above N-peptides. (A) TILs were stimulated with 5 μM class I-peptides, including N-peptides and D-neopeptides, for 5 days. 5 replicate wells were tested for proliferation, and proliferation was measured by 3H-thymidine incorporation assay. In each group, the N-peptides and their corresponding D-neopeptides are included, and the designed mutated positions are highlighted in the red box. The blue dotted line indicates the mean value of the no peptide control. The red dotted line indicates the mean value plus three standard deviations of the no peptide control. Values above the red dotted line were considered positive. (#) indicates the peptide that was used in the 1st to 4th vaccinations. (B) TILs were stimulated with 5 μM class II-peptides, including N-peptides and D-neopeptides, for 5 days. 5 replicate wells were tested for proliferation, and proliferation was measured by 3H-thymidine incorporation assay.

Predicted TSA/TAA-derived N-peptides and D-neopeptides

Purpose:The low mutational load of some cancers is considered one reason for the difficulty to develop effective tumor vaccines. To overcome this problem, we developed a strategy to design neopeptides through single amino acid mutations to enhance their immunogenicity.Experimental Design:Exome and RNA sequencing as well as in silico HLA-binding predictions to autologous HLA molecules were used to identify candidate neopeptides. Subsequently, in silico HLA-anchor placements were used to deduce putative T-cell receptor (TCR) contacts of peptides. Single amino acids of TCR contacting residues were then mutated by amino acid replacements. Overall, 175 peptides were synthesized and sets of 25 each containing both peptides designed to bind to HLA class I and II molecules applied in the vaccination. Upon development of a tumor recurrence, the tumor-infiltrating lymphocytes (TIL) were characterized in detail both at the bulk and clonal level.Results:The immune response of peripheral blood T cells to vaccine peptides, including natural peptides and designed neopeptides, gradually increased with repetitive vaccination, but remained low. In contrast, at the time of tumor recurrence, CD8+ TILs and CD4+ TILs responded to 45% and 100%, respectively, of the vaccine peptides. Furthermore, TIL-derived CD4+ T-cell clones showed strong responses and tumor cell lysis not only against the designed neopeptide but also against the unmutated natural peptides of the tumor.Conclusions:Turning tumor self-peptides into foreign antigens by introduction of designed mutations is a promising strategy to induce strong intratumoral CD4+ T-cell responses in a cold tumor like glioblastoma.

Supplementary Table 1. Somatic mutations of the patient's glioblastoma; Supplementary Table 2. See Excel File; Supplementary Table 3. Literature-derived, glioblastoma-associated TAAs; Supplementary Table 4. See Excel File; Supplementary Table 5. Over-/highly expressed genes that were chosen for the design of vaccine peptides; Supplementary Table 6. Peptide cocktails for vaccination; Supplementary Table 7. Antibodies for imaging mass cytometry.

Tab. S1: Genes covered in the applied sequencing Panel; Tab. S2: List of vaccine peptides

Melanoma often recurs after a latency period of several years, presenting a T cell–edited phenotype that reflects a role for CD8+ T cells in maintaining metastatic latency. Here, we report an investigation of a patient with multiple recurrent lesions, where poorly immunogenic melanoma phenotypes were found to evolve in the presence of autologous tumor antigen–specific CD8+ T cells. Melanoma cells from two of three late recurrent metastases, developing within a 6-year latency period, lacked HLA class I expression. CD8+ T cell–resistant, HLA class I–negative tumor cells became clinically apparent 1.5 and 6 years into stage IV disease. Genome profiling by SNP arrays revealed that HLA class I loss in both metastases originated from a shared chromosome 15q alteration and independently acquired focal B2M gene deletions. A third HLA class I haplotype-deficient lesion developed in year 3 of stage IV disease that acquired resistance toward dominant CD8+ T-cell clonotypes targeting stage III tumor cells. At an early stage, melanoma cells showed a dedifferentiated c-Junhigh/MITFlow phenotype, possibly associated with immunosuppression, which contrasted with a c-Junlow/MITFhigh phenotype of T cell–edited tumor cells derived from late metastases. In summary, our work shows how tumor recurrences after long-term latency evolve toward T-cell resistance by independent genetic events, as a means for immune escape and immunotherapeutic resistance. Cancer Res; 76(15); 4347–58. ©2016 AACR.

Additional information about experimental techniques and materials relevant to this study

Legends

Supplementary Figures S1-S6. Supplementary Fig. S1, localization of T cells in the periphery of metastasis Ma-Mel-86f. Supplementary Fig. S2, re-expression of HLA class I on B2M-transfected melanoma cells. Supplementary Fig. S3, no enrichment of CD4+ T cells and NK cells in metastasis Ma-Mel-86f. Supplementary Fig. S4, similar PD-L1 expression on Ma-Mel-86a and Ma-Mel-86c cells. Supplementary Fig. S5, autologous CD8+ T cells kill Ma-Mel-86c cells. Supplementary Fig. S6, autologous CD8+ T cells kill Ma-Mel-86a cells.

Cancer associated gene fusions (GF) are a potential source for highly immunogenic neo-antigens, but the lack of computational tools for accurate, sensitive identification of personal GFs has limited their targeting in personalized cancer immunotherapy. Here, we present EasyFuse, a machine learning computational pipeline for detecting cancer-specific GFs in transcriptome data obtained from human cancer samples. We provide an extensive experimental confirmation dataset and demonstrate that EasyFuse predicts personal GFs with high precision and sensitivity, outperforming previously described tools. By testing immunogenicity with autologous blood lymphocytes from patients with cancer, we detected pre-established CD4(+) and CD8(+) T-cell responses for 10 of 21 (48%), and for 1 of 30 (3%) of identified GFs, respectively. The high frequency of T-cell responses detected in cancer patients supports the relevance of individual GFs as neo-antigens that may be targeted in personalized immunotherapies, especially for tumors with low mutation burdens.

Purpose: The low mutational load of some cancers is considered one reason for the difficulty to develop effective tumor vaccines. To overcome this problem, we developed a strategy to design neopeptides through single amino acid mutations to enhance their immunogenicity. Experimental Design: Exome and RNA sequencing as well as in silico HLA-binding predictions to autologous HLA molecules were used to identify candidate neopeptides. Subsequently, in silico HLA-anchor placements were used to deduce putative T-cell receptor (TCR) contacts of peptides. Single amino acids of TCR contacting residues were then mutated by amino acid replacements. Overall, 175 peptides were synthesized and sets of 25 each containing both peptides designed to bind to HLA class I and II molecules applied in the vaccination. Upon development of a tumor recurrence, the tumor-infiltrating lymphocytes (TIL) were characterized in detail both at the bulk and clonal level. Results: The immune response of peripheral blood T cells to vaccine peptides, including natural peptides and designed neopeptides, gradually increased with repetitive vaccination, but remained low. In contrast, at the time of tumor recurrence, CD8+ TILs and CD4+ TILs responded to 45% and 100%, respectively, of the vaccine peptides. Furthermore, TIL-derived CD4+ T-cell clones showed strong responses and tumor cell lysis not only against the designed neopeptide but also against the unmutated natural peptides of the tumor. Conclusions: Turning tumor self-peptides into foreign antigens by introduction of designed mutations is a promising strategy to induce strong intratumoral CD4+ T-cell responses in a cold tumor like glioblastoma., Clinical Cancer Research, 28 (24), ISSN:1078-0432, ISSN:1557-3265

IntroductionThe B.1.1.529 (Omicron) SARS-CoV-2 variant has raised global concerns due to its high number of mutations and its rapid spread. It is of major importance to understand the impact of this variant on the acquired and induced immunity. Several preliminary studies have reported the impact of antibody binding and to this date, there are few studies on Omicron’s CD8+ T-cell immune escape.MethodsWe first assessed the impact of Omicron and B.1.617.2 (Delta) variant mutations on the SARS-CoV-2 spike epitopes submitted to the Immune Epitope Database (IEDB) with positive out-come on MHC ligand or T-cell assays (n=411). From those epitopes modified by a mutation, we found the corresponding homologous epitopes in Omicron and Delta. We then ran the netMHCpan computational MHC binding prediction on the pairs of IEDB epitopes and matching homologous epitopes over top 5 MHC I alleles on some selected populations. Lastly, we applied a Fisher test to find mutations enriched for homologous epitopes with decreased predicted binding affinity.ResultsWe found 31 and 78 IEDB epitopes modified by Delta and Omicron mutations, respectively. The IEDB spike protein epitopes redundantly cover the protein sequence. The WT pMHC with a strong predicted binding tend to have homologous mutated pMHC with decreased binding. A similar trend is observed in Delta over all HLA genes, while in Omicron only for HLA-B and HLA-C. Finally, we obtained one and seven mutations enriched for homologous mutated pMHC with decreased MHC binding affinity in Delta and Omicron, respectively. Three of the Omicron mutations, VYY143-145del, K417N and Y505H, are replacing an aromatic or large amino acid, which are reported to be enriched in immunogenic epitopes. K417N is common with Beta variants, while Y505H and VYY143-145del are novel Omicron mutations.ConclusionIn summary, pMHC with Delta and Omicron mutations show decreased MHC binding affinity, which results in a trend specific to SARS-CoV-2 variants. Such epitopes may decrease overall presentation on different HLA alleles suggesting evasion from CD8+ T-cell responses in specific HLA alleles. However, our results show B.1.1.529 (Omicron) will not totally evade the immune system through a CD8+ immune escape mechanism. Yet, we identified mutations in B.1.1.529 (Omicron) introducing amino acids associated with increased immunogenicity.AvailabilityAll the code and results from this study are available at https://github.com/TRON-bioinformatics/omicron-analysis.

Synchronous primary malignancies occur in a small proportion of head and neck squamous cell carcinoma (HNSCC) patients. Here, we analysed three synchronous primaries and a recurrence from one patient by comparing the genomic and transcriptomic profiles among the tumour samples and determining the recurrence origin. We found remarkable levels of heterogeneity among the primary tumours, and through the patterns of shared mutations, we traced the origin of the recurrence. Interestingly, the patient carried germline variants that might have predisposed him to carcinogenesis, together with a history of alcohol and tobacco consumption. The mutational signature analysis confirmed the impact of alcohol exposure, with Signature 16 present in all tumour samples. Characterisation of immune cell infiltration highlighted an immunosuppressive environment in all samples, which exceeded the potential activity of T cells. Studies such as the one described here have important clinical value and contribute to personalised treatment decisions for patients with synchronous primaries and matched recurrences.

Many approaches to identify therapeutically relevant neoantigens couple tumor sequencing with bioinformatic algorithms and inferred rules of tumor epitope immunogenicity. However, there are no reference data to compare these approaches, and the parameters governing tumor epitope immunogenicity remain unclear. Here, we assembled a global consortium wherein each participant predicted immunogenic epitopes from shared tumor sequencing data. 608 epitopes were subsequently assessed for T cell binding in patient-matched samples. By integrating peptide features associated with presentation and recognition, we developed a model of tumor epitope immunogenicity that filtered out 98% of non-immunogenic peptides with a precision above 0.70. Pipelines prioritizing model features had superior performance, and pipeline alterations leveraging them improved prediction performance. These findings were validated in an independent cohort of 310 epitopes prioritized from tumor sequencing data and assessed for T cell binding. This data resource enables identification of parameters underlying effective anti-tumor immunity and is available to the research community.

Our immune system plays a key role in health and disease as it is capable of responding to foreign antigens as well as acquired antigens from cancer cells. Latter are caused by somatic mutations, the so-called neoepitopes, and might be recognized by T cells if they are presented by HLA molecules on the surface of cancer cells. Personalized mutanome vaccines are a class of customized immunotherapies, which is dependent on the detection of individual cancer-specific tumor mutations and neoepitope (i.e., prediction, followed by a rational vaccine design, before on-demand production. The development of next generation sequencing (NGS) technologies and bioinformatic tools allows a large-scale analysis of each parameter involved in this process. Here, we provide an overview of the bioinformatic aspects involved in the design of personalized, neoantigen-based vaccines, including the detection of mutations and the subsequent prediction of potential epitopes, as well as methods for associated biomarker research, such as high-throughput sequencing of T-cell receptors (TCRs), followed by data analysis and the bioinformatics quantification of immune cell infiltration in cancer samples.

Antigen-encoding, lipoplex-formulated RNA (RNA-LPX) enables systemic delivery to lymphoid compartments and selective expression in resident antigen-presenting cells. We report here that the rejection of CT26 tumors, mediated by local radiotherapy (LRT), is further augmented in a CD8+ T cell-dependent manner by an RNA-LPX vaccine that encodes CD4+ T cell-recognized neoantigens (CD4 neoantigen vaccine). Whereas CD8+ T cells induced by LRT alone were primarily directed against the immunodominant gp70 antigen, mice treated with LRT plus the CD4 neoantigen vaccine rejected gp70-negative tumors and were protected from rechallenge with these tumors, indicating a potent poly-antigenic CD8+ T cell response and T cell memory. In the spleens of CD4 neoantigen-vaccinated mice, we found a high number of activated, poly-functional, Th1-like CD4+ T cells against ME1, the immunodominant CD4 neoantigen within the poly-neoantigen vaccine. LRT itself strongly increased CD8+ T cell numbers and clonal expansion. However, tumor infiltrates of mice treated with CD4 neoantigen vaccine/LRT, as compared to LRT alone, displayed a higher fraction of activated gp70-specific CD8+ T cells, lower PD-1/LAG-3 expression and contained ME1-specific IFNγ+ CD4+ T cells capable of providing cognate help. CD4 neoantigen vaccine/LRT treatment followed by anti-CTLA-4 antibody therapy further enhanced the efficacy with complete remission of gp70-negative CT26 tumors and survival of all mice. Our data highlight the power of combining synergistic modes of action and warrants further exploration of the presented treatment schema.

Our immune system plays a key role in health and disease as it is capable of responding to foreign antigens as well as acquired antigens from cancer cells. Latter are caused by somatic mutations, the so-called neoepitopes, and might be recognized by T cells if they are presented by HLA molecules on the surface of cancer cells. Personalized mutanome vaccines are a class of customized immunotherapies, which is dependent on the detection of individual cancer-specific tumor mutations and neoepitope (i.e., prediction, followed by a rational vaccine design, before on-demand production. The development of next generation sequencing (NGS) technologies and bioinformatic tools allows a large-scale analysis of each parameter involved in this process. Here, we provide an overview of the bioinformatic aspects involved in the design of personalized, neoantigen-based vaccines, including the detection of mutations and the subsequent prediction of potential epitopes, as well as methods for associated biomarker research, such as high-throughput sequencing of T-cell receptors (TCRs), followed by data analysis and the bioinformatics quantification of immune cell infiltration in cancer samples.

Bringing truly personalized cancer vaccination with tumour neoantigens to the clinic will require overcoming the challenges of optimized vaccine design, manufacturing and affordability, and identification of the most suitable clinical setting.

Identification of biomarkers is pivotal towards the proper development of cancer immunotherapies, in particular for designing the most effective regimens and identifying patient subgroups most likely to benefit from a given treatment. T-cell receptor (TCR) repertoire emerged as a promising biomarker in this respect, as it allows measurement of the dynamics of the T cell response both in time and in different compartments. In this work, we developed and evaluated an NGS-based mouse TCR profiling method (TCR alpha and TCR beta) for discovery and validation of biomarkers related to the antitumoral T-cell response in different compartments upon systemic administration of an RNA-based cancer vaccine (RNA-lipoplex). The in-house established mouse TCR profiling protocol was able to detect blood-spiked known-TCR sequences down to a frequency of 0,001% spiked cells for both TCR alpha and TCR beta chains and the relative frequencies of detected TCRs in the tumor correlated well with the degree of T-cell infiltration into the same tumors assessed by flow cytometry. Comparative analysis of changes in the TCR repertoire between blood and tumor upon repetitive vaccination with RNA-lipoplexes encoding immunodominant epitope of chicken ovalbumin (OVA257-264) revealed that richness as well as diversity of endogenous TCRs were generally lower in B16-OVA tumors compared to blood and decreased with further vaccination. Interestingly, top clones overlapped increasingly in blood and tumor until after the second vaccination, but drifted apart after the third vaccination, with the TCR repertoire in the tumor narrowing down. This overlap was less evident when mice were inoculated with B16-F10 tumors. Top clones within the same compartment were found to be highly variable between individual animals, complicating comparative analyses. Monitoring intra-individual TCR repertoire changes in the blood of a given mouse over time, however, indicated a tendency for decreasing richness and diversity following a single RNA vaccination, which may be pointing at repertoire adaptation and antigen focus. Supporting this notion, tumors isolated at late time-points exhibited a lower diversity than those investigated at earlier time-points and at baseline. In contrast to RNA vaccination, richness and diversity were reduced in response to treatment with an agonistic anti-CD40 antibody in blood and spleen but were similar among tumors. Consequently, richness and diversity in the periphery may not necessarily be predictive of corresponding repertoire changes in the tumor microenvironment. Citation Format: Lena Mareen Kranz, Barbara Schrörs, Thomas Bukur, Christian Albrecht, Lorieta Leppin, Martin Suchan, Marta Faryna, Eileen Powalsky, Rainer Hipfel, Özlem Türeci, Ugur Sahin, Mustafa Diken. Evaluation of systemic RNA-based cancer vaccine induced T-cell responses via mouse T-cell receptor (TCR) profiling [abstract]. In: Proceedings of the AACR Special Conference on Tumor Immunology and Immunotherapy; 2018 Nov 27-30; Miami Beach, FL. Philadelphia (PA): AACR; Cancer Immunol Res 2020;8(4 Suppl):Abstract nr B62.

T cells directed against mutant neo-epitopes drive cancer immunity. However, spontaneous immune recognition of mutations is inefficient. We recently introduced the concept of individualized mutanome vaccines and implemented an RNA-based poly-neo-epitope approach to mobilize immunity against a spectrum of cancer mutations. Here we report the first-in-human application of this concept in melanoma. We set up a process comprising comprehensive identification of individual mutations, computational prediction of neo-epitopes, and design and manufacturing of a vaccine unique for each patient. All patients developed T cell responses against multiple vaccine neo-epitopes at up to high single-digit percentages. Vaccine-induced T cell infiltration and neo-epitope-specific killing of autologous tumour cells were shown in post-vaccination resected metastases from two patients. The cumulative rate of metastatic events was highly significantly reduced after the start of vaccination, resulting in a sustained progression-free survival. Two of the five patients with metastatic disease experienced vaccine-related objective responses. One of these patients had a late relapse owing to outgrowth of β2-microglobulin-deficient melanoma cells as an acquired resistance mechanism. A third patient developed a complete response to vaccination in combination with PD-1 blockade therapy. Our study demonstrates that individual mutations can be exploited, thereby opening a path to personalized immunotherapy for patients with cancer.

Melanoma often recurs after a latency period of several years, presenting a T cell–edited phenotype that reflects a role for CD8+ T cells in maintaining metastatic latency. Here, we report an investigation of a patient with multiple recurrent lesions, where poorly immunogenic melanoma phenotypes were found to evolve in the presence of autologous tumor antigen–specific CD8+ T cells. Melanoma cells from two of three late recurrent metastases, developing within a 6-year latency period, lacked HLA class I expression. CD8+ T cell–resistant, HLA class I–negative tumor cells became clinically apparent 1.5 and 6 years into stage IV disease. Genome profiling by SNP arrays revealed that HLA class I loss in both metastases originated from a shared chromosome 15q alteration and independently acquired focal B2M gene deletions. A third HLA class I haplotype-deficient lesion developed in year 3 of stage IV disease that acquired resistance toward dominant CD8+ T-cell clonotypes targeting stage III tumor cells. At an early stage, melanoma cells showed a dedifferentiated c-Junhigh/MITFlow phenotype, possibly associated with immunosuppression, which contrasted with a c-Junlow/MITFhigh phenotype of T cell–edited tumor cells derived from late metastases. In summary, our work shows how tumor recurrences after long-term latency evolve toward T-cell resistance by independent genetic events, as a means for immune escape and immunotherapeutic resistance. Cancer Res; 76(15); 4347–58. ©2016 AACR.

The profound but frequently transient clinical responses to BRAFV600 inhibitor (BRAFi) treatment in melanoma emphasize the need for combinatorial therapies. Multiple clinical trials combining BRAFi and immunotherapy are under way to further enhance therapeutic responses. However, to which extent BRAFV600 inhibition may affect melanoma immunogenicity over time remains largely unknown. To support the development of an optimal treatment protocol, we studied the impact of prolonged BRAFi exposure on the recognition of melanoma cells by T cells in different patient models. We demonstrate that autologous CD8+ tumor-infiltrating lymphocytes (TILs) efficiently recognized short-term (3, 7 days) BRAFi-treated melanoma cells but were less responsive towards long-term (14, 21 days) exposed tumor cells. Those long-term BRAFi-treated melanoma cells showed a non-proliferative dedifferentiated phenotype and were less sensitive to four out of five CD8+ T cell clones, present in the preexisting TIL repertoire, of which three recognized shared antigens (Tyrosinase, Melan-A and CSPG4) and one being neoantigen-specific. Only a second neoantigen was steadily recognized independent of treatment duration. Notably, in all cases the impaired T cell activation was due to a time-dependent downregulation of their respective target antigens. Moreover, combinatorial treatment of melanoma cells with BRAFi and an inhibitor of its downstream kinase MEK had similar effects on T cell recognition. In summary, MAP kinase inhibitors (MAPKi) strongly alter the tumor antigen expression profile over time, favoring evolution of melanoma variants cross-resistant to both T cells and MAPKi. Our data suggest that simultaneous treatment with MAPKi and immunotherapy could be most effective for tumor elimination.

Tumour-specific mutations are ideal targets for cancer immunotherapy as they lack expression in healthy tissues and can potentially be recognized as neo-antigens by the mature T-cell repertoire. Their systematic targeting by vaccine approaches, however, has been hampered by the fact that every patient's tumour possesses a unique set of mutations ('the mutanome') that must first be identified. Recently, we proposed a personalized immunotherapy approach to target the full spectrum of a patient's individual tumour-specific mutations. Here we show in three independent murine tumour models that a considerable fraction of non-synonymous cancer mutations is immunogenic and that, unexpectedly, the majority of the immunogenic mutanome is recognized by CD4(+) T cells. Vaccination with such CD4(+) immunogenic mutations confers strong antitumour activity. Encouraged by these findings, we established a process by which mutations identified by exome sequencing could be selected as vaccine targets solely through bioinformatic prioritization on the basis of their expression levels and major histocompatibility complex (MHC) class II-binding capacity for rapid production as synthetic poly-neo-epitope messenger RNA vaccines. We show that vaccination with such polytope mRNA vaccines induces potent tumour control and complete rejection of established aggressively growing tumours in mice. Moreover, we demonstrate that CD4(+) T cell neo-epitope vaccination reshapes the tumour microenvironment and induces cytotoxic T lymphocyte responses against an independent immunodominant antigen in mice, indicating orchestration of antigen spread. Finally, we demonstrate an abundance of mutations predicted to bind to MHC class II in human cancers as well by employing the same predictive algorithm on corresponding human cancer types. Thus, the tailored immunotherapy approach introduced here may be regarded as a universally applicable blueprint for comprehensive exploitation of the substantial neo-epitope target repertoire of cancers, enabling the effective targeting of every patient's tumour with vaccines produced 'just in time'.

The profound but frequently transient clinical responses to BRAF(V600) inhibitor (BRAFi) treatment in melanoma emphasize the need for combinatorial therapies. Multiple clinical trials combining BRAFi and immunotherapy are under way to further enhance therapeutic responses. However, to which extent BRAF(V600) inhibition may affect melanoma immunogenicity over time remains largely unknown. To support the development of an optimal treatment protocol, we studied the impact of prolonged BRAFi exposure on the recognition of melanoma cells by T cells in different patient models. We demonstrate that autologous CD8(+) tumor-infiltrating lymphocytes (TILs) efficiently recognized short-term (3, 7 days) BRAFi-treated melanoma cells but were less responsive towards long-term (14, 21 days) exposed tumor cells. Those long-term BRAFi-treated melanoma cells showed a non-proliferative dedifferentiated phenotype and were less sensitive to four out of five CD8(+) T cell clones, present in the preexisting TIL repertoire, of which three recognized shared antigens (Tyrosinase, Melan-A and CSPG4) and one being neoantigen-specific. Only a second neoantigen was steadily recognized independent of treatment duration. Notably, in all cases the impaired T cell activation was due to a time-dependent downregulation of their respective target antigens. Moreover, combinatorial treatment of melanoma cells with BRAFi and an inhibitor of its downstream kinase MEK had similar effects on T cell recognition. In summary, MAP kinase inhibitors (MAPKi) strongly alter the tumor antigen expression profile over time, favoring evolution of melanoma variants cross-resistant to both T cells and MAPKi. Our data suggest that simultaneous treatment with MAPKi and immunotherapy could be most effective for tumor elimination.

// Barbara Schrors 1 , Silke Lubcke 1 , Volker Lennerz 1 , Martina Fatho 1 , Anne Bicker 2 , Catherine Wolfel 1 , Patrick Derigs 1 , Thomas Hankeln 2 , Dirk Schadendorf 3 , Annette Paschen 3 , Thomas Wolfel 1 1 Internal Medicine III, University Cancer Center (UCT) and Research Center for Immunotherapy (FZI), University Medical Center (UMC) of the Johannes Gutenberg University and German Cancer Consortium (DKTK), Partner Site Frankfurt/Mainz, Mainz, Germany 2 Institute for Molecular Genetics, Johannes Gutenberg University, Mainz, Germany 3 Department of Dermatology, University Hospital, University Duisburg-Essen and German Cancer Consortium (DKTK), Partner Site Essen/Dusseldorf, Essen, Germany Correspondence to: Thomas Wolfel, email: thomas.woelfel@unimedizin-mainz.de Keywords: melanoma, mutated neoantigens, high-throughput sequencing, immune escape, HLA class I loss Received: July 05, 2016 Accepted: February 27, 2017 Published: March 09, 2017 ABSTRACT 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, HERPUD1 G161S , INSIG1 S238F , MMS22L S437F and PRDM10 S1050F , 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.

Advances in nucleic acid sequencing technologies have revolutionized the field of genomics, allowing the efficient targeting of mutated neoantigens for personalized cancer vaccination. Due to their absence during negative selection of T cells and their lack of expression in healthy tissue, tumor mutations are considered as optimal targets for cancer immunotherapy. Preclinical and early clinical data suggest that synthetic mRNA can serve as potent drug format allowing the cost efficient production of highly efficient vaccines in a timely manner. In this review, we describe a process, which integrates next generation sequencing based cancer mutanome mapping,in silicotarget selection and prioritization approaches, and mRNA vaccine manufacturing and delivery into a process we refer to as MERIT (mutanome engineered RNA immunotherapy).

Summary The detection and prediction of true neoantigens is of great importance for the field of cancer immunotherapy. Wesearched the literature for proposed neoantigen features and integrated them into a toolbox called NEOantigen Feature toolbOX (NeoFox). NeoFox is an easy-to-use Python package that enables the annotation of neoantigen candidates with 16 neoantigen features. Availability and implementation NeoFox is freely available as an open source Python package released under the GNU General Public License (GPL) v3 license at https://github.com/TRON-Bioinformatics/neofox. Supplementary information Supplementary data are available at Bioinformatics online.