Cancer Neoantigens Research Articles

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.

Recurrent Neoantigens in Colorectal Cancer as Potential Immunotherapy Targets.

This study was aimed at investigating the mutations in colorectal cancer (CRC) for recurrent neoantigen identification. A total of 1779 samples with whole exome sequencing (WES) data were obtained from 7 published CRC cohorts. Common HLA genotypes were used to predict the probability of neoantigens at high-frequency mutants in the dataset. Based on the WES data, we not only obtained the most comprehensive CRC mutation landscape so far but also found 1550 mutations which could be identified in at least 5 patients, including KRAS G12D (8%), KRAS G12V (5.8%), PIK3CA E545K (3.5%), PIK3CA H1047R (2.5%), and BMPR2 N583Tfs∗44 (2.8%). These mutations can also be recognized by multiple common HLA molecules in Chinese and TCGA cohort as potential “public” neoantigens. Many of these mutations also have high mutation rates in metastatic pan-cancers, suggesting their value as therapeutic targets in different cancer types. Overall, our analysis provides recurrent neoantigens as potential cancer immunotherapy targets.

HLA-Arena: A Customizable Environment for the Structural Modeling and Analysis of Peptide-HLA Complexes for Cancer Immunotherapy.

PURPOSEHLA protein receptors play a key role in cellular immunity. They bind intracellular peptides and display them for recognition by T-cell lymphocytes. Because T-cell activation is partially driven by structural features of these peptide-HLA complexes, their structural modeling and analysis are becoming central components of cancer immunotherapy projects. Unfortunately, this kind of analysis is limited by the small number of experimentally determined structures of peptide-HLA complexes. Overcoming this limitation requires developing novel computational methods to model and analyze peptide-HLA structures.METHODSHere we describe a new platform for the structural modeling and analysis of peptide-HLA complexes, called HLA-Arena, which we have implemented using Jupyter Notebook and Docker. It is a customizable environment that facilitates the use of computational tools, such as APE-Gen and DINC, which we have previously applied to peptide-HLA complexes. By integrating other commonly used tools, such as MODELLER and MHCflurry, this environment includes support for diverse tasks in structural modeling, analysis, and visualization.RESULTSTo illustrate the capabilities of HLA-Arena, we describe 3 example workflows applied to peptide-HLA complexes. Leveraging the strengths of our tools, DINC and APE-Gen, the first 2 workflows show how to perform geometry prediction for peptide-HLA complexes and structure-based binding prediction, respectively. The third workflow presents an example of large-scale virtual screening of peptides for multiple HLA alleles.CONCLUSIONThese workflows illustrate the potential benefits of HLA-Arena for the structural modeling and analysis of peptide-HLA complexes. Because HLA-Arena can easily be integrated within larger computational pipelines, we expect its potential impact to vastly increase. For instance, it could be used to conduct structural analyses for personalized cancer immunotherapy, neoantigen discovery, or vaccine development.

Adaptive Nanoparticle Platforms for High Throughput Expansion and Detection of Antigen-Specific T cells.

T cells are critical players in disease; yet, their antigen-specificity has been difficult to identify, as current techniques are limited in terms of sensitivity, throughput, or ease of use. To address these challenges, we increased the throughput and translatability of magnetic nanoparticle-based artificial antigen presenting cells (aAPCs) to enrich and expand (E+E) murine or human antigen-specific T cells. We streamlined enrichment, expansion, and aAPC production processes by enriching CD8+ T cells directly from unpurified immune cells, increasing parallel processing capacity of aAPCs in a 96-well plate format, and designing an adaptive aAPC that enables multiplexed aAPC construction for E+E and detection. We applied these adaptive platforms to process and detect CD8+ T cells specific for rare cancer neoantigens, commensal bacterial cross-reactive epitopes, and human viral and melanoma antigens. These innovations dramatically increase the multiplexing ability and decrease the barrier to adopt for investigating antigen-specific T cell responses.

Structural basis for oligoclonal T cell recognition of a shared p53 cancer neoantigen

Adoptive cell therapy (ACT) with tumor-specific T cells can mediate cancer regression. The main target of tumor-specific T cells are neoantigens arising from mutations in self-proteins. Although the majority of cancer neoantigens are unique to each patient, and therefore not broadly useful for ACT, some are shared. We studied oligoclonal T-cell receptors (TCRs) that recognize a shared neoepitope arising from a driver mutation in the p53 oncogene (p53R175H) presented by HLA-A2. Here we report structures of wild-type and mutant p53–HLA-A2 ligands, as well as structures of three tumor-specific TCRs bound to p53R175H–HLA-A2. These structures reveal how a driver mutation in p53 rendered a self-peptide visible to T cells. The TCRs employ structurally distinct strategies that are highly focused on the mutation to discriminate between mutant and wild-type p53. The TCR–p53R175H–HLA-A2 complexes provide a framework for designing TCRs to improve potency for ACT without sacrificing specificity.

Benchmarking predictions of MHC class I restricted T cell epitopes in a comprehensively studied model system.

T cell epitope candidates are commonly identified using computational prediction tools in order to enable applications such as vaccine design, cancer neoantigen identification, development of diagnostics and removal of unwanted immune responses against protein therapeutics. Most T cell epitope prediction tools are based on machine learning algorithms trained on MHC binding or naturally processed MHC ligand elution data. The ability of currently available tools to predict T cell epitopes has not been comprehensively evaluated. In this study, we used a recently published dataset that systematically defined T cell epitopes recognized in vaccinia virus (VACV) infected C57BL/6 mice (expressing H-2Db and H-2Kb), considering both peptides predicted to bind MHC or experimentally eluted from infected cells, making this the most comprehensive dataset of T cell epitopes mapped in a complex pathogen. We evaluated the performance of all currently publicly available computational T cell epitope prediction tools to identify these major epitopes from all peptides encoded in the VACV proteome. We found that all methods were able to improve epitope identification above random, with the best performance achieved by neural network-based predictions trained on both MHC binding and MHC ligand elution data (NetMHCPan-4.0 and MHCFlurry). Impressively, these methods were able to capture more than half of the major epitopes in the top N = 277 predictions within the N = 767,788 predictions made for distinct peptides of relevant lengths that can theoretically be encoded in the VACV proteome. These performance metrics provide guidance for immunologists as to which prediction methods to use, and what success rates are possible for epitope predictions when considering a highly controlled system of administered immunizations to inbred mice. In addition, this benchmark was implemented in an open and easy to reproduce format, providing developers with a framework for future comparisons against new tools.

Radioiodine (RAI) in combination with durvalumab for recurrent/metastatic thyroid cancers.

6587 Background: Immune checkpoint blockade (ICB) has limited efficacy for radioiodine-refractory thyroid cancer. The high incidence of autoimmune thyroid disease and ICB-induced hypothyroidism suggests that loss of T cell tolerance to thyroid protein epitopes is common and can be activated by ICB to induce immune responses. We hypothesize that RAI can enhance presentation of thyroid protein immunogens and putative neoantigens in thyroid cancers to amplify the effectiveness of ICB. We studied the safety and efficacy of RAI plus the anti-PD-L1 agent durvalumab (durva) in recurrent/metastatic (R/M) patients (pts). Methods: Pts. had at least one RAI-avid tumor on the most recent RAI scan or one tumor on FDG PET with an SUVmax < 10. RECIST measurable disease was required. Any number of prior therapies was allowed. Pts were treated with durva 1500 mg IV every 4 weeks with recombinant human TSH (rhTSH)-stimulated RAI (100 mCi) administered in Cycle 1. Treatment beyond progression was allowed. The primary objective was to assess safety. Durva related dose limiting toxicities (DLTs) were monitored for 6 weeks after the first dose. Since no durva DLTs were observed in the first 6 pts, per protocol rules the trial accrued 11 pts total. Secondary objectives were assessing best overall response (BOR) per RECIST and progression-free survival (PFS). Results: 11 pts (7 female) were enrolled. Eight had prior drug therapy. No DLTs or > Grade 3 durva related adverse events (AEs) were observed. The most common non-laboratory AEs (regardless of attribution) were cough (7), hypertension (7), pain (6), edema (5), and fatigue/nausea/diarrhea/arthralgia/dry skin/dyspnea/edema (4 each). As of 2/6/20, 2 had partial response, 7 stable disease, and 2 progression of disease as BOR. Six pts had tumor regression. Four pts received treatment for > 6 months. Six are still on treatment. Analyses of research biopsies (bxs) (8 had pre-treatment bxs, 6 had an additional on-treatment bx) will be presented. Conclusions: Durva plus RAI is safe and well tolerated. The preliminary efficacy signal in this small cohort is promising. Understanding how RAI plus PD-L1 targeting impacts the tumor immune microenvironment may guide how RAI should be evaluated in future ICB trials. Clinical trial information: NCT03215095 .

Evidence of ongoing immune surveillance to cancer-associated phosphopeptides in healthy individuals

Abstract Dysregulated protein phosphorylation plays a major role in cancer cell malignancy. We showed that phosphopeptides are presented by MHC-I molecules, representing a new class of cancer neoantigens. We found that healthy donors with no evident prior cancer had robust CD8+ T cell responses to a cohort of HLA-B7+ leukemia-associated phosphopeptides. Here we report that CD8+CD45RO+ T cells purified from blood of different healthy donors showed responses to 2–25% of HLA-A2+ and 0–23% of HLA-B7+ cancer-associated phosphopeptides, confirming that individuals with no evident cancer history have pre-existing phosphopeptide specific immune memory. Six of 59 HLA-A2+ phosphopeptides were “immunodominant,” with memory responses evident in the majority of analyzed donors. This suggests that these phosphopeptides result from processes common in cellular transformation or viral infection. In contrast, no immunodominant HLA-B7+ phosphopeptides were identified among the 168 analyzed, suggesting that mechanisms driving their expression in healthy donors are less common. Robust day 14 responses were observed against a small cohort of HLA-B7+ phosphopeptides in 2 donors. Responses to some of these phosphopeptides could be detected ex vivo and were evident over 3 years, suggesting they reflected chronic antigen exposures. In contrast, responses to 2 phosphopeptides evident ex vivo diminished or disappeared over 14 days in culture, suggesting a recent acute immune response composed of terminal effectors unable to persist long term. These results provide evidence of ongoing immune surveillance in healthy individuals to phosphopeptides known to be expressed on cancers.

HIgh efficiency protocol for lymphocytes gene modification by lentiviral vector within an automated closed system

Background & Aim Gene therapy with genetically modified lymphocytes has been progressively entering in the clinical settings mainly as clinical trials and recently also with approved CAR-T cells. This therapeutic approach holds great promises for cancer therapy but is also limited by the current manufacturing methods. Most protocols rely on transduction with viral vectors followed by cell expansion; this method requires lengthy process optimization on the vector and on the transduction conditions and is often limited by the cell number that needs to be achieved for a clinical dose. In addition, variability of the cell product from batch to batch and manufacturing costs need to be taken into account. Our group isolated TCRs targeting viral antigens and is in the process of isolating TCRs targeting cancer neoantigens; we intend to develop a process for transduction of T-cells that will guarantee us a high and robust level of exogenous TCRs expression coupled with a significant cell yield. TCR-T cells manufactured with our new method will be used for adoptive cell therapy trials in cancer patients and patients affected by chronic viral hepatitis. Methods, Results & Conclusion PBMC were obtained from healthy donor buffy coat, CD4+ and CD8+ cells were enriched, activated and expanded in the CliniMacs Prodigy (Miltenyi) system. The TCT process provided by Miltenyi has been modified to increase its overall efficiency with our model lentiviral vector (pSSLSIN.cppt.MSCV-GFP). Parameters like cells number, percentage of transduced cells and cell phenotype were used to identify the best protocol. CD3/CD4 and CD3/CD8 enrichment was on average 1.5 fold compared to the initial starting material. The optimal time for lentiviral transduction was identified at 24h after cell activation with TransAct (Miltenyi). After 7 days, the gene modification efficiency was evaluated by flow cytometry and was not less 60% with high viability (96%) of the transduced cells and the expected phenotypes (CD3/CD4/CD8). We subsequently focused on cell yield, and starting from 100 × 106 cells we established protocol that allowed us to generate 2,3 × 109 cells in 10 days and with more than 95% of viability. Based on this optimized protocol, we are now transducing T-cells with a vector encoding TCRs for hepatitis C virus antigens, specifically the non-structural 3 and 5 antigens (NS3/NS5). We here report an optimized GMP-compliant protocol to generate high number of TCR-modified T cells that can be used for adoptive cell immunotherapy.

Cancer neoantigen prioritization through sensitive and reliable proteogenomics analysis

Genomics-based neoantigen discovery can be enhanced by proteomic evidence, but there remains a lack of consensus on the performance of different quality control methods for variant peptide identification in proteogenomics. We propose to use the difference between accurately predicted and observed retention times for each peptide as a metric to evaluate different quality control methods. To this end, we develop AutoRT, a deep learning algorithm with high accuracy in retention time prediction. Analysis of three cancer data sets with a total of 287 tumor samples using different quality control strategies results in substantially different numbers of identified variant peptides and putative neoantigens. Our systematic evaluation, using the proposed retention time metric, provides insights and practical guidance on the selection of quality control strategies. We implement the recommended strategy in a computational workflow named NeoFlow to support proteogenomics-based neoantigen prioritization, enabling more sensitive discovery of putative neoantigens.

Cancer neoantigens and immunogenicity: mutation position matters

ABSTRACTCancer mutations can elicit protective immunity. Computational methods are critical for selecting these neoantigens for immunotherapy. While significant progress has been made in the field in predicting peptide presentation, our understanding of which mutated peptide is recognized as foreign by T cells remains limited. We used mouse vaccination studies to examine the features of immunogenic neoantigens and demonstrated that the mutation position is an important criterion for predicting neoantigens.

Abstract B67: Synthetic DNA multi-neoantigen vaccine drives predominately MHC class I CD8+ T cell-mediated effector immunity impacting tumor challenge

Abstract T-cell recognition of cancer neoantigens is important for effective immune checkpoint blockade therapy, and there is increasing interest in developing personalized tumor neoantigen vaccines. Previous studies utilizing RNA and synthetic long peptide neoantigen vaccines in preclinical and early-phase clinical studies have shown immune responses predominantly driven by MHC class II CD4+ T cells. Although it has been established that CD4+ T cells are able to recognize tumor neoantigens, the majority of naturally occurring tumor antigen-specific killer T cells identified in patients have been of CD8+ origin, indicating that additional immunization strategies aimed to stimulate neoantigen-specific CD8+ T cells may be useful. Here, we report the first preclinical study utilizing a synthetic DNA vaccine platform to target tumor neoantigens in mice. For this study, we identified neoepitopes by sequencing syngeneic mouse tumors, and designed optimized DNA plasmids encoding long strings of neoepitopes separated by highly efficient cleavage sites preserving epitope integrity. We tested the immunogenicity of 84 independent neoepitopes encoded into optimized DNA plasmids delivered by adaptive electroporation in mice in vivo. We observed that this approach generated robust T-cell immunity against a similar proportion of epitopes compared to other vaccine platforms (20/84, 24%). Strikingly, however, the synthetic neoantigen DNA vaccine platform generated a much larger proportion of CD8+ T-cell responses compared to the prior studies. Synthetic neoantigen DNA vaccines generated 75% CD8+ only or CD4/CD8+ T-cell responses, and 25% CD4+ only T-cell responses, showing a dramatic and important CD8+ T-cell bias. Inclusion of only high-affinity MHC class I (<500nM) epitopes selected for a larger proportion of immunogenic epitopes, and for 100% CD8+ or CD8+/CD4+ T-cell epitopes. These neoantigen vaccines were able to control tumor growth therapeutically in vivo in both lung and ovarian cancer models, and T cells expanded from immunized mice were able to kill tumor cells ex vivo. Because of the potential for rapid synthesis of vaccine constructs, the capacity to deliver a large number of neoepitopes simultaneously, and the potent CD8+ stimulatory capacity, this advanced DNA vaccine platform represents a likely important new approach for cancer immunotherapy. Citation Format: Elizabeth K. Duperret, Alfredo Perales-Puchalt, Regina Stoltz, G.H. Hiranjith, Nitin Mandloi, James Barlow, Amitabha Chaudhuri, Niranjan Y. Sardesai, David B. Weiner. Synthetic DNA multi-neoantigen vaccine drives predominately MHC class I CD8+ T cell-mediated effector immunity impacting tumor challenge [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 B67.

Abstract A95: Neoantigen sequence similarity to pathogens and commensals determines immune phenotype of cancer samples and patient survival

Abstract The immune system has to destroy pathogens and cancer, while tolerating commensal species and self-proteins. Neoantigens mediate the immune response against tumors. While tumors often carry many different neoantigens, the immune response is ineffective in a number of cases. Accordingly, only a small subset of neoantigens are immunogenic and are able to induce destructive responses. Why is it so? Numerous studies have shown that neoantigens similar to pathogens are more immunogenic. Recently, the role of the commensal flora in antitumor immunity has also come into research focus. We propose that molecular mimicry of cancer neoantigens to certain pathogenic as well as commensal species determines whether immune-mediated destruction or tolerance is formed. Consequently, the amino acid sequences of neoantigens in a given sample determine its immune phenotype and, thus, the survival of cancer patients. We analyzed cancer samples of The Cancer Genome Atlas. We identified neoantigens using established methods, and defined T cell-exposed motifs on neoantigen sequences. We determined the prevalence of these motifs in 1,303 commensal and 183 pathogen species. We report significant and relevant differences in the motif signature of cancer samples belonging to different immune subtypes: i) Inflammatory type samples carried numerous motifs of pathogens, while ii) TGF-beta dominant samples carried motifs of commensal bacteria and pathogens associated with immune suppression. At the same time, iii) neoantigens of immunologically quiet samples contained no or only a few motifs associated with pathogens or the microbiome. We clustered patients based on the motif signature of their cancer samples. Patients in different clusters showed significantly different survival. The effect of motif signature remained significant after controlling for patient demographics and missense mutation count in a Cox model. We conclude that neoantigen sequence signature determines the type of immune response against cancer cells and, thus, patient survival. These modified sequences are able to induce not only immune-mediated destruction, but also immune tolerance, which depends on its similarity to pathogen and commensal species. Citation Format: Gergo Mihaly Balogh, Balazs Koncz, Leo Asztalos, Lajos Kemeny, Balazs Papp, Csaba Pal, Mate Manczinger. Neoantigen sequence similarity to pathogens and commensals determines immune phenotype of cancer samples and patient survival [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 A95.

Neoantigens in Hematologic Malignancies.

T cell cancer neoantigens are created from peptides derived from cancer-specific aberrant proteins, such as mutated and fusion proteins, presented in complex with human leukocyte antigens on the cancer cell surface. Because expression of the aberrant target protein is exclusive to malignant cells, immunotherapy directed against neoantigens should avoid “on-target, off-tumor” toxicity. The efficacy of neoantigen vaccines in melanoma and glioblastoma and of adoptive transfer of neoantigen-specific T cells in epithelial tumors indicates that neoantigens are valid therapeutic targets. Improvements in sequencing technology and innovations in antigen discovery approaches have facilitated the identification of neoantigens. In comparison to many solid tumors, hematologic malignancies have few mutations and thus fewer potential neoantigens. Despite this, neoantigens have been identified in a wide variety of hematologic malignancies. These include mutated nucleophosmin1 and PML-RARA in acute myeloid leukemia, ETV6-RUNX1 fusions and other mutated proteins in acute lymphoblastic leukemia, BCR-ABL1 fusions in chronic myeloid leukemia, driver mutations in myeloproliferative neoplasms, immunoglobulins in lymphomas, and proteins derived from patient-specific mutations in chronic lymphoid leukemias. We will review advances in the field of neoantigen discovery, describe the spectrum of identified neoantigens in hematologic malignancies, and discuss the potential of these neoantigens for clinical translation.

Mutation position is an important determinant for predicting cancer neoantigens.

Tumor-specific mutations can generate neoantigens that drive CD8 T cell responses against cancer. Next-generation sequencing and computational methods have been successfully applied to identify mutations and predict neoantigens. However, only a small fraction of predicted neoantigens are immunogenic. Currently, predicted peptide binding affinity for MHC-I is often the major criterion for prioritizing neoantigens, although little progress has been made toward understanding the precise functional relationship between affinity and immunogenicity. We therefore systematically assessed the immunogenicity of peptides containing single amino acid mutations in mouse tumor models and divided them into two classes of immunogenic mutations. The first comprises mutations at a nonanchor residue, for which we find that the predicted absolute binding affinity is predictive of immunogenicity. The second involves mutations at an anchor residue; here, predicted relative affinity (compared with the WT counterpart) is a better predictor. Incorporating these features into an immunogenicity model significantly improves neoantigen ranking. Importantly, these properties of neoantigens are also predictive in human datasets, suggesting that they can be used to prioritize neoantigens for individualized neoantigen-specific immunotherapies.

Recent developments in neoantigen-based cancer vaccines.

Cynics point out that a cure for cancer has been "around the corner" for the last 50 years. Nevertheless, the recent convergence of deep DNA, RNA, and proteomic technologies with enhanced understanding of the nuances of the adaptive immune system has generated great optimism amongst researchers. The extraordinary heterogeneity of various cancers, once thought to be a major therapeutic hurdle, may now be bypassed via "personalized" vaccine treatments. Specifically, these treatments involve the identification of MHC-bound peptides that are unique to a patient's cancer (neoantigens), followed by immunization with peptides, RNA, or DNA that encodes these neoantigens via various delivery systems, thus amplifying the immune system's response to the particular cancer. Such approaches have shown dramatic results in animal studies. Not surprisingly, then, the field of neoantigen-based immunotherapy has advanced at a spectacular rate, necessitating that interested individuals stay apprised of recent developments. Following an introduction to the subject, we thus focus on aspects that are particularly fast-moving; the cellular sources of neoantigens, which are surprisingly diverse, the tools that are used for their identification, and the status of the numerous clinical trials that are now being conducted.

In Silico Prediction of Tumor Neoantigens with TIminer.

Tumor neoantigens are at the core of immunological tumor control and response to immunotherapy. In silico prediction of tumor neoantigens from next-generation sequencing (NGS) data is possible but requires the assembly of complex, multistep computational pipelines and extensive data preprocessing. Using public data from two cancer cell lines, here we show how TIminer, a framework to perform immunogenomics analyses, can be easily used to assemble and run customized pipelines to predict cancer neoantigens from multisample NGS data.

Abstract B04: Identification of T-cell receptors targeting mutant KRAS in pancreatic cancer

Abstract Pathologic somatic gene mutations within the KRAS proto-oncogene occur in greater than 90% of pancreatic ductal adenocarcinomas (PDA). These mutations are often conserved involving the codon 12 position and most frequently result in G12D, G12V, or G12R transitions. Unfortunately, no mutant KRAS (mKRAS)-targeted therapies exist to date. We hypothesize conserved hot-spot KRAS mutations may serve as cancer neoantigens for targeted immune-based therapeutic approaches. Using a validated in silico epitope prediction pipeline, we identified mKRAS epitopes with predicted strong binding affinity to the highly prevalent MHC class I alleles HLA-A*03:01, HLA-A*11:01, and HLA-B*07:02. We then confirmed the processing, presentation, and binding of putative mKRAS epitopes using HPLC-tandem mass spectrometry and competitive peptide-binding assays. The immunogenicity of bona fide mKRAS epitopes was confirmed by the in vitro generation of mKRAS-specific CD8+ T cell responses using healthy donor blood samples that were restricted to HLA-A*03:01, -A*11:01, or -B*07:02 as determined by cytokine release assay and peptide/MHC multimer staining. Following T-cell receptor (TCR) sequencing, transgenic expression of mKRAS-specific TCRs on primary CD8+ T cells conferred cytotoxic potential against KRAS-mutated tumor cell lines expressing target HLA/mKRAS combinations. Importantly, mKRAS-specific CD8+ T cells did not cross-react with wild-type or alternatively mutated KRAS epitopes as measured by cytokine release or cytotoxicity assays. Based on these data, we initiated an adjuvant vaccine clinical study at the University of Pennsylvania Abramson Cancer Center targeting mKRAS in patients with resected PDA. The objective of this clinical study is to identify further mKRAS-specific TCR sequences for future adoptive T-cell therapy applications. To date, a total of 7 subjects have been enrolled, and 3 subjects have been vaccinated against targeted mKRAS short peptides utilizing a dendritic cell-based platform. Results from the first vaccinated subject have demonstrated the generation of a KRAS G12V-specific CD8+ T-cell response restricted to HLA-A*11:01 following vaccination with >10% mKRAS-specific T cells isolated following primary in vitro expansion of CD8+ T cells using antigen-pulsed dendritic cells as measured by peptide/MHC multimer staining. TCR sequencing has been completed and functional studies to validate antigen specificity and tumor cytotoxicity are in process. The results of these studies will serve to develop a widely applicable T-cell therapy for PDA patients, and will have implications for the large number of patients with other KRAS-mutated cancers. Citation Format: Adham S. Bear, Andrew J. Rech, Lee P. Richman, Mark H. O'Hara, Gerald P. Linette, Beatriz M. Carreno, Robert H. Vonderheide. Identification of T-cell receptors targeting mutant KRAS in pancreatic cancer [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer: Advances in Science and Clinical Care; 2019 Sept 6-9; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2019;79(24 Suppl):Abstract nr B04.

Pilot Study on the Feasibility, Safety and Immunogenicity of a Personalized Neoantigen-Targeted Immunotherapy (NeoPepVac) in Combination with Anti-PD-1 or Anti-PD-L1 in Advanced Solid Tumors

Abstract Background The majority of neoantigens arise from unique mutations which are not shared between individual patients. Thus, neoantigen-directed immunotherapy is a fully personalized treatment. Novel technical advances in next-generation sequencing and artificial intelligence (AI) allow fast and systematic prediction of cancer neoantigens for each individual patient. In this study, the proprietary AI platform PIONEERTM is used for fast and accurate identification of tumor-derived neoantigens to be included in an immunotherapy (NeoPepVac), tailored to each individual patient. NeoPepVac immunotherapy, consisting of 5-15 predicted neoantigens as synthetic peptides, is formulated with a novel cationic liposomal adjuvant (CAF09b) to strengthen CD8+ T-cell immunity towards cancer. Immune checkpoint inhibitors, targeting PD-1 or PD-L1, are administered before, during and after neoantigen-targeted immunotherapy to augment the activity of induced immune responses. Methods The study is designed as an open phase I/IIa trial. Patients with either unresectable or metastatic melanoma, non-small cell lung cancer (NSCLC) or bladder (urothelial) cancer who meet the criteria for treatment with anti-PD-1 or anti-PD-L1 are included. The NeoPepVac immunotherapy is given every second week, for a total of 6 immunizations. Blood samples are collected before, during and after NeoPepVac treatment to monitor the induction of a neoantigen-specific immune response and immune-related changes. Results Four patients have been included and treated with personalized NeoPepVac immunotherapy. So far, only CTCAE Grade 1 mild flu-like symptoms have been observed as NeoPepVac-related AEs at the first dose level (500 mg total peptide). At least 50% of the neoantigens are generating a specific T-cell response as determined by IFN-γ ELISPOT analyses of patient PBMCs. Conclusion So far the vaccine is well tolerated and safe. Neoantigen prediction and immunotherapy manufacturing is feasible within 6 weeks of patient enrolment using the PIONEERTM platform. Preliminary analyses indicate induction of NeoPepVac-specific immune responses. Clinical trial identification EudraCT: 2018-002892-16. Legal entity responsible for the study Inge Marie Svane. Funding Innovations fonden. Disclosure J.V. Kringelum: Full / Part-time employment, PhD, Director, Genomic Immuno-Oncology: Evaxion. T. Bogenrieder: Full / Part-time employment, CMO at Evaxion: Evaxion. B. Rono: Full / Part-time employment, PhD, Director, Cancer Vaccines: Evaxion. A.B. Sorensen: Full / Part-time employment, Project Manager, Personalized Immuno-oncology: Evaxion. N.V. Petersen: Full / Part-time employment: Evaxion. L.V. Andreasen: Leadership role: SSI. D. Christensen: Leadership role, Full / Part-time employment: SSI. P.L. Andersen: Leadership role, Full / Part-time employment: SSI. All other authors have declared no conflicts of interest.

An overview of immunoinformatics approaches and databases linking T cell receptor repertoires to their antigen specificity.

Recent advances in molecular and bioinformatic methods have greatly improved our ability to study the formation of an adaptive immune response towards foreign pathogens, self-antigens, and cancer neoantigens. T cell receptors (TCR) are the key players in this process that recognize peptides presented by major histocompatibility complex (MHC). Owing to the huge diversity of both TCR sequence variants and peptides they recognize, accumulation and complex analysis of large amounts of TCR-antigen specificity data is required for understanding the structure and features of adaptive immune responses towards pathogens, vaccines, cancer, as well as autoimmune responses. In the present review, we summarize recent efforts on gathering and interpreting TCR-antigen specificity data and outline the critical role of tighter integration with other immunoinformatics data sources that include epitope MHC restriction, TCR repertoire structure models, and TCR/peptide/MHC structural data. We suggest that such integration can lead to the ability to accurately annotate individual TCR repertoires, efficiently estimate epitope and neoantigen immunogenicity, and ultimately, in silico identify TCRs specific to yet unstudied antigens and predict self-peptides related to autoimmunity.