Candidate Neoantigens Research Articles

Intra-tumoral CD40 antibody with irreversible electroporation (IRE) in locally advanced pancreas cancer.

TPS787 Background: Irreversible electroporation (IRE) is a form of non-thermal tumor ablation that is currently in clinical use for selected patients with locally advanced pancreatic cancer (LAPC). Preclinical and clinical studies have suggested that IRE generates an in situ vaccination effect by releasing tumor neoantigens in the setting of inflammation. Our published data from syngeneic, orthotopic mouse models demonstrate that combination of IRE with local CD40 agonism induces systemic anti-tumor immune effects, including neoantigen-specific T-cell responses and inhibition of liver metastases (1). Mitazalimab, a second-generation CD40 agonistic IgG1 mAb, has demonstrated promising clinical antitumor activity when delivered systemically in combination with modified FOLFIRINOX in metastatic pancreatic cancer (2). Methods: This clinical trial (NCT06205849) is a phase I dose-escalation study of an agonistic CD40 antibody (mitazalimab) injected intratumorally at the time of surgical IRE in patients with LAPC who have not demonstrated distant disease progression after a minimum of 4 months of optimal standard of care (SOC) chemotherapy (modified FOLFIRINOX). The doses investigated are 25, 75 (starting dose), and 200 µg/kg, in an interval 3+3 design, with a maximum of 18 subjects. The primary endpoints are the rates of dose limiting toxicities (DLTs) and treatment-emergent adverse events (AEs). The secondary endpoints are progression-free survival (PFS) and overall survival (OS). At a liberal alpha of 25%, appropriate for this proof-of-concept early phase study, we have 80% power to detect an improvement in PFS from 12 months (based on prior studies of IRE alone) to 18 months, with an expected 16 evaluable subjects and an anticipated 11 observed events, using a one-sided log-rank test. Candidate neoantigens will be identified by profiling nucleic acids derived from tumor biopsies obtained intraoperatively prior to IRE using our Identification-Prioritization-Validation (IPV) bioinformatic pipeline (3). Blood samples will be collected pre-operatively and 12-weeks post-operatively to evaluate for evidence of neoantigen-specific T-cell reactivity and other neoantigen-independent analyses of immune response. We have enrolled two patients since the study opened in September 2024. 1. J. Shankara Narayanan et al., JITC 2023. 2. J. Van Laethem et al., Lancet Oncol 2024. 3. A. Miller et al., Sci Transl Med. 2024. Clinical trial information: NCT06205849 .

Unlocking novel T cell-based immunotherapy for hepatocellular carcinoma through neoantigen-driven T cell receptor isolation

BackgroundTumour-infiltrating T cells can mediate both antitumour immunity and promote tumour progression by creating an immunosuppressive environment. This dual role is especially relevant in hepatocellular carcinoma (HCC), characterised by a...

Identification of BRCA new prognostic targets and neoantigen candidates from fusion genes

Cancer-associated gene fusions serve as a potential source of highly immunogenic neoantigens. In this study, we identified fusion proteins from fusion genes and extracted fusion peptides to accurately predict Breast cancer (BRCA) neo-antigen candidates by high-throughput artificial intelligence computation. Firstly, Deepsurv was used to evaluate the prognosis of patients, providing a landscape of prognostic fusion genes in BRCA. Next, AGFusion was utilized to generate full-length fusion protein sequences and annotate functional domains. Advanced neural networks and Transformer-based analyses were implemented to predict the binding of fusion peptides to 112 types of HLA, thereby forming a new immunotherapy candidates’ library of BRCA neo-antigens (n = 7791, covering 88.41% of patients). Among them, 15 neo-antigens were validated and factually translated into mass spectrometry data of BRCA patients. Finally, AlphaFold2 was applied to predict the binding sites of these neo-antigens to MHC (HLA) molecules. Notably, we identified a prognostic neoantigen from the TBC1D4–COMMD6 fusion that significantly improves patient prognosis and extensively binds to 16 types of HLA alleles. These highly immunogenic and tumor-specific neoantigens offer emerging targets for personalized cancer immunotherapies and act as prospective predictors for tumor survival prognosis and responses to immune checkpoint therapies.

PVACview: an interactive visualization tool for efficient neoantigen prioritization and selection

BackgroundNeoantigen-targeting therapies including personalized vaccines have shown promise in the treatment of cancers, particularly when used in combination with checkpoint blockade therapy. At least 100 clinical trials involving these therapies have been initiated globally. Accurate identification and prioritization of neoantigens is crucial for designing these trials, predicting treatment response, and understanding mechanisms of resistance. With the advent of massively parallel DNA and RNA sequencing technologies, it is now possible to computationally predict neoantigens based on patient-specific variant information. However, numerous factors must be considered when prioritizing neoantigens for use in personalized therapies. Complexities such as alternative transcript annotations, various binding, presentation and immunogenicity prediction algorithms, and variable peptide lengths/registers all potentially impact the neoantigen selection process. There has been a rapid development of computational tools that attempt to account for these complexities. While these tools generate numerous algorithmic predictions for neoantigen characterization, results from these pipelines are difficult to navigate and require extensive knowledge of the underlying tools for accurate interpretation. This often leads to over-simplification of pipeline outputs to make them tractable, for example, limiting prediction to a single RNA isoform or only summarizing the top ranked of many possible peptide candidates. In addition to variant detection, gene expression, and predicted peptide binding affinities, recent studies have also demonstrated the importance of mutation location, allele-specific anchor locations, and variation of T-cell response to long versus short peptides. Due to the intricate nature and number of salient neoantigen features, presenting all relevant information to facilitate candidate selection for downstream applications is a difficult challenge that current tools fail to address.ResultsWe have created pVACview, the first interactive tool designed to aid in the prioritization and selection of neoantigen candidates for personalized neoantigen therapies including cancer vaccines. pVACview has a user-friendly and intuitive interface where users can upload, explore, select, and export their neoantigen candidates. The tool allows users to visualize candidates at multiple levels of detail including variant, transcript, peptide, and algorithm prediction information.ConclusionspVACview will allow researchers to analyze and prioritize neoantigen candidates with greater efficiency and accuracy in basic and translational settings. The application is available as part of the pVACtools software at pvactools.org and as an online server at pvacview.org.

A novel mouse model recapitulating the MMR-defective SCLC subtype uncovers an actionable sensitivity to immune checkpoint blockade

PurposeSmall cell lung cancer (SCLC) has an extremely poor prognosis. Despite high initial response rates to chemotherapy and modest survival improvements with the addition of immune checkpoint inhibitors (ICI), almost all patients experience relapse and fatal outcomes. Recent genomic insights uncovered extensive molecular heterogeneity in addition to the almost uniform loss of RB1 and TRP53. Additionally, defective DNA mismatch repair (MMR) has recently been described in some SCLC cases. Here, we generated a novel SCLC mouse model capturing MMR deficiency and assessed immunotherapy responses.MethodsWe developed an MMR-deficient genetically engineered mouse model (GEMM) of SCLC by introducing a conditional Msh2 gene, crucial for maintaining MMR integrity, into the standard Rb1fl/fl;Trp53fl/fl (RP) model. Genomic characteristics and preclinical therapy responses were evaluated by focusing on overall survival and whole exome sequencing (WES) analyses.ResultsMMR-defective SCLC tumors (Rb1fl/fl;Trp53fl/fl;Msh2fl/fl (RPM)) developed later than tumors in MMR-proficient mice. However, the time from tumor manifestation to death of the affected animals was substantially shortened (median survival 55 days in RP vs. 46.5 days in RPM), indicating increased aggressiveness of MMR-defective tumors. RPM tumors exhibited MMR deficiency, high tumor mutational burden (TMB), and an elevated load of candidate neoantigens, compared to RP lesions (p = 0.0106), suggesting increased immunogenicity. Importantly, the overall survival of RPM animals was significantly improved when exposed to ICI.ConclusionWe propose a novel RPM mouse model as a suitable system to mimic MMR-defective SCLC and tumors with high TMB. We provide in vivo evidence that Msh2 deficiency enhances ICI sensitivity. These findings could contribute to stratifying SCLC patients to immunotherapy, thereby improving treatment outcomes.

IMMU-03. EXPLORING CANCER-SPECIFIC T CELLS AND ANTIGENS IN GLIOBLASTOMA BASED ON SINGLE-CELL SEQUENCING OF CD8+ TILS

Abstract BACKGROUND The effectiveness of cancer immunotherapy against glioblastoma (GBM) remains limited. This study aims to identify cancer-specific antigens to develop antigen-based cancer immunotherapies for GBM. We investigated candidate tumor antigen-specific T cells in GBM by single-cell RNA sequencing (scRNA-seq) and single-cell TCR sequencing (scTCR-seq), and we explored candidate antigens through genetic analysis of tumor tissues. METHODS Flow cytometry analysis was conducted on fresh tumor digest samples to evaluate tumor-infiltrating lymphocytes (TILs) of GBM. Single-cell RNA and TCR sequencing were performed on CD8+ T cells in TILs. Both data generated by Cell Ranger software were loaded into Seurat at R studio. The dimensional reduction for clustering was performed with uniform manifold approximation and projection (UMAP). Additionally, we performed bulk RNA sequencing (RNA-seq) and whole exome sequencing (WES) on tumor tissues. RESULTS Two out of 20 patients (10%) exhibited abundant TILs in GBM. From these two patients, approximately 20,000 CD8+ T cells in total were analyzed in single-cell analysis. Clustering based on UMAP revealed a predominance of clusters expressing exhaustion markers, with high TCR clonality centered on exhausted T cell (TEX) clusters, like our previous date of lung cancer TILs recognizing tumor antigens. Moreover, within the TEX clusters, the expression of CXCL13, often reported as an antigen-specific marker in recent years, was significantly higher in our data compared to two publicly available datasets. Subsequently, based on RNA-seq and WES, 61 candidate neoantigens were estimated using machine-learning prediction algorithms from NEC Corporation. Furthermore, 5 overexpressed cancer/testis antigens, and 3 bacterial species-derived microbial peptides using Kraken2 were selected as antigen candidates. CONCLUSIONS We identified prospective tumor antigen-specific T cell subsets with high CXCL13 expression from two GBM patients. We plan to identify cancer-specific T cells and antigens from these candidates by T cell activation assay.

CTIM-20. IDENTIFYING CANDIDATE ANTIGEN TARGETS FOR PEDIATRIC BRAIN TUMOR IMMUNOTHERAPIES THROUGH COMPREHENSIVE IMMUNOGENOMIC ANALYSIS

Abstract BACKGROUND Identifying tumor rejection antigens continues to be a significant challenge in the development of effective immunotherapies and their application to pediatric brain tumors. METHODS To identify candidate antigen targets for Medulloblastoma (primary MB, n=170; recurrent MB, n=21), Diffuse Intrinsic Pontine Glioma (DIPG, n=10), and Pediatric High-Grade Astrocytoma (HGA, n=12) for adoptive cellular therapy, we conducted a comprehensive immunogenomic analysis of the transcription profiles of the most aggressive pediatric brain tumors and performed in silico antigen prediction across a wide range of antigen classes. IMPORTANCE OF THE STUDY Pediatric brain tumors are heterogeneous diseases, and current standard treatments do not adequately address this variability. Newer and safer patient-specific treatment approaches are needed for high-risk patients who do not respond to standard therapies. Cellular immunotherapy could be crucial for improving survival and reducing morbidity. For an effective immune response against these brain tumors, appropriate tumor antigens must be targeted. While subgroup-specific genetic mutations in medulloblastoma patients have been reported, the immunogenicity of these genetic alterations remains unknown. Using a custom antigen prediction pipeline, we identified potential tumor rejection antigens with significant implications for the development of cellular therapies for MB, DIPG, and HGA. RESULTS Antigen prediction analysis revealed that most patients express few candidate neoantigen targets that pass all filtering criteria. Overall, the proportion of predicted tumor-associated antigens (TAAs) was higher than that of neoantigens and gene fusions for all molecular subtypes, except for SHH, which exhibited a higher neoantigen burden. Notably, cancer-testis antigens and previously unrecognized neurodevelopmental antigens were found to be expressed in most patients across all medulloblastoma subgroups. Although the absolute immune cell content is predicted to be low, immune gene-signature analysis revealed subgroup-specific differences.

Neoantigen prioritization based on antigen processing and presentation.

Somatic mutations in tumor cells give rise to mutant proteins, fragments of which are often presented by MHC and serve as neoantigens. Neoantigens are tumor-specific and not expressed in healthy tissues, making them attractive targets for T-cell-based cancer immunotherapy. On the other hand, since most somatic mutations differ from patient to patient, neoantigen-targeted immunotherapy is personalized medicine and requires their identification in each patient. Computational algorithms and machine learning methods have been developed to prioritize neoantigen candidates. In fact, since the number of clinically relevant neoantigens present in a patient is generally limited, this process is like finding a needle in a haystack. Nevertheless, MHC presentation of neoantigens is not random but follows certain rules, and the efficiency of neoantigen detection may be further improved with technological innovations. In this review, we discuss current approaches to the detection of clinically relevant neoantigens, with a focus on antigen processing and presentation.

The T cell receptor β chain repertoire of tumor infiltrating lymphocytes improves neoantigen prediction and prioritization.

In the realm of cancer immunotherapy, the meticulous selection of neoantigens plays a fundamental role in enhancing personalized treatments. Traditionally, this selection process has heavily relied on predicting the binding of peptides to human leukocyte antigens (pHLA). Nevertheless, this approach often overlooks the dynamic interaction between tumor cells and the immune system. In response to this limitation, we have developed an innovative prediction algorithm rooted in machine learning, integrating T cell receptor β chain (TCRβ) profiling data from colorectal cancer (CRC) patients for a more precise neoantigen prioritization. TCRβ sequencing was conducted to profile the TCR repertoire of tumor-infiltrating lymphocytes (TILs) from 28 CRC patients. The data unveiled both intra-tumor and inter-patient heterogeneity in the TCRβ repertoires of CRC patients, likely resulting from the stochastic utilization of V and J segments in response to neoantigens. Our novel combined model integrates pHLA binding information with pHLA-TCR binding to prioritize neoantigens, resulting in heightened specificity and sensitivity compared to models using individual features alone. The efficacy of our proposed model was corroborated through ELISpot assays on long peptides, performed on four CRC patients. These assays demonstrated that neoantigen candidates prioritized by our combined model outperformed predictions made by the established tool NetMHCpan. This comprehensive assessment underscores the significance of integrating pHLA binding with pHLA-TCR binding analysis for more effective immunotherapeutic strategies.

The T cell receptor β chain repertoire of tumor infiltrating lymphocytes improves neoantigen prediction and prioritization

In the realm of cancer immunotherapy, the meticulous selection of neoantigens plays a fundamental role in enhancing personalized treatments. Traditionally, this selection process has heavily relied on predicting the binding of peptides to human leukocyte antigens (pHLA). Nevertheless, this approach often overlooks the dynamic interaction between tumor cells and the immune system. In response to this limitation, we have developed an innovative prediction algorithm rooted in machine learning, integrating T cell receptor β chain (TCRβ) profiling data from colorectal cancer (CRC) patients for a more precise neoantigen prioritization. TCRβ sequencing was conducted to profile the TCR repertoire of tumor-infiltrating lymphocytes (TILs) from 28 CRC patients. The data unveiled both intra-tumor and inter-patient heterogeneity in the TCRβ repertoires of CRC patients, likely resulting from the stochastic utilization of V and J segments in response to neoantigens. Our novel combined model integrates pHLA binding information with pHLA-TCR binding to prioritize neoantigens, resulting in heightened specificity and sensitivity compared to models using individual features alone. The efficacy of our proposed model was corroborated through ELISpot assays on long peptides, performed on four CRC patients. These assays demonstrated that neoantigen candidates prioritized by our combined model outperformed predictions made by the established tool NetMHCpan. This comprehensive assessment underscores the significance of integrating pHLA binding with pHLA-TCR binding analysis for more effective immunotherapeutic strategies.

Neoantigen immunogenicity landscapes and evolution of tumor ecosystems during immunotherapy with nivolumab.

Neoantigen immunoediting drives immune checkpoint blockade efficacy, yet the molecular features of neoantigens and how neoantigen immunogenicity shapes treatment response remain poorly understood. To address these questions, 80 patients with non-small cell lung cancer were enrolled in the biomarker cohort of CheckMate 153 (CA209-153), which collected radiographic guided biopsy samples before treatment and during treatment with nivolumab. Early loss of mutations and neoantigens during therapy are both associated with clinical benefit. We examined 1,453 candidate neoantigens, including many of which that had reduced cancer cell fraction after treatment with nivolumab, and identified 196 neopeptides that were recognized by T cells. Mapping these neoantigens to clonal dynamics, evolutionary trajectories and clinical response revealed a strong selection against immunogenic neoantigen-harboring clones. We identified position-specific amino acid and physiochemical features related to immunogenicity and developed an immunogenicity score. Nivolumab-induced microenvironmental evolution in non-small cell lung cancer shared some similarities with melanoma, yet critical differences were apparent. This study provides unprecedented molecular portraits of neoantigen landscapes underlying nivolumab's mechanism of action.

Cancer Immunotherapies Ignited by a Thorough Machine Learning-Based Selection of Neoantigens.

Identification of neoantigens, derived from somatic DNA alterations, emerges as a promising strategy for cancer immunotherapies. However, not all somatic mutations result in immunogenicity, hence, efficient tools to predict the immunogenicity of neoepitopes are needed. A pipeline is presented that provides a comprehensive solution for the identification of neoepitopes based on genomic sequencing data. The pipeline consists of a data pre-processing step and three machine learning predictive steps. The pre-processing step analyzes genomic data for different types of alterations, produces a list of all possible antigens, and determines the human leukocyte antigen (HLA) type and T-cell receptor (TCR) repertoire. The first predictive step performs a classification into antigens and neoantigens, selecting neoantigens for further consideration. The next step predicts the strength of binding between neoantigens and available major histocompatibility complexes of class I (MHC-I). The third step is engaged to predict the likelihood of inducing an immune response. Neoepitopes satisfying all three predictive stages are assumed to be potent candidates to ensure immunogenicity. The predictive pipeline is used in two regimes: selecting neoantigens from patients' sequencing data and generating novel neoantigen candidates. Two different techniques - Monte Carlo and Reinforcement Learning - are implemented to facilitate the generativeregime.

Identification of pancreatic adenocarcinoma immune subtype associated with tumor neoantigen from aberrant alternative splicing.

Pancreatic adenocarcinoma (PAAD) is referred to as an immunologically "cold" tumor that responds poorly to immunotherapy. A fundamental theory that explains the low immunogenicity of PAAD is the dramatically low tumor mutation burden (TMB) of PAAD tumors, which fails to induce sufficient immune response. Alternative splicing of pre-mRNA, which could alter the proteomic diversity of many cancers, has been reported to be involved in neoantigen production. Therefore, we aim to identify novel PAAD antigens and immune subtypes through systematic bioinformatics research. Data for splicing analysis were downloaded from The Cancer Genome Atlas (TCGA) SpliceSeq database. Among the available algorithms, we chose CIBERSORT to evaluate the immune cell distribution among PAADs. The TCGA-PAAD expression matrix was used to construct a co-expression network. Single-cell analysis was performed based on the Seurat workflow. Integrated analysis of aberrantly upregulated genes, alternatively spliced genes, genes associated with nonsense-mediated RNA decay (NMD) factors, antigen presentation and overall survival (OS) in TCGA-PAAD revealed that PLEC is a promising neoantigen for PAAD-targeted therapy. We identified a C2 TCGA-PAAD subtype that had better prognosis and more CD8+ T-cell infiltration. We propose a novel immune subtyping system for PAAD to indicate patient prognosis and opportunities for immunotherapy, such as immune checkpoint (ICP) inhibitors. In conclusion, the present study used a transcriptome-guided approach to screen neoantigen candidates based on alternative splicing, NMD factors, and antigen-presenting signatures for PAAD. A prognosis model with guidance of immunotherapy will aid in patient selection for appropriate treatment.

Downregulation of HNRNPA1 induced neoantigen generation via regulating alternative splicing

BackgroundImmunotherapies effectively treat human malignancies, but the low response and resistance are major obstacles. Neoantigen is an emerging target for tumor immunotherapy that can enhance anti-tumor immunity and improve immunotherapy. Aberrant alternative splicing is an important source of neoantigens. HNRNPA1, an RNA splicing factor, was found to be upregulated in the majority of tumors and play an important role in the tumor immunosuppressive microenvironment.MethodsWhole transcriptome sequencing was performed on shHNRNPA1 SKOV3 cells and transcriptomic data of shHNRNPA1 HepG2, MCF-7M, K562, and B-LL cells were downloaded from the GEO database. Enrichment analysis was performed to elucidate the mechanisms underlying the activation of anti-tumor immunity induced by HNRNPA1 knockdown. mRNA alternative splicing was analyzed and neoantigens were predicted by JCAST v.0.3.5 and Immune epitope database. The immunogenicity of candidate neoantigens was calculated by Class I pMHC Immunogenicity and validated by the IFN-γ ELISpot assay. The effect of shHNRNPA1 on tumor growth and immune cells in vivo was evaluated by xenograft model combined with immunohistochemistry.ResultsHNRNPA1 was upregulated in a majority of malignancies and correlated with immunosuppressive status of the tumor immune microenvironment. Downregulation of HNRNPA1 could induce the activation of immune-related pathways and biological processes. Disruption of HNRNPA1 resulted in aberrant alternative splicing events and generation of immunogenic neoantigens. Downregulation of HNRNPA1 inhibited tumor growth and increased CD8+ T cell infiltration in vivo.ConclusionOur study demonstrated that targeting HNRNPA1 could produce immunogenic neoantigens that elicit anti-tumor immunity by inducing abnormal mRNA splicing. It suggests that HNRNPA1 may be a potential target for immunotherapy.

The identification of effective tumor-suppressing neoantigens using a tumor-reactive TIL TCR-pMHC ternary complex

Neoantigens are ideal targets for cancer immunotherapy because they are expressed de novo in tumor tissue but not in healthy tissue and are therefore recognized as foreign by the immune system. Advances in next-generation sequencing and bioinformatics technologies have enabled the quick identification and prediction of tumor-specific neoantigens; however, only a small fraction of predicted neoantigens are immunogenic. To improve the predictability of immunogenic neoantigens, we developed the in silico neoantigen prediction workflows VACINUSpMHC and VACINUSTCR: VACINUSpMHC incorporates physical binding between peptides and MHCs (pMHCs), and VACINUSTCR integrates T cell reactivity to the pMHC complex through deep learning-based pairing with T cell receptors (TCRs) of putative tumor-reactive CD8 tumor-infiltrating lymphocytes (TILs). We then validated our neoantigen prediction workflows both in vitro and in vivo in patients with hepatocellular carcinoma (HCC) and in a B16F10 mouse melanoma model. The predictive abilities of VACINUSpMHC and VACINUSTCR were confirmed in a validation cohort of 8 patients with HCC. Of a total of 118 neoantigen candidates predicted by VACINUSpMHC, 48 peptides were ultimately selected using VACINUSTCR. In vitro validation revealed that among the 48 predicted neoantigen candidates, 13 peptides were immunogenic. Assessment of the antitumor efficacy of the candidate neoepitopes using a VACINUSTCR in vivo mouse model suggested that vaccination with the predicted neoepitopes induced neoantigen-specific T cell responses and enabled the trafficking of neoantigen-specific CD8 + T cell clones into the tumor tissue, leading to tumor suppression. This study showed that the prediction of immunogenic neoantigens can be improved by integrating a tumor-reactive TIL TCR-pMHC ternary complex.

A phase I pilot study of personalized neoantigen peptide-based vaccine in combination with pembrolizumab in advanced solid tumors (PNeoVCA).

TPS2700 Background: Though immunotherapy with anti-PD-1 immune checkpoint inhibitors (ICIs) has led to improvements in clinical outcomes in patients with cancer, when used as monotherapy in the advanced setting, only 18-25% of patients respond to ICI. Notably, ICI frequently lacks anti-tumor activity in immune-cold cancers where T cell priming is missing. Recent breakthroughs in identifying personal neoantigens via a comprehensive analysis of cancer sequencing data have brought increased attention to neoantigen cancer vaccines. Personalized cancer vaccines targeting neoantigens have shown early promise. While neoantigens have recently been investigated in some cancer types, the current neoantigen prediction algorithms have often focused on the MHC class-I subtype, single nucleotide mutations (SNM), small insertions and deletions (INDEL). We recently developed an informatics workflow, REAL-neo, for the identification, quality control (QC), and prioritization of both class-I and class-II human leukocyte antigen (HLA) bound neoantigens that arise from somatic SNM, INDEL, aberrant RNA splicing, and gene fusions, generating much more potent neoantigen candidates. Furthermore, we demonstrated robust T-cell response and prolonged survival by combining ICI and cancer vaccines in preclinical models. This single-arm phase I clinical trial is in progress to assess the safety, feasibility, and immunogenicity of personalized neoantigen vaccines in combination with pembrolizumab in patients with advanced solid cancers. Methods: The vaccine consists of up to 20 peptides (15-30 mer) comprising patient-specific neoantigens identified from tumor DNA and mRNA sequencing data using REAL-neo and delivered with GM-CSF as an adjuvant. The first cohort of three patients will be treated at dose level 1 consisting of 4 or 5 peptides at 300 mcg/peptide and GM-CSF 125 mcg per injection site in each of four limbs. After Cohort 1 is deemed safe, the study will expand to cohort 2, when patients will be enrolled at the same dose level for vaccine plus Pembrolizumab 200 mg i.v. Neoantigen vaccine will be given via subcutaneous injection on days 1, 4, 8, 15, and 21 (cohorts 1 and 2) and weeks 5 and 8 (booster dose for cohort 2 only). Cohort 1 has been completed without dose-limiting toxicity (DLT). Enrollment in Cohort 2 began in January 2024. AEs are assessed according to CTCAE v5. and safety findings are reviewed by data safety medical board (DSMB). Key eligibility criteria include 1) histologically confirmed locally advanced or metastatic solid malignancies and 2) cancer progression after at least one line of the standard-of-care (SOC) systemic treatment. For cohort 2, patients must be eligible to receive pembrolizumab per SOC or the treating physician’s judgment. Clinical trial information: NCT05269381 .

Comprehensive profiling of cancer neoantigens from aberrant RNA splicing

BackgroundCancer neoantigens arise from protein-altering somatic mutations in tumor and rank among the most promising next-generation immuno-oncology agents when used in combination with immune checkpoint inhibitors. We previously developed a...

IMPROVE: a feature model to predict neoepitope immunogenicity through broad-scale validation of T-cell recognition.

Mutation-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. To 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. We 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. Overall, IMPROVE was found to significantly advance the identification of neoepitopes compared to other current methods.

Abstract 3110: An enhanced and cost-efficient method for neoantigen immunogenicity screening method: Double Barcode Neoepitope ScanTM

Abstract Neoantigens, HLA bound peptides generated by tumor-specific mutations, have emerged as promising targets for cancer immunotherapeutic. However, the significant challenge in neoantigen-based therapies is that the neoantigens are rare and difficult to predict. Therefore, in addition to prediction algorithms, the optimization of validation methods is essential for the reliable selection of highly immunogenic neoepitopes. In this study, we have developed a cost-effective screening method to validate large scale of neoantigen candidates. The DNA library-based methods have been employed for antigen selection and T cell receptor identification. We improved the DNA library-based neoantigen screening method by adding two barcodes to each minigene transcribing multiple neoantigen candidates and pooled synthesis of minigenes. We initially designed the barcodes to allow analysis in a 10 by 10 format for screening of hundreds of neoantigens so that PCR with each barcode result in amplification of ten candidates. The second barcode allowed the deconvolution of immunogenicity results without the need for synthesis of individual peptides. In this method B cell was used as antigen-presenting cell, which were co-transfected with mRNA for a minigene pool, 41BBL, and OX40L after expansion with CD40L/IL4/IL21. The resulting B cells were co-cultured with T cells and T cell responses were evaluated using ELISpot. To validate the method the TESLA consortium reported tetramer positive neoantigens were included in minigene. In results, the neoantigens including each barcode were amplified to form a pool of ten neoantigens as a minigene and confirmed that the neoantigens were equally amplified by nested PCR. The neoantigen pools synthesized as mRNA were transfected for presentation in B cells. The result of the targeting a neoantigen pool containing tetramer-positive neoantigens of the TESLA consortium showed a positive for ELISpot. In conclusion, our optimized screening method, employing the double barcode system, offers a time- and cost-efficient approach for the selection of immunogenic neoantigens without the need for individual peptides. This method will facilitate large-scale screening to identify actual neoantigens with immunogenic potential, contributing to the development of neoantigen-targeted vaccines. Citation Format: Seong Gwang Kim, Min-Ho Jung, Hyun-Ji Hwang, Sejin Oh, Seong-Eui Hong, Soonmyung Paik, Hae-Suk Kim. An enhanced and cost-efficient method for neoantigen immunogenicity screening method: Double Barcode Neoepitope ScanTM [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3110.

Abstract 5007: Ex vivo evaluation of mRNA lipid nanoparticle cancer vaccines

Abstract Lipid nanoparticle vaccines have recently been developed broadly against pathogens and recently, in precision medicine targeting mutations in different tumors. Evaluating vaccine candidates typically require animal models using orthotopic or allogenic transplantations to evaluate efficiency. Evaluating vaccine efficacy using ex vivo systems is faster and less technically demanding. Here we aim to develop individualized cancer vaccines based on neoantigen identified within established human organoids or directly from patient tissue. The selected neoantigens are enriched for epitopes identified on gene fusions. Identified gene fusions encoding predicted immunogenic proteins are inserted into a vaccine backbone and used to make mRNA lipid nanoparticle vaccines. The neoantigen candidates are then evaluated in a personalized ex vivo testbed. Within the ex vivo testbed personal tumor neoantigens are tested on the patients own isolated CD8+ T cells and monocytes. Monocytes are in vitro differentiated into monocyte derived dendritic cells (moDCs), which specialize in antigen cross presentation. The moDCs are inoculated with mRNA lipid nanoparticle vaccines or loaded with peptides. The moDCs are then co-cultured with syngeneic T cells. Vaccine candidates are evaluated based on CD8+ T cell activation and cytotoxicity in the co-culture ex vivo testbed. Our pipeline offers a fast and efficient means of evaluating individualized vaccine candidates. Effective vaccines developed through this process can be administered as a standalone therapy or in combination with checkpoint inhibitors, to generate a strong durable immune response against a variety of solid tumor types. Citation Format: Tommy Lidström, Mara Blanco Arauzo, Nickolas Karlowatz, Mikael Johansson, Mattias Forsell, Jonas Nilsson. Ex vivo evaluation of mRNA lipid nanoparticle cancer vaccines [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5007.