pVACview: an interactive visualization tool for efficient neoantigen prioritization and selection.

Background: Neoantigen 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 are underway globally. Accurate identification and prioritization of neoantigens is highly relevant to 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. Results: We 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 across three different levels, including variant, transcript and peptide information. Conclusions: pVACview 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 pipeline at pvactools.org and as an online server at pvacview.org.

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

Background: 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. Methods: 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. Results: 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. Conclusions: 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.

Candidate tumor-specific CD8+ T cell subsets identified in the malignant pleural effusion of advanced lung cancer patients by single-cell analysis.

Isolation of tumor-specific T cells and their antigen receptors (TCRs) from malignant pleural effusions (MPE) may facilitate the development of TCR-transduced adoptive cellular immunotherapy products for advanced lung cancer patients. However, the characteristics and markers of tumor-specific T-cells in MPE are largely undefined. To this end, to establish the phenotypes and antigen specificities of CD8+ T cells, we performed single-cell RNA and TCR sequencing of samples from three advanced lung cancer patients. Dimensionality reduction on a total of 4,983 CD8+ T cells revealed 10 clusters including naïve, memory, and exhausted phenotypes. We focused particularly on exhausted T cell clusters and tested their TCR reactivity against neoantigens predicted from autologous cancer cell lines. Four different TCRs specific for the same neoantigen and one orphan TCR specific for the autologous cell line were identified from one of the patients. Differential gene expression analysis in tumor-specific T cells relative to the other T cells identified CXCL13, as a candidate gene expressed by tumor-specific T cells. In addition to expressing CXCL13, tumor-specific T cells were present in a higher proportion of T cells co-expressing PDCD1(PD-1)/TNFRSF9(4-1BB). Furthermore, flow cytometric analyses in advanced lung cancer patients with MPE documented that those with high PD-1/4-1BB expression have a better prognosis in the subset of 57 adenocarcinoma patients (p = .039). These data suggest that PD-1/4-1BB co-expression might identify tumor-specific CD8+ T cells in MPE, which are associated with patients' prognosis. (233 words).

Transformers meets neoantigen detection: a systematic literature review.

Cancer immunology offers a new alternative to traditional cancer treatments, such as radiotherapy and chemotherapy. One notable alternative is the development of personalized vaccines based on cancer neoantigens. Moreover, Transformers are considered a revolutionary development in artificial intelligence with a significant impact on natural language processing (NLP) tasks and have been utilized in proteomics studies in recent years. In this context, we conducted a systematic literature review to investigate how Transformers are applied in each stage of the neoantigen detection process. Additionally, we mapped current pipelines and examined the results of clinical trials involving cancer vaccines.

The effect of smoking on tumor immunoediting: Friend or foe?

The recognition of smoking as an independent risk factor for lung cancer has become a widely accepted within the realm of respiratory medicine. The emergence of tumor immunotherapy has notably enhanced the prognosis for numerous late-stage cancer patients. Nevertheless, some studies have noted a tendency for lung cancer patients who smoke to derive greater benefit from immunotherapy. This observation has sparked increased interest in the interaction between smoking and the immune response to tumors in lung cancer. The concept of cancer immunoediting has shed light on the intricate and nuanced relationship between the immune system and tumors. Starting from the perspectives of immune surveillance, immune equilibrium, and immune evasion, this narrative review explores how smoking undermines the immune response against tumor cells and induces the generation of tumor neoantigens, and examines other behaviors that trigger tumor immune evasion. By elucidating these aspects, the review concludes that smoking is not conducive to tumor immunoediting.

Artificial intelligence and neoantigens: paving the path for precision cancer immunotherapy.

Cancer immunotherapy has witnessed rapid advancement in recent years, with a particular focus on neoantigens as promising targets for personalized treatments. The convergence of immunogenomics, bioinformatics, and artificial intelligence (AI) has propelled the development of innovative neoantigen discovery tools and pipelines. These tools have revolutionized our ability to identify tumor-specific antigens, providing the foundation for precision cancer immunotherapy. AI-driven algorithms can process extensive amounts of data, identify patterns, and make predictions that were once challenging to achieve. However, the integration of AI comes with its own set of challenges, leaving space for further research. With particular focus on the computational approaches, in this article we have explored the current landscape of neoantigen prediction, the fundamental concepts behind, the challenges and their potential solutions providing a comprehensive overview of this rapidly evolving field.

Dendritic cell subsets and implications for cancer immunotherapy.

Dendritic cells (DCs) play a central role in the orchestration of effective T cell responses against tumors. However, their functional behavior is context-dependent. DC type, transcriptional program, location, intratumoral factors, and inflammatory milieu all impact DCs with regard to promoting or inhibiting tumor immunity. The following review introduces important facets of DC function, and how subset and phenotype can affect the interplay of DCs with other factors in the tumor microenvironment. It will also discuss how current cancer treatment relies on DC function, and survey the myriad ways with which immune therapy can more directly harness DCs to enact antitumor cytotoxicity.

Public neoantigens in breast cancer immunotherapy (Review).

Among women globally, breast cancer is the most prevalent cancer and the leading cause of cancer­related death. Interestingly, though genetic mutations contribute to the disease, <15% of women diagnosed with breast cancer have a family history of the disease, suggesting a prevalence of sporadic genetic mutations in breast cancer development. In the rapidly rising field of cancer genomics, neoantigen­based immunotherapy has come to the fore. The investigation of novel proteins arising from unique somatic mutations or neoantigens have opened a new pathway for both individualized and public cancer treatments. Because they are shared among individuals with similar genetic changes, public neoantigens provide an opportunity for 'off­the­shelf' anticancer therapies, potentially extending the benefits to a wider patient group. The present review aimed to highlight the role of shared or public neoantigens as therapeutic targets for patients with breast cancer, emphasizing common hotspot mutations of certain genes identified in breast cancer. The clinical utilization of public neoantigen­based therapies for breast cancer treatment were also discussed.

The neoantigens derived from transposable elements - A hidden treasure for cancer immunotherapy.

Neoantigen-based therapy is a promising approach that selectively activates the immune system of the host to recognize and eradicate cancer cells. Preliminary clinical trials have validated the feasibility, safety, and immunogenicity of personalized neoantigen-directed vaccines, enhancing their effectiveness and broad applicability in immunotherapy. While many ongoing oncological trials concentrate on neoantigens derived from mutations, these targets do not consistently provoke an immune response in all patients harboring the mutations. Additionally, tumors like ovarian cancer, which have a low tumor mutational burden (TMB), may be less amenable to mutation-based neoantigen therapies. Recent advancements in next-generation sequencing and bioinformatics have uncovered a rich source of neoantigens from non-canonical RNAs associated with transposable elements (TEs). Considering the substantial presence of TEs in the human genome and the proven immunogenicity of TE-derived neoantigens in various tumor types, this review investigates the latest findings on TE-derived neoantigens, examining their clinical implications, challenges, and unique advantages in enhancing tumor immunotherapy.

Downregulation of HNRNPA1 induced neoantigen generation via regulating alternative splicing.

BACKGROUND: Immunotherapies 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. METHODS: Whole 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. RESULTS: HNRNPA1 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. CONCLUSION: Our 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.

Inhibition of DNMTs increases neoantigen-reactive T-cell toxicity against microsatellite-stable colorectal cancer in combination with radiotherapy.

The therapeutic efficacy of immunotherapy is limited in the majority of colorectal cancer patients due to the low mutational and neoantigen burdens in this immunogenically "cold" microsatellite stability-colorectal cancer (MSS-CRC) cohort. Here, we showed that DNA methyltransferase (DNMT) inhibition upregulated neoantigen-bearing gene expression in MSS-CRC, resulting in increased neoantigen presentation by MHC class I in tumor cells and leading to increased neoantigen-specific T-cell activation in combination with radiotherapy. The cytotoxicity of neoantigen-reactive T cells (NRTs) to DNMTi-treated cancer cells was highly cytotoxic, and these cells secreted high IFNγ levels targeting MSS-CRC cells after ex vivo expansion of NRTs with DNMTi-treated tumor antigens. Moreover, the therapeutic efficacy of NRTs further increased when NRTs were combined with radiotherapy in vivo. Administration of DNMTi-augmented NRTs and radiotherapy achieved an ∼50 % complete response and extended survival time in an immunocompetent MSS-CRC animal model. Moreover, remarkably, splenocytes from these mice exhibited neoantigen-specific T-cell responses, indicating that radiotherapy in combination with DNMTi-augmented NRTs prolonged and increased neoantigen-specific T-cell toxicity in MSS-CRC patients. In addition, these DNMTi-augmented NRTs markedly increase the therapeutic efficacy of cancer vaccines and immune checkpoint inhibitors (ICIs). These data suggest that a combination of radiotherapy and epi-immunotherapeutic agents improves the function of ex vivo-expanded neoantigen-reactive T cells and increases the tumor-specific cytotoxic effector population to enhance therapeutic efficacy in MSS-CRC.

MONET: a database for prediction of neoantigens derived from microsatellite loci.

Background: Microsatellite instability (MSI) secondary to mismatch repair (MMR) deficiency is characterized by insertions and deletions (indels) in short DNA sequences across the genome. These indels can generate neoantigens, which are ideal targets for precision immune interception. However, current neoantigen databases lack information on neoantigens arising from coding microsatellites. To address this gap, we introduce The MicrOsatellite Neoantigen Discovery Tool (MONET). Method: MONET identifies potential mutated tumor-specific neoantigens (neoAgs) by predicting frameshift mutations in coding microsatellite sequences of the human genome. Then MONET annotates these neoAgs with key features such as binding affinity, stability, expression, frequency, and potential pathogenicity using established algorithms, tools, and public databases. A user-friendly web interface (https://monet.mdanderson.org/) facilitates access to these predictions. Results: MONET predicts over 4 million and 15 million Class I and Class II potential frameshift neoAgs, respectively. Compared to existing databases, MONET demonstrates superior coverage (>85% vs. <25%) using a set of experimentally validated neoAgs. Conclusion: MONET is a freely available, user-friendly web tool that leverages publicly available resources to identify neoAgs derived from microsatellite loci. This systems biology approach empowers researchers in the field of precision immune interception.

Identification of an HLA-A*11:01-restricted neoepitope of mutant PIK3CA and its specific T cell receptors for cancer immunotherapy targeting hotspot driver mutations.

Hotspot driver mutations presented by human leukocyte antigens might be recognized by anti-tumor T cells. Based on their advantages of tumor-specificity and immunogenicity, neoantigens derived from hotspot mutations, such as PIK3CAH1047L, may serve as emerging targets for cancer immunotherapies. NetMHCpan V4.1 was utilized for predicting neoepitopes of PIK3CA hotspot mutation. Using in vitro stimulation, antigen-specific T cells targeting the HLA-A*11:01-restricted PIK3CA mutation were isolated from healthy donor-derived peripheral blood mononuclear cells. T cell receptors (TCRs) were cloned using single-cell PCR and sequencing. Their functionality was assessed through T cell activation markers, cytokine production and cytotoxic response to cancer cell lines pulsed with peptides or transduced genes of mutant PIK3CA. Immunogenic mutant antigens from PIK3CA and their corresponding CD8+ T cells were identified. These PIK3CA mutation-specific CD8+ T cells were subsequently enriched, and their TCRs were isolated. The TCR clones exhibited mutation-specific and HLA-restricted reactivity, demonstrating varying degrees of functional avidity. Identified TCR genes were transferred into CD8+ Jurkat cells and primary T cells deficient of endogenous TCRs. TCR-expressing cells demonstrated specific recognition and reactivity against the PIK3CAH1047L peptide presented by HLA-A*11:01-expressing K562 cells. Furthermore, mutation-specific TCR-T cells demonstrated an elevation in cytokine production and profound cytotoxic effects against HLA-A*11:01+ malignant cell lines harboring PIK3CAH1047L. Our data demonstrate the immunogenicity of an HLA-A*11:01-restricted PIK3CA hotspot mutation and its targeting therapeutic potential, together with promising candidates of TCR-T cell therapy.

In Silico: Predicting Intrinsic Features of HLA Class-I Restricted Neoantigens.

Mutation-containing immunogenic peptides from tumor cells, also named as neoantigens, have various amino acid descriptors and physical-chemical properties characterized intrinsic features, which are useful in prioritizing the immunogenicity potentials of neoantigens and predicting patients' survival. Here, we describe a glioma neoantigen intrinsic feature database, GNIFdb, that hosts computationally predicted HLA-I restricted neoantigens of gliomas, their intrinsic features, and the tools for calculating intrinsic features and predicting overall survival of gliomas. We illustrate the application of GNIFdb in searching for possible neoantigen candidates from ATF6 that plays important roles in tumor growth and resistance to radiotherapy in glioblastoma. We also demonstrate the application of intrinsic feature associated tools in GNIFdb to predict the overall survival of primary IDH wild-type glioblastoma.

RNA vaccines for cancer: Principles to practice.

Vaccines are the most impactful medicines to improve health. Though potent against pathogens, vaccines for cancer remain an unfulfilled promise. However, recent advances in RNA technology coupled with scientific and clinical breakthroughs have spurred rapid discovery and potent delivery of tumor antigens at speed and scale, transforming cancer vaccines into a tantalizing prospect. Yet, despite being at a pivotal juncture, with several randomized clinical trials maturing in upcoming years, several critical questions remain: which antigens, tumors, platforms, and hosts can trigger potent immunity with clinical impact? Here, we address these questions with a principled framework of cancer vaccination from antigen detection to delivery. With this framework, we outline features of emergent RNA technology that enable rapid, robust, real-time vaccination with somatic mutation-derived neoantigens-an emerging "ideal" antigen class-and highlight latent features that have sparked the belief that RNA could realize the enduring vision for vaccines against cancer.

Individualized neoantigen peptide immunization of a metastatic pancreatic cancer patient: a case report of combined tumor and liquid biopsy.

This report details a case of pancreatic cancer with liver metastasis that exhibited a positive immune response to personalized immunization therapy. Our study involved the identification of neoantigens and their corresponding immunogenic peptides using an in-house bioinformatic pipeline. This process included the identification of somatic mutations through DNA/RNA sequencing of solid tumor tissue and blood liquid biopsy. Computational prediction techniques were then employed to identify novel epitopes, followed by the design and manufacture of patient-specific immunization peptides. In combination with standard-of-care chemotherapy, the patient received a sequence of 5 biweekly prime injections followed by 2 boost injections 2 and 5 months later. The peptides were emulsified in Montanide and the injection-site was conditioned with nivolumab and imiquimod. The combined regimen of peptide immunization and chemotherapy resulted in a notable decline in CA19-9 tumor marker levels following both prime and boost applications. Subsequent MRI assessments revealed a reduction in the size of liver metastases several months post-immunization initiation. Importantly, the patient showed and improved overall survival and reported an improved quality of life without experiencing significant treatment-related adverse effects. This case underscores the potential benefits of personalized peptide-based immunization as an adjunctive therapy in the treatment of advanced pancreatic cancer, showcasing promising outcomes in tumor marker reduction, tumor shrinkage, and enhanced patient well-being.

DeepHLApan: A Deep Learning Approach for the Prediction of Peptide-HLA Binding and Immunogenicity.

Neoantigens are crucial in distinguishing cancer cells from normal ones and play a significant role in cancer immunotherapy. The field of bioinformatics prediction for tumor neoantigens has rapidly developed, focusing on the prediction of peptide-HLA binding affinity. In this chapter, we introduce a user-friendly tool named DeepHLApan, which utilizes deep learning techniques to predict neoantigens by considering both peptide-HLA binding affinity and immunogenicity. We provide the application of DeepHLApan, along with the source code, docker version, and web-server. These resources are freely available at https://github.com/zjupgx/deephlapan and http://pgx.zju.edu.cn/deephlapan/ .

Tumor immunogenicity regulates host immune responses, and conventional dendritic cell type 2 uptakes the majority of tumor antigens in an orthotopic lung cancer model.

Human lung cancer carries high genetic alterations, expressing high tumor-specific neoantigens. Although orthotopic murine lung cancer models recapitulate many characteristics of human lung cancers, genetically engineered mouse models have fewer somatic mutations than human lung cancer, resulting in scarce immune cell infiltration and deficient immune responses. The endogenous mouse lung cancer model driven by Kras mutation and Trp53 deletion (KP model) has minimal immune infiltration because of a scarcity of neoantigens. Fine-tuning tumor antigenicity to trigger the appropriate level of antitumor immunity would be key to investigating immune responses against human lung cancer. We engineered the KP model to express antigens of OVA peptides (minOVA) as neoantigens along with ZsGreen, a traceable fluorescent conjugate. The KP model expressing minOVA exhibited stronger immunogenicity with higher immune cell infiltration comprised of CD8+ T cells and CD11c+ dendritic cells (DCs). Consequentially, the KP model expressing minOVA exhibits suppressed tumor growth compared to its origin. We further analyzed tumor-infiltrated DCs. The majority of ZsGreen conjugated with minOVA was observed in the conventional type 2 DCs (cDC2), where cDC1 has minimal. These data indicate that tumor immunogenicity regulates host immune responses, and tumor neoantigen is mostly recognized by cDC2 cells, which may play a critical role in initiating anti-tumor immune responses in an orthotopic murine lung cancer model.

A journey to your self: The vague definition of immune self and its practical implications.

The identification of immunogenic peptides has become essential in an increasing number of fields in immunology, ranging from tumor immunotherapy to vaccine development. The nature of the adaptive immune response is shaped by the similarity between foreign and self-protein sequences, a concept extensively applied in numerous studies. Can we precisely define the degree of similarity to self? Furthermore, do we accurately define immune self? In the current work, we aim to unravel the conceptual and mechanistic vagueness hindering the assessment of self-similarity. Accordingly, we demonstrate the remarkably low consistency among commonly employed measures and highlight potential avenues for future research.