Identification of neoantigens derived from alternative splicing and RNA modification

Neoantigen-based immunotherapy: advancing precision medicine in cancer and glioblastoma treatment through discovery and innovation

Neoantigen-based immunotherapy has emerged as a transformative approach in cancer treatment, offering precision medicine strategies that target tumor-specific antigens derived from genetic, transcriptomic, and proteomic alterations unique to cancer cells. These neoantigens serve as highly specific targets for personalized therapies, promising more effective and tailored treatments. The aim of this article is to explore the advances in neoantigen-based therapies, highlighting successful treatments such as vaccines, tumor-infiltrating lymphocyte (TIL) therapy, T-cell receptor-engineered T cells therapy (TCR-T), and chimeric antigen receptor T cells therapy (CAR-T), particularly in cancer types like glioblastoma (GBM). Advances in technologies such as next-generation sequencing, RNA-based platforms, and CRISPR gene editing have accelerated the identification and validation of neoantigens, moving them closer to clinical application. Despite promising results, challenges such as tumor heterogeneity, immune evasion, and resistance mechanisms persist. The integration of AI-driven tools and multi-omic data has refined neoantigen discovery, while combination therapies are being developed to address issues like immune suppression and scalability. Additionally, the article discusses the ongoing development of personalized immunotherapies targeting tumor mutations, emphasizing the need for continued collaboration between computational and experimental approaches. Ultimately, the integration of cutting-edge technologies in neoantigen research holds the potential to revolutionize cancer care, offering hope for more effective and targeted treatments.

Predictive marker for response to trifluridine/tipiracil plus bevacizumab in metastatic colorectal cancer patients

OBJECTIVE

Trifluridine/tipiracil (FTD/TPI) is one of the options for late-line treatment of colorectal cancer (CRC). However, the specific patient populations that would particularly benefit from it remain unclear. This study attempted to identify predictive markers of chemotherapy efficacy with trifluridine/tipiracil (FTD/TPI), focusing on the RNA-editing enzyme adenosine deaminase acting on RNA 1 (ADAR1) expression and neutrophil-lymphocyte ratio (NLR).

METHODS

To assess the effectiveness of FTD/TPI in CRC patients, we retrospectively analyzed 72 CRC patients at Okayama University Hospital from 2014 to 2022.

RESULTS

Adding bevacizumab to FTD/TPI resulted in a more prolonged progression-free survival (PFS), consistent with the SUNLIGHT study findings (p = 0.0028). Among the participants, those with a high NLR had a shorter PFS (p = 0.0395). Moreover, high ADAR1 expression was associated with longer PFS (p = 0.0151). In multivariate analysis, low ADAR1 (HR = 3.43, p = 0.01) and absence of bevacizumab (HR = 4.25, p = 0.01) were identified as factors shortening PFS. The high ADAR1 group demonstrated fewer cases of progressive disease and a higher proportion of stable disease than the low ADAR1 group (p = 0.0288). Low NLR and high ADAR1 were predictive markers of prolonged PFS in the bevacizumab-treated group (p = 0.0036).

CONCLUSION

Low NLR and high ADAR1 were predictive markers for a positive response to the FTD/TPI plus bevacizumab regimen associated with prolonged PFS. The FTD/TPI plus bevacizumab regimen should be proactively implemented in the low NLR and high ADAR1 subgroups.

Generation of tumor neoantigens by RNA splicing perturbation

Immunotherapy has revolutionized cancer treatment, but the limited availability of tumor-specific neoantigens still remains a challenge. The potential of alternative mRNA splicing-derived neoantigens as a source of new immunotherapy targets has gained significant attention. Tumors exhibit unique splicing changes and splicing factor mutations which are prevalent in various cancers and play a crucial role in neoantigen production. We present advances in splicing modulation approaches, including small-molecule drugs, decoy and splice-switching antisense oligonucleotides (SSOs), CRISPR, small interfering RNAs (siRNAs), and nonsense-mediated RNA decay (NMD) inhibition, that can be adapted to enhance antitumor immune responses. Finally, we explore the clinical implications of these approaches, highlighting their potential to transform cancer immunotherapy and broaden its efficacy.

Mechanisms of immune tolerance breakdown in paraneoplastic neurological syndromes

Paraneoplastic neurological syndromes (PNS) are rare autoimmune disorders triggered by the presence of a cancer. The autoimmunity is herein directed against proteins expressed both in the tumor and in the nervous system, namely the onconeural antigens, against which are directed specific autoantibodies, each of them characterizing a neurological syndrome. The mechanisms of the immune tolerance breakdown in PNS leading to the production of specific autoantibodies directed against the nervous system and leading to the immune attack begins to be explained. Each syndrome is associated with a specific histo-molecular subtype of tumor suggesting a link between the PNS genesis and oncogenesis. The expression of the onconeural antigen by these tumors is insufficient to explain the immune tolerance breakdown. In some PNS tumors, alterations of the antigen have been identified: mutations, gene copy number variation and overexpression of transcript and protein. But in others PNS, no such molecular alterations of the onconeural antigens have been demonstrated. In these cases, other mechanisms of neoantigen generation that may be involved remain to be deciphered. Cancer outcomes of PNS tumors are also characterized by the high frequency of lymph node metastasis at diagnosis. At the primary tumor site, the antitumor immune reaction seems to be particularly intense and characterized by a prominence of B-cell and Ig-secreting plasma cells that may generate the autoantibody secretion. The immune control mechanisms leading to such organization of the immune attack are not known to date. Renewed research efforts are thus needed to better understand the mechanism of immune tolerance breakdown in each PNS and determine potential targets to meet the therapeutic challenges posed by these rare disorders.

Molecular mimicry as a mechanism of viral immune evasion and autoimmunity

Mimicry of host protein structures, or 'molecular mimicry', is a common mechanism employed by viruses to evade the host's immune system. Short linear amino acid (AA) molecular mimics can elicit cross-reactive antibodies and T cells from the host, but the prevalence of such mimics throughout the human virome has not been fully explored. Here we evaluate 134 human-infecting viruses and find significant usage of linear mimicry across the virome, particularly those in the Herpesviridae and Poxviridae families. Furthermore, host proteins related to cellular replication and inflammation, autosomes, the X chromosome, and thymic cells are enriched as viral mimicry targets. Finally, we find that short linear mimicry from Epstein-Barr virus (EBV) is higher in auto-antibodies found in patients with multiple sclerosis than previously appreciated. Our results thus hint that human-infecting viruses leverage mimicry in the course of their infection, and that such mimicry may contribute to autoimmunity, thereby prompting potential targets for therapies.

Computational immunogenomic approaches to predict response to cancer immunotherapies

Cancer immunogenomics is an emerging field that bridges genomics and immunology. The establishment of large-scale genomic collaborative efforts along with the development of new single-cell transcriptomic techniques and multi-omics approaches have enabled characterization of the mutational and transcriptional profiles of many cancer types and helped to identify clinically actionable alterations as well as predictive and prognostic biomarkers. Researchers have developed computational approaches and machine learning algorithms to accurately obtain clinically useful information from genomic and transcriptomic sequencing data from bulk tissue or single cells and explore tumours and their microenvironment. The rapid growth in sequencing and computational approaches has resulted in the unmet need to understand their true potential and limitations in enabling improvements in the management of patients with cancer who are receiving immunotherapies. In this Review, we describe the computational approaches currently available to analyse bulk tissue and single-cell sequencing data from cancer, stromal and immune cells, as well as how best to select the most appropriate tool to address various clinical questions and, ultimately, improve patient outcomes.

Utilizing immunogenomic approaches to prioritize targetable neoantigens for personalized cancer immunotherapy

Advancements in sequencing technologies and bioinformatics algorithms have expanded our ability to identify tumor-specific somatic mutation-derived antigens (neoantigens). While recent studies have shown neoantigens to be compelling targets for cancer immunotherapy due to their foreign nature and high immunogenicity, the need for increasingly accurate and cost-effective approaches to rapidly identify neoantigens remains a challenging task, but essential for successful cancer immunotherapy. Currently, gene expression analysis and algorithms for variant calling can be used to generate lists of mutational profiles across patients, but more care is needed to curate these lists and prioritize the candidate neoantigens most capable of inducing an immune response. A growing amount of evidence suggests that only a handful of somatic mutations predicted by mutational profiling approaches act as immunogenic neoantigens. Hence, unbiased screening of all candidate neoantigens predicted by Whole Genome Sequencing/Whole Exome Sequencing may be necessary to more comprehensively access the full spectrum of immunogenic neoepitopes. Once putative cancer neoantigens are identified, one of the largest bottlenecks in translating these neoantigens into actionable targets for cell-based therapies is identifying the cognate T cell receptors (TCRs) capable of recognizing these neoantigens. While many TCR-directed screening and validation assays have utilized bulk samples in the past, there has been a recent surge in the number of single-cell assays that provide a more granular understanding of the factors governing TCR-pMHC interactions. The goal of this review is to provide an overview of existing strategies to identify candidate neoantigens using genomics-based approaches and methods for assessing neoantigen immunogenicity. Additionally, applications, prospects, and limitations of some of the current single-cell technologies will be discussed. Finally, we will briefly summarize some of the recent models that have been used to predict TCR antigen specificity and analyze the TCR receptor repertoire.

Proteogenomic analysis reveals RNA as a source for tumor-agnostic neoantigen identification

Systemic pan-tumor analyses may reveal the significance of common features implicated in cancer immunogenicity and patient survival. Here, we provide a comprehensive multi-omics data set for 32 patients across 25 tumor types for proteogenomic-based discovery of neoantigens. By using an optimized computational approach, we discover a large number of tumor-specific and tumor-associated antigens. To create a pipeline for the identification of neoantigens in our cohort, we combine DNA and RNA sequencing with MS-based immunopeptidomics of tumor specimens, followed by the assessment of their immunogenicity and an in-depth validation process. We detect a broad variety of non-canonical HLA-binding peptides in the majority of patients demonstrating partially immunogenicity. Our validation process allows for the selection of 32 potential neoantigen candidates. The majority of neoantigen candidates originates from variants identified in the RNA data set, illustrating the relevance of RNA as a still understudied source of cancer antigens. This study underlines the importance of RNA-centered variant detection for the identification of shared biomarkers and potentially relevant neoantigen candidates.

Identification and Targeting of Mutant Neoantigens in Multiple Myeloma Treatment

Multiple myeloma (MM) is malignant disease characterized by the clonal proliferation of plasma cells in the bone marrow, leading to anemia, immunosuppression, and other symptoms, that is generally hard to treat. In MM, the immune system is likely exposed to neoplasia-associated neoantigens for several years before the tumor onset. Different types of neoantigens have been identified. Public or shared neoantigens derive from tumor-specific modifications often reported in several patients or across diverse tumors. They are intriguing therapeutic targets because they are frequently observed, and they have an oncogenic effect. Only a small number of public neoantigens have been recognized. Most of the neoantigens that have been identified are patient-specific or "private", necessitating a personalized approach for adaptive cell treatment. It was demonstrated that the targeting of a single greatly immunogenic neoantigen may be appropriate for tumor control. The purpose of this review was to analyze the neoantigens present in patients with MM, and to evaluate the possibility of using their presence as a prognostic factor or as a therapeutic target. We reviewed the most recent literature on neoantigen treatment strategies and on the use of bispecific, trispecific, and conjugated antibodies for the treatment of MM. Finally, a section was dedicated to the use of CAR-T in relapsed and refractory patients.

Neoepitopes prediction strategies: an integration of cancer genomics and immunoinformatics approaches

A major near-term medical impact of the genomic technology revolution will be the elucidation of mechanisms of cancer pathogenesis, leading to improvements in the diagnosis of cancer and the selection of cancer treatment. Next-generation sequencing technologies have accelerated the characterization of a tumor, leading to the comprehensive discovery of all the major alterations in a given cancer genome, followed by the translation of this information using computational and immunoinformatics approaches to cancer diagnostics and therapeutic efforts. In the current article, we review various components of cancer immunoinformatics applied to a series of fields of cancer research, including computational tools for cancer mutation detection, cancer mutation and immunological databases, and computational vaccinology.

Artificial intelligence applied in neoantigen identification facilitates personalized cancer immunotherapy

The field of cancer neoantigen investigation has developed swiftly in the past decade. Predicting novel and true neoantigens derived from large multi-omics data became difficult but critical challenges. The rise of Artificial Intelligence (AI) or Machine Learning (ML) in biomedicine application has brought benefits to strengthen the current computational pipeline for neoantigen prediction. ML algorithms offer powerful tools to recognize the multidimensional nature of the omics data and therefore extract the key neoantigen features enabling a successful discovery of new neoantigens. The present review aims to outline the significant technology progress of machine learning approaches, especially the newly deep learning tools and pipelines, that were recently applied in neoantigen prediction. In this review article, we summarize the current state-of-the-art tools developed to predict neoantigens. The standard workflow includes calling genetic variants in paired tumor and blood samples, and rating the binding affinity between mutated peptide, MHC (I and II) and T cell receptor (TCR), followed by characterizing the immunogenicity of tumor epitopes. More specifically, we highlight the outstanding feature extraction tools and multi-layer neural network architectures in typical ML models. It is noted that more integrated neoantigen-predicting pipelines are constructed with hybrid or combined ML algorithms instead of conventional machine learning models. In addition, the trends and challenges in further optimizing and integrating the existing pipelines are discussed.

Neoantigens: promising targets for cancer therapy

Recent advances in neoantigen research have accelerated the development and regulatory approval of tumor immunotherapies, including cancer vaccines, adoptive cell therapy and antibody-based therapies, especially for solid tumors. Neoantigens are newly formed antigens generated by tumor cells as a result of various tumor-specific alterations, such as genomic mutation, dysregulated RNA splicing, disordered post-translational modification, and integrated viral open reading frames. Neoantigens are recognized as non-self and trigger an immune response that is not subject to central and peripheral tolerance. The quick identification and prediction of tumor-specific neoantigens have been made possible by the advanced development of next-generation sequencing and bioinformatic technologies. Compared to tumor-associated antigens, the highly immunogenic and tumor-specific neoantigens provide emerging targets for personalized cancer immunotherapies, and serve as prospective predictors for tumor survival prognosis and immune checkpoint blockade responses. The development of cancer therapies will be aided by understanding the mechanism underlying neoantigen-induced anti-tumor immune response and by streamlining the process of neoantigen-based immunotherapies. This review provides an overview on the identification and characterization of neoantigens and outlines the clinical applications of prospective immunotherapeutic strategies based on neoantigens. We also explore their current status, inherent challenges, and clinical translation potential.

AS-CMC: a pan-cancer database of alternative splicing for molecular classification of cancer

Alternative splicing (AS) is a post-transcriptional regulation that leads to the complexity of the transcriptome. Despite the growing importance of AS in cancer research, the role of AS has not been systematically studied, especially in understanding cancer molecular classification. Herein, we analyzed the molecular subtype-specific regulation of AS using The Cancer Genome Atlas data and constructed a web-based database, named Alternative Splicing for Cancer Molecular Classification (AS-CMC). Our system harbors three analysis modules for exploring subtype-specific AS events, evaluating their phenotype association, and performing pan-cancer comparison. The number of subtype-specific AS events was found to be diverse across cancer types, and some differentially regulated AS events were recurrently found in multiple cancer types. We analyzed a subtype-specific AS in exon 11 of mitogen-activated protein kinase kinase 7 (MAP3K7) as an example of a pan-cancer AS biomarker. This AS marker showed significant association with the survival of patients with stomach adenocarcinoma. Our analysis revealed AS as an important determinant for cancer molecular classification. AS-CMC is the first web-based resource that provides a comprehensive tool to explore the biological implications of AS events, facilitating the discovery of novel AS biomarkers.

Heterogeneous RNA editing and influence of ADAR2 on mesothelioma chemoresistance and the tumor microenvironment

We previously observed increased levels of adenosine-deaminase-acting-on-dsRNA (Adar)-dependent RNA editing during mesothelioma development in mice exposed to asbestos. The aim of this study was to characterize and assess the role of ADAR-dependent RNA editing in mesothelioma. We found that tumors and mesothelioma primary cultures have higher ADAR-mediated RNA editing compared to mesothelial cells. Unsupervised clustering of editing in different genomic regions revealed heterogeneity between tumor samples as well as mesothelioma primary cultures. ADAR2 expression levels are higher in BRCA1-associated protein 1 wild-type tumors, with corresponding changes in RNA editing in transcripts and 3'UTR. ADAR2 knockdown and rescue models indicated a role in cell proliferation, altered cell cycle, increased sensitivity to antifolate treatment, and type-1 interferon signaling upregulation, leading to changes in the microenvironment in vivo. Our data indicate that RNA editing contributes to mesothelioma heterogeneity and highlights an important role of ADAR2 not only in growth regulation in mesothelioma but also in chemotherapy response, in addition to regulating inflammatory response downstream of sensing nucleic acid structures.

Characterization of Somatic Mutations That Affect Neoantigens in Non-Small Cell Lung Cancer

Purpose

Immune checkpoint inhibitors (ICIs) have recently emerged as an important option for treating patients with advanced non-small cell lung cancer (NSCLC). Neoantigens are important biomarkers and potential immunotherapy targets that play important roles in the prognosis and treatment of patients with NSCLC. This study aimed to evaluate and characterize the relationships between somatic mutations and potential neoantigens in specimens from patients who underwent surgical treatment for NSCLC.

Patients and Methods

This prospective study evaluated specimens from patients with NSCLC who underwent surgical treatment at the Peking Union Medical College, China, from June 2019 to September 2019. Whole-exome sequencing was performed for tumor tissues and corresponding normal tissues. Candidate neoantigens were predicted using generative software, and the relationships between various mutation characteristics and number of neoantigens were evaluated.

Results

Neoantigen-related gene mutations were less frequent than mutations affecting the whole genome. Genes with high neoantigen burden had more types and higher frequencies of mutations. The number of candidate neoantigens was positively correlated with missense mutations, code shift insertions/deletions, split-site variations, and nonsense mutations. However, in the multiple linear regression analysis, only missense mutations were positively correlated with the number of neoantigens. The number of neoantigens was also positively correlated with base transversions (A>C/C>A, T>G/G>T, and C>G/G>C) and negatively correlated with base transitions (A>G/G>A and C>T/T>C).

Conclusion

The number of candidate neoantigens in NSCLC specimens was associated with mutation frequency, type of mutation, and type of base substitution.

Shedding light on the hidden human proteome expands immunopeptidome in cancer

Unrestrained cellular growth and immune escape of a tumor are associated with the incidental errors of the genome and transcriptome. Advances in next-generation sequencing have identified thousands of genomic and transcriptomic aberrations that generate variant peptides that assemble the hidden proteome, further expanding the immunopeptidome. Emerging next-generation sequencing technologies and a number of computational methods estimated the abundance of immune infiltration from bulk transcriptome have advanced our understanding of tumor microenvironments. Here, we will characterize several major types of tumor-specific antigens arising from single-nucleotide variants, insertions and deletions, gene fusion, alternative splicing, RNA editing and non-coding RNAs. Finally, we summarize the current state-of-the-art computational and experimental approaches or resources and provide an integrative pipeline for the identification of candidate tumor antigens. Together, the systematic investigation of the hidden proteome in cancer will help facilitate the development of effective and durable immunotherapy targets for cancer.

Discovery and Pharmacological Characterization of JNJ-64619178, a Novel Small-Molecule Inhibitor of PRMT5 with Potent Antitumor Activity

The protein arginine methyltransferase 5 (PRMT5) methylates a variety of proteins involved in splicing, multiple signal transduction pathways, epigenetic control of gene expression, and mechanisms leading to protein expression required for cellular proliferation. Dysregulation of PRMT5 is associated with clinical features of several cancers, including lymphomas, lung cancer, and breast cancer. Here, we describe the characterization of JNJ-64619178, a novel, selective, and potent PRMT5 inhibitor, currently in clinical trials for patients with advanced solid tumors, non-Hodgkin's lymphoma, and lower-risk myelodysplastic syndrome. JNJ-64619178 demonstrated a prolonged inhibition of PRMT5 and potent antiproliferative activity in subsets of cancer cell lines derived from various histologies, including lung, breast, pancreatic, and hematological malignancies. In primary acute myelogenous leukemia samples, the presence of splicing factor mutations correlated with a higher ex vivo sensitivity to JNJ-64619178. Furthermore, the potent and unique mechanism of inhibition of JNJ-64619178, combined with highly optimized pharmacological properties, led to efficient tumor growth inhibition and regression in several xenograft models in vivo, with once-daily or intermittent oral-dosing schedules. An increase in splicing burden was observed upon JNJ-64619178 treatment. Overall, these observations support the continued clinical evaluation of JNJ-64619178 in patients with aberrant PRMT5 activity-driven tumors.

Targeting the Atf7ip-Setdb1 Complex Augments Antitumor Immunity by Boosting Tumor Immunogenicity

Substantial progress has been made in understanding how tumors escape immune surveillance. However, few measures to counteract tumor immune evasion have been developed. Suppression of tumor antigen expression is a common adaptive mechanism that cancers use to evade detection and destruction by the immune system. Epigenetic modifications play a critical role in various aspects of immune invasion, including the regulation of tumor antigen expression. To identify epigenetic regulators of tumor antigen expression, we established a transplantable syngeneic tumor model of immune escape with silenced antigen expression and used this system as a platform for a CRISPR-Cas9 suppressor screen for genes encoding epigenetic modifiers. We found that disruption of the genes encoding either of the chromatin modifiers activating transcription factor 7-interacting protein (Atf7ip) or its interacting partner SET domain bifurcated histone lysine methyltransferase 1 (Setdb1) in tumor cells restored tumor antigen expression. This resulted in augmented tumor immunogenicity concomitant with elevated endogenous retroviral (ERV) antigens and mRNA intron retention. ERV disinhibition was associated with a robust type I interferon response and increased T-cell infiltration, leading to rejection of cells lacking intact Atf7ip or Setdb1. ATF7IP or SETDB1 expression inversely correlated with antigen processing and presentation pathways, interferon signaling, and T-cell infiltration and cytotoxicity in human cancers. Our results provide a rationale for targeting Atf7ip or Setdb1 in cancer immunotherapy.

Implications of Antigen Selection on T Cell-Based Immunotherapy

Many immunotherapies rely on CD8+ effector T cells to recognize and kill cognate tumor cells. These T cell-based immunotherapies include adoptive cell therapy, such as CAR T cells or transgenic TCR T cells, and anti-cancer vaccines which expand endogenous T cell populations. Tumor mutation burden and the choice of antigen are among the most important aspects of T cell-based immunotherapies. Here, we highlight various classes of cancer antigens, including self, neojunction-derived, human endogenous retrovirus (HERV)-derived, and somatic nucleotide variant (SNV)-derived antigens, and consider their utility in T cell-based immunotherapies. We further discuss the respective anti-tumor/anti-self-properties that influence both the degree of immunotolerance and potential off-target effects associated with each antigen class.

Update on the most promising biomarkers of response to immune checkpoint inhibitors in clear cell renal cell carcinoma

In the last few years, the standard of care for metastatic clear cell renal cell carcinoma (mccRCC) has changed dramatically with the emergence of the immune checkpoint inhibitors (ICI): anti-PD(L)-1 used as a monotherapy or as in combination either with an anti CTLA-4 or with an anti-angiogenic molecule (VEGFR tyrosine kinase inhibitor (TKI)). These combinations are now recommended in first line setting for mccRCC, according to the last European recommendations. In the face of these new therapeutic options, the question of selecting the best treatment arises as well as the optimal sequence. Predictive biomarkers are required to guide the therapeutic choice and provide a personalized treatment for each patient. This narrative review will provide an overview of the main predictive biomarkers assessed in mccRCC treatment, with a particular focus on mRNA panel signatures.

Main Strategies for the Identification of Neoantigens

Genetic instability of tumors leads to the appearance of numerous tumor-specific somatic mutations that could potentially result in the production of mutated peptides that are presented on the cell surface by the MHC molecules. Peptides of this kind are commonly called neoantigens. Their presence on the cell surface specifically distinguishes tumors from healthy tissues. This feature makes neoantigens a promising target for immunotherapy. The rapid evolution of high-throughput genomics and proteomics makes it possible to implement these techniques in clinical practice. In particular, they provide useful tools for the investigation of neoantigens. The most valuable genomic approach to this problem is whole-exome sequencing coupled with RNA-seq. High-throughput mass-spectrometry is another option for direct identification of MHC-bound peptides, which is capable of revealing the entire MHC-bound peptidome. Finally, structure-based predictions could significantly improve the understanding of physicochemical and structural features that affect the immunogenicity of peptides. The development of pipelines combining such tools could improve the accuracy of the peptide selection process and decrease the required time. Here we present a review of the main existing approaches to investigating the neoantigens and suggest a possible ideal pipeline that takes into account all modern trends in the context of neoantigen discovery.

Towards new horizons: characterization, classification and implications of the tumour antigenic repertoire

Immune-checkpoint inhibition provides an unmatched level of durable clinical efficacy in various malignancies. Such therapies promote the activation of antigen-specific T cells, although the precise targets of these T cells remain unknown. Exploiting these targets holds great potential to amplify responses to treatment, such as by combining immune-checkpoint inhibition with therapeutic vaccination or other antigen-directed treatments. In this scenario, the pivotal hurdle remains the definition of valid HLA-restricted tumour antigens, which requires several levels of evidence before targets can be established with sufficient confidence. Suitable antigens might include tumour-specific antigens with alternative or wild-type sequences, tumour-associated antigens and cryptic antigens that exceed exome boundaries. Comprehensive antigen classification is required to enable future clinical development and the definition of innovative treatment strategies. Furthermore, clinical development remains challenging with regard to drug manufacturing and regulation, as well as treatment feasibility. Despite these challenges, treatments based on diligently curated antigens combined with a suitable therapeutic platform have the potential to enable optimal antitumour efficacy in patients, either as monotherapies or in combination with other established immunotherapies. In this Review, we summarize the current state-of-the-art approaches for the identification of candidate tumour antigens and provide a structured terminology based on their underlying characteristics.

Immune-checkpoint inhibition has transformed the treatment of patients with advanced-stage cancers. Nonetheless, the specific antigens targeted by T cells that are activated or reactivated by these agents remain largely unknown. In this Review, the authors describe the characterization and classification of tumour antigens including descriptions of the most appropriate detection methods, and discuss potential regulatory issues regarding the use of tumour antigen-based therapeutics.

Immune-checkpoint inhibition has profoundly changed the paradigm for the care of several malignancies. Although these therapies activate antigen-specific T cells, the precise mechanisms of action and their specific targets remain largely unknown.

Anticancer immunotherapies encompass two fundamentally different therapeutic principles based on knowledge of their therapeutic targets, that either have been characterized (antigen-aware) or have remained elusive (antigen-unaware).

HLA-presented tumour antigens of potential therapeutic relevance can comprise alternative or wild-type amino acid sequences and can be subdivided into different categories based on their mechanisms of formation.

The available methods for the detection of HLA-presented antigens come with intrinsic challenges and limitations and, therefore, warrant multiple lines of evidence of robust tumour specificity before being considered for clinical use.

Knowledge obtained using various antigen-detection strategies can be combined with different therapeutic platforms to create individualized therapies that hold great promise, including when combined with already established immunotherapies.

Tailoring immunotherapies while taking into account the substantial heterogeneity of malignancies as well as that of HLA loci not only requires innovative science, but also demands innovative approaches to trial design and drug regulation.

Hypoxia-induced alternative splicing: the 11th Hallmark of Cancer

Hypoxia-induced alternative splicing is a potent driving force in tumour pathogenesis and progression. In this review, we update currents concepts of hypoxia-induced alternative splicing and how it influences tumour biology. Following brief descriptions of tumour-associated hypoxia and the pre-mRNA splicing process, we review the many ways hypoxia regulates alternative splicing and how hypoxia-induced alternative splicing impacts each individual hallmark of cancer. Hypoxia-induced alternative splicing integrates chemical and cellular tumour microenvironments, underpins continuous adaptation of the tumour cellular microenvironment responsible for metastatic progression and plays clear roles in oncogene activation and autonomous tumour growth, tumor suppressor inactivation, tumour cell immortalization, angiogenesis, tumour cell evasion of programmed cell death and the anti-tumour immune response, a tumour-promoting inflammatory response, adaptive metabolic re-programming, epithelial to mesenchymal transition, invasion and genetic instability, all of which combine to promote metastatic disease. The impressive number of hypoxia-induced alternative spliced protein isoforms that characterize tumour progression, classifies hypoxia-induced alternative splicing as the 11th hallmark of cancer, and offers a fertile source of potential diagnostic/prognostic markers and therapeutic targets.