Identification of tumor antigens with immunopeptidomics

Antigen self-presenting dendrosomes swallowing nanovaccines boost antigens and STING agonists codelivery for cancer immunotherapy

Cancer vaccines show promise by eliciting tumor-specific cytotoxic T lymphocytes (CTL) responses. Efficient cytosolic co-delivery of antigens and adjuvants to dendritic cells (DCs) is crucial for vaccines to induce anti-tumor immunity. However, peptide- or nucleic acid-based biomolecules like tumor antigens and STING agonist cyclic-di-GMP (cdGMP) are prone to endosomal degradation, resulting in low cytosolic delivery and CTL response rates. Cationic nanocarriers can improve cytosolic delivery, but their positive charges induce off-target effects. Here, we develop cationic poly(ester amide) based nanoparticles co-loaded with antigens and adjuvant cdGMP (NP(cG, OVA)) for efficient cytosolic delivery and swallow them within antigen self-presenting DCs-derived dendrosomes (ODs) for lymph nodes (LNs) homing. The constructed dendrosomes swallowing nanovaccines ODs/NP(cG, OVA) demonstrated significantly reduced liver accumulation and enhanced LNs and DCs targeting compared to NP(cG, OVA). ODs/NP(cG, OVA) effectively cross-dressed the antigen epitopes on the shell to DCs and facilitated internalization of NP(cG, OVA), realizing DCs cytosolic co-delivery of antigens and adjuvants, thereby promoting antigen presentation, maturation and inflammatory cytokines secretion of DCs. Consequently, DCs stimulated by ODs/NP(cG, OVA) effectively induced activation, proliferation, and differentiation of antigen-specific CTLs that provided robust immune protection against tumor invasion. This work presents a powerful vaccine strategy for cancer immunotherapy.

SILAC-based quantification reveals modulation of the immunopeptidome in BRAF and MEK inhibitor sensitive and resistant melanoma cells

Background

The immunopeptidome is constantly monitored by T cells to detect foreign or aberrant HLA peptides. It is highly dynamic and reflects the current cellular state, enabling the immune system to recognize abnormal cellular conditions, such as those present in cancer cells. To precisely determine how changes in cellular processes, such as those induced by drug treatment, affect the immunopeptidome, quantitative immunopeptidomics approaches are essential.

Methods

To meet this need, we developed a pulsed SILAC-based method for quantitative immunopeptidomics. Metabolic labeling with lysine, arginine, and leucine enabled isotopic labeling of nearly all HLA peptides across all allotypes (> 90% on average). We established a data analysis workflow that integrates the de novo sequencing-based tool Peptide-PRISM for comprehensive HLA peptide identification with MaxQuant for accurate quantification.

Results

We employed this strategy to explore the modulation of the immunopeptidome upon MAPK pathway inhibition (MAPKi) and to investigate alterations associated with early cellular responses to inhibitor treatment and acquired resistance to MAPKi. Our analyses demonstrated significant changes in the immunopeptidome early during MAPKi treatment and in the resistant state. Moreover, we identified putative tumor-specific cryptic HLA peptides linked to these processes that might represent exploitable targets for cancer immunotherapy.

Conclusions

We have developed a new mass spectrometric approach that allowed us to investigate the effects of common MAPK inhibitors on the immunopeptidome of melanoma cells. This finally led to the discovery of new potential targets for cancer immunotherapy.

Relevance of mutation-derived neoantigens and non-classical antigens for anticancer therapies

Efficacy of cancer immunotherapies relies on correct recognition of tumor antigens by lymphocytes, eliciting thus functional responses capable of eliminating tumor cells. Therefore, important efforts have been carried out in antigen identification, with the aim of understanding mechanisms of response to immunotherapy and to design safer and more efficient strategies. In addition to classical tumor-associated antigens identified during the last decades, implementation of next-generation sequencing methodologies is enabling the identification of neoantigens (neoAgs) arising from mutations, leading to the development of new neoAg-directed therapies. Moreover, there are numerous non-classical tumor antigens originated from other sources and identified by new methodologies. Here, we review the relevance of neoAgs in different immunotherapies and the results obtained by applying neoAg-based strategies. In addition, the different types of non-classical tumor antigens and the best approaches for their identification are described. This will help to increase the spectrum of targetable molecules useful in cancer immunotherapies.

Immunogenic peptides putatively from intratumor microbes: Opportunities for colorectal cancer treatment

Recent evidence has confirmed the presence of intratumor microbes, yet their impact on the immunopeptidome remains largely unexplored. Here we introduced an integrated strategy to identify the immunopeptidome originated from intratumor microbes. Analyzing 10 colorectal cancer (CRC) patients, we identified 154 putative microbe-derived human leukocyte antigen (HLA)-I ligands. Predominantly bacterial in origin, these peptides were notably abundant in Fusobacterium nucleatum, the most prevalent bacterium differentiating between normal and tumor tissues. We discovered 20 peptides originating from F. nucleatum, thirteen of which, including two peptides shared across multiple patients, were tumor specific. Validation experiments confirmed that the putative microbe-derived peptide could activate CD8+ T cell responses. Our findings indicate that HLA-I molecules are capable of presenting intratumor microbe-derived peptides in CRC, potentially contributing to CD8+ T cell-mediated immunity and suggesting potential strategies for cancer immunotherapy.

Proteomics Can Rise to the Challenge of Pseudogenes' Coding Nature

Throughout the past decade, technological advances in genomics and transcriptomics have revealed pervasive translation throughout mammalian genomes. These putative proteins are usually excluded from proteomics analyses, as they are absent from common protein repositories. A sizable portion of these noncanonical proteins is translated from pseudogenes. Pseudogenes are commonly termed defective copies of coding genes unable to produce proteins. Here, we suggest that proteomics can help in their annotation. First, we define important terms and review specific examples underlining the caveats in pseudogene annotation and their coding potential. Then, we will discuss the challenges inherent to pseudogenes that have thus far rendered complex their confidence in omics data. Finally, we identify recent developments in experimental procedures, instrumentation, and computational methods in proteomics that put the field in a unique position to solve the pseudogene annotation conundrum.

Proteogenomics offers a novel avenue in neoantigen identification for cancer immunotherapy

Cancer neoantigens are tumor-specific non-synonymous mutant peptides that activate the immune system to produce an anti-tumor response. Personalized cancer vaccines based on neoantigens are currently one of the most promising therapeutic approaches for cancer treatment. By utilizing the unique mutations within each patient's tumor, these vaccines aim to elicit a strong and specific immune response against cancer cells. However, the identification of neoantigens remains challenging due to the low accuracy of current prediction tools and the high false-positive rate of candidate neoantigens. Since the concept of "proteogenomics" emerged in 2004, it has evolved rapidly with the increased sequencing depth of next-generation sequencing technologies and the maturation of mass spectrometry-based proteomics technologies to become a more comprehensive approach to neoantigen identification, allowing the discovery of high-confidence candidate neoantigens. In this review, we summarize the reason why cancer neoantigens have become attractive targets for immunotherapy, the mechanism of cancer vaccines and the advances in cancer immunotherapy. Considerations relevant to the application emerging of proteogenomics technologies for neoantigen identification and challenges in this field are described.

Proteomics appending a complementary dimension to precision oncotherapy

Recent advances in high-throughput proteomic profiling technologies have facilitated the precise quantification of numerous proteins across multiple specimens concurrently. Researchers have the opportunity to comprehensively analyze the molecular signatures in plentiful medical specimens or disease pattern cell lines. Along with advances in data analysis and integration, proteomics data could be efficiently consolidated and employed to recognize precise elementary molecular mechanisms and decode individual biomarkers, guiding the precision treatment of tumors. Herein, we review a broad array of proteomics technologies and the progress and methods for the integration of proteomics data and further discuss how to better merge proteomics in precision medicine and clinical settings.

Advancements in mass spectrometry-based proteomics: a new era in pathology research and diagnostics

BACKGROUND

Mass spectrometry (MS)-based proteomics is rapidly transforming pathology research and diagnostics by enabling comprehensive studies of protein expression and post-translational modifications (PTMs).

OBJECTIVE

This article discusses recent advancements in MS-based proteomics, focusing on emerging technologies in sample preparation, MS instrumentation, and data analysis. These developments are scrutinized for their applications in clinical cohort studies and molecular pathology diagnostics.

MATERIALS AND METHODS

The article reviews innovations in automated sample preparation, chromatography systems, advanced MS technologies, and proteomic data analysis in the context of pathology. Specific applications such as liquid biopsy, spike-in heavy peptide panels, immunopeptidomics, and PTM screening are highlighted alongside opportunities for data integration.

RESULTS

Recent technological improvements have significantly increased the throughput, precision, and scope of proteomic studies, enabling the analysis of large clinical cohorts and small specimens with unprecedented sensitivity. Advanced MS techniques have broadened applications, opening new avenues for discovery and diagnosis of marker proteins and therapeutic targets.

CONCLUSION

Advancements in MS-based proteomics have created new opportunities in clinical research and diagnostics. By facilitating more comprehensive and integrated analyses of proteomes, these technologies are set to play a pivotal role in the future of personalized medicine and pathology research.

New insights for the development of efficient DNA vaccines

Despite the great potential of DNA vaccines for a broad range of applications, ranging from prevention of infections, over treatment of autoimmune and allergic diseases to cancer immunotherapies, the implementation of such therapies for clinical treatment is far behind the expectations up to now. The main reason is the poor immunogenicity of DNA vaccines in humans. Consequently, the improvement of the performance of DNA vaccines in vivo is required. This mini-review provides an overview of the current state of DNA vaccines and the various strategies to enhance the immunogenic potential of DNA vaccines, including (i) the optimization of the DNA construct itself regarding size, nuclear transfer and transcriptional regulation; (ii) the use of appropriate adjuvants; and (iii) improved delivery, for example, by careful choice of the administration route, physical methods such as electroporation and nanomaterials that may allow cell type-specific targeting. Moreover, combining nanoformulated DNA vaccines with other immunotherapies and prime-boost strategies may help to enhance success of treatment.

Beneath the Surface: Neoantigens beyond Chromosomal DNA Mutations

The conventional wisdom is that the overwhelming majority of neoantigens arise from chromosomal DNA mutations; however, recent studies show that posttranscriptional and posttranslational events can also generate neoantigens. This commentary provides an overview of known and potential sources of nonchromosomal neoantigens, emerging technologies, and clinical trials that may move this field forward to redefine immunologically "hot/cold" tumors and develop next-generation immunotherapeutic approaches.

Validation and quantification of peptide antigens presented on MHCs using SureQuant

Vaccines and immunotherapies that target peptide-major histocompatibility complexes (peptide-MHCs) have the potential to address multiple unmet medical needs in cancer and infectious disease. Designing vaccines and immunotherapies to target peptide-MHCs requires accurate identification of target peptides in infected or cancerous cells or tissue, and may require absolute or relative quantification to identify abundant targets and measure changes in presentation under different treatment conditions. Internal standard parallel reaction monitoring (also known as 'SureQuant') can be used to validate and/or quantify MHC peptides previously identified by using untargeted methods such as data-dependent acquisition. SureQuant MHC has three main use cases: (i) conclusive confirmation of the identities of putative MHC peptides via comparison with an internal synthetic stable isotope labeled (SIL) peptide standard; (ii) accurate relative quantification by using pre-formed heavy isotope-labeled peptide-MHC complexes (hipMHCs) containing SIL peptides as internal controls for technical variation; and (iii) absolute quantification of each target peptide by using different amounts of hipMHCs loaded with synthetic peptides containing one, two or three SIL amino acids to provide an internal standard curve. Absolute quantification can help determine whether the abundance of a peptide-MHC is sufficient for certain therapeutic modalities. SureQuant MHC therefore provides unique advantages for immunologists seeking to confidently validate antigenic targets and understand the dynamics of the MHC repertoire. After synthetic standards are ordered (3-4 weeks), this protocol can be carried out in 3-4 days and is suitable for individuals with mass spectrometry experience who are comfortable with customizing instrument methods.

ImmuneApp for HLA-I epitope prediction and immunopeptidome analysis

Advances in mass spectrometry accelerates the characterization of HLA ligandome, necessitating the development of efficient methods for immunopeptidomics analysis and (neo)antigen prediction. We develop ImmuneApp, an interpretable deep learning framework trained on extensive HLA ligand datasets, which improves the prediction of HLA-I epitopes, prioritizes neoepitopes, and enhances immunopeptidomics deconvolution. ImmuneApp extracts informative embeddings and identifies key residues for pHLA binding. We also present a more accurate model-based deconvolution approach and systematically analyzed 216 multi-allelic immunopeptidomics samples, identifying 835,551 ligands restricted to over 100 HLA-I alleles. Our investigation reveals the effectiveness of the composite model, denoted as ImmuneApp-MA, which integrates mono- and multi-allelic data to enhance predictive performance. Leveraging ImmuneApp-MA as a pre-trained model, we built ImmuneApp-Neo, an immunogenicity predictor that outperforms existing methods for prioritizing immunogenic neoepitope. ImmuneApp demonstrates its utility across various immunopeptidomics datasets, which will promote the discovery of novel neoantigens and the development of new immunotherapies.

A comprehensive proteogenomic pipeline for neoantigen discovery to advance personalized cancer immunotherapy

The accurate identification and prioritization of antigenic peptides is crucial for the development of personalized cancer immunotherapies. Publicly available pipelines to predict clinical neoantigens do not allow direct integration of mass spectrometry immunopeptidomics data, which can uncover antigenic peptides derived from various canonical and noncanonical sources. To address this, we present an end-to-end clinical proteogenomic pipeline, called NeoDisc, that combines state-of-the-art publicly available and in-house software for immunopeptidomics, genomics and transcriptomics with in silico tools for the identification, prediction and prioritization of tumor-specific and immunogenic antigens from multiple sources, including neoantigens, viral antigens, high-confidence tumor-specific antigens and tumor-specific noncanonical antigens. We demonstrate the superiority of NeoDisc in accurately prioritizing immunogenic neoantigens over recent prioritization pipelines. We showcase the various features offered by NeoDisc that enable both rule-based and machine-learning approaches for personalized antigen discovery and neoantigen cancer vaccine design. Additionally, we demonstrate how NeoDisc's multiomics integration identifies defects in the cellular antigen presentation machinery, which influence the heterogeneous tumor antigenic landscape.

Molecular targets and strategies in the development of nucleic acid cancer vaccines: from shared to personalized antigens

Recent breakthroughs in cancer immunotherapies have emphasized the importance of harnessing the immune system for treating cancer. Vaccines, which have traditionally been used to promote protective immunity against pathogens, are now being explored as a method to target cancer neoantigens. Over the past few years, extensive preclinical research and more than a hundred clinical trials have been dedicated to investigating various approaches to neoantigen discovery and vaccine formulations, encouraging development of personalized medicine. Nucleic acids (DNA and mRNA) have become particularly promising platform for the development of these cancer immunotherapies. This shift towards nucleic acid-based personalized vaccines has been facilitated by advancements in molecular techniques for identifying neoantigens, antigen prediction methodologies, and the development of new vaccine platforms. Generating these personalized vaccines involves a comprehensive pipeline that includes sequencing of patient tumor samples, data analysis for antigen prediction, and tailored vaccine manufacturing. In this review, we will discuss the various shared and personalized antigens used for cancer vaccine development and introduce strategies for identifying neoantigens through the characterization of gene mutation, transcription, translation and post translational modifications associated with oncogenesis. In addition, we will focus on the most up-to-date nucleic acid vaccine platforms, discuss the limitations of cancer vaccines as well as provide potential solutions, and raise key clinical and technical considerations in vaccine development.

Protein isoform-centric therapeutics: expanding targets and increasing specificity

Most protein-coding genes produce multiple protein isoforms; however, these isoforms are commonly neglected in drug discovery. The expression of protein isoforms can be specific to a disease, tissue and/or developmental stage, and this specific expression can be harnessed to achieve greater drug specificity than pan-targeting of all gene products and to enable improved treatments for diseases caused by aberrant protein isoform production. In recent years, several protein isoform-centric therapeutics have been developed. Here, we collate these studies and clinical trials to highlight three distinct but overlapping modes of action for protein isoform-centric drugs: isoform switching, isoform introduction or depletion, and modulation of isoform activity. In addition, we discuss how protein isoforms can be used clinically as targets for cell type-specific drug delivery and immunotherapy, diagnostic biomarkers and sources of cancer neoantigens. Collectively, we emphasize the value of a focus on isoforms as a route to discovering drugs with greater specificity and fewer adverse effects. This approach could enable the targeting of proteins for which pan-inhibition of all isoforms is toxic and poorly tolerated.

Microproteins unveiling new dimensions in cancer

In the complex landscape of cancer biology, the discovery of microproteins has triggered a paradigm shift, thereby, challenging the conventional conceptions of gene regulation. Though overlooked for years, these entities encoded by the small open reading frames (100-150 codons), have a significant impact on various cellular processes. As precision medicine pioneers delve deeper into the genome and proteome, microproteins have come into the limelight. Typically characterized by a single protein domain that directly binds to the target protein complex and regulates their assembly, these microproteins have been shown to play a key role in fundamental biological processes such as RNA processing, DNA repair, and metabolism regulation. Techniques for identification and characterization, such as ribosome profiling and proteogenomic approaches, have unraveled unique mechanisms by which these microproteins regulate cell signaling or pathological processes in most diseases including cancer. However, the functional relevance of these microproteins in cancer remains unclear. In this context, the current review aims to "rethink the essence of these genes" and explore "how these hidden players-microproteins orchestrate the signaling cascades of cancer, both as accelerators and brakes.".

Exploring the dynamic landscape of immunopeptidomics: Unravelling posttranslational modifications and navigating bioinformatics terrain

Immunopeptidomics is becoming an increasingly important field of study. The capability to identify immunopeptides with pivotal roles in the human immune system is essential to shift the current curative medicine towards personalized medicine. Throughout the years, the field has matured, giving insight into the current pitfalls. Nowadays, it is commonly accepted that generalizing shotgun proteomics workflows is malpractice because immunopeptidomics faces numerous challenges. While many of these difficulties have been addressed, the road towards the ideal workflow remains complicated. Although the presence of Posttranslational modifications (PTMs) in the immunopeptidome has been demonstrated, their identification remains highly challenging despite their significance for immunotherapies. The large number of unpredictable modifications in the immunopeptidome plays a pivotal role in the functionality and these challenges. This review provides a comprehensive overview of the current advancements in immunopeptidomics. We delve into the challenges associated with identifying PTMs within the immunopeptidome, aiming to address the current state of the field.

Evidence for widespread translation of 5' untranslated regions

Ribosome profiling experiments support the translation of a range of novel human open reading frames. By contrast, most peptides from large-scale proteomics experiments derive from just one source, 5' untranslated regions. Across the human genome we find evidence for 192 translated upstream regions, most of which would produce protein isoforms with extended N-terminal ends. Almost all of these N-terminal extensions are from highly abundant genes, which suggests that the novel regions we detect are just the tip of the iceberg. These upstream regions have characteristics that are not typical of coding exons. Their GC-content is remarkably high, even higher than 5' regions in other genes, and a large majority have non-canonical start codons. Although some novel upstream regions have cross-species conservation - five have orthologues in invertebrates for example - the reading frames of two thirds are not conserved beyond simians. These non-conserved regions also have no evidence of purifying selection, which suggests that much of this translation is not functional. In addition, non-conserved upstream regions have significantly more peptides in cancer cell lines than would be expected, a strong indication that an aberrant or noisy translation initiation process may play an important role in translation from upstream regions.

MHC-I-presented non-canonical antigens expand the cancer immunotherapy targets in acute myeloid leukemia

Identification of tumor neoantigens is indispensable for the development of cancer immunotherapies. However, we are still lacking knowledge about the potential neoantigens derived from sequences outside protein-coding regions. Here, we comprehensively characterized the immunopeptidome landscape by integrating multi-omics data in acute myeloid leukemia (AML). Both canonical and non-canonical MHC-associated peptides (MAPs) in AML were identified. We found that the quality and characteristics of ncMAPs are comparable or superior to cMAPs, suggesting ncMAPs are indispensable sources for tumor neoantigens. We further proposed a computational framework to prioritize the neoantigens by integrating additional transcriptome and immunopeptidome in normal tissues. Notably, 6 of prioritized 13 neoantigens were derived from ncMAPs. The expressions of corresponding source genes are highly related to infiltrations of immune cells. Finally, a risk model was developed, which exhibited good performance for clinical prognosis in AML. Our findings expand potential cancer immunotherapy targets and provide in-depth insights into AML treatment, laying a new foundation for precision therapies in AML.

Microproteins encoded by noncanonical ORFs are a major source of tumor-specific antigens in a liver cancer patient meta-cohort

The expression of tumor-specific antigens during cancer progression can trigger an immune response against the tumor. Here, we investigate if microproteins encoded by noncanonical open reading frames (ncORFs) are a relevant source of tumor-specific antigens. We analyze RNA sequencing data from 117 hepatocellular carcinoma (HCC) tumors and matched healthy tissue together with ribosome profiling and immunopeptidomics data. Combining human leukocyte antigen-epitope binding predictions and experimental validation experiments, we conclude that around 40% of the tumor-specific antigens in HCC are likely to be derived from ncORFs, including two peptides that can trigger an immune response in humanized mice. We identify a subset of 33 tumor-specific long noncoding RNAs expressing novel cancer antigens shared by more than 10% of the HCC samples analyzed, which, when combined, cover a large proportion of the patients. The results of the study open avenues for extending the range of anticancer vaccines.

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.

IMMUNOTAR - Integrative prioritization of cell surface targets for cancer immunotherapy

Cancer remains a leading cause of mortality globally. Recent improvements in survival have been facilitated by the development of less toxic immunotherapies; however, identifying targets for immunotherapies remains a challenge in the field. To address this challenge, we developed IMMUNOTAR, a computational tool that systematically prioritizes and identifies candidate immunotherapeutic targets. IMMUNOTAR integrates user-provided RNA-sequencing or proteomics data with quantitative features extracted from publicly available databases based on predefined optimal immunotherapeutic target criteria and quantitatively prioritizes potential surface protein targets. We demonstrate the utility and flexibility of IMMUNOTAR using three distinct datasets, validating its effectiveness in identifying both known and new potential immunotherapeutic targets within the analyzed cancer phenotypes. Overall, IMMUNOTAR enables the compilation of data from multiple sources into a unified platform, allowing users to simultaneously evaluate surface proteins across diverse criteria. By streamlining target identification, IMMUNOTAR empowers researchers to efficiently allocate resources and accelerate immunotherapy development.

Cellular control of protein levels: A systems biology perspective

How cells regulate protein levels is a central question of biology. Over the past decades, molecular biology research has provided profound insights into the mechanisms and the molecular machinery governing each step of the gene expression process, from transcription to protein degradation. Recent advances in transcriptomics and proteomics have complemented our understanding of these fundamental cellular processes with a quantitative, systems-level perspective. Multi-omic studies revealed significant quantitative, kinetic and functional differences between the genome, transcriptome and proteome. While protein levels often correlate with mRNA levels, quantitative investigations have demonstrated a substantial impact of translation and protein degradation on protein expression control. In addition, protein-level regulation appears to play a crucial role in buffering protein abundances against undesirable mRNA expression variation. These findings have practical implications for many fields, including gene function prediction and precision medicine.

HLAIImaster: a deep learning method with adaptive domain knowledge predicts HLA II neoepitope immunogenic responses

While significant strides have been made in predicting neoepitopes that trigger autologous CD4+ T cell responses, accurately identifying the antigen presentation by human leukocyte antigen (HLA) class II molecules remains a challenge. This identification is critical for developing vaccines and cancer immunotherapies. Current prediction methods are limited, primarily due to a lack of high-quality training epitope datasets and algorithmic constraints. To predict the exogenous HLA class II-restricted peptides across most of the human population, we utilized the mass spectrometry data to profile >223 000 eluted ligands over HLA-DR, -DQ, and -DP alleles. Here, by integrating these data with peptide processing and gene expression, we introduce HLAIImaster, an attention-based deep learning framework with adaptive domain knowledge for predicting neoepitope immunogenicity. Leveraging diverse biological characteristics and our enhanced deep learning framework, HLAIImaster is significantly improved against existing tools in terms of positive predictive value across various neoantigen studies. Robust domain knowledge learning accurately identifies neoepitope immunogenicity, bridging the gap between neoantigen biology and the clinical setting and paving the way for future neoantigen-based therapies to provide greater clinical benefit. In summary, we present a comprehensive exploitation of the immunogenic neoepitope repertoire of cancers, facilitating the effective development of "just-in-time" personalized vaccines.

Metabolic rewiring and communication in cancer immunity

The immune system shapes tumor development and progression. Although immunotherapy has transformed cancer treatment, its overall efficacy remains limited, underscoring the need to uncover mechanisms to improve therapeutic effects. Metabolism-associated processes, including intracellular metabolic reprogramming and intercellular metabolic crosstalk, are emerging as instructive signals for anti-tumor immunity. Here, we first summarize the roles of intracellular metabolic pathways in controlling immune cell function in the tumor microenvironment. How intercellular metabolic communication regulates anti-tumor immunity, and the impact of metabolites or nutrients on signaling events, are also discussed. We then describe how targeting metabolic pathways in tumor cells or intratumoral immune cells or via nutrient-based interventions may boost cancer immunotherapies. Finally, we conclude with discussions on profiling and functional perturbation methods of metabolic activity in intratumoral immune cells, and perspectives on future directions. Uncovering the mechanisms for metabolic rewiring and communication in the tumor microenvironment may enable development of novel cancer immunotherapies.

Identification of HLA-A*11:01 and A*02:01-Restricted EBV Peptides Using HLA Peptidomics

Epstein-Barr Virus (EBV) is closely linked to nasopharyngeal carcinoma (NPC), notably prevalent in southern China. Although type II latency of EBV plays a crucial role in the development of NPC, some lytic genes and intermittent reactivation are also critical for viral propagation and tumor progression. Since T cell-mediated immunity is effective in targeted killing of EBV-positive cells, it is important to identify EBV-derived peptides presented by highly prevalent human leukocyte antigen class I (HLA-I) molecules throughout the EBV life cycle. Here, we constructed an EBV-positive NPC cell model to evaluate the presentation of EBV lytic phase peptides on streptavidin-tagged specific HLA-I molecules. Utilizing a mass spectrometry (LC-MS/MS)-based immunopeptidomic approach, we characterized eleven novel EBV peptides as well as two previously identified peptides. Furthermore, we determined these peptides were immunogenic and could stimulate PBMCs from EBV VCA/NA-IgA positive donors in an NPC endemic southern Chinese population. Overall, this work demonstrates that highly prevalent HLA-I-specific EBV peptides can be captured and functionally presented to elicit immune responses in an in vitro model, which provides insight into the epitopes presented during EBV lytic cycle and reactivation. It expands the range of viral targets for potential NPC early diagnosis and treatment.

Recent Findings on Therapeutic Cancer Vaccines: An Updated Review

Cancer remains one of the global leading causes of death and various vaccines have been developed over the years against it, including cell-based, nucleic acid-based, and viral-based cancer vaccines. Although many vaccines have been effective in in vivo and clinical studies and some have been FDA-approved, there are major limitations to overcome: (1) developing one universal vaccine for a specific cancer is difficult, as tumors with different antigens are different for different individuals, (2) the tumor antigens may be similar to the body's own antigens, and (3) there is the possibility of cancer recurrence. Therefore, developing personalized cancer vaccines with the ability to distinguish between the tumor and the body's antigens is indispensable. This paper provides a comprehensive review of different types of cancer vaccines and highlights important factors necessary for developing efficient cancer vaccines. Moreover, the application of other technologies in cancer therapy is discussed. Finally, several insights and conclusions are presented, such as the possibility of using cold plasma and cancer stem cells in developing future cancer vaccines, to tackle the major limitations in the cancer vaccine developmental process.

Advancing nanotechnology for neoantigen-based cancer theranostics

Neoantigens play a pivotal role in the field of tumour therapy, encompassing the stimulation of anti-tumour immune response and the enhancement of tumour targeting capability. Nonetheless, numerous factors directly influence the effectiveness of neoantigens in bolstering anti-tumour immune responses, including neoantigen quantity and specificity, uptake rates by antigen-presenting cells (APCs), residence duration within the tumour microenvironment (TME), and their ability to facilitate the maturation of APCs for immune response activation. Nanotechnology assumes a significant role in several aspects, including facilitating neoantigen release, promoting neoantigen delivery to antigen-presenting cells, augmenting neoantigen uptake by dendritic cells, shielding neoantigens from protease degradation, and optimizing interactions between neoantigens and the immune system. Consequently, the development of nanotechnology synergistically enhances the efficacy of neoantigens in cancer theranostics. In this review, we provide an overview of neoantigen sources, the mechanisms of neoantigen-induced immune responses, and the evolution of precision neoantigen-based nanomedicine. This encompasses various therapeutic modalities, such as neoantigen-based immunotherapy, phototherapy, radiotherapy, chemotherapy, chemodynamic therapy, and other strategies tailored to augment precision in cancer therapeutics. We also discuss the current challenges and prospects in the application of neoantigen-based precision nanomedicine, aiming to expedite its clinical translation.

From antigens to immune responses: Shaping the future of TB detection and prevention

OBJECTIVES

Tuberculosis (TB) remains a global health challenge due to various factors, including delayed diagnoses leading to the spread of infection, limited efficacy of current vaccination strategies, and emergence of drug-resistant strains. Here, we explore the significance of Mycobacterium tuberculosis (Mtb)-specific antigens to overcome these challenges.

METHODS

A narrative review exploring the dynamics of Mtb-specific antigens and the related T cell immune responses across the TB spectrum.

RESULTS

A variety of antigens are expressed at different stages of Mtb infection, driving its diverse antigenic landscape and associated T cell functional heterogeneity. Recent advances in high-coverage genomic and proteomic approaches may lead to the identification and characterization of antigens/epitopes within the context of TB.

CONCLUSION

Factors such as magnitude of memory response, cytokine profile, immunodominance, and conservation of epitopes should be emphasized as crucial parameters in assessing the potential efficacy of these antigens in diagnostics or vaccine research. Recognizing the antigenic repertoire of Mtb changes with the infection stage, it is important to assess the availability of different subsets of Mtb antigens across the spectrum of infection for more precise disease classifications. Targeting specific antigens holds promise as a pathway for developing specific immunological biomarkers to predict TB reactivation in populations.

From structural design to delivery: mRNA therapeutics for cancer immunotherapy

mRNA therapeutics have emerged as powerful tools for cancer immunotherapy in accordance with their superiority in expressing all sequence-known proteins in vivo. In particular, with a small dosage of delivered mRNA, antigen-presenting cells (APCs) can synthesize mutant neo-antigens and multi-antigens and present epitopes to T lymphocytes to elicit antitumor effects. In addition, expressing receptors like chimeric antigen receptor (CAR), T-cell receptor (TCR), CD134, and immune-modulating factors including cytokines, interferons, and antibodies in specific cells can enhance immunological response against tumors. With the maturation of in vitro transcription (IVT) technology, large-scale and pure mRNA encoding specific proteins can be synthesized quickly. However, the clinical translation of mRNA-based anticancer strategies is restricted by delivering mRNA into target organs or cells and the inadequate endosomal escape efficiency of mRNA. Recently, there have been some advances in mRNA-based cancer immunotherapy, which can be roughly classified as modifications of the mRNA structure and the development of delivery systems, especially the lipid nanoparticle platforms. In this review, the latest strategies for overcoming the limitations of mRNA-based cancer immunotherapies and the recent advances in delivering mRNA into specific organs and cells are summarized. Challenges and opportunities for clinical applications of mRNA-based cancer immunotherapy are also discussed.

Are we getting closer to a successful neoantigen cancer vaccine?

Although significant advances in immunotherapy have revolutionized the treatment of many cancer types over the past decade, the field of vaccine therapy, an important component of cancer immunotherapy, despite decades-long intense efforts, is still transmitting signals of promises and awaiting strong data on efficacy to proceed with regulatory approval. The field of cancer vaccines faces standard challenges, such as tumor-induced immunosuppression, immune response in inhibitory tumor microenvironment (TME), intratumor heterogeneity (ITH), permanently evolving cancer mutational landscape leading to neoantigens, and less known obstacles: neoantigen gain/loss upon immunotherapy, the timing and speed of appearance of neoantigens and responding T cell clonotypes and possible involvement of immune interference/heterologous immunity, in the complex interplay between evolving tumor epitopes and the immune system. In this review, we discuss some key issues related to challenges hampering the development of cancer vaccines, along with the current approaches focusing on neoantigens. We summarize currently well-known ideas/rationales, thus revealing the need for alternative vaccine approaches. Such a discussion should stimulate vaccine researchers to apply out-of-box, unconventional thinking in search of new avenues to deal with critical, often yet unaddressed challenges on the road to a new generation of therapeutics and vaccines.

Overexpression of Transmembrane Phosphatase with Tensin homology (TPTE) in prostate cancer is clinically significant, suggesting its potential as a valuable biomarker

PURPOSE

Cancer testis antigens (CTAs) are a family of proteins typically expressed in male testicles but overexpressed in various cancer cell types. Transmembrane Phosphatase with Tensin homology (TPTE) is expressed only in the testis of healthy individuals and is a member of the family of CTAs. The current study, for the first time, examined the significance of TPTE expression in prostate cancer (PCa) tissues by generating a novel antibody marker targeting TPTE protein.

METHODS

Polyclonal antibodies were prepared for TPTE-p1 and TPTE-p2 peptides, which are derived from the extracellular domains of TPTE. Anti-TPTE-p2 antibody was then used to study the extent and pattern of TPTE expression in 102 PCa and 48 benign prostatic hyperplasia (BPH) tissue samples by immunohistochemistry. The viability of cancer cell lines (PC-3 and MCF-7 cells) was also evaluated in the presence of anti-TPTE-p2 antibody using the MTT test.

RESULTS

The immunohistochemical analysis demonstrated a significant increase in cytoplasmic and membrane TPTE expression in the PCa samples compared to the BPH group (both P < 0.0001). Cytoplasmic TPTE expression was positively correlated with Gleason score and PSA levels (P = 0.03 and P = 0.001, respectively). Significant correlations were identified between the levels of PSA and perineural invasion and the membrane expression (P = 0.01, P = 0.04, respectively). Moreover, anti-TPTE-p2 antibody inhibited PC-3 and MCF-7 cells proliferation compared to the control group for 24 h (P < 0.001 and P = 0.001, respectively) as well as for 48 h (P = 0.001 and P = 0.001, respectively).

CONCLUSION

Our findings indicate that increased TPTE expression is associated with progression of disease. The ability of anti-TPTE-p2 antibody to recognize and target the TPTE protein makes it a potential biomarker to assess and/or target the PCa.

Immune Specific and Tumor-Dependent mRNA Vaccines for Cancer Immunotherapy: Reprogramming Clinical Translation into Tumor Editing Therapy

Extensive research into mRNA vaccines for cancer therapy in preclinical and clinical trials has prepared the ground for the quick development of immune-specific mRNA vaccines during the COVID-19 pandemic. Therapeutic cancer vaccines based on mRNA are well tolerated, and are an attractive choice for future cancer immunotherapy. Ideal personalized tumor-dependent mRNA vaccines could stimulate both humoral and cellular immunity by overcoming cancer-induced immune suppression and tumor relapse. The stability, structure, and distribution strategies of mRNA-based vaccines have been improved by technological innovations, and patients with diverse tumor types are now being enrolled in numerous clinical trials investigating mRNA vaccine therapy. Despite the fact that therapeutic mRNA-based cancer vaccines have not yet received clinical approval, early clinical trials with mRNA vaccines as monotherapy and in conjunction with checkpoint inhibitors have shown promising results. In this review, we analyze the most recent clinical developments in mRNA-based cancer vaccines and discuss the optimal platforms for the creation of mRNA vaccines. We also discuss the development of the cancer vaccines' clinical research, paying particular attention to their clinical use and therapeutic efficacy, which could facilitate the design of mRNA-based vaccines in the near future.

Nanocatalytic Anti-Tumor Immune Regulation

Immunotherapy has brought a new dawn for human being to defeat cancer. Although existing immunotherapy regimens (CAR-T, etc.) have made breakthroughs in the treatments of hematological cancer and few solid tumors such as melanoma, the therapeutic efficacy on most solid tumors is still far from being satisfactory. In recent years, the researches on tumor immunotherapy based on nanocatalytic materials are under rapid development, and significant progresses have been made. Nanocatalytic medicine has been demonstrated to be capable of overcoming the limitations of current clinicnal treatments by using toxic chemodrugs, and exhibits highly attractive advantages over traditional therapies, such as the enhanced and sustained therapeutic efficacy based on the durable catalytic activity, remarkably reduced harmful side-effects without using traditional toxic chemodrugs, and so on. Most recently, nanocatalytic medicine has been introduced in the immune-regulation for disease treatments, especially, in the immunoactivation for tumor therapies. This article presents the most recent progresses in immune-response activations by nanocatalytic medicine-initiated chemical reactions for tumor immunotherapy, and elucidates the mechanism of nanocatalytic medicines in regulating anti-tumor immunity. By reviewing the current research progress in the emerging field, this review will further highlight the great potential and broad prospects of nanocatalysis-based anti-tumor immune-therapeutics.

Low-dose decitabine enhances the efficacy of viral cancer vaccines for immunotherapy

Cancer immunotherapy requires a specific antitumor CD8+ T cell-driven immune response; however, upon genetic and epigenetic alterations of the antigen processing and presenting components, cancer cells escape the CD8+ T cell recognition. As a result, poorly immunogenic tumors are refractory to conventional immunotherapy. In this context, the use of viral cancer vaccines in combination with hypomethylating agents represents a promising strategy to prevent cancer from escaping immune system recognition. In this study, we evaluated the sensitivity of melanoma (B16-expressing ovalbumin) and metastatic triple-negative breast cancer (4T1) cell lines to FDA-approved low-dose decitabine in combination with PeptiCRAd, an adenoviral anticancer vaccine. The two models showed different sensitivity to decitabine in vitro and in vivo when combined with PeptiCRAd. In particular, mice bearing syngeneic 4T1 cancer showed higher tumor growth control when receiving the combinatorial treatment compared to single controls in association with a higher expression of MHC class I on cancer cells and reduction in Tregs within the tumor microenvironment. Furthermore, remodeling of the CD8+ T cell infiltration and cytotoxic activity toward cancer cells confirmed the effect of decitabine in enhancing anticancer vaccines in immunotherapy regimens.

Immunopeptidomics-based identification of naturally presented non-canonical circRNA-derived peptides

Circular RNAs (circRNAs) are covalently closed non-coding RNAs lacking the 5' cap and the poly-A tail. Nevertheless, it has been demonstrated that certain circRNAs can undergo active translation. Therefore, aberrantly expressed circRNAs in human cancers could be an unexplored source of tumor-specific antigens, potentially mediating anti-tumor T cell responses. This study presents an immunopeptidomics workflow with a specific focus on generating a circRNA-specific protein fasta reference. The main goal of this workflow is to streamline the process of identifying and validating human leukocyte antigen (HLA) bound peptides potentially originating from circRNAs. We increase the analytical stringency of our workflow by retaining peptides identified independently by two mass spectrometry search engines and/or by applying a group-specific FDR for canonical-derived and circRNA-derived peptides. A subset of circRNA-derived peptides specifically encoded by the region spanning the back-splice junction (BSJ) are validated with targeted MS, and with direct Sanger sequencing of the respective source transcripts. Our workflow identifies 54 unique BSJ-spanning circRNA-derived peptides in the immunopeptidome of melanoma and lung cancer samples. Our approach enlarges the catalog of source proteins that can be explored for immunotherapy.

Thunder-DDA-PASEF enables high-coverage immunopeptidomics and is boosted by MS2Rescore with MS2PIP timsTOF fragmentation prediction model

Human leukocyte antigen (HLA) class I peptide ligands (HLAIps) are key targets for developing vaccines and immunotherapies against infectious pathogens or cancer cells. Identifying HLAIps is challenging due to their high diversity, low abundance, and patient individuality. Here, we develop a highly sensitive method for identifying HLAIps using liquid chromatography-ion mobility-tandem mass spectrometry (LC-IMS-MS/MS). In addition, we train a timsTOF-specific peak intensity MS2PIP model for tryptic and non-tryptic peptides and implement it in MS2Rescore (v3) together with the CCS predictor from ionmob. The optimized method, Thunder-DDA-PASEF, semi-selectively fragments singly and multiply charged HLAIps based on their IMS and m/z. Moreover, the method employs the high sensitivity mode and extended IMS resolution with fewer MS/MS frames (300 ms TIMS ramp, 3 MS/MS frames), doubling the coverage of immunopeptidomics analyses, compared to the proteomics-tailored DDA-PASEF (100 ms TIMS ramp, 10 MS/MS frames). Additionally, rescoring boosts the HLAIps identification by 41.7% to 33%, resulting in 5738 HLAIps from as little as one million JY cell equivalents, and 14,516 HLAIps from 20 million. This enables in-depth profiling of HLAIps from diverse human cell lines and human plasma. Finally, profiling JY and Raji cells transfected to express the SARS-CoV-2 spike protein results in 16 spike HLAIps, thirteen of which have been reported to elicit immune responses in human patients.

Making sense of mRNA translational "noise"

The importance of translation fidelity has been apparent since the discovery of genetic code. It is commonly believed that translation deviating from the main coding region is to be avoided at all times inside cells. However, ribosome profiling and mass spectrometry have revealed pervasive noncanonical translation. Both the scope and origin of translational "noise" are just beginning to be appreciated. Although largely overlooked, those translational "noises" are associated with a wide range of cellular functions, such as producing unannotated protein products. Furthermore, the dynamic nature of translational "noise" is responsive to stress conditions, highlighting the beneficial effect of translational "noise" in stress adaptation. Mechanistic investigation of translational "noise" will provide better insight into the mechanisms of translational regulation. Ultimately, they are not "noise" at all but represent a signature of cellular activities under pathophysiological conditions. Deciphering translational "noise" holds the therapeutic and diagnostic potential in a wide spectrum of human diseases.

Current Technologies and Future Perspectives in Immunotherapy towards a Clinical Oncology Approach

Immunotherapy is now established as a potent therapeutic paradigm engendering antitumor immune response against a wide range of malignancies and other diseases by modulating the immune system either through the stimulation or suppression of immune components such as CD4+ T cells, CD8+ T cells, B cells, monocytes, macrophages, dendritic cells, and natural killer cells. By targeting several immune checkpoint inhibitors or blockers (e.g., PD-1, PD-L1, PD-L2, CTLA-4, LAG3, and TIM-3) expressed on the surface of immune cells, several monoclonal antibodies and polyclonal antibodies have been developed and already translated clinically. In addition, natural killer cell-based, dendritic cell-based, and CAR T cell therapies have been also shown to be promising and effective immunotherapeutic approaches. In particular, CAR T cell therapy has benefited from advancements in CRISPR-Cas9 genome editing technology, allowing the generation of several modified CAR T cells with enhanced antitumor immunity. However, the emerging SARS-CoV-2 infection could hijack a patient's immune system by releasing pro-inflammatory interleukins and cytokines such as IL-1β, IL-2, IL-6, and IL-10, and IFN-γ and TNF-α, respectively, which can further promote neutrophil extravasation and the vasodilation of blood vessels. Despite the significant development of advanced immunotherapeutic technologies, after a certain period of treatment, cancer relapses due to the development of resistance to immunotherapy. Resistance may be primary (where tumor cells do not respond to the treatment), or secondary or acquired immune resistance (where tumor cells develop resistance gradually to ICIs therapy). In this context, this review aims to address the existing immunotherapeutic technologies against cancer and the resistance mechanisms against immunotherapeutic drugs, and explain the impact of COVID-19 on cancer treatment. In addition, we will discuss what will be the future implementation of these strategies against cancer drug resistance. Finally, we will emphasize the practical steps to lay the groundwork for enlightened policy for intervention and resource allocation to care for cancer patients.

Advancements and hurdles in the development of a vaccine for triple-negative breast cancer: A comprehensive review of multi-omics and immunomics strategies

Triple-Negative Breast Cancer (TNBC) presents a significant challenge in oncology due to its aggressive behavior and limited therapeutic options. This review explores the potential of immunotherapy, particularly vaccine-based approaches, in addressing TNBC. It delves into the role of immunoinformatics in creating effective vaccines against TNBC. The review first underscores the distinct attributes of TNBC and the importance of tumor antigens in vaccine development. It then elaborates on antigen detection techniques such as exome sequencing, HLA typing, and RNA sequencing, which are instrumental in identifying TNBC-specific antigens and selecting vaccine candidates. The discussion then shifts to the in-silico vaccine development process, encompassing antigen selection, epitope prediction, and rational vaccine design. This process merges computational simulations with immunological insights. The role of Artificial Intelligence (AI) in expediting the prediction of antigens and epitopes is also emphasized. The review concludes by encapsulating how Immunoinformatics can augment the design of TNBC vaccines, integrating tumor antigens, advanced detection methods, in-silico strategies, and AI-driven insights to advance TNBC immunotherapy. This could potentially pave the way for more targeted and efficacious treatments.

Scaling up robust immunopeptidomics technologies for a global T cell surveillance digital network

The human immunopeptidome plays a central role in disease susceptibility and resistance. In our opinion, the development of immunopeptidomics and other peptide sequencing technologies should be prioritized during the next decade, particularly within the framework of the Human Immunopeptidome Project initiative. In this context, we present bold ideas, fresh arguments, and call upon industrial partners and funding organizations to support and champion this important initiative that we believe has the potential to save countless lives in the future.

Mass Spectrometry-Based Immunopeptidomics of Peptides Presented on Human Leukocyte Antigen Proteins

Human leukocyte antigen (HLA) proteins are a group of glycoproteins that are expressed at the cell surface, where they present peptides to T cells through physical interactions with T-cell receptors (TCRs). Hence, characterizing the set of peptides presented by HLA proteins, referred to hereafter as the immunopeptidome, is fundamental for neoantigen identification, immunotherapy, and vaccine development. As a result, different methods have been used over the years to identify peptides presented by HLA proteins, including competition assays, peptide microarrays, and yeast display systems. Nonetheless, over the last decade, mass spectrometry-based immunopeptidomics (MS-immunopeptidomics) has emerged as the gold-standard method for identifying peptides presented by HLA proteins. MS-immunopeptidomics enables the direct identification of the immunopeptidome in different tissues and cell types in different physiological and pathological states, for example, solid tumors or virally infected cells. Despite its advantages, it is still an experimentally and computationally challenging technique with different aspects that need to be considered before planning an MS-immunopeptidomics experiment, while conducting the experiment and with analyzing and interpreting the results. Hence, we aim in this chapter to provide an overview of this method and discuss different practical considerations at different stages starting from sample collection until data analysis. These points should aid different groups aiming at utilizing MS-immunopeptidomics, as well as, identifying future research directions to improve the method.

Deep Learning-Assisted Analysis of Immunopeptidomics Data

Liquid chromatography-coupled mass spectrometry (LC-MS/MS) is the primary method to obtain direct evidence for the presentation of disease- or patient-specific human leukocyte antigen (HLA). However, compared to the analysis of tryptic peptides in proteomics, the analysis of HLA peptides still poses computational and statistical challenges. Recently, fragment ion intensity-based matching scores assessing the similarity between predicted and observed spectra were shown to substantially increase the number of confidently identified peptides, particularly in use cases where non-tryptic peptides are analyzed. In this chapter, we describe in detail three procedures on how to benefit from state-of-the-art deep learning models to analyze and validate single spectra, single measurements, and multiple measurements in mass spectrometry-based immunopeptidomics. For this, we explain how to use the Universal Spectrum Explorer (USE), online Oktoberfest, and offline Oktoberfest. For intensity-based scoring, Oktoberfest uses fragment ion intensity and retention time predictions from the deep learning framework Prosit, a deep neural network trained on a very large number of synthetic peptides and tandem mass spectra generated within the ProteomeTools project. The examples shown highlight how deep learning-assisted analysis can increase the number of identified HLA peptides, facilitate the discovery of confidently identified neo-epitopes, or provide assistance in the assessment of the presence of cryptic peptides, such as spliced peptides.

Tumour circular RNAs elicit anti-tumour immunity by encoding cryptic peptides

Emerging data have shown that previously defined noncoding genomes might encode peptides that bind human leukocyte antigen (HLA) as cryptic antigens to stimulate adaptive immunity1,2. However, the significance and mechanisms of action of cryptic antigens in anti-tumour immunity remain unclear. Here mass spectrometry of the HLA class I (HLA-I) peptidome coupled with ribosome sequencing of human breast cancer samples identified HLA-I-binding cryptic antigenic peptides that were noncanonically translated by a tumour-specific circular RNA (circRNA): circFAM53B. The cryptic peptides efficiently primed naive CD4+ and CD8+ T cells in an antigen-specific manner and induced anti-tumour immunity. Clinically, the expression of circFAM53B and its encoded peptides was associated with substantial infiltration of antigen-specific CD8+ T cells and better survival in patients with breast cancer and patients with melanoma. Mechanistically, circFAM53B-encoded peptides had strong binding affinity to both HLA-I and HLA-II molecules. In vivo, administration of vaccines consisting of tumour-specific circRNA or its encoded peptides in mice bearing breast cancer tumours or melanoma induced enhanced infiltration of tumour-antigen-specific cytotoxic T cells, which led to effective tumour control. Overall, our findings reveal that noncanonical translation of circRNAs can drive efficient anti-tumour immunity, which suggests that vaccination exploiting tumour-specific circRNAs may serve as an immunotherapeutic strategy against malignant tumours.

Peptidomics Strategies to Evaluate Cancer Diagnosis, Prognosis, and Treatment

Peptides have potential bioactive functions, and the peptidomics landscape has been broadly investigated for various diseases, including cancer. In this chapter, we reviewed the past four years of literature available and selected 16 peer-reviewed publications exploring peptidomics in diagnosis, prognosis, and treatment in cancer research. We highlighted their main aims, mass spectrometry-based peptidomics, multi-omics, data-driven and in silico strategies, functional assays, and clinical applications. Moreover, we underscored several levels of difficulties in translating the peptidomics findings to clinical practice, aiming to learn with the accumulated knowledge and guide upcoming studies. Finally, this review reinforces the peptidomics robustness in discovering potential candidates for monitoring the several stages of cancer disease and therapeutic treatment, leveraging the management of cancer patients in the future.

Investigative needle core biopsies for multi-omics in Glioblastoma

Glioblastoma (GBM) is a primary brain cancer with an abysmal prognosis and few effective therapies. The ability to investigate the tumor microenvironment before and during treatment would greatly enhance both understanding of disease response and progression, as well as the delivery and impact of therapeutics. Stereotactic biopsies are a routine surgical procedure performed primarily for diagnostic histopathologic purposes. The role of investigative biopsies - tissue sampling for the purpose of understanding tumor microenvironmental responses to treatment using integrated multi-modal molecular analyses ('Multi-omics") has yet to be defined. Secondly, it is unknown whether comparatively small tissue samples from brain biopsies can yield sufficient information with such methods. Here we adapt stereotactic needle core biopsy tissue in two separate patients. In the first patient with recurrent GBM we performed highly resolved multi-omics analysis methods including single cell RNA sequencing, spatial-transcriptomics, metabolomics, proteomics, phosphoproteomics, T-cell clonotype analysis, and MHC Class I immunopeptidomics from biopsy tissue that was obtained from a single procedure. In a second patient we analyzed multi-regional core biopsies to decipher spatial and genomic variance. We also investigated the utility of stereotactic biopsies as a method for generating patient derived xenograft models in a separate patient cohort. Dataset integration across modalities showed good correspondence between spatial modalities, highlighted immune cell associated metabolic pathways and revealed poor correlation between RNA expression and the tumor MHC Class I immunopeptidome. In conclusion, stereotactic needle biopsy cores are of sufficient quality to generate multi-omics data, provide data rich insight into a patient's disease process and tumor immune microenvironment and can be of value in evaluating treatment responses.

One sentence summary

Integrative multi-omics analysis of stereotactic needle core biopsies in glioblastoma.

How does TCR-T cell therapy exhibit a superior anti-tumor efficacy

TCR-engineered T cells have achieved great progress in solid tumor therapy, some of which have been applicated in clinical trials. Deep knowledge about the current progress of TCR-T in tumor therapy would be beneficial to understand the direction. Here, we classify tumor antigens into tumor-associated antigens, tumor-specific antigens, tumor antigens expressed by oncogenic viruses, and tumor antigens caused by abnormal protein modification; Then we detail the TCR-T cell therapy effects targeting those tumor antigens in clinical or preclinical trials, and propose that neoantigen specific TCR-T cell therapy is expected to be a promising approach for solid tumors; Furthermore, we summarize the optimization strategies, such as tumor microenvironment, TCR pairing and affinity, to improve the therapeutic effect of TCR-T. Overall, this review provides inspiration for the antigen selection and therapy strategies of TCR-T in the future.

Oncolytic Viruses in the Era of Omics, Computational Technologies, and Modeling: Thesis, Antithesis, and Synthesis

Oncolytic viruses (OVs) are the frontier therapy for refractory cancers, especially in integration with immunomodulation strategies. In cancer immunovirotherapy, the many available "omics" and systems biology technologies generate at a fast pace a challenging huge amount of data, where apparently clashing information mirrors the complexity of individual clinical situations and OV used. In this review, we present and discuss how currently big data analysis, on one hand and, on the other, simulation, modeling, and computational technologies, provide invaluable support to interpret and integrate "omic" information and drive novel synthetic biology and personalized OV engineering approaches for effective immunovirotherapy. Altogether, these tools, possibly aided in the future by artificial intelligence as well, will allow for the blending of the information into OV recombinants able to achieve tumor clearance in a patient-tailored way. Various endeavors to the envisioned "synthesis" of turning OVs into personalized theranostic agents are presented.