Direct identification of clinically relevant neoepitopes presented on native human melanoma tissue by mass spectrometry

Enhancing Mass spectrometry-based tumor immunopeptide identification: machine learning filter leveraging HLA binding affinity, aliphatic index and retention time deviation

Accurately identifying neoantigens is crucial for developing effective cancer vaccines and improving tumor immunotherapy. Mass spectrometry-based immunopeptidomics has emerged as a promising approach to identifying human leukocyte antigen (HLA) peptides presented on the surface of cancer cells, but false-positive identifications remain a significant challenge. In this study, liquid chromatography-tandem mass spectrometry-based proteomics and next-generation sequencing were utilized to identify HLA-presenting neoantigenic peptides resulting from non-synonymous single nucleotide variations in tumor tissues from 18 patients with renal cell carcinoma or pancreatic cancer. Machine learning was utilized to evaluate Mascot identifications through the prediction of MS/MS spectral consistency, and four descriptors for each candidate sequence: the max Mascot ion score, predicted HLA binding affinity, aliphatic index and retention time deviation, were selected as important features in filtering out identifications with inadequate fragmentation consistency. This suggests that incorporating rescoring filters based on peptide physicochemical characteristics could enhance the identification rate of MS-based immunopeptidomics compared to the traditional Mascot approach predominantly used for proteomics, indicating the potential for optimizing neoantigen identification pipelines as well as clinical applications.

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.

mRNA vaccine development and applications: A special focus on tumors (Review)

Cancer is characterized by unlimited proliferation and metastasis, and traditional therapeutic strategies usually result in the acquisition of drug resistance, thus highlighting the need for more personalized treatment. mRNA vaccines transfer the gene sequences of exogenous target antigens into human cells through transcription and translation to stimulate the body to produce specific immune responses against the encoded proteins, so as to enable the body to obtain immune protection against said antigens; this approach may be adopted for personalized cancer therapy. Since the recent coronavirus pandemic, the development of mRNA vaccines has seen substantial progress and widespread adoption. In the present review, the development of mRNA vaccines, their mechanisms of action, factors influencing their function and the current clinical applications of the vaccine are discussed. A focus is placed on the application of mRNA vaccines in cancer, with the aim of highlighting unique advances and the remaining challenges of this novel and promising therapeutic approach.

Rescoring Peptide Spectrum Matches: Boosting Proteomics Performance by Integrating Peptide Property Predictors Into Peptide Identification

Rescoring of peptide spectrum matches originating from database search engines enabled by peptide property predictors is exceeding the performance of peptide identification from traditional database search engines. In contrast to the peptide spectrum match scores calculated by traditional database search engines, rescoring peptide spectrum matches generates scores based on comparing observed and predicted peptide properties, such as fragment ion intensities and retention times. These newly generated scores enable a more efficient discrimination between correct and incorrect peptide spectrum matches. This approach was shown to lead to substantial improvements in the number of confidently identified peptides, facilitating the analysis of challenging datasets in various fields such as immunopeptidomics, metaproteomics, proteogenomics, and single-cell proteomics. In this review, we summarize the key elements leading up to the recent introduction of multiple data-driven rescoring pipelines. We provide an overview of relevant post-processing rescoring tools, introduce prominent data-driven rescoring pipelines for various applications, and highlight limitations, opportunities, and future perspectives of this approach and its impact on mass spectrometry-based proteomics.

Bacterial Biohybrids for Invasion of Tumor Cells Promote Antigen Cross-Presentation Through Gap Junction

Due to inherent differences in cellular composition and metabolic behavior with host cells, tumor-harbored bacteria can discriminatorily affect tumor immune landscape. However, the mechanisms by which intracellular bacteria affect antigen presentation process between tumor cells and antigen-presenting cells (APCs) are largely unknown. The invasion behavior of attenuated Salmonella VNP20009 (VNP) into tumor cells is investigated and an attempt is made to modulate this behavior by modifying positively charged polymers on the surface of VNP. It is found that non-toxic chitosan oligosaccharide (COS) modified VNP (VNP@COS) bolsters the formation of gap junction between tumor cells and APCs by enhancing the ability of VNP to infect tumor cells. On this basis, a bacterial biohybrid is designed to promote in situ antigen cross-presentation through intracellular bacteria induced gap junction. This bacterial biohybrid also enhances the expression of major histocompatibility complex class I molecules on the surface of tumor cells through the incorporation of Mdivi-1 coupled with VNP@COS. This strategic integration serves to heighten the immunogenic exposure of tumor antigens; while, preserving the cytotoxic potency of T cells. A strategy is proposed to precisely controlling the function and local effects of microorganisms within tumors.

Immunogenicity of Non-Mutated Ovarian Cancer-Specific Antigens

Epithelial ovarian cancer (EOC) has not significantly benefited from advances in immunotherapy, mainly because of the lack of well-defined actionable antigen targets. Using proteogenomic analyses of primary EOC tumors, we previously identified 91 aberrantly expressed tumor-specific antigens (TSAs) originating from unmutated genomic sequences. Most of these TSAs derive from non-exonic regions, and their expression results from cancer-specific epigenetic changes. The present study aimed to evaluate the immunogenicity of 48 TSAs selected according to two criteria: presentation by highly prevalent HLA allotypes and expression in a significant fraction of EOC tumors. Using targeted mass spectrometry analyses, we found that pulsing with synthetic TSA peptides leads to a high-level presentation on dendritic cells. TSA abundance correlated with the predicted binding affinity to the HLA allotype. We stimulated naïve CD8 T cells from healthy blood donors with TSA-pulsed dendritic cells and assessed their expansion with two assays: MHC-peptide tetramer staining and TCR Vβ CDR3 sequencing. We report that these TSAs can expand sizeable populations of CD8 T cells and, therefore, represent attractive targets for EOC immunotherapy.

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

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

Molecular basis for antibody recognition of multiple drug-peptide/MHC complexes

The HapImmuneTM platform exploits covalent inhibitors as haptens for creating major histocompatibility complex (MHC)-presented tumor-specific neoantigens by design, combining targeted therapies with immunotherapy for the treatment of drug-resistant cancers. A HapImmune antibody, R023, recognizes multiple sotorasib-conjugated KRAS(G12C) peptides presented by different human leukocyte antigens (HLAs). This high specificity to sotorasib, coupled with broad HLA-binding capability, enables such antibodies, when reformatted as T cell engagers, to potently and selectively kill sotorasib-resistant KRAS(G12C) cancer cells expressing different HLAs upon sotorasib treatment. The loosening of HLA restriction could increase the patient population that can benefit from this therapeutic approach. To understand the molecular basis for its unconventional binding capability, we used single-particle cryogenic electron microscopy to determine the structures of R023 bound to multiple sotorasib-peptide conjugates presented by different HLAs. R023 forms a pocket for sotorasib between the VH and VL domains, binds HLAs in an unconventional, angled way, with VL making most contacts with them, and makes few contacts with the peptide moieties. This binding mode enables the antibody to accommodate different hapten-peptide conjugates and to adjust its conformation to different HLAs presenting hapten-peptides. Deep mutational scanning validated the structures and revealed distinct levels of mutation tolerance by sotorasib- and HLA-binding residues. Together, our structural information and sequence landscape analysis reveal key features for achieving MHC-restricted recognition of multiple hapten-peptide antigens, which will inform the development of next-generation therapeutic antibodies.

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.

The electrostatic landscape of MHC-peptide binding revealed using inception networks

Predictive modeling of macromolecular recognition and protein-protein complementarity represents one of the cornerstones of biophysical sciences. However, such models are often hindered by the combinatorial complexity of interactions at the molecular interfaces. Exemplary of this problem is peptide presentation by the highly polymorphic major histocompatibility complex class I (MHC-I) molecule, a principal component of immune recognition. We developed human leukocyte antigen (HLA)-Inception, a deep biophysical convolutional neural network, which integrates molecular electrostatics to capture non-bonded interactions for predicting peptide binding motifs across 5,821 MHC-I alleles. These predictions of generated motifs correlate strongly with experimental peptide binding and presentation data. Beyond molecular interactions, the study demonstrates the application of predicted motifs in analyzing MHC-I allele associations with HIV disease progression and patient response to immune checkpoint inhibitors. A record of this paper's transparent peer review process is included in the supplemental information.

Multicellular ecotypes shape progression of lung adenocarcinoma from ground-glass opacity toward advanced stages

Lung adenocarcinoma is a type of cancer that exhibits a wide range of clinical radiological manifestations, from ground-glass opacity (GGO) to pure solid nodules, which vary greatly in terms of their biological characteristics. Our current understanding of this heterogeneity is limited. To address this gap, we analyze 58 lung adenocarcinoma patients via machine learning, single-cell RNA sequencing (scRNA-seq), and whole-exome sequencing, and we identify six lung multicellular ecotypes (LMEs) correlating with distinct radiological patterns and cancer cell states. Notably, GGO-associated neoantigens in early-stage cancers are recognized by CD8+ T cells, indicating an immune-active environment, while solid nodules feature an immune-suppressive LME with exhausted CD8+ T cells, driven by specific stromal cells such as CTHCR1+ fibroblasts. This study also highlights EGFR(L858R) neoantigens in GGO samples, suggesting potential CD8+ T cell activation. Our findings offer valuable insights into lung adenocarcinoma heterogeneity, suggesting avenues for targeted therapies in early-stage disease.

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.

FusionVAC22_01: a phase I clinical trial evaluating a DNAJB1-PRKACA fusion transcript-based peptide vaccine combined with immune checkpoint inhibition for fibrolamellar hepatocellular carcinoma and other tumor entities carrying the oncogenic driver fusion

The DNAJB1-PRKACA fusion transcript was identified as the oncogenic driver of tumor pathogenesis in fibrolamellar hepatocellular carcinoma (FL-HCC), also known as fibrolamellar carcinoma (FLC), as well as in other tumor entities, thus representing a broad target for novel treatment in multiple cancer entities. FL-HCC is a rare primary liver tumor with a 5-year survival rate of only 45%, which typically affects young patients with no underlying primary liver disease. Surgical resection is the only curative treatment option if no metastases are present at diagnosis. There is no standard of care for systemic therapy. Peptide-based vaccines represent a low side-effect approach relying on specific immune recognition of tumor-associated human leucocyte antigen (HLA) presented peptides. The induction (priming) of tumor-specific T-cell responses against neoepitopes derived from gene fusion transcripts by peptide-vaccination combined with expansion of the immune response and optimization of immune function within the tumor microenvironment achieved by immune-checkpoint-inhibition (ICI) has the potential to improve response rates and durability of responses in malignant diseases. The phase I clinical trial FusionVAC22_01 will enroll patients with FL-HCC or other cancer entities carrying the DNAJB1-PRKACA fusion transcript that are locally advanced or metastatic. Two doses of the DNAJB1-PRKACA fusion-based neoepitope vaccine Fusion-VAC-XS15 will be applied subcutaneously (s.c.) with a 4-week interval in combination with the anti-programmed cell death-ligand 1 (PD-L1) antibody atezolizumab starting at day 15 after the first vaccination. Anti-PD-L1 will be applied every 4 weeks until end of the 54-week treatment phase or until disease progression or other reason for study termination. Thereafter, patients will enter a 6 months follow-up period. The clinical trial reported here was approved by the Ethics Committee II of the University of Heidelberg (Medical faculty of Mannheim) and the Paul-Ehrlich-Institute (P-00540). Clinical trial results will be published in peer-reviewed journals.

Trial registration numbers

EU CT Number: 2022-502869-17-01 and ClinicalTrials.gov Registry (NCT05937295).

TripHLApan: predicting HLA molecules binding peptides based on triple coding matrix and transfer learning

Human leukocyte antigen (HLA) recognizes foreign threats and triggers immune responses by presenting peptides to T cells. Computationally modeling the binding patterns between peptide and HLA is very important for the development of tumor vaccines. However, it is still a big challenge to accurately predict HLA molecules binding peptides. In this paper, we develop a new model TripHLApan for predicting HLA molecules binding peptides by integrating triple coding matrix, BiGRU + Attention models, and transfer learning strategy. We have found the main interaction site regions between HLA molecules and peptides, as well as the correlation between HLA encoding and binding motifs. Based on the discovery, we make the preprocessing and coding closer to the natural biological process. Besides, due to the input being based on multiple types of features and the attention module focused on the BiGRU hidden layer, TripHLApan has learned more sequence level binding information. The application of transfer learning strategies ensures the accuracy of prediction results under special lengths (peptides in length 8) and model scalability with the data explosion. Compared with the current optimal models, TripHLApan exhibits strong predictive performance in various prediction environments with different positive and negative sample ratios. In addition, we validate the superiority and scalability of TripHLApan's predictive performance using additional latest data sets, ablation experiments and binding reconstitution ability in the samples of a melanoma patient. The results show that TripHLApan is a powerful tool for predicting the binding of HLA-I and HLA-II molecular peptides for the synthesis of tumor vaccines. TripHLApan is publicly available at https://github.com/CSUBioGroup/TripHLApan.git.

Birinapant Reshapes the Tumor Immunopeptidome and Enhances Antigen Presentation

Birinapant, an antagonist of the inhibitor of apoptosis proteins, upregulates MHCs in tumor cells and displays a better tumoricidal effect when used in combination with immune checkpoint inhibitors, indicating that Birinapant may affect the antigen presentation pathway; however, the mechanism remains elusive. Based on high-resolution mass spectrometry and in vitro and in vivo models, we adopted integrated genomics, proteomics, and immunopeptidomics strategies to study the mechanism underlying the regulation of tumor immunity by Birinapant from the perspective of antigen presentation. Firstly, in HT29 and MCF7 cells, Birinapant increased the number and abundance of immunopeptides and source proteins. Secondly, a greater number of cancer/testis antigen peptides with increased abundance and more neoantigens were identified following Birinapant treatment. Moreover, we demonstrate the existence and immunogenicity of a neoantigen derived from insertion/deletion mutation. Thirdly, in HT29 cell-derived xenograft models, Birinapant administration also reshaped the immunopeptidome, and the tumor exhibited better immunogenicity. These data suggest that Birinapant can reshape the tumor immunopeptidome with respect to quality and quantity, which improves the presentation of CTA peptides and neoantigens, thus enhancing the immunogenicity of tumor cells. Such changes may be vital to the effectiveness of combination therapy, which can be further transferred to the clinic or aid in the development of new immunotherapeutic strategies to improve the anti-tumor immune response.

Untranslated regions (UTRs) are a potential novel source of neoantigens for personalised immunotherapy

Background

Neoantigens, mutated tumour-specific antigens, are key targets of anti-tumour immunity during checkpoint inhibitor (CPI) treatment. Their identification is fundamental to designing neoantigen-directed therapy. Non-canonical neoantigens arising from the untranslated regions (UTR) of the genome are an overlooked source of immunogenic neoantigens. Here, we describe the landscape of UTR-derived neoantigens and release a computational tool, PrimeCUTR, to predict UTR neoantigens generated by start-gain and stop-loss mutations.

Methods

We applied PrimeCUTR to a whole genome sequencing dataset of pre-treatment tumour samples from CPI-treated patients (n = 341). Cancer immunopeptidomic datasets were interrogated to identify MHC class I presentation of UTR neoantigens.

Results

Start-gain neoantigens were predicted in 72.7% of patients, while stop-loss mutations were found in 19.3% of patients. While UTR neoantigens only accounted 2.6% of total predicted neoantigen burden, they contributed 12.4% of neoantigens with high dissimilarity to self-proteome. More start-gain neoantigens were found in CPI responders, but this relationship was not significant when correcting for tumour mutational burden. While most UTR neoantigens are private, we identified two recurrent start-gain mutations in melanoma. Using immunopeptidomic datasets, we identify two distinct MHC class I-presented UTR neoantigens: one from a recurrent start-gain mutation in melanoma, and one private to Jurkat cells.

Conclusion

PrimeCUTR is a novel tool which complements existing neoantigen discovery approaches and has potential to increase the detection yield of neoantigens in personalised therapeutics, particularly for neoantigens with high dissimilarity to self. Further studies are warranted to confirm the expression and immunogenicity of UTR neoantigens.

Challenges in developing personalized neoantigen cancer vaccines

The recent success of cancer immunotherapies has highlighted the benefit of harnessing the immune system for cancer treatment. Vaccines have a long history of promoting immunity to pathogens and, consequently, vaccines targeting cancer neoantigens have been championed as a tool to direct and amplify immune responses against tumours while sparing healthy tissue. In recent years, extensive preclinical research and more than one hundred clinical trials have tested different strategies of neoantigen discovery and vaccine formulations. However, despite the enthusiasm for neoantigen vaccines, proof of unequivocal efficacy has remained beyond reach for the majority of clinical trials. In this Review, we focus on the key obstacles pertaining to vaccine design and tumour environment that remain to be overcome in order to unleash the true potential of neoantigen vaccines in cancer therapy.

Cancer neoepitopes viewed through negative selection and peripheral tolerance: a new path to cancer vaccines

A proportion of somatic mutations in tumors create neoepitopes that can prime T cell responses that target the MHC I-neoepitope complexes on tumor cells, mediating tumor control or rejection. Despite the compelling centrality of neoepitopes to cancer immunity, we know remarkably little about what constitutes a neoepitope that can mediate tumor control in vivo and what distinguishes such a neoepitope from the vast majority of similar candidate neoepitopes that are inefficacious in vivo. Studies in mice as well as clinical trials have begun to reveal the unexpected paradoxes in this area. Because cancer neoepitopes straddle that ambiguous ground between self and non-self, some rules that are fundamental to immunology of frankly non-self antigens, such as viral or model antigens, do not appear to apply to neoepitopes. Because neoepitopes are so similar to self-epitopes, with only small changes that render them non-self, immune response to them is regulated at least partially the way immune response to self is regulated. Therefore, neoepitopes are viewed and understood here through the clarifying lens of negative thymic selection. Here, the emergent questions in the biology and clinical applications of neoepitopes are discussed critically and a mechanistic and testable framework that explains the complexity and translational potential of these wonderful antigens is proposed.

A functional identification platform reveals frequent, spontaneous neoantigen-specific T cell responses in patients with cancer

The clinical impact of tumor-specific neoantigens as both immunotherapeutic targets and biomarkers has been impeded by the lack of efficient methods for their identification and validation from routine samples. We have developed a platform that combines bioinformatic analysis of tumor exomes and transcriptional data with functional testing of autologous peripheral blood mononuclear cells (PBMCs) to simultaneously identify and validate neoantigens recognized by naturally primed CD4+ and CD8+ T cell responses across a range of tumor types and mutational burdens. The method features a human leukocyte antigen (HLA)-agnostic bioinformatic algorithm that prioritizes mutations recognized by patient PBMCs at a greater than 40% positive predictive value followed by a short-term in vitro functional assay, which allows interrogation of 50 to 75 expressed mutations from a single 50-ml blood sample. Neoantigens validated by this method include both driver and passenger mutations, and this method identified neoantigens that would not have been otherwise detected using an in silico prediction approach. These findings reveal an efficient approach to systematically validate clinically actionable neoantigens and the T cell receptors that recognize them and demonstrate that patients across a variety of human cancers have a diverse repertoire of neoantigen-specific T cells.

COSMIC-based mutation database enhances identification efficiency of HLA-I immunopeptidome

BACKGROUND

Neoantigens have emerged as a promising area of focus in tumor immunotherapy, with several established strategies aiming to enhance their identification. Human leukocyte antigen class I molecules (HLA-I), which present intracellular immunopeptides to T cells, provide an ideal source for identifying neoantigens. However, solely relying on a mutation database generated through commonly used whole exome sequencing (WES) for the identification of HLA-I immunopeptides, may result in potential neoantigens being missed due to limitations in sequencing depth and sample quality.

METHOD

In this study, we constructed and evaluated an extended database for neoantigen identification, based on COSMIC mutation database. This study utilized mass spectrometry-based proteogenomic profiling to identify the HLA-I immunopeptidome enriched from HepG2 cell. HepG2 WES-based and the COSMIC-based mutation database were generated and utilized to identify HepG2-specific mutant immunopeptides.

RESULT

The results demonstrated that COSMIC-based database identified 5 immunopeptides compared to only 1 mutant peptide identified by HepG2 WES-based database, indicating its effectiveness in identifying mutant immunopeptides. Furthermore, HLA-I affinity of the mutant immunopeptides was evaluated through NetMHCpan and peptide-docking modeling to validate their binding to HLA-I molecules, demonstrating the potential of mutant peptides identified by the COSMIC-based database as neoantigens.

CONCLUSION

Utilizing the COSMIC-based mutation database is a more efficient strategy for identifying mutant peptides from HLA-I immunopeptidome without significantly increasing the false positive rate. HepG2 specific WES-based database may exclude certain mutant peptides due to WES sequencing depth or sample heterogeneity. The COSMIC-based database can effectively uncover potential neoantigens within the HLA-I immunopeptidomes.

Test-Time Training for Deep MS/MS Spectrum Prediction Improves Peptide Identification

In bottom-up proteomics, peptide-spectrum matching is critical for peptide and protein identification. Recently, deep learning models have been used to predict tandem mass spectra of peptides, enabling the calculation of similarity scores between the predicted and experimental spectra for peptide-spectrum matching. These models follow the supervised learning paradigm, which trains a general model using paired peptides and spectra from standard data sets and directly employs the model on experimental data. However, this approach can lead to inaccurate predictions due to differences between the training data and the experimental data, such as sample types, enzyme specificity, and instrument calibration. To tackle this problem, we developed a test-time training paradigm that adapts the pretrained model to generate experimental data-specific models, namely, PepT3. PepT3 yields a 10-40% increase in peptide identification depending on the variability in training and experimental data. Intriguingly, when applied to a patient-derived immunopeptidomic sample, PepT3 increases the identification of tumor-specific immunopeptide candidates by 60%. Two-thirds of the newly identified candidates are predicted to bind to the patient's human leukocyte antigen isoforms. To facilitate access of the model and all the results, we have archived all the intermediate files in Zenodo.org with identifier 8231084.

MARS an improved de novo peptide candidate selection method for non-canonical antigen target discovery in cancer

Understanding the nature and extent of non-canonical human leukocyte antigen (HLA) presentation in tumour cells is a priority for target antigen discovery for the development of next generation immunotherapies in cancer. We here employ a de novo mass spectrometric sequencing approach with a refined, MHC-centric analysis strategy to detect non-canonical MHC-associated peptides specific to cancer without any prior knowledge of the target sequence from genomic or RNA sequencing data. Our strategy integrates MHC binding rank, Average local confidence scores, and peptide Retention time prediction for improved de novo candidate Selection; culminating in the machine learning model MARS. We benchmark our model on a large synthetic peptide library dataset and reanalysis of a published dataset of high-quality non-canonical MHC-associated peptide identifications in human cancer. We achieve almost 2-fold improvement for high quality spectral assignments in comparison to de novo sequencing alone with an estimated accuracy of above 85.7% when integrated with a stepwise peptide sequence mapping strategy. Finally, we utilize MARS to detect and validate lncRNA-derived peptides in human cervical tumour resections, demonstrating its suitability to discover novel, immunogenic, non-canonical peptide sequences in primary tumour tissue.

Splicing neoantigen discovery with SNAF reveals shared targets for cancer immunotherapy

Immunotherapy has emerged as a crucial strategy to combat cancer by "reprogramming" a patient's own immune system. Although immunotherapy is typically reserved for patients with a high mutational burden, neoantigens produced from posttranscriptional regulation may provide an untapped reservoir of common immunogenic targets for new targeted therapies. To comprehensively define tumor-specific and likely immunogenic neoantigens from patient RNA-Seq, we developed Splicing Neo Antigen Finder (SNAF), an easy-to-use and open-source computational workflow to predict splicing-derived immunogenic MHC-bound peptides (T cell antigen) and unannotated transmembrane proteins with altered extracellular epitopes (B cell antigen). This workflow uses a highly accurate deep learning strategy for immunogenicity prediction (DeepImmuno) in conjunction with new algorithms to rank the tumor specificity of neoantigens (BayesTS) and to predict regulators of mis-splicing (RNA-SPRINT). T cell antigens from SNAF were frequently evidenced as HLA-presented peptides from mass spectrometry (MS) and predict response to immunotherapy in melanoma. Splicing neoantigen burden was attributed to coordinated splicing factor dysregulation. Shared splicing neoantigens were found in up to 90% of patients with melanoma, correlated to overall survival in multiple cancer cohorts, induced T cell reactivity, and were characterized by distinct cells of origin and amino acid preferences. In addition to T cell neoantigens, our B cell focused pipeline (SNAF-B) identified a new class of tumor-specific extracellular neoepitopes, which we termed ExNeoEpitopes. ExNeoEpitope full-length mRNA predictions were tumor specific and were validated using long-read isoform sequencing and in vitro transmembrane localization assays. Therefore, our systematic identification of splicing neoantigens revealed potential shared targets for therapy in heterogeneous cancers.

The SysteMHC Atlas v2.0, an updated resource for mass spectrometry-based immunopeptidomics

The SysteMHC Atlas v1.0 was the first public repository dedicated to mass spectrometry-based immunopeptidomics. Here we introduce a newly released version of the SysteMHC Atlas v2.0 (https://systemhc.sjtu.edu.cn), a comprehensive collection of 7190 MS files from 303 allotypes. We extended and optimized a computational pipeline that allows the identification of MHC-bound peptides carrying on unexpected post-translational modifications (PTMs), thereby resulting in 471K modified peptides identified over 60 distinct PTM types. In total, we identified approximately 1.0 million and 1.1 million unique peptides for MHC class I and class II immunopeptidomes, respectively, indicating a 6.8-fold increase and a 28-fold increase to those in v1.0. The SysteMHC Atlas v2.0 introduces several new features, including the inclusion of non-UniProt peptides, and the incorporation of several novel computational tools for FDR estimation, binding affinity prediction and motif deconvolution. Additionally, we enhanced the user interface, upgraded website framework, and provided external links to other resources related. Finally, we built and provided various spectral libraries as community resources for data mining and future immunopeptidomic and proteomic analysis. We believe that the SysteMHC Atlas v2.0 is a unique resource to provide key insights to the immunology and proteomics community and will accelerate the development of vaccines and immunotherapies.

Breast cancer immunopeptidomes contain numerous shared tumor antigens

Hormone receptor-positive breast cancer (HR+) is immunologically cold and has not benefited from advances in immunotherapy. In contrast, subsets of triple-negative breast cancer (TNBC) display high leukocytic infiltration and respond to checkpoint blockade. CD8+ T cells, the main effectors of anticancer responses, recognize MHC I-associated peptides (MAPs). Our work aimed to characterize the repertoire of MAPs presented by HR+ and TNBC tumors. Using mass spectrometry, we identified 57,094 unique MAPs in 26 primary breast cancer samples. MAP source genes highly overlapped between both subtypes. We identified 25 tumor-specific antigens (TSAs) mainly deriving from aberrantly expressed regions. TSAs were most frequently identified in TNBC samples and were more shared among The Cancer Genome Atlas (TCGA) database TNBC than HR+ samples. In the TNBC cohort, the predicted number of TSAs positively correlated with leukocytic infiltration and overall survival, supporting their immunogenicity in vivo. We detected 49 tumor-associated antigens (TAAs), some of which derived from cancer-associated fibroblasts. Functional expansion of specific T cell assays confirmed the in vitro immunogenicity of several TSAs and TAAs. Our study identified attractive targets for cancer immunotherapy in both breast cancer subtypes. The higher prevalence of TSAs in TNBC tumors provides a rationale for their responsiveness to checkpoint blockade.

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.

IMBAS-MS Discovers Organ-Specific HLA Peptide Patterns in Plasma

Distinction of non-self from self is the major task of the immune system. Immunopeptidomics studies the peptide repertoire presented by the human leukocyte antigen (HLA) protein, usually on tissues. However, HLA peptides are also bound to plasma soluble HLA (sHLA), but little is known about their origin and potential for biomarker discovery in this readily available biofluid. Currently, immunopeptidomics is hampered by complex workflows and limited sensitivity, typically requiring several mL of plasma. Here, we take advantage of recent improvements in the throughput and sensitivity of mass spectrometry (MS)-based proteomics to develop a highly sensitive, automated, and economical workflow for HLA peptide analysis, termed Immunopeptidomics by Biotinylated Antibodies and Streptavidin (IMBAS). IMBAS-MS quantifies more than 5000 HLA class I peptides from only 200 μl of plasma, in just 30 min. Our technology revealed that the plasma immunopeptidome of healthy donors is remarkably stable throughout the year and strongly correlated between individuals with overlapping HLA types. Immunopeptides originating from diverse tissues, including the brain, are proportionately represented. We conclude that sHLAs are a promising avenue for immunology and potentially for precision oncology.

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.

CD8+ T Cell-Dependent Antitumor Activity In Vivo of a Mass Spectrometry-Identified Neoepitope despite Undetectable CD8+ Immunogenicity In Vitro

Identification of neoepitopes that can control tumor growth in vivo remains a challenge even 10 y after the first genomics-defined cancer neoepitopes were identified. In this study, we identify a neoepitope, resulting from a mutation in the junction plakoglobin (Jup) gene (chromosome 11), from the mouse colon cancer line MC38-FABF (C57BL/6). This neoepitope, Jup mutant (JupMUT), was detected during mass spectrometry of MHC class I-eluted peptides from the tumor. JupMUT has a predicted binding affinity of 564 nM for the Kb molecule and a higher predicted affinity of 82 nM for Db. However, whereas structural modeling of JupMUT and its unmutated counterpart Jup wild-type indicates that there are little conformational differences between the two epitopes bound to Db, large structural divergences are predicted between the two epitopes bound to Kb. Together with in vitro binding data with RMA-S cells, these data suggest that Kb rather than Db is the relevant MHC class I molecule of JupMUT. Immunization of naive C57BL/6 mice with JupMUT elicits CD8-dependent tumor control of a MC38-FABF challenge. Despite the CD8 dependence of JupMUT-mediated tumor control in vivo, CD8+ T cells from JupMUT-immunized mice do not produce higher levels of IFN-γ than do naive mice. The structural and immunological characteristics of JupMUT are substantially different from those of many other neoepitopes that have been shown to mediate tumor control.

T cell receptor therapeutics: immunological targeting of the intracellular cancer proteome

The T cell receptor (TCR) complex is a naturally occurring antigen sensor that detects, amplifies and coordinates cellular immune responses to epitopes derived from cell surface and intracellular proteins. Thus, TCRs enable the targeting of proteins selectively expressed by cancer cells, including neoantigens, cancer germline antigens and viral oncoproteins. As such, TCRs have provided the basis for an emerging class of oncology therapeutics. Herein, we review the current cancer treatment landscape using TCRs and TCR-like molecules. This includes adoptive cell transfer of T cells expressing endogenous or engineered TCRs, TCR bispecific engagers and antibodies specific for human leukocyte antigen (HLA)-bound peptides (TCR mimics). We discuss the unique complexities associated with the clinical development of these therapeutics, such as HLA restriction, TCR retrieval, potency assessment and the potential for cross-reactivity. In addition, we highlight emerging clinical data that establish the antitumour potential of TCR-based therapies, including tumour-infiltrating lymphocytes, for the treatment of diverse human malignancies. Finally, we explore the future of TCR therapeutics, including emerging genome editing methods to safely enhance potency and strategies to streamline patient identification.

TOFIMS mass spectrometry-based immunopeptidomics refines tumor antigen identification

T cell recognition of human leukocyte antigen (HLA)-presented tumor-associated peptides is central for cancer immune surveillance. Mass spectrometry (MS)-based immunopeptidomics represents the only unbiased method for the direct identification and characterization of naturally presented tumor-associated peptides, a key prerequisite for the development of T cell-based immunotherapies. This study reports on the implementation of ion mobility separation-based time-of-flight (TOFIMS) MS for next-generation immunopeptidomics, enabling high-speed and sensitive detection of HLA-presented peptides. Applying TOFIMS-based immunopeptidomics, a novel extensive benignTOFIMS dataset was generated from 94 primary benign samples of solid tissue and hematological origin, which enabled the expansion of benign reference immunopeptidome databases with > 150,000 HLA-presented peptides, the refinement of previously described tumor antigens, as well as the identification of frequently presented self antigens and not yet described tumor antigens comprising low abundant mutation-derived neoepitopes that might serve as targets for future cancer immunotherapy development.

Transcriptome-based identification of tumor-reactive and bystander CD8+ T cell receptor clonotypes in human pancreatic cancer

Pancreatic ductal adenocarcinoma (PDAC) is generally refractory to immune checkpoint blockade, although patients with genetically unstable tumors can show modest therapeutic benefit. We previously demonstrated the presence of tumor-reactive CD8+ T cells in PDAC samples. Here, we charted the tumor-infiltrating T cell repertoire in PDAC by combining single-cell transcriptomics with functional testing of T cell receptors (TCRs) for reactivity against autologous tumor cells. On the basis of a comprehensive dataset including 93 tumor-reactive and 65 bystander TCR clonotypes, we delineated a gene signature that effectively distinguishes between these T cell subsets in PDAC, as well as in other tumor indications. This revealed a high frequency of tumor-reactive TCR clonotypes in three genetically unstable samples. In contrast, the T cell repertoire in six genetically stable PDAC tumors was largely dominated by bystander T cells. Nevertheless, multiple tumor-reactive TCRs were successfully identified in each of these samples, thereby providing a perspective for personalized immunotherapy in this treatment-resistant indication.

Development of a semi-automated MHC-associated peptide proteomics (MAPPs) method using streptavidin bead-based immunoaffinity capture and nano LC-MS/MS to support immunogenicity risk assessment in drug development

Major histocompatibility complex (MHC)-Associated Peptide Proteomics (MAPPs) is an ex vivo method used to assess the immunogenicity risk of biotherapeutics. MAPPs can identify potential T-cell epitopes within the biotherapeutic molecule. Using adalimumab treated human monocyte derived dendritic cells (DCs) and a pan anti-HLA-DR antibody (Ab), we systematically automated and optimized biotin/streptavidin (SA)-capture antibody coupling, lysate incubation with capture antibody, as well as the washing and elution steps of a MAPPs method using functionalized magnetic beads and a KingFisher Magnetic Particle processor. Automation of these steps, combined with capturing using biotinylated-Ab/SA magnetic beads rather than covalently bound antibody, improved reproducibility as measured by minimal inter-and intra-day variability, as well as minimal analyst-to-analyst variability. The semi-automated MAPPs workflow improved sensitivity, allowing for a lower number of cells per analysis. The method was assessed using five different biotherapeutics with varying immunogenicity rates ranging from 0.1 to 48% ADA incidence in the clinic. Biotherapeutics with ≥10%immunogenicity incidence consistently presented more peptides (1.8-28 fold) and clusters (10-21 fold) compared to those with <10% immunogenicity incidence. Our semi-automated MAPPs method provided two main advantages over a manual workflow- the robustness and reproducibility affords confidence in the epitopes identified from as few as 5 to 10 donors and the method workflow can be readily adapted to incorporate different capture Abs in addition to anti-HLA-DR. The incorporation of semi-automated MAPPs with biotinylated-Ab/SA bead-based capture in immunogenicity screening strategies allows the generation of more consistent and reliable data, helping to improve immunogenicity prediction capabilities in drug development. MHC associated peptide proteomics (MAPPs), Immunogenicity risk assessment, in vitro/ex vivo, biotherapeutics, Major Histocompatibility Complex Class II (MHC II), LC-MS, Immunoaffinity Capture, streptavidin magnetic beads.

Recent advances in immunopeptidomic-based tumor neoantigen discovery

The role of aberrantly expressed proteins in tumors in driving immune-mediated control of cancer has been well documented for more than five decades. Today, we know that both aberrantly expressed normal proteins as well as mutant proteins (neoantigens) can function as tumor antigens in both humans and mice. Next-generation sequencing (NGS) and high-resolution mass spectrometry (MS) technologies have made significant advances since the early 2010s, enabling detection of rare but clinically relevant neoantigens recognized by T cells. MS profiling of tumor-specific immunopeptidomes remains the most direct method to identify mutant peptides bound to cellular MHC. However, the need for use of large numbers of cells or significant amounts of tumor tissue to achieve neoantigen detection has historically limited the application of MS. Newer, more sensitive MS technologies have recently demonstrated the capacities to detect neoantigens from fewer cells. Here, we highlight recent advancements in immunopeptidomics-based characterization of tumor-specific neoantigens. Various tumor antigen categories and neoantigen identification approaches are also discussed. Furthermore, we summarize recent reports that achieved successful tumor neoantigen detection by MS using a variety of starting materials, MS acquisition modes, and novel ion mobility devices.

Immune Surveillance of Acute Myeloid Leukemia Is Mediated by HLA-Presented Antigens on Leukemia Progenitor Cells

Therapy-resistant leukemia stem and progenitor cells (LSC) are a main cause of acute myeloid leukemia (AML) relapse. LSC-targeting therapies may thus improve outcome of patients with AML. Here we demonstrate that LSCs present HLA-restricted antigens that induce T-cell responses allowing for immune surveillance of AML. Using a mass spectrometry-based immunopeptidomics approach, we characterized the antigenic landscape of patient LSCs and identified AML- and AML/LSC-associated HLA-presented antigens absent from normal tissues comprising nonmutated peptides, cryptic neoepitopes, and neoepitopes of common AML driver mutations of NPM1 and IDH2. Functional relevance of shared AML/LSC antigens is illustrated by presence of their cognizant memory T cells in patients. Antigen-specific T-cell recognition and HLA class II immunopeptidome diversity correlated with clinical outcome. Together, these antigens shared among AML and LSCs represent prime targets for T cell-based therapies with potential of eliminating residual LSCs in patients with AML.

SIGNIFICANCE

The elimination of therapy-resistant leukemia stem and progenitor cells (LSC) remains a major challenge in the treatment of AML. This study identifies and functionally validates LSC-associated HLA class I and HLA class II-presented antigens, paving the way to the development of LSC-directed T cell-based immunotherapeutic approaches for patients with AML. See related commentary by Ritz, p. 430 . This article is featured in Selected Articles from This Issue, p. 419.

Targeting of intracellular oncoproteins with peptide-centric CARs

The majority of oncogenic drivers are intracellular proteins, constraining their immunotherapeutic targeting to mutated peptides (neoantigens) presented by individual human leukocyte antigen (HLA) allotypes1. However, most cancers have a modest mutational burden that is insufficient for generating responses using neoantigen-based therapies2,3. Neuroblastoma is a paediatric cancer that harbours few mutations and is instead driven by epigenetically deregulated transcriptional networks4. Here we show that the neuroblastoma immunopeptidome is enriched with peptides derived from proteins essential for tumorigenesis. We focused on targeting the unmutated peptide QYNPIRTTF discovered on HLA-A*24:02, which is derived from the neuroblastoma-dependency gene and master transcriptional regulator PHOX2B. To target QYNPIRTTF, we developed peptide-centric chimeric antigen receptors (PC-CARs) through a counter panning strategy using predicted potentially cross-reactive peptides. We further proposed that PC-CARs can recognize peptides on additional HLA allotypes when presenting a similar overall molecular surface. Informed by our computational modelling results, we show that PHOX2B PC-CARs also recognize QYNPIRTTF presented by HLA-A*23:01, the most common non-A2 allele in people with African ancestry. Finally, we demonstrate potent and specific killing of neuroblastoma cells expressing these HLAs in vitro and complete tumour regression in mice. These data suggest that PC-CARs have the potential to expand the pool of immunotherapeutic targets to include non-immunogenic intracellular oncoproteins and allow targeting through additional HLA allotypes in a clinical setting.

Correlative CD4 and CD8 T-cell immunodominance in humans and mice: Implications for preclinical testing

Antigen-specific T-cell recognition is restricted by Major Histocompatibility Complex (MHC) molecules, and differences between CD4 and CD8 immunogenicity in humans and animal species used in preclinical vaccine testing are yet to be fully understood. In this study, we addressed this matter by analyzing experimentally identified epitopes based on published data curated in the Immune Epitopes DataBase (IEDB) database. We first analyzed SARS-CoV-2 spike (S) and nucleoprotein (N), which are two common targets of the immune response and well studied in both human and mouse systems. We observed a weak but statistically significant correlation between human and H-2b mouse T-cell responses (CD8 S specific (r = 0.206, p = 1.37 × 10-13); CD4 S specific (r = 0.118, p = 2.63 × 10-5) and N specific (r = 0.179, p = 2.55 × 10-4)). Due to intrinsic differences in MHC molecules across species, we also investigated the association between the immunodominance of common Human Leukocyte Antigen (HLA) alleles for which HLA transgenic mice are available, namely, A*02:01, B*07:02, DRB1*01:01, and DRB1*04:01, and found higher significant correlations for both CD8 and CD4 (maximum r = 0.702, p = 1.36 × 10-31 and r = 0.594, p = 3.04-122, respectively). Our results further indicated that some regions are commonly immunogenic between humans and mice (either H-2b or HLA transgenic) but that others are human specific. Finally, we noted a significant correlation between CD8 and CD4 S- (r = 0.258, p = 7.33 × 1021) and N-specific (r = 0.369, p = 2.43 × 1014) responses, suggesting that discrete protein subregions can be simultaneously recognized by T cells. These findings were confirmed in other viral systems, providing general guidance for the use of murine models to test T-cell immunogenicity of viral antigens destined for human use.

DNMT and HDAC inhibition induces immunogenic neoantigens from human endogenous retroviral element-derived transcripts

Immunotherapies targeting cancer-specific neoantigens have revolutionized the treatment of cancer patients. Recent evidence suggests that epigenetic therapies synergize with immunotherapies, mediated by the de-repression of endogenous retroviral element (ERV)-encoded promoters, and the initiation of transcription. Here, we use deep RNA sequencing from cancer cell lines treated with DNA methyltransferase inhibitor (DNMTi) and/or Histone deacetylase inhibitor (HDACi), to assemble a de novo transcriptome and identify several thousand ERV-derived, treatment-induced novel polyadenylated transcripts (TINPATs). Using immunopeptidomics, we demonstrate the human leukocyte antigen (HLA) presentation of 45 spectra-validated treatment-induced neopeptides (t-neopeptides) arising from TINPATs. We illustrate the potential of the identified t-neopeptides to elicit a T-cell response to effectively target cancer cells. We further verify the presence of t-neopeptides in AML patient samples after in vivo treatment with the DNMT inhibitor Decitabine. Our findings highlight the potential of ERV-derived neoantigens in epigenetic and immune therapies.

Current perspectives on mass spectrometry-based immunopeptidomics: the computational angle to tumor antigen discovery

Identification of tumor antigens presented by the human leucocyte antigen (HLA) molecules is essential for the design of effective and safe cancer immunotherapies that rely on T cell recognition and killing of tumor cells. Mass spectrometry (MS)-based immunopeptidomics enables high-throughput, direct identification of HLA-bound peptides from a variety of cell lines, tumor tissues, and healthy tissues. It involves immunoaffinity purification of HLA complexes followed by MS profiling of the extracted peptides using data-dependent acquisition, data-independent acquisition, or targeted approaches. By incorporating DNA, RNA, and ribosome sequencing data into immunopeptidomics data analysis, the proteogenomic approach provides a powerful means for identifying tumor antigens encoded within the canonical open reading frames of annotated coding genes and non-canonical tumor antigens derived from presumably non-coding regions of our genome. We discuss emerging computational challenges in immunopeptidomics data analysis and tumor antigen identification, highlighting key considerations in the proteogenomics-based approach, including accurate DNA, RNA and ribosomal sequencing data analysis, careful incorporation of predicted novel protein sequences into reference protein database, special quality control in MS data analysis due to the expanded and heterogeneous search space, cancer-specificity determination, and immunogenicity prediction. The advancements in technology and computation is continually enabling us to identify tumor antigens with higher sensitivity and accuracy, paving the way toward the development of more effective cancer immunotherapies.

Precision medicine in the era of multi-omics: can the data tsunami guide rational treatment decision?

Precision medicine for cancer is rapidly moving to an approach that integrates multiple dimensions of the biology in order to model mechanisms of cancer progression in each patient. The discovery of multiple drivers per tumor challenges medical decision that faces several treatment options. Drug sensitivity depends on the actionability of the target, its clonal or subclonal origin and coexisting genomic alterations. Sequencing has revealed a large diversity of drivers emerging at treatment failure, which are potential targets for clinical trials or drug repurposing. To effectively prioritize therapies, it is essential to rank genomic alterations based on their proven actionability. Moving beyond primary drivers, the future of precision medicine necessitates acknowledging the intricate spatial and temporal heterogeneity inherent in cancer. The advent of abundant complex biological data will make artificial intelligence algorithms indispensable for thorough analysis. Here, we will discuss the advancements brought by the use of high-throughput genomics, the advantages and limitations of precision medicine studies and future perspectives in this field.

What do cancer-specific CD8+ T cells see? The contribution of immunopeptidomics

Immunopeptidomics is the survey of all peptides displayed on a cell or tissue when bound to human leukocyte antigen (HLA) molecules using tandem mass spectrometry. When attempting to determine the targets of tumour-specific CD8+ T cells, a survey of the potential ligands in tumour tissues is invaluable, and, in comparison with in-silico predictions, provides greater certainty of the existence of individual epitopes, as immunopeptidomics-confirmed CD8+ T-cell epitopes are known to be immunogenic, and direct observation should avoid the risk of autoreactivity which could arise following immunisation with structural homologues. The canonical sources of CD8+ T-cell tumour specific epitopes, such as tumour associated antigens, may be well conserved between patients and tumour types, but are often only weakly immunogenic. Direct observation of tumour-specific neoantigens by immunopeptidomics is rare, although valuable. Thus, there has been increasing interest in the non-canonical origins of tumour-reactive CD8+ T-cell epitopes, such as those arising from proteasomal splicing events, translational/turnover defects and alternative open reading frame reads. Such epitopes can be identified in silico, although validation is more challenging. Non-self CD8+ T-cell epitopes such as viral epitopes may be useful in certain cancer types with known viral origins, however these have been relatively unexplored with immunopeptidomics to date, possibly due to the paucity of source viral proteins in tumour tissues. This review examines the latest evidence for canonical, non-canonical and non-human CD8+ T-cell epitopes identified by immunopeptidomics, and concludes that the relative contribution for each of these sources to anti-tumour CD8+ T-cell reactivity is currently uncertain.

The landscape of MHC-presented phosphopeptides yields actionable shared tumor antigens for cancer immunotherapy across multiple HLA alleles

BACKGROUND

Certain phosphorylated peptides are differentially presented by major histocompatibility complex (MHC) molecules on cancer cells characterized by aberrant phosphorylation. Phosphopeptides presented in complex with the human leukocyte antigen HLA-A*02:01 provide a stability advantage over their non-phosphorylated counterparts. This stability is thought to contribute to enhanced immunogenicity. Whether tumor-associated phosphopeptides presented by other common alleles exhibit immunogenicity and structural characteristics similar to those presented by A*02:01 is unclear. Therefore, we determined the identity, structural features, and immunogenicity of phosphopeptides presented by the prevalent alleles HLA-A*03:01, HLA-A*11:01, HLA-C*07:01, and HLA-C*07:02.

METHODS

We isolated peptide-MHC complexes by immunoprecipitation from 11 healthy and neoplastic tissue samples using mass spectrometry, and then combined the resulting data with public immunopeptidomics data sets to assemble a curated set of phosphopeptides presented by 96 samples spanning 20 distinct healthy and neoplastic tissue types. We determined the biochemical features of selected phosphopeptides by in vitro binding assays and in silico docking, and their immunogenicity by analyzing healthy donor T cells for phosphopeptide-specific multimer binding and cytokine production.

RESULTS

We identified a subset of phosphopeptides presented by HLA-A*03:01, A*11:01, C*07:01 and C*07:02 on multiple tumor types, particularly lymphomas and leukemias, but not healthy tissues. These phosphopeptides are products of genes essential to lymphoma and leukemia survival. The presented phosphopeptides generally exhibited similar or worse binding to A*03:01 than their non-phosphorylated counterparts. HLA-C*07:01 generally presented phosphopeptides but not their unmodified counterparts. Phosphopeptide binding to HLA-C*07:01 was dependent on B-pocket interactions that were absent in HLA-C*07:02. While HLA-A*02:01 and HLA-A*11:01 phosphopeptide-specific T cells could be readily detected in an autologous setting even when the non-phosphorylated peptide was co-presented, HLA-A*03:01 or HLA-C*07:01 phosphopeptides were repeatedly non-immunogenic, requiring use of allogeneic T cells to induce phosphopeptide-specific T cells.

CONCLUSIONS

Phosphopeptides presented by multiple alleles that are differentially expressed on tumors constitute tumor-specific antigens that could be targeted for cancer immunotherapy, but the immunogenicity of such phosphopeptides is not a general feature. In particular, phosphopeptides presented by HLA-A*02:01 and A*11:01 exhibit consistent immunogenicity, while phosphopeptides presented by HLA-A*03:01 and C*07:01, although appropriately presented, are not immunogenic. Thus, to address an expanded patient population, phosphopeptide-targeted immunotherapies should be wary of allele-specific differences.

What Can Ribo-Seq, Immunopeptidomics, and Proteomics Tell Us About the Noncanonical Proteome?

Ribosome profiling (Ribo-Seq) has proven transformative for our understanding of the human genome and proteome by illuminating thousands of noncanonical sites of ribosome translation outside the currently annotated coding sequences (CDSs). A conservative estimate suggests that at least 7000 noncanonical ORFs are translated, which, at first glance, has the potential to expand the number of human protein CDSs by 30%, from ∼19,500 annotated CDSs to over 26,000 annotated CDSs. Yet, additional scrutiny of these ORFs has raised numerous questions about what fraction of them truly produce a protein product and what fraction of those can be understood as proteins according to conventional understanding of the term. Adding further complication is the fact that published estimates of noncanonical ORFs vary widely by around 30-fold, from several thousand to several hundred thousand. The summation of this research has left the genomics and proteomics communities both excited by the prospect of new coding regions in the human genome but searching for guidance on how to proceed. Here, we discuss the current state of noncanonical ORF research, databases, and interpretation, focusing on how to assess whether a given ORF can be said to be "protein coding."

Identification of neoepitope reactive T-cell receptors guided by HLA-A*03:01 and HLA-A*11:01 immunopeptidomics

BACKGROUND

Tumor-specific mutated proteins can create immunogenic non-self, mutation-containing 'neoepitopes' that are attractive targets for adoptive T-cell therapies. To avoid the complexity of defining patient-specific, private neoepitopes, there has been major interest in targeting common shared mutations in driver genes using off-the-shelf T-cell receptors (TCRs) engineered into autologous lymphocytes. However, identifying the precise naturally processed neoepitopes to pursue is a complex and challenging process. One method to definitively demonstrate whether an epitope is presented at the cell surface is to elute peptides bound to a specific major histocompatibility complex (MHC) allele and analyze them by mass spectrometry (MS). These MS data can then be prospectively applied to isolate TCRs specific to the neoepitope.

METHODS

We created mono-allelic cell lines expressing one class I HLA allele and one common mutated oncogene in order to eliminate HLA deconvolution requirements and increase the signal of recovered peptides. MHC-bound peptides on the surface of these cell lines were immunoprecipitated, purified, and analyzed using liquid chromatography-tandem mass spectrometry, producing a list of mutation-containing minimal epitopes. To validate the immunogenicity of these neoepitopes, HLA-transgenic mice were vaccinated using the minimal peptides identified by MS in order to generate neoepitope-reactive TCRs. Specificity of these candidate TCRs was confirmed by peptide titration and recognition of transduced targets.

RESULTS

We identified precise neoepitopes derived from mutated isoforms of KRAS, EGFR, BRAF, and PIK3CA presented by HLA-A*03:01 and/or HLA-A*11:01 across multiple biological replicates. From our MS data, we were able to successfully isolate murine TCRs that specifically recognize four HLA-A*11:01 restricted neoepitopes (KRAS G13D, PIK3CA E545K, EGFR L858R and BRAF V600E) and three HLA-A*03:01 restricted neoepitopes (KRAS G12V, EGFR L858R and BRAF V600E).

CONCLUSIONS

Our data show that an MS approach can be used to demonstrate which shared oncogene-derived neoepitopes are processed and presented by common HLA alleles, and those MS data can rapidly be used to develop TCRs against these common tumor-specific antigens. Although further characterization of these neoepitope-specific murine TCRs is required, ultimately, they have the potential to be used clinically for adoptive cell therapy.

Antigen presentation in cancer - mechanisms and clinical implications for immunotherapy

Over the past decade, the emergence of effective immunotherapies has revolutionized the clinical management of many types of cancers. However, long-term durable tumour control is only achieved in a fraction of patients who receive these therapies. Understanding the mechanisms underlying clinical response and resistance to treatment is therefore essential to expanding the level of clinical benefit obtained from immunotherapies. In this Review, we describe the molecular mechanisms of antigen processing and presentation in tumours and their clinical consequences. We examine how various aspects of the antigen-presentation machinery (APM) shape tumour immunity. In particular, we discuss genomic variants in HLA alleles and other APM components, highlighting their influence on the immunopeptidomes of both malignant cells and immune cells. Understanding the APM, how it is regulated and how it changes in tumour cells is crucial for determining which patients will respond to immunotherapy and why some patients develop resistance. We focus on recently discovered molecular and genomic alterations that drive the clinical outcomes of patients receiving immune-checkpoint inhibitors. An improved understanding of how these variables mediate tumour-immune interactions is expected to guide the more precise administration of immunotherapies and reveal potentially promising directions for the development of new immunotherapeutic approaches.

Mass spectrometry-based proteomics as an emerging tool in clinical laboratories

Mass spectrometry (MS)-based proteomics have been increasingly implemented in various disciplines of laboratory medicine to identify and quantify biomolecules in a variety of biological specimens. MS-based proteomics is continuously expanding and widely applied in biomarker discovery for early detection, prognosis and markers for treatment response prediction and monitoring. Furthermore, making these advanced tests more accessible and affordable will have the greatest healthcare benefit.This review article highlights the new paradigms MS-based clinical proteomics has created in microbiology laboratories, cancer research and diagnosis of metabolic disorders. The technique is preferred over conventional methods in disease detection and therapy monitoring for its combined advantages in multiplexing capacity, remarkable analytical specificity and sensitivity and low turnaround time.Despite the achievements in the development and adoption of a number of MS-based clinical proteomics practices, more are expected to undergo transition from bench to bedside in the near future. The review provides insights from early trials and recent progresses (mainly covering literature from the NCBI database) in the application of proteomics in clinical laboratories.

A Lysine Residue at the C-Terminus of MHC Class I Ligands Correlates with Low C-Terminal Proteasomal Cleavage Probability

The majority of peptides presented by MHC class I result from proteasomal protein turnover. The specialized immunoproteasome, which is induced during inflammation, plays a major role in antigenic peptide generation. However, other cellular proteases can, either alone or together with the proteasome, contribute peptides to MHC class I loading non-canonically. We used an immunopeptidomics workflow combined with prediction software for proteasomal cleavage probabilities to analyze how inflammatory conditions affect the proteasomal processing of immune epitopes presented by A549 cells. The treatment of A549 cells with IFNγ enhanced the proteasomal cleavage probability of MHC class I ligands for both the constitutive proteasome and the immunoproteasome. Furthermore, IFNγ alters the contribution of the different HLA allotypes to the immunopeptidome. When we calculated the HLA allotype-specific proteasomal cleavage probabilities for MHC class I ligands, the peptides presented by HLA-A*30:01 showed characteristics hinting at a reduced C-terminal proteasomal cleavage probability independently of the type of proteasome. This was confirmed by HLA-A*30:01 ligands from the immune epitope database, which also showed this effect. Furthermore, two additional HLA allotypes, namely, HLA-A*03:01 and HLA-A*11:01, presented peptides with a markedly reduced C-terminal proteasomal cleavage probability. The peptides eluted from all three HLA allotypes shared a peptide binding motif with a C-terminal lysine residue, suggesting that this lysine residue impairs proteasome-dependent HLA ligand production and might, in turn, favor peptide generation by other cellular proteases.

Proteogenomic identification of an immunogenic antigen derived from human endogenous retrovirus in renal cell carcinoma

CD8+ T cells can recognize tumor antigens displayed by HLA class I molecules and eliminate tumor cells. Despite their low tumor mutation burden, immune checkpoint blockade (ICB) is often beneficial in patients with renal cell carcinoma (RCC). Here, using a proteogenomic approach, we directly and comprehensively explored the HLA class I-presenting peptidome of RCC tissues and demonstrated that the immunopeptidomes contain a small subset of peptides derived from human endogenous retroviruses (hERV). A comparison between tumor and normal kidney tissues revealed tumor-associated hERV antigens, one of which was immunogenic and recognized by host tumor-infiltrating lymphocytes (TIL). Stimulation with the hERV antigen induced reactive CD8+ T cells in healthy donor-derived (HD-derived) peripheral blood mononuclear cells (PBMC). These results highlight the presence of antitumor CD8+ T cell surveillance against hERV3895 antigens, suggesting their clinical applications in patients with RCC.

Peptide-based vaccine for cancer therapies

Different strategies based on peptides are available for cancer treatment, in particular to counter-act the progression of tumor growth and disease relapse. In the last decade, in the context of therapeutic strategies against cancer, peptide-based vaccines have been evaluated in different tumor models. The peptides selected for cancer vaccine development can be classified in two main type: tumor-associated antigens (TAAs) and tumor-specific antigens (TSAs), which are captured, internalized, processed and presented by antigen-presenting cells (APCs) to cell-mediated immunity. Peptides loaded onto MHC class I are recognized by a specific TCR of CD8+ T cells, which are activated to exert their cytotoxic activity against tumor cells presenting the same peptide-MHC-I complex. This process is defined as active immunotherapy as the host's immune system is either de novo activated or restimulated to mount an effective, tumor-specific immune reaction that may ultimately lead to tu-mor regression. However, while the preclinical data have frequently shown encouraging results, therapeutic cancer vaccines clinical trials, including those based on peptides have not provided satisfactory data to date. The limited efficacy of peptide-based cancer vaccines is the consequence of several factors, including the identification of specific target tumor antigens, the limited immunogenicity of peptides and the highly immunosuppressive tumor microenvironment (TME). An effective cancer vaccine can be developed only by addressing all such different aspects. The present review describes the state of the art for each of such factors.

The Value of Microbes in Cancer Neoantigen Immunotherapy

Tumor neoantigens are widely used in cancer immunotherapy, and a growing body of research suggests that microbes play an important role in these neoantigen-based immunotherapeutic processes. The human body and its surrounding environment are filled with a large number of microbes that are in long-term interaction with the organism. The microbiota can modulate our immune system, help activate neoantigen-reactive T cells, and play a great role in the process of targeting tumor neoantigens for therapy. Recent studies have revealed the interconnection between microbes and neoantigens, which can cross-react with each other through molecular mimicry, providing theoretical guidance for more relevant studies. The current applications of microbes in immunotherapy against tumor neoantigens are mainly focused on cancer vaccine development and immunotherapy with immune checkpoint inhibitors. This article summarizes the related fields and suggests the importance of microbes in immunotherapy against neoantigens.