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

Strategies for neoantigen screening and immunogenicity validation in cancer immunotherapy (Review)

Cancer immunotherapy stimulates and enhances antitumor immune responses to eliminate cancer cells. Neoantigens, which originate from specific mutations within tumor cells, are key targets in cancer immunotherapy. Neoantigens manifest as abnormal peptide fragments or protein segments that are uniquely expressed in tumor cells, making them highly immunogenic. As a result, they activate the immune system, particularly T cell‑mediated immune responses, effectively identifying and eliminating tumor cells. Certain tumor‑associated antigens that are abnormally expressed in normal host proteins in cancer cells are promising targets for immunotherapy. Neoantigens derived from mutated proteins in cancer cells offer true cancer specificity and are often highly immunogenic. Furthermore, most neoantigens are unique to each patient, highlighting the need for personalized treatment strategies. The precise identification and screening of neoantigens are key for improving treatment efficacy and developing individualized therapeutic plans. The neoantigen prediction process involves somatic mutation identification, human leukocyte antigen (HLA) typing, peptide processing and peptide‑HLA binding prediction. The present review summarizes the major current methods used for neoantigen screening, available computational tools and the advantages and limitations of various techniques. Additionally, the present review aimed to summarize experimental strategies for validating the immunogenicity of the predicted neoantigens, which will determine whether these neoantigens can effectively trigger immune responses, as well as challenges encountered during neoantigen screening, providing relevant recommendations for the optimization of neoantigen‑based immunotherapy.

Methodological approach to identify immunogenic epitopes candidates for vaccines against emerging pathogens tailored to defined HLA populations

Vaccines stimulate cells of the adaptive immune system, generating a protective and lasting memory, and are the main public health strategy to protect the world population from emerging pathogens such as the SARS-CoV-2 virus, responsible for millions of deaths in the recent COVID-19 pandemic. Several in-silico algorithms have facilitated the selection of antigens as vaccine candidates; however, their predictive capacity remains limited and it is necessary to continue training them, using information obtained in immunological assays. In this work, the SARS-CoV-2 proteome was sampled using a series of concatenated algorithms that allowed us to define a series of candidate viral peptides for a vaccine against SARS-CoV-2 in individuals from Colombian, whose haplotypes for HLA-I and II were incorporated as part of the algorithm. The immunogenicity of the peptides predicted with three tools or with the combination of them was evaluated and found that short peptides predicted and selected as highly immunogenic peptides were capable of expanding memory CD8 T lymphocytes with an activation phenotype. Altogether, our results outline a pipeline that combines a bioinformatic and immunological approach useful to select immunogenic epitopes from emerging pathogens as vaccine candidates tailored to the population's HLA-Haplotypes.

Neoantigen enriched biomimetic nanovaccine for personalized cancer immunotherapy

Personalized cancer vaccines elicit robust T cell immunity and anti-tumour potency, but identifying tumour-specific antigens remains challenging, severely constraining the therapeutic window. Biomimetic nanovaccines employing cancer cell membranes display inherent biocompatibility and stimulate T-cell responses against diverse tumour antigens, though tumours develop multiple mechanisms to reduce antigen presentation. Here we demonstrate a rapid and general strategy to fabricate personalized nanovaccines based on Antigen-Enriched tumor Cell Membranes (AECM) for early intervention. Interferon-γ potently stimulates antigen presentation across a broad range of cancer cell types. By coupling the generated AECM with PC7A adjuvant, a stimulator of interferon genes (STING)-activating polymer, the AECM@PC7A nanovaccine induces robust poly-neoepitopic T-cell responses even at low dosage, achieving significant tumour regression and metastasis inhibition in multiple murine cancer models. This anti-tumor response relies on MHC-I restricted antigen presentation and CD8+ T-cell activation, with dendritic cells presenting AECM antigens predominantly via cross-dressing to prime T-cells. AECM@PC7A exhibits remarkable anti-tumor efficacy when compared to vaccines with diverse formulations, and demonstrates therapeutic potential in post-surgical and humanized xenograft tumor models. This proof-of-concept study provides a promising universal avenue for the rapid development of personalized cancer vaccines applicable to early intervention for a broad range of patients.

Tumor antigens preferentially derive from unmutated genomic sequences in melanoma and non-small cell lung cancer

Melanoma and non-small cell lung cancer (NSCLC) display exceptionally high mutational burdens. Hence, immune targeting in these cancers has primarily focused on tumor antigens (TAs) predicted to derive from nonsynonymous mutations. Using comprehensive proteogenomic analyses, we identified 589 TAs in cutaneous melanoma (n = 505) and NSCLC (n = 90). Of these, only 1% were derived from mutated sequences, which was explained by a low RNA expression of most nonsynonymous mutations and their localization outside genomic regions proficient for major histocompatibility complex (MHC) class I-associated peptide generation. By contrast, 99% of TAs originated from unmutated genomic sequences specific to cancer (aberrantly expressed tumor-specific antigens (aeTSAs), n = 220), overexpressed in cancer (tumor-associated antigens (TAAs), n = 165) or specific to the cell lineage of origin (lineage-specific antigens (LSAs), n = 198). Expression of aeTSAs was epigenetically regulated, and most were encoded by noncanonical genomic sequences. aeTSAs were shared among tumor samples, were immunogenic and could contribute to the response to immune checkpoint blockade observed in previous studies, supporting their immune targeting across cancers.

Comparison of Shared Class I HLA-Bound Noncanonical Neoepitopes between Normal and Neoplastic Tissues of Pancreatic Adenocarcinoma

PURPOSE

Developing T-cell or vaccine therapies for pancreatic ductal adenocarcinoma (PDAC) has been challenging because of a lack of knowledge regarding immunodominant, cancer-specific antigens as PDAC are characterized by a scarcity of genomic mutation-associated neoepitopes, and effective approaches to discover them are limited.

EXPERIMENTAL DESIGN

An advanced mass spectrometry approach was employed to compare the immunopeptidome of PDAC tissues and matched normal tissues from the same patients.

RESULTS

This study identified HLA class I-binding variant peptides derived from canonical proteins, which had single amino-acid substitutions not attributed to genetic mutations or RNA editing. These amino-acid substitutions appeared to result from translational errors. The variant peptides were predominantly found in tumor tissues, with certain peptides common among multiple patients. Importantly, several of these variant peptides were more immunogenic than their wild-type counterparts.

CONCLUSIONS

The shared noncanonical neoepitopes identified in this study offer promising candidates for vaccine and T-cell therapy development, potentially providing new avenues for immunotherapy in PDAC. See related commentary by Yuan et al., p. 1821.

Harnessing Noncanonical Proteins for Next-Generation Drug Discovery and Diagnosis

Noncanonical proteins, encoded by previously overlooked genomic regions (part of the "dark genome"), are emerging as crucial players in human health and disease, expanding our understanding of the "dark proteome." This review explores their landscape, including proteins derived from long non-coding RNAs, circular RNAs, and alternative open reading frames. Recent advances in ribosome profiling, mass spectrometry, and proteogenomics have unveiled their involvement in critical cellular processes. We examine their roles in cancer, neurological disorders, cardiovascular diseases, and infectious diseases, highlighting their potential as novel biomarkers and therapeutic targets. The review addresses challenges in identifying and characterizing these proteins, particularly recently evolved ones, and discusses implications for drug discovery, including cancer immunotherapy and neoantigen sources. By synthesizing recent findings, we underscore the significance of noncanonical proteins in expanding our understanding of the human genome and proteome, and their promise in developing innovative diagnostic tools and targeted therapies. This overview aims to stimulate further research into this unexplored biological space, potentially revolutionizing approaches to disease treatment and personalized medicine.

Engineered Proteins and Chemical Tools to Probe the Cell Surface Proteome

The cell surface proteome, or surfaceome, is the hub for cells to interact and communicate with the outside world. Many disease-associated changes are hard-wired within the surfaceome, yet approved drugs target less than 50 cell surface proteins. In the past decade, the proteomics community has made significant strides in developing new technologies tailored for studying the surfaceome in all its complexity. In this review, we first dive into the unique characteristics and functions of the surfaceome, emphasizing the necessity for specialized labeling, enrichment, and proteomic approaches. An overview of surfaceomics methods is provided, detailing techniques to measure changes in protein expression and how this leads to novel target discovery. Next, we highlight advances in proximity labeling proteomics (PLP), showcasing how various enzymatic and photoaffinity proximity labeling techniques can map protein-protein interactions and membrane protein complexes on the cell surface. We then review the role of extracellular post-translational modifications, focusing on cell surface glycosylation, proteolytic remodeling, and the secretome. Finally, we discuss methods for identifying tumor-specific peptide MHC complexes and how they have shaped therapeutic development. This emerging field of neo-protein epitopes is constantly evolving, where targets are identified at the proteome level and encompass defined disease-associated PTMs, complexes, and dysregulated cellular and tissue locations. Given the functional importance of the surfaceome for biology and therapy, we view surfaceomics as a critical piece of this quest for neo-epitope target discovery.

PCI-DB: a novel primary tissue immunopeptidome database to guide next-generation peptide-based immunotherapy development

BACKGROUND

Various cancer immunotherapies rely on the T cell-mediated recognition of peptide antigens presented on human leukocyte antigens (HLA). However, the identification and selection of naturally presented peptide targets for the development of personalized as well as off-the-shelf immunotherapy approaches remain challenging.

METHODS

Over 10,000 raw mass spectrometry (MS) files from over 3,000 tissue samples were analyzed, summing to approximately seven terabytes of data. The raw MS data were processed using the standardized and open-source nf-core pipelines MHCquant2 and epitopeprediction, providing a uniform procedure for data handling. A global false discovery rate was applied to minimize false-positive identifications.

RESULTS

Here, we introduce the open-access Peptides for Cancer Immunotherapy Database (PCI-DB, https://pci-db.org/), a comprehensive resource of immunopeptidome data originating from various malignant and benign primary tissues that provides the research community with a convenient tool to facilitate the identification of peptide targets for immunotherapy development. The PCI-DB includes >6.6 million HLA class I and >3.4 million HLA class II peptides from over 40 tissue types and cancer entities. First application of the database provided insights into the representation of cancer-testis antigens across malignant and benign tissues, enabling the identification and characterization of cross-tumor entity and entity-specific tumor-associated antigens (TAAs) as well as naturally presented neoepitopes from frequent cancer mutations. Further, we used the PCI-DB to design personalized peptide vaccines for two patients suffering from metastatic cancer. In a retrospective analysis, PCI-DB enabled the composition of both a multi-peptide vaccine comprising non-mutated, highly frequent TAAs matching the immunopeptidome of the individual patient's tumor and a neoepitope-based vaccine matching the mutational profile of a patient with cancer. Both vaccine approaches induced potent and long-lasting T-cell responses, accompanied by long-term survival of these patients with advanced cancer.

CONCLUSION

The PCI-DB provides a highly versatile tool to broaden the understanding of cancer-related antigen presentation and, ultimately, supports the development of novel immunotherapies.

Identification of post-translationally modified MHC class I-associated peptides as potential cancer immunotherapeutic targets

Over the past three decades, the Hunt laboratory has developed advancements in mass spectrometry-based technologies to enable the identification of peptides bound to major histocompatibility complex (MHC) molecules. The MHC class I processing pathway is responsible for presenting these peptides to circulating cytotoxic T cells, allowing them to recognize and eliminate malignant cells, many of which have aberrant signaling. Professor Hunt hypothesized that due to the dysregulation in phosphorylation in cancer, that abnormal phosphopeptides are likely presented by this pathway, and went on to discover the first phosphopeptide presented by the MHC processing pathway. Thereafter, the laboratory continued to sequence MHC-associated phosphopeptides and contributed several improved methods for their enrichment, detection, and sequencing. This manuscript summarizes the most recent advancements in identification of modified MHC-associated peptides and includes the cumulative list of phosphopeptides sequenced by the Hunt lab. Further, many other post-translational modifications (PTMs) were found to modify MHC peptides, including O-GlcNAcylation, methylation, and kynurenine; in total, we present here a list of 2,450 MHC-associated PTM peptides. Many of these were disease specific and found across several patients, thus highlighting their potential as cancer immunotherapy targets. We are sharing this list with the field in hopes that it might be used in investigating this potential. Overall, the Hunt lab's contributions have significantly advanced our understanding of antigen presentation and dysregulation of PTMs, supporting modern immunotherapy and vaccine development efforts.

diaPASEF Analysis for HLA-I Peptides Enables Quantification of Common Cancer Neoantigens

Human leukocyte antigen class I (HLA-I) molecules present short peptide sequences from endogenous or foreign proteins to cytotoxic T cells. The low abundance of HLA-I peptides poses significant technical challenges for their identification and accurate quantification. While mass spectrometry is currently a method of choice for direct system-wide identification of cellular immunopeptidomes, there is still a need for enhanced sensitivity in detecting and quantifying tumor-specific epitopes. As gas phase separation in data-dependent MS data acquisition increased HLA-I peptide detection by up to 50%, here, we aimed to evaluate the performance of data-independent acquisition (DIA) in combination with parallel accumulation serial fragmentation ion mobility (diaPASEF) for high-sensitivity identification of HLA presented peptides. Our streamlined diaPASEF workflow enabled identification of 11,412 unique peptides from 12.5 million A375 cells and 3426 8-11mers from as low as 500,000 cells with high reproducibility. By taking advantage of HLA binder-specific in silico predicted spectral libraries, we were able to further increase the number of identified HLA-I peptides. We applied SILAC-DIA to a mixture of labeled HLA-I peptides, calculated heavy-to-light ratios for 7742 peptides across five conditions and demonstrated that diaPASEF achieves high quantitative accuracy up to 5-fold dilution. Finally, we identified and quantified shared neoantigens in a monoallelic C1R cell line model. By spiking in heavy synthetic peptides, we verified the identification of the peptide sequences and calculated relative abundances for 13 neoantigens. Taken together, diaPASEF analysis workflows for HLA-I peptides can increase the peptidome coverage for lower sample amounts. The sensitivity and quantitative precision provided by DIA can enable the detection and quantification of less abundant peptide species such as neoantigens across samples from the same background.

Highly catalytic CoFe-prussian blue analogue/ZIF-67 yolk-shell nanocube-decorated MBene nanosheets for ultrasensitive electrochemical cancer-specific neoantigen biosensor

Neoantigens exclusively presented by human leukocyte antigens (HLAs) on cancer cell surfaces are newly discovered and highly cancer-specific biomarkers for cancer diagnosis. The current available method for detecting neoantigens is predominantly based on Mass spectrometry with inevitable limitations of high cost, complexity and isotope labels. In this work, we describe the development of an innovative catalytic electrochemical biosensor for ultrasensitive detection of neoantigen in cell lysates. Such biosensor design involves the synthesis of new and highly catalytic CoFe-prussian blue analogue/ZIF-67 yolk-shell nanocube-decorated MBene nanosheets (CoFe-PBA/ZIF-67/MBene) and the derivatization of electrochemically inert neoantigen to electroactive molecule. The as synthesized CoFe-PBA/ZIF-67/MBene nanocomposite exhibits large specific surface area, excellent conductivity and abundant active sites for electrochemical oxidation of the derivatized neoantigens for the yield of considerably amplified currents for sensitive detection of target neoantigen with low to 0.047 nM detection limit. The biosensor can also be applied for monitoring low levels of HLA-presented neoantigen complexes in cell lysates, offering new insights into methodological advancements in neoantigen analyses for cancer research.

CAR T cells based on fully human T cell receptor-mimetic antibodies exhibit potent antitumor activity in vivo

Monoclonal antibody therapies have transformed the lives of patients across a diverse range of diseases. However, antibodies can usually only access extracellular proteins, including the extracellular portions of membrane proteins that are expressed on the cell surface. In contrast, T cell receptors (TCRs) survey the entire cellular proteome when processed and presented as peptides in association with human leukocyte antigen (pHLA complexes). Antibodies that mimic TCRs by recognizing pHLA complexes have the potential to extend the reach of antibodies to this larger pool of targets and provide increased binding affinity and specificity. A major challenge in developing TCR mimetic (TCRm) antibodies is the limited sequence differences between the target pHLA complex relative to the large global repertoire of pHLA complexes. Here, we provide a comprehensive strategy for generating fully human TCRm antibodies across multiple HLA alleles, beginning with pHLA target discovery and validation and culminating in the engineering of TCRm-based chimeric antigen receptor T cells with potent antitumor activity. By incorporating mass spectrometry, bioinformatic predictions, HLA-humanized mice, antibody screening, and cryo-electron microscopy, we have established a pipeline to identify additional pHLA complex-specific antibodies with therapeutic potential.

Tailoring nanovectors for optimal neoantigen vaccine efficacy

The primary objective of neoantigen vaccines is to elicit a robust anti-tumor immune response by generating neoantigen-specific T cells that can eradicate tumor cells. Despite substantial advancements in personalized neoantigen prediction using next-generation sequencing, machine learning, and mass spectrometry, challenges remain in efficiently expanding neoantigen-specific T cell populations in vivo. This challenge impedes the widespread clinical application of neoantigen vaccines. Nanovector-based neoantigen delivery systems have emerged as a promising solutions to this challenge. These nanovectors offer several advantages, such as enhanced stability, targeted intracellular delivery, sustained release, and improved antigen-presenting cell (APC) activation. Notably, they effectively deliver various neoantigen vaccine formulations (DC cell-based, synthetic long peptide (SLP)-based or DNA/mRNA-based) to APCs or T cells, thereby activating both CD4+ T and CD8+ T cells. This ultimately induces a specific anti-tumor immune response. This review focuses on recent innovations in neoantigen vaccine delivery vectors. We aim to identify optimal design parameters for vectors tailored to different neoantigen vaccine types, with an emphasis on enhancing the tumor microenvironment and stimulating the production of neoantigen-specific cytotoxic T cells. By maximizing the potential of these delivery systems, we aim to accelerate the clinical translation of neoantigen nanovaccines and advance cancer immunotherapy.

Peptide hydrogel platform encapsulating manganese ions and high-density lipoprotein nanoparticle-mimicking nanovaccines for the prevention and treatment of gastric cancer

BACKGROUND

Advanced gastric cancer remains a significant global health challenge, with limited therapeutic options available. In contrast, immunotherapy have emerged as promising alternatives, offering greater potency in treating advanced gastric cancer. However, the development of novel and efficient immunotherapeutic strategy is crucial to enhance the body's immune response against gastric cancer.

METHODS

This study developed a single-injection peptide hydrogel-based nanovaccine therapy for gastric cancer treatment. The therapy utilizes a RADA32 peptide hydrogel, which is sensitive to metal ion concentration, to encapsulate manganese ions and HPPS nanovaccines. The HPPS nanovaccines contain antigen peptide and CpG-ODN, designed to activate both the toll-like receptor 9 (TLR9) and cGAS-STING signaling pathways in antigen-presenting cells. This design aims to facilitate a stable and sustained release of the nanovaccine, thereby enhancing the body's effective recognition and response to antigens.

RESULTS

The efficacy of the system was confirmed using the model antigen OVA and the gastric cancer-specific antigen MG7-related peptide. The results demonstrated that the nanovaccine effectively activated the immune response, leading to enhanced recognition and response to the antigens. This activation of both TLR9 and cGAS-STING pathways in antigen-presenting cells was crucial for the observed immune response, highlighting the potential of this approach to stimulate a robust and sustained immune response against gastric cancer.

CONCLUSIONS

This study presents a novel strategy for clinical anti-tumor vaccine administration, offering a promising approach for the prevention and treatment of gastric cancer. The single-injection peptide hydrogel-based nanovaccine system provides a convenient and effective method to enhance the body's immune response against gastric cancer. This approach could potentially be expanded to other types of cancer, providing a versatile platform for cancer immunotherapy.

Individualized Neoantigen-Directed Melanoma Therapy

Individualized neoantigen-directed therapy represents a groundbreaking approach in melanoma treatment that leverages the patient's own immune system to target cancer cells. This innovative strategy involves the identification of unique immunogenic neoantigens (mutated proteins specific to an individual's tumor) and the development of therapeutic vaccines that either consist of peptide sequences or RNA encoding these neoantigens. The goal of these therapies is to induce neoantigen-specific immune responses, enabling the immune system to recognize and destroy cancer cells presenting the targeted neoantigens. This individualized approach is particularly advantageous given the genetic heterogeneity of melanoma, which exhibits distinct mutations among different patients. In contrast to traditional therapies, neoantigen-directed therapy offers a tailored treatment that potentially reduces off-target side effects and enhances therapeutic efficacy. Recent advances in neoantigen prediction and vaccine development have facilitated clinical trials exploring the combination of neoantigen vaccines with immune checkpoint inhibitors. These trials have shown promising clinical outcomes, underscoring the potential of this personalized approach. This review provides an overview of the rationale behind neoantigen-directed therapies and summarizes the current state of knowledge regarding personalized neoantigen vaccines in melanoma treatment.

Tumour-wide RNA splicing aberrations generate actionable public neoantigens

T cell-based immunotherapies hold promise in treating cancer by leveraging the immune system's recognition of cancer-specific antigens1. However, their efficacy is limited in tumours with few somatic mutations and substantial intratumoural heterogeneity2-4. Here we introduce a previously uncharacterized class of tumour-wide public neoantigens originating from RNA splicing aberrations in diverse cancer types. We identified T cell receptor clones capable of recognizing and targeting neoantigens derived from aberrant splicing in GNAS and RPL22. In cases with multi-site biopsies, we detected the tumour-wide expression of the GNAS neojunction in glioma, mesothelioma, prostate cancer and liver cancer. These neoantigens are endogenously generated and presented by tumour cells under physiologic conditions and are sufficient to trigger cancer cell eradication by neoantigen-specific CD8+ T cells. Moreover, our study highlights a role for dysregulated splicing factor expression in specific cancer types, leading to recurrent patterns of neojunction upregulation. These findings establish a molecular basis for T cell-based immunotherapies addressing the challenges of intratumoural heterogeneity.

Immunogenic cryptic peptides dominate the antigenic landscape of ovarian cancer

Increased infiltration of CD3+ and CD8+ T cells into ovarian cancer (OC) is linked to better prognosis, but the specific antigens involved are unclear. Recent reports suggest that HLA class I can present peptides from noncoding genomic regions, known as noncanonical or cryptic peptides, but their immunogenicity is underexplored. To address this, we used immunopeptidomic analysis and RNA sequencing on five metastatic OC samples, which identified 311 cryptic peptides (40 to 83 per patient). Despite comprising less than 1% of total peptides, cryptic peptides from noncoding transcripts emerged as the predominant antigen class when compared to the other major classes of known tumor-specific and tumor-associated antigens in OC samples. Notably, nearly 70% of the prioritized cryptic peptides elicited T cell activation, as evidenced by increased 4-1BB and IFN-γ expression in autologous CD8+ T cells. This study reveals noncoding cryptic peptides as an important class of immunogenic antigens in OC.

Leveraging mRNA technology for antigen based immuno-oncology therapies

The application of messenger RNA (mRNA) technology in antigen-based immuno-oncology therapies represents a significant advancement in cancer treatment. Cancer vaccines are an effective combinatorial partner to sensitize the host immune system to the tumor and boost the efficacy of immune therapies. Selecting suitable tumor antigens is the key step to devising effective vaccinations and amplifying the immune response. Tumor neoantigens are de novo epitopes derived from somatic mutations, avoiding T-cell central tolerance of self-epitopes and inducing immune responses to tumors. The identification and prioritization of patient-specific tumor neoantigens are based on advanced computational algorithms taking advantage of the profiling with next-generation sequencing considering factors involved in human leukocyte antigen (HLA)-peptide-T-cell receptor (TCR) complex formation, including peptide presentation, HLA-peptide affinity, and TCR recognition. This review discusses the development and clinical application of mRNA vaccines in oncology, with a particular focus on recent clinical trials and the computational workflows and methodologies for identifying both shared and individual antigens. While this review centers on therapeutic mRNA vaccines targeting existing tumors, it does not cover preventative vaccines. Preclinical experimental validations are crucial in cancer vaccine development, but we emphasize the computational approaches that facilitate neoantigen selection and design, highlighting their role in advancing mRNA vaccine development. The versatility and rapid development potential of mRNA make it an ideal platform for personalized neoantigen immunotherapy. We explore various strategies for antigen target identification, including tumor-associated and tumor-specific antigens and the computational tools used to predict epitopes capable of eliciting strong immune responses. We address key design considerations for enhancing the immunogenicity and stability of mRNA vaccines, as well as emerging trends and challenges in the field. This comprehensive overview highlights the therapeutic potential of mRNA-based cancer vaccines and underscores ongoing research efforts aimed at optimizing these therapies for improved clinical outcomes.

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.

HLA class II neoantigen presentation for CD4+ T cell surveillance in HLA class II-negative colorectal cancer

Neoantigen-reactive CD4+ T cells play a key role in the anti-tumor immune response. However, the majority of epithelial tumors are negative for HLA class II (HLA-II) surface expression, and less is known about the processing of HLA-II antigens. Here, we directly identified naturally presented HLA-II neoantigens in HLA-II negative colorectal cancer (CRC) tissue using a proteogenomic approach. The neoantigens were immunogenic and induced patient CD4+ T cells with a Th1-like memory phenotype that produced IFN-γ, IL2 and TNF-α. Multiplex immunohistochemistry (IHC) demonstrated an interaction between Th cells and HLA-II-positive antigen-presenting cells (APCs) at the invasive margin and within the tertiary lymphoid structures (TLS). In our CRC cohort, the density of stromal APCs was associated with HLA-II antigen presentation in the tumor microenvironment (TME), and the number of TLS was positively correlated with the number of somatic mutations in the tumors. These results demonstrate the presence of neoantigen-specific CD4+ surveillance in HLA-II-negative CRC and suggest a potential role for macrophages and dendritic cells (DCs) at the invasive margin and in TLS for antigen presentation. Stromal APCs in the TME can potentially be used as a source for HLA-II neoantigen identification.

Towards designing improved cancer immunotherapy targets with a peptide-MHC-I presentation model, HLApollo

Based on the success of cancer immunotherapy, personalized cancer vaccines have emerged as a leading oncology treatment. Antigen presentation on MHC class I (MHC-I) is crucial for the adaptive immune response to cancer cells, necessitating highly predictive computational methods to model this phenomenon. Here, we introduce HLApollo, a transformer-based model for peptide-MHC-I (pMHC-I) presentation prediction, leveraging the language of peptides, MHC, and source proteins. HLApollo provides end-to-end treatment of MHC-I sequences and deconvolution of multi-allelic data, using a negative-set switching strategy to mitigate misassigned negatives in unlabelled ligandome data. HLApollo shows a 12.65% increase in average precision (AP) on ligandome data and a 4.1% AP increase on immunogenicity test data compared to next-best models. Incorporating protein features from protein language models yields further gains and reduces the need for gene expression measurements. Guided by clinical use, we demonstrate pan-allelic generalization which effectively captures rare alleles in underrepresented ancestries.

Chemotherapeutic agents and leucine deprivation induce codon-biased aberrant protein production in cancer

Messenger RNA (mRNA) translation is a tightly controlled process frequently deregulated in cancer. Key to this deregulation are transfer RNAs (tRNAs), whose expression, processing and post-transcriptional modifications are often altered in cancer to support cellular transformation. In conditions of limiting levels of amino acids, this deregulated control of protein synthesis leads to aberrant protein production in the form of ribosomal frameshifting or misincorporation of non-cognate amino acids. Here, we studied leucine, an essential amino acid coded by six different codons. Surprisingly, we found that leucine deprivation leads to ribosomal stalling and aberrant protein production in various cancer cell types, predominantly at one codon, UUA. Similar effects were observed after treatment with chemotherapeutic agents, implying a shared mechanism controlling the downstream effects on mRNA translation. In both conditions, a limitation in the availability of tRNALeu(UAA) for protein production was shown to be the cause for this dominant effect on UUA codons. The induced aberrant proteins can be processed and immune-presented as neoepitopes and can direct T-cell killing. Altogether, we uncovered a novel mode of interplay between DNA damage, regulation of tRNA availability for mRNA translation and aberrant protein production in cancer that could be exploited for anti-cancer therapy.

High-resolution profile of neoantigen-specific TCR activation links moderate stimulation to increased resilience of engineered TCR-T cells

Neoantigen-specific T cell receptors (neoTCRs) promise safe, personalized anti-tumor immunotherapy. However, detailed assessment of neoTCR-characteristics affecting therapeutic efficacy is mostly missing. Previously, we identified diverse neoTCRs restricted to different neoantigens in a melanoma patient. In this work, we now combine single-cell TCR-sequencing and RNA-sequencing after neoantigen-specific restimulation of peripheral blood-derived CD8+ T cells of this patient. We detect neoTCRs with specificity for the previously detected neoantigens and perform fine-characterization of neoTCR-transgenic (tg) T cells in vitro and in vivo. We describe a heterogeneous spectrum of TCR-intrinsic activation patterns in response to a shared neoepitope ranging from previously detected more highly frequent neoTCRs with moderate activation to rare ones with initially stronger activation. Experimental restimulation of adoptively transferred neoTCR-tg T cells in a xenogeneic rechallenge tumor model demonstrates superior anti-tumor responses of moderate neoTCR-tg T cells upon repeated tumor contact. These insights have significant implications for the selection of TCRs for therapeutic engineering of TCR-tg T cells.

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.

RNA splicing junction landscape reveals abundant tumor-specific transcripts in human cancer

RNA splicing is a critical process governing gene expression and transcriptomic diversity. Despite its importance, a detailed examination of transcript variation at the splicing junction level remains scarce. Here, we perform a thorough analysis of RNA splicing junctions in 34,775 samples across multiple sample types. We identified 29,051 tumor-specific transcripts (TSTs) in pan-cancer, with a majority of these TSTs being unannotated. Our findings show that TSTs are positively correlated with tumor stemness and linked to unfavorable outcomes in cancer patients. Additionally, TSTs display mutual exclusivity with somatic mutations and are overrepresented in transposable-element-derived transcripts possessing oncogenic functions. Importantly, TSTs can generate putative neoantigens for immunotherapy. Moreover, TSTs can be detected in blood extracellular vesicles from cancer patients. Our results shed light on the intricacies of RNA splicing and offer promising avenues for cancer diagnosis and therapy.

Neoantigen prioritization based on antigen processing and presentation

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

Probiotic neoantigen delivery vectors for precision cancer immunotherapy

Microbial systems have been synthetically engineered to deploy therapeutic payloads in vivo1,2. With emerging evidence that bacteria naturally home in on tumours3,4 and modulate antitumour immunity5,6, one promising application is the development of bacterial vectors as precision cancer vaccines2,7. Here we engineered probiotic Escherichia coli Nissle 1917 as an antitumour vaccination platform optimized for enhanced production and cytosolic delivery of neoepitope-containing peptide arrays, with increased susceptibility to blood clearance and phagocytosis. These features enhance both safety and immunogenicity, achieving a system that drives potent and specific T cell-mediated anticancer immunity that effectively controls or eliminates tumour growth and extends survival in advanced murine primary and metastatic solid tumours. We demonstrate that the elicited antitumour immune response involves recruitment and activation of dendritic cells, extensive priming and activation of neoantigen-specific CD4+ and CD8+ T cells, broader activation of both T and natural killer cells, and a reduction of tumour-infiltrating immunosuppressive myeloid and regulatory T and B cell populations. Taken together, this work leverages the advantages of living medicines to deliver arrays of tumour-specific neoantigen-derived epitopes within the optimal context to induce specific, effective and durable systemic antitumour immunity.

3D genome contributes to MHC-II neoantigen prediction

Reliable and ultra-fast DNA and RNA sequencing have been achieved with the emergence of high-throughput sequencing technology. When combining the results of DNA and RNA sequencing for tumor cells of cancer patients, neoantigens that potentially stimulate the immune response of either CD4+ or CD8+ T cells can be identified. However, due to the abundance of somatic mutations and the high polymorphic nature of human leukocyte antigen (HLA) it is challenging to accurately predict the neoantigens. Moreover, comparing to HLA-I presented peptides, the HLA-II presented peptides are more variable in length, making the prediction of HLA-II loaded neoantigens even harder. A number of computational approaches have been proposed to address this issue but none of them considers the DNA origin of the neoantigens from the perspective of 3D genome. Here we investigate the DNA origins of the immune-positive and non-negative HLA-II neoantigens in the context of 3D genome and discovered that the chromatin 3D architecture plays an important role in more effective HLA-II neoantigen prediction. We believe that the 3D genome information will help to increase the precision of HLA-II neoantigen discovery and eventually benefit precision and personalized medicine in cancer immunotherapy.

IMGT/RobustpMHC: robust training for class-I MHC peptide binding prediction

The accurate prediction of peptide-major histocompatibility complex (MHC) class I binding probabilities is a critical endeavor in immunoinformatics, with broad implications for vaccine development and immunotherapies. While recent deep neural network based approaches have showcased promise in peptide-MHC (pMHC) prediction, they have two shortcomings: (i) they rely on hand-crafted pseudo-sequence extraction, (ii) they do not generalize well to different datasets, which limits the practicality of these approaches. While existing methods rely on a 34 amino acid pseudo-sequence, our findings uncover the involvement of 147 positions in direct interactions between MHC and peptide. We further show that neural architectures can learn the intricacies of pMHC binding using even full sequences. To this end, we present PerceiverpMHC that is able to learn accurate representations on full-sequences by leveraging efficient transformer based architectures. Additionally, we propose IMGT/RobustpMHC that harnesses the potential of unlabeled data in improving the robustness of pMHC binding predictions through a self-supervised learning strategy. We extensively evaluate RobustpMHC on eight different datasets and showcase an overall improvement of over 6% in binding prediction accuracy compared to state-of-the-art approaches. We compile CrystalIMGT, a crystallography-verified dataset presenting a challenge to existing approaches due to significantly different pMHC distributions. Finally, to mitigate this distribution gap, we further develop a transfer learning pipeline.

Identification of immunogenic HLA class I and II neoantigens using surrogate immunopeptidomes

Neoantigens arising from somatic mutations are tumor specific and induce antitumor host T cell responses. However, their sequences are individual specific and need to be identified for each patient for therapeutic applications. Here, we present a proteogenomic approach for neoantigen identification, named Neoantigen Selection using a Surrogate Immunopeptidome (NESSIE). This approach uses an autologous wild-type immunopeptidome as a surrogate for the tumor immunopeptidome and allows human leukocyte antigen (HLA)-agnostic identification of both HLA class I (HLA-I) and HLA class II (HLA-II) neoantigens. We demonstrate the direct identification of highly immunogenic HLA-I and HLA-II neoantigens using NESSIE in patients with colorectal cancer and endometrial cancer. Fresh or frozen tumor samples are not required for analysis, making it applicable to many patients in clinical settings. We also demonstrate tumor prevention by vaccination with selected neoantigens in a preclinical mouse model. This approach may benefit personalized T cell-mediated immunotherapies.

A Chemical Approach to Assess the Impact of Post-translational Modification on MHC Peptide Binding and Effector Cell Engagement

The human major histocompatibility complex (MHC) plays a pivotal role in the presentation of peptidic fragments from proteins, which can originate from self-proteins or from nonhuman antigens, such as those produced by viruses or bacteria. To prevent cytotoxicity against healthy cells, thymocytes expressing T cell receptors (TCRs) that recognize self-peptides are removed from circulation (negative selection), thus leaving T cells that recognize nonself-peptides. Current understanding suggests that post-translationally modified (PTM) proteins and the resulting peptide fragments they generate following proteolysis are largely excluded from negative selection; this feature means that PTMs can generate nonself-peptides that potentially contribute to the development of autoreactive T cells and subsequent autoimmune diseases. Although it is well-established that PTMs are prevalent in peptides present on MHCs, the precise mechanisms by which PTMs influence the antigen presentation machinery remain poorly understood. In the present work, we introduce chemical modifications mimicking PTMs on synthetic peptides. This is the first systematic study isolating the impact of PTMs on MHC binding and also their impact on TCR recognition. Our findings reveal various ways PTMs alter antigen presentation, which could have implications for tumor neoantigen presentation.

Machine Learning Strategies to Tackle Data Challenges in Mass Spectrometry-Based Proteomics

In computational proteomics, machine learning (ML) has emerged as a vital tool for enhancing data analysis. Despite significant advancements, the diversity of ML model architectures and the complexity of proteomics data present substantial challenges in the effective development and evaluation of these tools. Here, we highlight the necessity for high-quality, comprehensive data sets to train ML models and advocate for the standardization of data to support robust model development. We emphasize the instrumental role of key data sets like ProteomeTools and MassIVE-KB in advancing ML applications in proteomics and discuss the implications of data set size on model performance, highlighting that larger data sets typically yield more accurate models. To address data scarcity, we explore algorithmic strategies such as self-supervised pretraining and multitask learning. Ultimately, we hope that this discussion can serve as a call to action for the proteomics community to collaborate on data standardization and collection efforts, which are crucial for the sustainable advancement and refinement of ML methodologies in the field.

Crucial Parameters for Immunopeptidome Characterization: A Systematic Evaluation

Immunopeptidomics is the area of knowledge focused on the study of peptides assembled in the major histocompatibility complex (MHC), or human leukocyte antigen (HLA) in humans, which could activate the immune response via specific and selective T cell recognition. Advances in high-sensitivity mass spectrometry have enabled the detailed identification and quantification of the immunopeptidome, significantly impacting fields like oncology, infections, and autoimmune diseases. Current immunopeptidomics approaches primarily focus on workflows to identify immunopeptides from HLA molecules, requiring the isolation of the HLA from relevant cells or tissues. Common critical steps in these workflows, such as cell lysis, HLA immunoenrichment, and peptide isolation, significantly influence outcomes. A systematic evaluation of these steps led to the creation of an 'Immunopeptidome Score' to enhance the reproducibility and robustness of these workflows. This score, derived from LC-MS/MS datasets (ProteomeXchange identifier PXD038165), in combination with available information from public databases, aids in optimizing the immunopeptidome characterization process. The 'Immunopeptidome Score' has been applied in a systematic analysis of protein extraction, HLA immunoprecipitation, and peptide recovery yields across several tumor cell lines enabling the selection of peptides with optimal features and, therefore, the identification of potential biomarker and therapeutic targets.

Epigenetic therapy potentiates transposable element transcription to create tumor-enriched antigens in glioblastoma cells

Inhibiting epigenetic modulators can transcriptionally reactivate transposable elements (TEs). These TE transcripts often generate unique peptides that can serve as immunogenic antigens for immunotherapy. Here, we ask whether TEs activated by epigenetic therapy could appreciably increase the antigen repertoire in glioblastoma, an aggressive brain cancer with low mutation and neoantigen burden. We treated patient-derived primary glioblastoma stem cell lines, an astrocyte cell line and primary fibroblast cell lines with epigenetic drugs, and identified treatment-induced, TE-derived transcripts that are preferentially expressed in cancer cells. We verified that these transcripts could produce human leukocyte antigen class I-presented antigens using liquid chromatography with tandem mass spectrometry pulldown experiments. Importantly, many TEs were also transcribed, even in proliferating nontumor cell lines, after epigenetic therapy, which suggests that targeted strategies like CRISPR-mediated activation could minimize potential side effects of activating unwanted genomic regions. The results highlight both the need for caution and the promise of future translational efforts in harnessing treatment-induced TE-derived antigens for targeted immunotherapy.

optiPRM: A Targeted Immunopeptidomics LC-MS Workflow With Ultra-High Sensitivity for the Detection of Mutation-Derived Tumor Neoepitopes From Limited Input Material

Personalized cancer immunotherapies such as therapeutic vaccines and adoptive transfer of T cell receptor-transgenic T cells rely on the presentation of tumor-specific peptides by human leukocyte antigen class I molecules to cytotoxic T cells. Such neoepitopes can for example arise from somatic mutations and their identification is crucial for the rational design of new therapeutic interventions. Liquid chromatography mass spectrometry (LC-MS)-based immunopeptidomics is the only method to directly prove actual peptide presentation and we have developed a parameter optimization workflow to tune targeted assays for maximum detection sensitivity on a per peptide basis, termed optiPRM. Optimization of collision energy using optiPRM allows for the improved detection of low abundant peptides that are very hard to detect using standard parameters. Applying this to immunopeptidomics, we detected a neoepitope in a patient-derived xenograft from as little as 2.5 × 106 cells input. Application of the workflow on small patient tumor samples allowed for the detection of five mutation-derived neoepitopes in three patients. One neoepitope was confirmed to be recognized by patient T cells. In conclusion, optiPRM, a targeted MS workflow reaching ultra-high sensitivity by per peptide parameter optimization, makes the identification of actionable neoepitopes possible from sample sizes usually available in the clinic.

Advancements and Challenges in Peptide-Based Cancer Vaccination: A Multidisciplinary Perspective

With the continuous advancements in tumor immunotherapy, researchers are actively exploring new treatment methods. Peptide therapeutic cancer vaccines have garnered significant attention for their potential in improving patient outcomes. Despite its potential, only a single peptide-based cancer vaccine has been approved by the U.S. Food and Drug Administration (FDA). A comprehensive understanding of the underlying mechanisms and current development status is crucial for advancing these vaccines. This review provides an in-depth analysis of the production principles and therapeutic mechanisms of peptide-based cancer vaccines, highlights the commonly used peptide-based cancer vaccines, and examines the synergistic effects of combining these vaccines with immunotherapy, targeted therapy, radiotherapy, and chemotherapy. While some studies have yielded suboptimal results, the potential of combination therapies remains substantial. Additionally, we addressed the management and adverse events associated with peptide-based cancer vaccines, noting their relatively higher safety profile compared to traditional radiotherapy and chemotherapy. Lastly, we also discussed the roles of adjuvants and targeted delivery systems in enhancing vaccine efficacy. In conclusion, this review comprehensively outlines the current landscape of peptide-based cancer vaccination and underscores its potential as a pivotal immunotherapy approach.

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.

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.