High-Throughput Stability Screening of Neoantigen/HLA Complexes Improves Immunogenicity Predictions

RNA vaccines for cancer: Principles to practice

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

Development and Clinical Applications of Therapeutic Cancer Vaccines with Individualized and Shared Neoantigens

Neoantigens, presented as peptides on the surfaces of cancer cells, have recently been proposed as optimal targets for immunotherapy in clinical practice. The promising outcomes of neoantigen-based cancer vaccines have inspired enthusiasm for their broader clinical applications. However, the individualized tumor-specific antigens (TSA) entail considerable costs and time due to the variable immunogenicity and response rates of these neoantigens-based vaccines, influenced by factors such as neoantigen response, vaccine types, and combination therapy. Given the crucial role of neoantigen efficacy, a number of bioinformatics algorithms and pipelines have been developed to improve the accuracy rate of prediction through considering a series of factors involving in HLA-peptide-TCR complex formation, including peptide presentation, HLA-peptide affinity, and TCR recognition. On the other hand, shared neoantigens, originating from driver mutations at hot mutation spots (e.g., KRASG12D), offer a promising and ideal target for the development of therapeutic cancer vaccines. A series of clinical practices have established the efficacy of these vaccines in patients with distinct HLA haplotypes. Moreover, increasing evidence demonstrated that a combination of tumor associated antigens (TAAs) and neoantigens can also improve the prognosis, thus expand the repertoire of shared neoantigens for cancer vaccines. In this review, we provide an overview of the complex process involved in identifying personalized neoantigens, their clinical applications, advances in vaccine technology, and explore the therapeutic potential of shared neoantigen strategies.

Advances in Therapeutic Cancer Vaccines, Their Obstacles, and Prospects Toward Tumor Immunotherapy

Over the past few decades, cancer immunotherapy has experienced a significant revolution due to the advancements in immune checkpoint inhibitors (ICIs) and adoptive cell therapies (ACTs), along with their regulatory approvals. In recent times, there has been hope in the effectiveness of cancer vaccines for therapy as they have been able to stimulate de novo T-cell reactions against tumor antigens. These tumor antigens include both tumor-associated antigen (TAA) and tumor-specific antigen (TSA). Nevertheless, the constant quest to fully achieve these abilities persists. Therefore, this review offers a broad perspective on the existing status of cancer immunizations. Cancer vaccine design has been revolutionized due to the advancements made in antigen selection, the development of antigen delivery systems, and a deeper understanding of the strategic intricacies involved in effective antigen presentation. In addition, this review addresses the present condition of clinical tests and deliberates on their approaches, with a particular emphasis on the immunogenicity specific to tumors and the evaluation of effectiveness against tumors. Nevertheless, the ongoing clinical endeavors to create cancer vaccines have failed to produce remarkable clinical results as a result of substantial obstacles, such as the suppression of the tumor immune microenvironment, the identification of suitable candidates, the assessment of immune responses, and the acceleration of vaccine production. Hence, there are possibilities for the industry to overcome challenges and enhance patient results in the coming years. This can be achieved by recognizing the intricate nature of clinical issues and continuously working toward surpassing existing limitations.

Estrogen Receptor Mutations as Novel Targets for Immunotherapy in Metastatic Estrogen Receptor-positive Breast Cancer

Estrogen receptor-positive (ER+) breast cancer is not considered immunogenic and, to date, has been proven resistant to immunotherapy. Endocrine therapy remains the cornerstone of treatment for ER+ breast cancers. However, constitutively activating mutations in the estrogen receptor alpha (ESR1) gene can emerge during treatment, rendering tumors resistant to endocrine therapy. Although these mutations represent a pathway of resistance, they also represent a potential source of neoepitopes that can be targeted by immunotherapy. In this study, we investigated ESR1 mutations as novel targets for breast cancer immunotherapy. Using machine learning algorithms, we identified ESR1-derived peptides predicted to form stable complexes with HLA-A*0201. We then validated the binding affinity and stability of the top predicted peptides through in vitro binding and dissociation assays and showed that these peptides bind HLA-A*0201 with high affinity and stability. Using tetramer assays, we confirmed the presence and expansion potential of antigen-specific CTLs from healthy female donors. Finally, using in vitro cytotoxicity assays, we showed the lysis of peptide-pulsed targets and breast cancer cells expressing common ESR1 mutations by expanded antigen-specific CTLs. Ultimately, we identified five peptides derived from the three most common ESR1 mutations (D538G, Y537S, and E380Q) and their associated wild-type peptides, which were the most immunogenic. Overall, these data confirm the immunogenicity of epitopes derived from ESR1 and highlight the potential of these peptides to be targeted by novel immunotherapy strategies.

SIGNIFICANCE

Estrogen receptor (ESR1) mutations have emerged as a key factor in endocrine therapy resistance. We identified and validated five novel, immunogenic ESR1-derived peptides that could be targeted through vaccine-based immunotherapy.

Molecular insights into the HLA-B35 molecules' classification associated with HIV control

Human leukocyte antigen (HLA) class I molecules have been shown to influence the immune response to HIV infection and acquired immunodeficiency syndrome progression. Polymorphisms within the HLA-B35 molecules divide the family into two groups, namely, Px and PY. The Px group is associated with deleterious effects and accelerated disease progression in HIV+ patients, whereas the PY group is not. The classification is based on the preferential binding of a tyrosine at the C-terminal part of the peptide in the PY group, and a nontyrosine residue in the Px group. However, there is a lack of knowledge on the molecular differences between the two groups. Here, we have investigated three HLA-B35 molecules, namely, HLA-B*35:01 (PY), HLA-B*35:03 (Px) and HLA-B*35:05 (unclassified). We selected an HIV-derived peptide, NY9, and demonstrated that it can trigger a polyfunctional CD8+ T-cell response in HLA-B*35:01+ /HIV+ patients. We determined that in the complex with the NY9 peptide, the PY molecule was more stable than the Px molecule. We solved the crystal structures of the three HLA molecules in complex with the NY9 peptide, and structural similarities with HLA-B*35:01 would classify the HLA-B*35:05 within the PY group. Interestingly, we found that HLA-B*35:05 can also bind a small molecule in its cleft, suggesting that small drugs could bind as well.

Structural and physical features that distinguish tumor-controlling from inactive cancer neoepitopes

Neoepitopes arising from amino acid substitutions due to single nucleotide polymorphisms are targets of T cell immune responses to cancer and are of significant interest in the development of cancer vaccines. However, understanding the characteristics of rare protective neoepitopes that truly control tumor growth has been a challenge, due to their scarcity as well as the challenge of verifying true, neoepitope-dependent tumor control in humans. Taking advantage of recent work in mouse models that circumvented these challenges, here, we compared the structural and physical properties of neoepitopes that range from fully protective to immunologically inactive. As neoepitopes are derived from self-peptides that can induce immune tolerance, we studied not only how the various neoepitopes differ from each other but also from their wild-type counterparts. We identified multiple features associated with protection, including features that describe how neoepitopes differ from self as well as features associated with recognition by diverse T cell receptor repertoires. We demonstrate both the promise and limitations of neoepitope structural analysis and predictive modeling and illustrate important aspects that can be incorporated into neoepitope prediction pipelines.

Therapeutic cancer vaccines: advancements, challenges, and prospects

With the development and regulatory approval of immune checkpoint inhibitors and adoptive cell therapies, cancer immunotherapy has undergone a profound transformation over the past decades. Recently, therapeutic cancer vaccines have shown promise by eliciting de novo T cell responses targeting tumor antigens, including tumor-associated antigens and tumor-specific antigens. The objective was to amplify and diversify the intrinsic repertoire of tumor-specific T cells. However, the complete realization of these capabilities remains an ongoing pursuit. Therefore, we provide an overview of the current landscape of cancer vaccines in this review. The range of antigen selection, antigen delivery systems development the strategic nuances underlying effective antigen presentation have pioneered cancer vaccine design. Furthermore, this review addresses the current status of clinical trials and discusses their strategies, focusing on tumor-specific immunogenicity and anti-tumor efficacy assessment. However, current clinical attempts toward developing cancer vaccines have not yielded breakthrough clinical outcomes due to significant challenges, including tumor immune microenvironment suppression, optimal candidate identification, immune response evaluation, and vaccine manufacturing acceleration. Therefore, the field is poised to overcome hurdles and improve patient outcomes in the future by acknowledging these clinical complexities and persistently striving to surmount inherent constraints.

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."

The screening, identification, design and clinical application of tumor-specific neoantigens for TCR-T cells

Recent advances in neoantigen research have accelerated the development of tumor immunotherapies, including adoptive cell therapies (ACTs), cancer vaccines and antibody-based therapies, particularly for solid tumors. With the development of next-generation sequencing and bioinformatics technology, the rapid identification and prediction of tumor-specific antigens (TSAs) has become possible. Compared with tumor-associated antigens (TAAs), highly immunogenic TSAs provide new targets for personalized tumor immunotherapy and can be used as prospective indicators for predicting tumor patient survival, prognosis, and immune checkpoint blockade response. Here, the identification and characterization of neoantigens and the clinical application of neoantigen-based TCR-T immunotherapy strategies are summarized, and the current status, inherent challenges, and clinical translational potential of these strategies are discussed.

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.

Usefulness of Docking and Molecular Dynamics in Selecting Tumor Neoantigens to Design Personalized Cancer Vaccines: A Proof of Concept

Personalized cancer vaccines based on neoantigens are a new and promising treatment for cancer; however, there are still multiple unresolved challenges to using this type of immunotherapy. Among these, the effective identification of immunogenic neoantigens stands out, since the in silico tools used generate a significant portion of false positives. Inclusion of molecular simulation techniques can refine the results these tools produce. In this work, we explored docking and molecular dynamics to study the association between the stability of peptide-HLA complexes and their immunogenicity, using as a proof of concept two HLA-A2-restricted neoantigens that were already evaluated in vitro. The results obtained were in accordance with the in vitro immunogenicity, since the immunogenic neoantigen ASTN1 remained bound at both ends to the HLA-A2 molecule. Additionally, molecular dynamic simulation suggests that position 1 of the peptide has a more relevant role in stabilizing the N-terminus than previously proposed. Likewise, the mutations may have a "delocalized" effect on the peptide-HLA interaction, which means that the mutated amino acid influences the intensity of the interactions of distant amino acids of the peptide with the HLA. These findings allow us to propose the inclusion of molecular simulation techniques to improve the identification of neoantigens for cancer vaccines.

Impact of peptide:HLA complex stability for the identification of SARS-CoV-2-specific CD8+T cells

Induction of a lasting protective immune response is dependent on presentation of epitopes to patrolling T cells through the HLA complex. While peptide:HLA (pHLA) complex affinity alone is widely exploited for epitope selection, we demonstrate that including the pHLA complex stability as a selection parameter can significantly reduce the high false discovery rate observed with predicted affinity. In this study, pHLA complex stability was measured on three common class I alleles and 1286 overlapping 9-mer peptides derived from the SARS-CoV-2 Spike protein. Peptides were pooled based on measured stability and predicted affinity. Strikingly, stability of the pHLA complex was shown to strongly select for immunogenic epitopes able to activate functional CD8⁺T cells. This result was observed across the three studied alleles and in both vaccinated and convalescent COVID-19 donors. Deconvolution of peptide pools showed that specific CD8⁺T cells recognized one or two dominant epitopes. Moreover, SARS-CoV-2 specific CD8⁺T cells were detected by tetramer-staining across multiple donors. In conclusion, we show that stability analysis of pHLA is a key factor for identifying immunogenic epitopes.

What can Ribo-seq and proteomics tell us about the non-canonical proteome?

Ribosome profiling (Ribo-seq) has proven transformative for our understanding of the human genome and proteome by illuminating thousands of non-canonical sites of ribosome translation outside of the currently annotated coding sequences (CDSs). A conservative estimate suggests that at least 7,000 non-canonical open reading frames (ORFs) are translated, which, at first glance, has the potential to expand the number of human protein-coding sequences by 30%, from ∼19,500 annotated CDSs to over 26,000. 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 non-canonical 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 non-canonical ORF research, databases, and interpretation, focusing on how to assess whether a given ORF can be said to be "protein-coding".

In brief

The human genome encodes thousands of non-canonical open reading frames (ORFs) in addition to protein-coding genes. As a nascent field, many questions remain regarding non-canonical ORFs. How many exist? Do they encode proteins? What level of evidence is needed for their verification? Central to these debates has been the advent of ribosome profiling (Ribo-seq) as a method to discern genome-wide ribosome occupancy, and immunopeptidomics as a method to detect peptides that are processed and presented by MHC molecules and not observed in traditional proteomics experiments. This article provides a synthesis of the current state of non-canonical ORF research and proposes standards for their future investigation and reporting.

Highlights

Combined use of Ribo-seq and proteomics-based methods enables optimal confidence in detecting non-canonical ORFs and their protein products.Ribo-seq can provide more sensitive detection of non-canonical ORFs, but data quality and analytical pipelines will impact results.Non-canonical ORF catalogs are diverse and span both high-stringency and low-stringency ORF nominations.A framework for standardized non-canonical ORF evidence will advance the research field.

Adoptive neoantigen-reactive T cell therapy: improvement strategies and current clinical researches

Neoantigens generated by non-synonymous mutations of tumor genes can induce activation of neoantigen-reactive T (NRT) cells which have the ability to resist the growth of tumors expressing specific neoantigens. Immunotherapy based on NRT cells has made preeminent achievements in melanoma and other solid tumors. The process of manufacturing NRT cells includes identification of neoantigens, preparation of neoantigen expression vectors or peptides, induction and activation of NRT cells, and analysis of functions and phenotypes. Numerous improvement strategies have been proposed to enhance the potency of NRT cells by engineering TCR, promoting infiltration of T cells and overcoming immunosuppressive factors in the tumor microenvironment. In this review, we outline the improvement of the preparation and the function assessment of NRT cells, and discuss the current status of clinical trials related to NRT cell immunotherapy.

Advances of mRNA vaccine in tumor: a maze of opportunities and challenges

High-frequency mutations in tumor genomes could be exploited as an asset for developing tumor vaccines. In recent years, with the tremendous breakthrough in genomics, intelligence algorithm, and in-depth insight of tumor immunology, it has become possible to rapidly target genomic alterations in tumor cell and rationally select vaccine targets. Among a variety of candidate vaccine platforms, the early application of mRNA was limited by instability low efficiency and excessive immunogenicity until the successful development of mRNA vaccines against SARS-COV-2 broken of technical bottleneck in vaccine preparation, allowing tumor mRNA vaccines to be prepared rapidly in an economical way with good performance of stability and efficiency. In this review, we systematically summarized the classification and characteristics of tumor antigens, the general process and methods for screening neoantigens, the strategies of vaccine preparations and advances in clinical trials, as well as presented the main challenges in the current mRNA tumor vaccine development.

The Emerging Role of uORF-Encoded uPeptides and HLA uLigands in Cellular and Tumor Biology

Recent technological advances have facilitated the detection of numerous non-canonical human peptides derived from regulatory regions of mRNAs, long non-coding RNAs, and other cryptic transcripts. In this review, we first give an overview of the classification of these novel peptides and summarize recent improvements in their annotation and detection by ribosome profiling, mass spectrometry, and individual experimental analysis. A large fraction of the novel peptides originates from translation at upstream open reading frames (uORFs) that are located within the transcript leader sequence of regular mRNA. In humans, uORF-encoded peptides (uPeptides) have been detected in both healthy and malignantly transformed cells and emerge as important regulators in cellular and immunological pathways. In the second part of the review, we focus on various functional implications of uPeptides. As uPeptides frequently act at the transition of translational regulation and individual peptide function, we describe the mechanistic modes of translational regulation through ribosome stalling, the involvement in cellular programs through protein interaction and complex formation, and their role within the human leukocyte antigen (HLA)-associated immunopeptidome as HLA uLigands. We delineate how malignant transformation may lead to the formation of novel uORFs, uPeptides, or HLA uLigands and explain their potential implication in tumor biology. Ultimately, we speculate on a potential use of uPeptides as peptide drugs and discuss how uPeptides and HLA uLigands may facilitate translational inhibition of oncogenic protein messages and immunotherapeutic approaches in cancer therapy.

Recombination and mutation shape variations in the major histocompatibility complex

The major histocompatibility complex (MHC) is closely associated with numerous diseases, but its high degree of polymorphism complicates the discovery of disease-associated variants. In principle, recombination and de novo mutations are two critical factors responsible for MHC polymorphisms. However, direct evidence for this hypothesis is lacking. Here, we report the generation of fine-scale MHC recombination and de novo mutation maps of ∼5 Mb by deep sequencing (> 100×) of the MHC genome for 17 MHC recombination and 30 non-recombination Han Chinese families (a total of 190 individuals). Recombination hotspots and Han-specific breakpoints are located in close proximity at haplotype block boundaries. The average MHC de novo mutation rate is higher than the genome-wide de novo mutation rate, particularly in MHC recombinant individuals. Notably, mutation and recombination generated polymorphisms are located within and outside linkage disequilibrium regions of the MHC, respectively, and evolution of the MHC locus was mainly controlled by positive selection. These findings provide insights on the evolutionary causes of the MHC diversity and may facilitate the identification of disease-associated genetic variants.

Personalized pancreatic cancer therapy: from the perspective of mRNA vaccine

Pancreatic cancer is characterized by inter-tumoral and intra-tumoral heterogeneity, especially in genetic alteration and microenvironment. Conventional therapeutic strategies for pancreatic cancer usually suffer resistance, highlighting the necessity for personalized precise treatment. Cancer vaccines have become promising alternatives for pancreatic cancer treatment because of their multifaceted advantages including multiple targeting, minimal nonspecific effects, broad therapeutic window, low toxicity, and induction of persistent immunological memory. Multiple conventional vaccines based on the cells, microorganisms, exosomes, proteins, peptides, or DNA against pancreatic cancer have been developed; however, their overall efficacy remains unsatisfactory. Compared with these vaccine modalities, messager RNA (mRNA)-based vaccines offer technical and conceptional advances in personalized precise treatment, and thus represent a potentially cutting-edge option in novel therapeutic approaches for pancreatic cancer. This review summarizes the current progress on pancreatic cancer vaccines, highlights the superiority of mRNA vaccines over other conventional vaccines, and proposes the viable tactic for designing and applying personalized mRNA vaccines for the precise treatment of pancreatic cancer.

Tumor associated antigen PRAME exhibits dualistic functions that are targetable in diffuse large B-cell lymphoma

PRAME is a prominent member of the cancer testis antigen family of proteins, which triggers autologous T cell–mediated immune responses. Integrative genomic analysis in diffuse large B cell lymphoma (DLBCL) uncovered recurrent and highly focal deletions of 22q11.22, including the PRAME gene, which were associated with poor outcome. PRAME-deleted tumors showed cytotoxic T cell immune escape and were associated with cold tumor microenvironments. In addition, PRAME downmodulation was strongly associated with somatic EZH2 Y641 mutations in DLBCL. In turn, PRC2-regulated genes were repressed in isogenic PRAME-KO lymphoma cell lines, and PRAME was found to directly interact with EZH2 as a negative regulator. EZH2 inhibition with EPZ-6438 abrogated these extrinsic and intrinsic effects, leading to PRAME expression and microenvironment restoration in vivo. Our data highlight multiple functions of PRAME during lymphomagenesis and provide a preclinical rationale for synergistic therapies combining epigenetic reprogramming with PRAME-targeted therapies.

Identification of neoantigens for individualized therapeutic cancer vaccines

Somatic mutations in cancer cells can generate tumour-specific neoepitopes, which are recognized by autologous T cells in the host. As neoepitopes are not subject to central immune tolerance and are not expressed in healthy tissues, they are attractive targets for therapeutic cancer vaccines. Because the vast majority of cancer mutations are unique to the individual patient, harnessing the full potential of this rich source of targets requires individualized treatment approaches. Many computational algorithms and machine-learning tools have been developed to identify mutations in sequence data, to prioritize those that are more likely to be recognized by T cells and to design tailored vaccines for every patient. In this Review, we fill the gaps between the understanding of basic mechanisms of T cell recognition of neoantigens and the computational approaches for discovery of somatic mutations and neoantigen prediction for cancer immunotherapy. We present a new classification of neoantigens, distinguishing between guarding, restrained and ignored neoantigens, based on how they confer proficient antitumour immunity in a given clinical context. Such context-based differentiation will contribute to a framework that connects neoantigen biology to the clinical setting and medical peculiarities of cancer, and will enable future neoantigen-based therapies to provide greater clinical benefit.

A novel and efficient approach to high-throughput production of HLA-E/peptide monomer for T-cell epitope screening

Over the past two decades, there has been a great interest in the study of HLA-E-restricted αβ T cells during bacterial and viral infections, including recently SARS-CoV-2 infection. Phenotyping of these specific HLA-E-restricted T cells requires new tools such as tetramers for rapid cell staining or sorting, as well as for the identification of new peptides capable to bind to the HLA-E pocket. To this aim, we have developed an optimal photosensitive peptide to generate stable HLA-E/pUV complexes allowing high-throughput production of new HLA-E/peptide complexes by peptide exchange. We characterized the UV exchange by ELISA and improved the peptide exchange readout using size exclusion chromatography. This novel approach for complex quantification is indeed very important to perform tetramerization of MHC/peptide complexes with the high quality required for detection of specific T cells. Our approach allows the rapid screening of peptides capable of binding to the non-classical human HLA-E allele, paving the way for the development of new therapeutic approaches based on the detection of HLA-E-restricted T cells.

MHC Class I Deficiency in Solid Tumors and Therapeutic Strategies to Overcome It

It is now well accepted that the immune system can control cancer growth. However, tumors escape immune-mediated control through multiple mechanisms and the downregulation or loss of major histocompatibility class (MHC)-I molecules is a common immune escape mechanism in many cancers. MHC-I molecules present antigenic peptides to cytotoxic T cells, and MHC-I loss can render tumor cells invisible to the immune system. In this review, we examine the dysregulation of MHC-I expression in cancer, explore the nature of MHC-I-bound antigenic peptides recognized by immune cells, and discuss therapeutic strategies that can be used to overcome MHC-I deficiency in solid tumors, with a focus on the role of natural killer (NK) cells and CD4 T cells.

Characterization of CD8 T Cell-Mediated Mutations in the Immunodominant Epitope GP33-41 of Lymphocytic Choriomeningitis Virus

Cytotoxic T lymphocytes (CTLs) represent key immune effectors of the host response against chronic viruses, due to their cytotoxic response to virus-infected cells. In response to this selection pressure, viruses may accumulate escape mutations that evade CTL-mediated control. To study the emergence of CTL escape mutations, we employed the murine chronic infection model of lymphocytic choriomeningitis virus (LCMV). We developed an amplicon-based next-generation sequencing pipeline to detect low frequency mutations in the viral genome and identified non-synonymous mutations in the immunodominant LCMV CTL epitope, GP33-41, in infected wildtype mice. Infected Rag2-deficient mice lacking CTLs did not contain such viral mutations. By using transgenic mice with T cell receptors specific to GP33-41, we characterized the emergence of viral mutations in this epitope under varying selection pressure. We investigated the two most abundant viral mutations by employing reverse genetically engineered viral mutants encoding the respective mutations. These experiments provided evidence that these mutations prevent activation and expansion of epitope-specific CD8 T cells. Our findings on the mutational dynamics of CTL escape mutations in a widely-studied viral infection model contributes to our understanding of how chronic viruses interact with their host and evade the immune response. This may guide the development of future treatments and vaccines against chronic infections.

Peptide-MHC I complex stability measured by nanoscale differential scanning fluorimetry reveals molecular mechanism of thermal denaturation

Recombinant major histocompatibility complex class I molecules are used in diagnostic and therapeutic approaches in cancer immunotherapy, with many studies exploring their binding to antigenic peptides. Current techniques for kinetic peptide binding studies are hampered by high sample consumption, low throughput, interference with protein stability, and/or high background signal. Here, we validate nanoscale differential scanning fluorimetry (nanoDSF), a method using the tryptophan fluorescence of class I molecules, for class I/peptide binding, and we use it to determine the molecular mechanism of the thermal denaturation of HLA-A*02:01.

Treatment of an aggressive orthotopic murine glioblastoma model with combination checkpoint blockade and a multivalent neoantigen vaccine

BACKGROUND

Although clinical trials testing immunotherapies in glioblastoma (GBM) have yielded mixed results, new strategies targeting tumor-specific somatic coding mutations, termed "neoantigens," represent promising therapeutic approaches. We characterized the microenvironment and neoantigen landscape of the aggressive CT2A GBM model in order to develop a platform to test combination checkpoint blockade and neoantigen vaccination.

METHODS

Flow cytometric analysis was performed on intracranial CT2A and GL261 tumor-infiltrating lymphocytes (TILs). Whole-exome DNA and RNA sequencing of the CT2A murine GBM was employed to identify expressed, somatic mutations. Predicted neoantigens were identified using the pVAC-seq software suite, and top-ranking candidates were screened for reactivity by interferon-gamma enzyme linked immunospot assays. Survival analysis was performed comparing neoantigen vaccination, anti-programmed cell death ligand 1 (αPD-L1), or combination therapy.

RESULTS

Compared with the GL261 model, CT2A exhibited immunologic features consistent with human GBM including reduced αPD-L1 sensitivity and hypofunctional TILs. Of the 29 CT2A neoantigens screened, we identified neoantigen-specific CD8+ T-cell responses in the intracranial TIL and draining lymph nodes to two H2-Kb restricted (Epb4H471L and Pomgnt1R497L) and one H2-Db restricted neoantigen (Plin2G332R). Survival analysis showed that therapeutic neoantigen vaccination with Epb4H471L, Pomgnt1R497L, and Plin2G332R, in combination with αPD-L1 treatment was superior to αPD-L1 alone.

CONCLUSIONS

We identified endogenous neoantigen specific CD8+ T cells within an αPD-L1 resistant murine GBM and show that neoantigen vaccination significantly augments survival benefit in combination with αPD-L1 treatment. These observations provide important preclinical correlates for GBM immunotherapy trials and support further investigation into the effects of multimodal immunotherapeutic interventions on antiglioma immunity.

KEY POINTS

1. Neoantigen vaccines combined with checkpoint blockade may be promising treatments.2. CT2A tumors exhibit features of human GBM microenvironments.3. Differential scanning fluorimetry assays may complement in silico neoantigen prediction tools.

SARS-CoV-2 mutations in MHC-I-restricted epitopes evade CD8+ T cell responses

CD8⁺ T cell immunity to SARS-CoV-2 has been implicated in COVID-19 severity and virus control. Here, we identified nonsynonymous mutations in MHC-I-restricted CD8⁺ T cell epitopes after deep sequencing of 747 SARS-CoV-2 virus isolates. Mutant peptides exhibited diminished or abrogated MHC-I binding in a cell-free in vitro assay. Reduced MHC-I binding of mutant peptides was associated with decreased proliferation, IFN-γ production and cytotoxic activity of CD8⁺ T cells isolated from HLA-matched COVID-19 patients. Single cell RNA sequencing of ex vivo expanded, tetramer-sorted CD8⁺ T cells from COVID-19 patients further revealed qualitative differences in the transcriptional response to mutant peptides. Our findings highlight the capacity of SARS-CoV-2 to subvert CD8⁺ T cell surveillance through point mutations in MHC-I-restricted viral epitopes.

Most non-canonical proteins uniquely populate the proteome or immunopeptidome

Combining RNA sequencing, ribosome profiling, and mass spectrometry, we elucidate the contribution of non-canonical translation to the proteome and major histocompatibility complex (MHC) class I immunopeptidome. Remarkably, of 14,498 proteins identified in three human B cell lymphomas, 2,503 are non-canonical proteins. Of these, 28% are novel isoforms and 72% are cryptic proteins encoded by ostensibly non-coding regions (60%) or frameshifted canonical genes (12%). Cryptic proteins are translated as efficiently as canonical proteins, have more predicted disordered residues and lower stability, and critically generate MHC-I peptides 5-fold more efficiently per translation event. Translating 5' "untranslated" regions hinders downstream translation of genes involved in transcription, translation, and antiviral responses. Novel protein isoforms show strong enrichment for signaling pathways deregulated in cancer. Only a small fraction of cryptic proteins detected in the proteome contribute to the MHC-I immunopeptidome, demonstrating the high preferential access of cryptic defective ribosomal products to the class I pathway.

DeepAntigen: a novel method for neoantigen prioritization via 3D genome and deep sparse learning

MOTIVATION

The mutations of cancers can encode the seeds of their own destruction, in the form of T-cell recognizable immunogenic peptides, also known as neoantigens. It is computationally challenging, however, to accurately prioritize the potential neoantigen candidates according to their ability of activating the T-cell immunoresponse, especially when the somatic mutations are abundant. Although a few neoantigen prioritization methods have been proposed to address this issue, advanced machine learning model that is specifically designed to tackle this problem is still lacking. Moreover, none of the existing methods considers the original DNA loci of the neoantigens in the perspective of 3D genome which may provide key information for inferring neoantigens' immunogenicity.

RESULTS

In this study, we discovered that DNA loci of the immunopositive and immunonegative MHC-I neoantigens have distinct spatial distribution patterns across the genome. We therefore used the 3D genome information along with an ensemble pMHC-I coding strategy, and developed a group feature selection-based deep sparse neural network model (DNN-GFS) that is optimized for neoantigen prioritization. DNN-GFS demonstrated increased neoantigen prioritization power comparing to existing sequence-based approaches. We also developed a webserver named deepAntigen (http://yishi.sjtu.edu.cn/deepAntigen) that implements the DNN-GFS as well as other machine learning methods. We believe that this work provides a new perspective toward more accurate neoantigen prediction which eventually contribute to personalized cancer immunotherapy.

AVAILABILITY AND IMPLEMENTATION

Data and implementation are available on webserver: http://yishi.sjtu.edu.cn/deepAntigen.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

The presentation of SARS-CoV-2 peptides by the common HLA-A∗02:01 molecule

CD8+ T cells are crucial for anti-viral immunity; however, understanding T cell responses requires the identification of epitopes presented by human leukocyte antigens (HLA). To date, few SARS-CoV-2-specific CD8+ T cell epitopes have been described. Internal viral proteins are typically more conserved than surface proteins and are often the target of CD8+ T cells. Therefore, we have characterized eight peptides derived from the internal SARS-CoV-2 nucleocapsid protein predicted to bind HLA-A^∗02:01, the most common HLA molecule in the global population. We determined not all peptides could form a complex with HLA-A^∗02:01, and the six crystal structures determined revealed that some peptides adopted a mobile conformation. We therefore provide a molecular understanding of SARS-CoV-2 CD8+ T cell epitopes. Furthermore, we show that there is limited pre-existing CD8+ T cell response toward these epitopes in unexposed individuals. Together, these data show that SARS-CoV-2 nucleocapsid might not contain potent epitopes restricted to HLA-A^∗02:01.

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HLA-A∗02:01 individuals have limited pre-existing immunity to SARS-CoV-2 nucleocapsid

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High-resolution crystal structures of HLA-A∗02:01 presenting SARS-CoV-2 peptides

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Structural analysis of pHLA shows stability influences peptide immunogenicity

Thermostability profiling of MHC-bound peptides: a new dimension in immunopeptidomics and aid for immunotherapy design

The features of peptide antigens that contribute to their immunogenicity are not well understood. Although the stability of peptide-MHC (pMHC) is known to be important, current assays assess this interaction only for peptides in isolation and not in the context of natural antigen processing and presentation. Here, we present a method that provides a comprehensive and unbiased measure of pMHC stability for thousands of individual ligands detected simultaneously by mass spectrometry (MS). The method allows rapid assessment of intra-allelic and inter-allelic differences in pMHC stability and reveals profiles of stability that are broader than previously appreciated. The additional dimensionality of the data facilitated the training of a model which improves the prediction of peptide immunogenicity, specifically of cancer neoepitopes. This assay can be applied to any cells bearing MHC or MHC-like molecules, offering insight into not only the endogenous immunopeptidome, but also that of neoepitopes and pathogen-derived sequences.

Changing the landscape of tumor immunology: novel tools to examine T cell specificity

Immunotherapy has established itself as a stalwart arm in patient care and with precision medicine forms the new paradigm in cancer treatment. T cells are an important group of immune cells capable of potent cancer immune surveillance and immunity. The advent of bioinformatics, particularly more recent advances incorporating algorithms employing machine learning, provide a seemingly limitless ability for T cell analysis and hypothesis generation. Such endeavors have become indispensable to research efforts accelerating and evolving to such an extent that there exists an appreciable gap between knowledge and proof of function and application. Exciting new technologies such as DNA barcoding, cytometry by time-of-flight (CyTOF), and peptide-exchangeable pHLA multimers inclusive of rare and difficult HLA alleles offer high-throughput cell-by-cell analytical capabilities. These outstanding recent contributions to T cell research will help close this gap and potentially bring practical benefit to patients.

Identification and validation of 174 COVID-19 vaccine candidate epitopes reveals low performance of common epitope prediction tools

The outbreak of SARS-CoV-2 (2019-nCoV) virus has highlighted the need for fast and efficacious vaccine development. Stimulation of a proper immune response that leads to protection is highly dependent on presentation of epitopes to circulating T-cells via the HLA complex. SARS-CoV-2 is a large RNA virus and testing of all of its overlapping peptides in vitro to deconvolute an immune response is not feasible. Therefore HLA-binding prediction tools are often used to narrow down the number of peptides to test. We tested NetMHC suite tools' predictions by using an in vitro peptide-MHC stability assay. We assessed 777 peptides that were predicted to be good binders across 11 MHC alleles in a complex-stability assay and tested a selection of 19 epitope-HLA-binding prediction tools against the assay. In this investigation of potential SARS-CoV-2 epitopes we found that current prediction tools vary in performance when assessing binding stability, and they are highly dependent on the MHC allele in question. Designing a COVID-19 vaccine where only a few epitope targets are included is therefore a very challenging task. Here, we present 174 SARS-CoV-2 epitopes with high prediction binding scores, validated to bind stably to 11 HLA alleles. Our findings may contribute to the design of an efficacious vaccine against COVID-19.

Cancer neoantigen: Boosting immunotherapy

Tumor neoantigen has a high degree of immunogenicity. As one of the emerging methods of tumor immunotherapy, the vaccine developed against it has served to clinical trials of various solid tumors, especially in the treatment of melanoma. Currently, a variety of immunotherapy methods have been applied to the treatment of the tumor. However, other therapeutic methods have the disadvantages of low specificity and prominent side effects. Treatments require tumor antigen with higher immunogenicity as the target of immune attack. This review will recommend the identification of neoantigen, the influencing factors of neoantigen, and the application of personalized vaccines for neoantigen in metastatic tumors such as malignant melanoma.

Bioinformatic methods for cancer neoantigen prediction

Tumor cells accumulate aberrations not present in normal cells, leading to presentation of neoantigens on MHC molecules on their surface. These non-self neoantigens distinguish tumor cells from normal cells to the immune system and are thus targets for cancer immunotherapy. The rapid development of molecular profiling platforms, such as next-generation sequencing, has enabled the generation of large datasets characterizing tumor cells. The simultaneous development of algorithms has enabled rapid and accurate processing of these data. Bioinformatic software tools encoding the algorithms can be strung together in a workflow to identify neoantigens. Here, with a focus on high-throughput sequencing, we review state-of-the art bioinformatic tools along with the steps and challenges involved in neoantigen identification and recognition.

T Cell Receptors for Gene Transfer in Adoptive T Cell Therapy

The past decade has seen enormous progress in cancer immunotherapy. Checkpoint inhibitors are a class of immunotherapy that act to recruit endogenous T cells of a patient's immune system against cancer-associated peptide- MHC antigens. In this process, mutated antigenic peptides referred to as neoantigens often serve as the target on cancer cells that are recognized by the T cell receptor (TCR) on endogenous T cells. Another successful immunotherapy has involved adoptive T cell therapy, where therapeutic doses of T cells expressing a gene for an anti-cancer receptor are delivered to a patient. This approach has been used primarily against hematopoietic cancers using synthetic receptors called chimeric antigen receptors (CARs). CARs typically contain an antibody fragment (single-chain Fv, scFv) against a cancer cell surface antigen such as the B cell molecule CD19. While therapeutic CARs (and full antibodies) target antigens expressed on cell surfaces, TCRs can target a much larger array of intracellular proteins by binding to any cellular peptide associated with an MHC product. These cancer targets include self-peptides from aberrantly expressed/overexpressed proteins or neoantigens. In this review, we discuss the use of TCRs in adoptive T cell therapy and their target antigens. We focus on two properties that impact sensitivity, potency, and possible toxic cross-reactivity of TCR-mediated therapy: (1) the affinity of the TCR for the target antigen, and (2) the density of the target antigen. Finally, we provide a comprehensive listing of the current clinical trials that involve TCRs in adoptive T cell cancer therapy.