An unbiased approach to defining bona fide cancer neoepitopes that elicit immune-mediated cancer rejection

Dynamic allostery in the peptide/MHC complex enables TCR neoantigen selectivity

The inherent cross-reactivity of the T cell receptor (TCR) is balanced by high specificity, which often manifests in confounding ways not easily interpretable from static structures. We show here that TCR discrimination between an HLA-A*03:01 (HLA-A3)-restricted public neoantigen derived from mutant PIK3CA and its wild-type (WT) counterpart emerges from motions within the HLA binding groove that vary with the identity of the peptide's first primary anchor. The motions form a dynamic gate that in the complex with the WT peptide impedes a large conformational change required for TCR binding. The more rigid neoantigen is insusceptible to this limiting dynamic, and with the gate open, is able to transit its central tryptophan residue underneath the peptide backbone to the contralateral side of the HLA-A3 peptide binding groove, facilitating TCR binding. Our findings reveal a novel mechanism driving TCR specificity for a cancer neoantigen that is rooted in the dynamic and allosteric nature of peptide/MHC-I complexes, with implications for resolving long-standing and often confounding questions about the determinants of T cell specificity.

Tumor Burden Dictates the Neoantigen Features Required to Generate an Effective Cancer Vaccine

Tumor neoantigens (nAg) represent a promising target for cancer immunotherapy. The identification of nAgs that can generate T-cell responses and have therapeutic activity has been challenging. Here, we sought to unravel the features of nAgs required to induce tumor rejection. We selected clinically validated Great Ape-derived adenoviral vectors (GAd) as a nAg delivery system for differing numbers and combinations of nAgs. We assessed their immunogenicity and efficacy in murine models of low to high disease burden, comparing multi-epitope versus mono-epitope vaccines. We demonstrated that the breadth of immune response is critical for vaccine efficacy and having multiple immunogenic nAgs encoded in a single vaccine improves efficacy. The contribution of each single neoantigen was examined, leading to the identification of 2 nAgs able to induce CD8+ T cell-mediated tumor rejection. They were both active as individual nAgs in a setting of prophylactic vaccination, although to different extents. However, the efficacy of these single nAgs was lost in a setting of therapeutic vaccination in tumor-bearing mice. The presence of CD4+ T-cell help restored the efficacy for only the most expressed of the two nAgs, demonstrating a key role for CD4+ T cells in sustaining CD8+ T-cell responses and the necessity of an efficient recognition of the targeted epitopes on cancer cells by CD8+ T cells for an effective antitumor response. This study provides insight into understanding the determinants of nAgs relevant for effective treatment and highlights features that could contribute to more effective antitumor vaccines. See related Spotlight by Slingluff Jr, p. 382.

NeoMUST: an accurate and efficient multi-task learning model for neoantigen presentation

Accurate identification of neoantigens is important for advancing cancer immunotherapies. This study introduces Neoantigen MUlti-taSk Tower (NeoMUST), a model employing multi-task learning to effectively capture task-specific information across related tasks. Our results show that NeoMUST rivals existing algorithms in predicting the presentation of neoantigens via MHC-I molecules, while demonstrating a significantly shorter training time for enhanced computational efficiency. The use of multi-task learning enables NeoMUST to leverage shared knowledge and task dependencies, leading to improved performance metrics and a significant reduction in the training time. NeoMUST, implemented in Python, is freely accessible at the GitHub repository. Our model will facilitate neoantigen prediction and empower the development of effective cancer immunotherapeutic approaches.

Activated B-Cells enhance epitope spreading to support successful cancer immunotherapy

Immune checkpoint therapies (ICT) have transformed the treatment of cancer over the past decade. However, many patients do not respond or suffer relapses. Successful immunotherapy requires epitope spreading, but the slow or inefficient induction of functional antitumoral immunity delays the benefit to patients or causes resistances. Therefore, understanding the key mechanisms that support epitope spreading is essential to improve immunotherapy. In this review, we highlight the major role played by B-cells in breaking immune tolerance by epitope spreading. Activated B-cells are key Antigen-Presenting Cells (APC) that diversify the T-cell response against self-antigens, such as ribonucleoproteins, in autoimmunity but also during successful cancer immunotherapy. This has important implications for the design of future cancer vaccines.

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

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

Distinct sets of molecular characteristics define tumor-rejecting neoantigens

Challenges in identifying tumor-rejecting neoantigens limit the efficacy of neoantigen vaccines to treat cancers, including cutaneous squamous cell carcinoma (cSCC). A minority of human cSCC tumors shared neoantigens, supporting the need for personalized vaccines. Using a UV-induced mouse cSCC model which recapitulated the mutational signature and driver mutations found in human disease, we found that CD8 T cells constrain cSCC. Two MHC class I neoantigens were identified that constrained cSCC growth. Compared to the wild-type peptides, one tumor-rejecting neoantigen exhibited improved MHC binding and the other had increased solvent accessibility of the mutated residue. Across known neoantigens that do not impact MHC binding, structural modeling of the peptide/MHC complexes indicated that increased solvent accessibility, which will facilitate TCR recognition of the neoantigen, distinguished tumor-rejecting from non-immunogenic neoantigens. This work reveals characteristics of tumor-rejecting neoantigens that may be of considerable importance in identifying optimal vaccine candidates in cSCC and other cancers.

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.

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

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

Long-Read Sequencing Reveals Alternative Splicing-Driven, Shared Immunogenic Neoepitopes Regardless of SF3B1 Status in Uveal Melanoma

Tumor-specific neoepitopes are promising targets in cancer immunotherapy. However, the identification of functional tumor-specific neoepitopes remains challenging. In addition to the most common source, single-nucleotide variants (SNV), alternative splicing (AS) represents another rich source of neoepitopes and can be utilized in cancers with low SNVs such as uveal melanoma (UM). UM, the most prevalent adult ocular malignancy, has poor clinical outcomes due to a lack of effective therapies. Recent studies have revealed the promise of harnessing tumor neoepitopes to treat UM. Previous studies have focused on neoepitope targets associated with mutations in splicing factor 3b subunit 1 (SF3B1), a key splicing factor; however, little is known about the neoepitopes that are commonly shared by patients independent of SF3B1 status. To identify the AS-derived neoepitopes regardless of SF3B1 status, we herein used a comprehensive nanopore long-read-sequencing approach to elucidate the landscape of AS and novel isoforms in UM. We also performed high-resolution mass spectrometry to further validate the presence of neoepitope candidates and analyzed their structures using the AlphaFold2 algorithm. We experimentally evaluated the antitumor effects of these neoepitopes and found they induced robust immune responses by stimulating interferon (IFN)γ production and activating T cell-based UM tumor killing. These results provide novel insights into UM-specific neoepitopes independent of SF3B1 and lay the foundation for developing therapies by targeting these actionable neoepitopes.

Immunosurveillance shapes the emergence of neo-epitope landscapes of sarcomas, revealing prime targets for immunotherapy

T cells recognize tumor-derived mutated peptides presented on MHC by tumors. The recognition of these neo-epitopes leads to rejection of tumors, an event that is critical for successful cancer immunosurveillance. Determination of tumor-rejecting neo-epitopes in human tumors has proved difficult, though recently developed systems approaches are becoming increasingly useful at evaluating their immunogenicity. We have used the differential aggretope index to determine the neo-epitope burden of sarcomas and observed a conspicuously titrated antigenic landscape, ranging from the highly antigenic osteosarcomas to the low antigenic leiomyosarcomas and liposarcomas. We showed that the antigenic landscape of the tumors inversely reflected the historical T cell responses in the tumor-bearing patients. We predicted that highly antigenic tumors with poor antitumor T cell responses, such as osteosarcomas, would be responsive to T cell-based immunotherapy regimens and demonstrated this in a murine osteosarcoma model. Our study presents a potentially novel pipeline for determining antigenicity of human tumors, provides an accurate predictor of potential neo-epitopes, and will be an important indicator of which cancers to target with T cell-enhancing immunotherapy.

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.

Cancer vaccines based on whole-tumor lysate or neoepitopes with validated HLA binding outperform those with predicted HLA-binding affinity

Antigen selection and prioritization represent crucial determinants of vaccines' efficacy. Here, we compare two personalized dendritic cell-based vaccination strategies using whole-tumor lysate or neoantigens. Data in mouse and in cancer patients demonstrate that peptide vaccines using neoantigens predicted on the sole basis of in silico peptide-major histocompatibility complex (MHC) binding affinity underperform relative to whole-tumor-lysate vaccines. In contrast, effective in vitro peptide-MHC binding affinity and peptide immunogenicity significantly improve the prioritization of tumor-rejecting neoepitopes and result in more efficacious vaccines.

Mouse models for immuno-oncology

Realizing the clinical promise of cancer immunotherapy is hindered by gaps in our knowledge of in vivo mechanisms underlying treatment response as well as treatment limiting toxicity. Preclinical in vivo model systems and technologies are required to address these knowledge gaps and to surmount the challenges faced in the clinical application of immunotherapy. Mice are commonly used for basic and translational research to support development and testing of immune interventions, including for cancer. Here, we discuss the advantages and the limitations of current models as well as future developments.

The Neoantigen Landscape of the Coding and Noncoding Cancer Genome Space

Tumor mutation burden (TMB) is a measure to predict patient responsiveness to immune checkpoint immunotherapy because with increased mutation frequency, the likelihood of a greater neoantigen burden is increased. Although neoantigen prediction tools exist, tumor neoantigen burden has not been adopted as a measure to predict immunotherapy response. With both measures, current guidelines are limited to the coding regions, but ectopic expression of sequences in the noncoding space may potentially be a source of neoantigens. A pan-cancer cohort of 574 advanced disease stage patients with whole genome and transcriptome sequencing was analyzed to report mutation burden and neoantigen counts within the coding and noncoding regions. The efficacy of tumor neoantigen burden, reported as tumor neoantigen count (TNC), including neoantigens derived from the expression of noncoding regions, compared with TMB as a predictor of response to immunotherapy for 80 patients who had received treatment, was evaluated. TMB was found to be the best predictor of response to immunotherapy, whereas expression-derived TNC from the noncoding regions did not improve prediction of response. Therefore, there is minimal benefit in extending the calculation of TNC to the noncoding space for the purposes of predicting response. However, it is likely that there is a wealth of neoantigens derived from the noncoding space that may impact patient outcomes and treatments.

Physicochemical Heuristics for Identifying High Fidelity, Near-Native Structural Models of Peptide/MHC Complexes

There is long-standing interest in accurately modeling the structural features of peptides bound and presented by class I MHC proteins. This interest has grown with the advent of rapid genome sequencing and the prospect of personalized, peptide-based cancer vaccines, as well as the development of molecular and cellular therapeutics based on T cell receptor recognition of peptide-MHC. However, while the speed and accessibility of peptide-MHC modeling has improved substantially over the years, improvements in accuracy have been modest. Accuracy is crucial in peptide-MHC modeling, as T cell receptors are highly sensitive to peptide conformation and capturing fine details is therefore necessary for useful models. Studying nonameric peptides presented by the common class I MHC protein HLA-A*02:01, here we addressed a key question common to modern modeling efforts: from a set of models (or decoys) generated through conformational sampling, which is best? We found that the common strategy of decoy selection by lowest energy can lead to substantial errors in predicted structures. We therefore adopted a data-driven approach and trained functions capable of predicting near native decoys with exceptionally high accuracy. Although our implementation is limited to nonamer/HLA-A*02:01 complexes, our results serve as an important proof of concept from which improvements can be made and, given the significance of HLA-A*02:01 and its preference for nonameric peptides, should have immediate utility in select immunotherapeutic and other efforts for which structural information would be advantageous.

Identification of Neoantigens in Cancer Cells as Targets for Immunotherapy

The clinical benefits of immune checkpoint blockage (ICB) therapy have been widely reported. In patients with cancer, researchers have demonstrated the clinical potential of antitumor cytotoxic T cells that can be reinvigorated or enhanced by ICB. Compared to self-antigens, neoantigens derived from tumor somatic mutations are believed to be ideal immune targets in tumors. Candidate tumor neoantigens can be identified through immunogenomic or immunopeptidomic approaches. Identification of neoantigens has revealed several points of the clinical relevance. For instance, tumor mutation burden (TMB) may be an indicator of immunotherapy. In various cancers, mutation rates accompanying neoantigen loads may be indicative of immunotherapy. Furthermore, mismatch repair-deficient tumors can be eradicated by T cells in ICB treatment. Hence, immunotherapies using vaccines or adoptive T-cell transfer targeting neoantigens are potential innovative strategies. However, significant efforts are required to identify the optimal epitopes. In this review, we summarize the recent progress in the identification of neoantigens and discussed preclinical and clinical studies based on neoantigens. We also discuss the issues remaining to be addressed before clinical applications of these new therapeutic strategies can be materialized.

Influence of T Cell-Mediated Immune Surveillance on Somatic Mutation Occurrences in Melanoma

Background

Neoantigens are presented on the cancer cell surface by peptide-restricted human leukocyte antigen (HLA) proteins and can subsequently activate cognate T cells. It has been hypothesized that the observed somatic mutations in tumors are shaped by immunosurveillance.

Methods

We investigated all somatic mutations identified in The Cancer Genome Atlas (TCGA) Skin Cutaneous Melanoma (SKCM) samples. By applying a computational algorithm, we calculated the binding affinity of the resulting neo-peptides and their corresponding wild-type peptides with the major histocompatibility complex (MHC) Class I complex. We then examined the relationship between binding affinity alterations and mutation frequency.

Results

Our results show that neoantigens derived from recurrent mutations tend to have lower binding affinities with the MHC Class I complex compared to peptides from non-recurrent mutations. Tumor samples harboring recurrent SKCM mutations exhibited lower immune infiltration levels, indicating a relatively colder immune microenvironment.

Conclusions

These results suggested that the occurrences of somatic mutations in melanoma have been shaped by immunosurveillance. Mutations that lead to neoantigens with high MHC class I binding affinity are more likely to be eliminated and thus are less likely to be present in tumors.

The Cancer Epitope Database and Analysis Resource: A Blueprint for the Establishment of a New Bioinformatics Resource for Use by the Cancer Immunology Community

Recent years have witnessed a dramatic rise in interest towards cancer epitopes in general and particularly neoepitopes, antigens that are encoded by somatic mutations that arise as a consequence of tumorigenesis. There is also an interest in the specific T cell and B cell receptors recognizing these epitopes, as they have therapeutic applications. They can also aid in basic studies to infer the specificity of T cells or B cells characterized in bulk and single-cell sequencing data. The resurgence of interest in T cell and B cell epitopes emphasizes the need to catalog all cancer epitope-related data linked to the biological, immunological, and clinical contexts, and most importantly, making this information freely available to the scientific community in a user-friendly format. In parallel, there is also a need to develop resources for epitope prediction and analysis tools that provide researchers access to predictive strategies and provide objective evaluations of their performance. For example, such tools should enable researchers to identify epitopes that can be effectively used for immunotherapy or in defining biomarkers to predict the outcome of checkpoint blockade therapies. We present here a detailed vision, blueprint, and work plan for the development of a new resource, the Cancer Epitope Database and Analysis Resource (CEDAR). CEDAR will provide a freely accessible, comprehensive collection of cancer epitope and receptor data curated from the literature and provide easily accessible epitope and T cell/B cell target prediction and analysis tools. The curated cancer epitope data will provide a transparent benchmark dataset that can be used to assess how well prediction tools perform and to develop new prediction tools relevant to the cancer research community.

SC1: A Tool for Interactive Web-Based Single-Cell RNA-Seq Data Analysis

Single-cell RNA-Seq (scRNA-Seq) is critical for studying cellular function and phenotypic heterogeneity as well as the development of tissues and tumors. In this study, we present SC1 a web-based highly interactive scRNA-Seq data analysis tool publicly accessible at https://sc1.engr.uconn.edu. The tool presents an integrated workflow for scRNA-Seq analysis, implements a novel method of selecting informative genes based on term-frequency inverse-document-frequency scores, and provides a broad range of methods for clustering, differential expression analysis, gene enrichment, interactive visualization, and cell cycle analysis. The tool integrates other single-cell omics data modalities such as T-cell receptor (TCR)-Seq and supports several single-cell sequencing technologies. In just a few steps, researchers can generate a comprehensive analysis and gain powerful insights from their scRNA-Seq data.