Genomic and bioinformatic profiling of mutational neoepitopes reveals new rules to predict anticancer immunogenicity

Antigen-specificity measurements are the key to understanding T cell responses

Antigen-specific T cells play a central role in the adaptive immune response and come in a wide range of phenotypes. T cell receptors (TCRs) mediate the antigen-specificities found in T cells. Importantly, high-throughput TCR sequencing provides a fingerprint which allows tracking of specific T cells and their clonal expansion in response to particular antigens. As a result, many studies have leveraged TCR sequencing in an attempt to elucidate the role of antigen-specific T cells in various contexts. Here, we discuss the published approaches to studying antigen-specific T cells and their specific TCR repertoire. Further, we discuss how these methods have been applied to study the TCR repertoire in various diseases in order to characterize the antigen-specific T cells involved in the immune control of disease.

A novel transcriptional signature identifies T-cell infiltration in high-risk paediatric cancer

BACKGROUND

Molecular profiling of the tumour immune microenvironment (TIME) has enabled the rational choice of immunotherapies in some adult cancers. In contrast, the TIME of paediatric cancers is relatively unexplored. We speculated that a more refined appreciation of the TIME in childhood cancers, rather than a reliance on commonly used biomarkers such as tumour mutation burden (TMB), neoantigen load and PD-L1 expression, is an essential prerequisite for improved immunotherapies in childhood solid cancers.

METHODS

We combined immunohistochemistry (IHC) with RNA sequencing and whole-genome sequencing across a diverse spectrum of high-risk paediatric cancers to develop an alternative, expression-based signature associated with CD8⁺ T-cell infiltration of the TIME. Furthermore, we explored transcriptional features of immune archetypes and T-cell receptor sequencing diversity, assessed the relationship between CD8⁺ and CD4⁺ abundance by IHC and deconvolution predictions and assessed the common adult biomarkers such as neoantigen load and TMB.

RESULTS

A novel 15-gene immune signature, Immune Paediatric Signature Score (IPASS), was identified. Using this signature, we estimate up to 31% of high-risk cancers harbour infiltrating T-cells. In addition, we showed that PD-L1 protein expression is poorly correlated with PD-L1 RNA expression and TMB and neoantigen load are not predictive of T-cell infiltration in paediatrics. Furthermore, deconvolution algorithms are only weakly correlated with IHC measurements of T-cells.

CONCLUSIONS

Our data provides new insights into the variable immune-suppressive mechanisms dampening responses in paediatric solid cancers. Effective immune-based interventions in high-risk paediatric cancer will require individualised analysis of the TIME.

Integrating immunopeptidome analysis for the design and development of cancer vaccines

The repertoire of naturally presented peptides within the MHC (major histocompatibility complex) or HLA (human leukocyte antigens) system on the cellular surface of every mammalian cell is referred to as ligandome or immunopeptidome. This later gained momentum upon the discovery of CD8 + T cells able to recognize and kill cancer cells in an MHC-I antigen-restricted manner. Indeed, cancer immune surveillance relies on T cell recognition of MHC-I-restricted peptides, making the identification of those peptides the core for designing T cell-based cancer vaccines. Moreover, the breakthrough of antibodies targeting immune checkpoint molecules has led to a new and strong interest in discovering suitable targets for CD8 +T cells. Therapeutic cancer vaccines are designed for the artificial generation and/or stimulation of CD8 +T cells; thus, their combination with ICIs to unleash the breaks of the immune system comes as a natural consequence to enhance anti-tumor efficacy. In this context, the identification and knowledge of peptide candidates take advantage of the fast technology updates in immunopeptidome and mass spectrometric methodologies, paying the way to the rational design of vaccines for immunotherapeutic approaches. In this review, we discuss mainly the role of immunopeptidome analysis and its application for the generation of therapeutic cancer vaccines with main focus on HLA-I peptides. Here, we review cancer vaccine platforms based on two different preparation methods: pathogens (viruses and bacteria) and not (VLPs, nanoparticles, subunits vaccines) that exploit discoveries in the ligandome field to generate and/or enhance anti-tumor specific response. Finally, we discuss possible drawbacks and future challenges in the field that remain still to be addressed.

Contemplating immunopeptidomes to better predict them

The identification of T-cell epitopes is key for a complete molecular understanding of immune recognition mechanisms in infectious diseases, autoimmunity and cancer. T-cell epitopes further provide targets for personalized vaccines and T-cell therapy, with several therapeutic applications in cancer immunotherapy and elsewhere. T-cell epitopes consist of short peptides displayed on Major Histocompatibility Complex (MHC) molecules. The recent advances in mass spectrometry (MS) based technologies to profile the ensemble of peptides displayed on MHC molecules - the so-called immunopeptidome - had a major impact on our understanding of antigen presentation and MHC ligands. On the one hand, these techniques enabled researchers to directly identify hundreds of thousands of peptides presented on MHC molecules, including some that elicited T-cell recognition. On the other hand, the data collected in these experiments revealed fundamental properties of antigen presentation pathways and significantly improved our ability to predict naturally presented MHC ligands and T-cell epitopes across the wide spectrum of MHC alleles found in human and other organisms. Here we review recent computational developments to analyze experimentally determined immunopeptidomes and harness these data to improve our understanding of antigen presentation and MHC binding specificities, as well as our ability to predict MHC ligands. We further discuss the strengths and limitations of the latest approaches to move beyond predictions of antigen presentation and tackle the challenges of predicting TCR recognition and immunogenicity.

Systems Biology Approaches for the Improvement of Oncolytic Virus-Based Immunotherapies

Oncolytic virus (OV)-based immunotherapy is mainly dependent on establishing an efficient cell-mediated antitumor immunity. OV-mediated antitumor immunity elicits a renewed antitumor reactivity, stimulating a T-cell response against tumor-associated antigens (TAAs) and recruiting natural killer cells within the tumor microenvironment (TME). Despite the fact that OVs are unspecific cancer vaccine platforms, to further enhance antitumor immunity, it is crucial to identify the potentially immunogenic T-cell restricted TAAs, the main key orchestrators in evoking a specific and durable cytotoxic T-cell response. Today, innovative approaches derived from systems biology are exploited to improve target discovery in several types of cancer and to identify the MHC-I and II restricted peptide repertoire recognized by T-cells. Using specific computation pipelines, it is possible to select the best tumor peptide candidates that can be efficiently vectorized and delivered by numerous OV-based platforms, in order to reinforce anticancer immune responses. Beyond the identification of TAAs, system biology can also support the engineering of OVs with improved oncotropism to reduce toxicity and maintain a sufficient portion of the wild-type virus virulence. Finally, these technologies can also pave the way towards a more rational design of armed OVs where a transgene of interest can be delivered to TME to develop an intratumoral gene therapy to enhance specific immune stimuli.

Peptide Vaccines in Melanoma: Chemical Approaches towards Improved Immunotherapeutic Efficacy

Cancer of the skin is by far the most common of all cancers. Although the incidence of melanoma is relatively low among skin cancers, it can account for a high number of skin cancer deaths. Since the start of deeper insight into the mechanisms of melanoma tumorigenesis and their strong interaction with the immune system, the development of new therapeutical strategies has been continuously rising. The high number of melanoma cell mutations provides a diverse set of antigens that the immune system can recognize and use to distinguish tumor cells from normal cells. Peptide-based synthetic anti-tumor vaccines are based on tumor antigens that elicit an immune response due to antigen-presenting cells (APCs). Although targeting APCs with peptide antigens is the most important assumption for vaccine development, peptide antigens alone are poorly immunogenic. The immunogenicity of peptide antigens can be improved not only by synthetic modifications but also by the assistance of adjuvants and/or delivery systems. The current review summarizes the different chemical approaches for the development of effective peptide-based vaccines for the immunotherapeutic treatment of advanced melanoma.

Improved predictions of antigen presentation and TCR recognition with MixMHCpred2.2 and PRIME2.0 reveal potent SARS-CoV-2 CD8+ T-cell epitopes

The recognition of pathogen or cancer-specific epitopes by CD8⁺ T cells is crucial for the clearance of infections and the response to cancer immunotherapy. This process requires epitopes to be presented on class I human leukocyte antigen (HLA-I) molecules and recognized by the T-cell receptor (TCR). Machine learning models capturing these two aspects of immune recognition are key to improve epitope predictions. Here, we assembled a high-quality dataset of naturally presented HLA-I ligands and experimentally verified neo-epitopes. We then integrated these data in a refined computational framework to predict antigen presentation (MixMHCpred2.2) and TCR recognition (PRIME2.0). The depth of our training data and the algorithmic developments resulted in improved predictions of HLA-I ligands and neo-epitopes. Prospectively applying our tools to SARS-CoV-2 proteins revealed several epitopes. TCR sequencing identified a monoclonal response in effector/memory CD8⁺ T cells against one of these epitopes and cross-reactivity with the homologous peptides from other coronaviruses.

Artificial intelligence applied in neoantigen identification facilitates personalized cancer immunotherapy

The field of cancer neoantigen investigation has developed swiftly in the past decade. Predicting novel and true neoantigens derived from large multi-omics data became difficult but critical challenges. The rise of Artificial Intelligence (AI) or Machine Learning (ML) in biomedicine application has brought benefits to strengthen the current computational pipeline for neoantigen prediction. ML algorithms offer powerful tools to recognize the multidimensional nature of the omics data and therefore extract the key neoantigen features enabling a successful discovery of new neoantigens. The present review aims to outline the significant technology progress of machine learning approaches, especially the newly deep learning tools and pipelines, that were recently applied in neoantigen prediction. In this review article, we summarize the current state-of-the-art tools developed to predict neoantigens. The standard workflow includes calling genetic variants in paired tumor and blood samples, and rating the binding affinity between mutated peptide, MHC (I and II) and T cell receptor (TCR), followed by characterizing the immunogenicity of tumor epitopes. More specifically, we highlight the outstanding feature extraction tools and multi-layer neural network architectures in typical ML models. It is noted that more integrated neoantigen-predicting pipelines are constructed with hybrid or combined ML algorithms instead of conventional machine learning models. In addition, the trends and challenges in further optimizing and integrating the existing pipelines are discussed.

Advancements in Cancer Immunotherapies

Recent work has suggested involvement of the immune system in biological therapies specifically targeting tumor microenvironment. Substantial advancement in the treatment of malignant tumors utilizing immune cells, most importantly T cells that play a key role in cell-mediated immunity, have led to success in clinical trials. Therefore, this article focuses on the therapeutic approaches and developmental strategies to treat cancer. This review emphasizes the immunomodulatory response, the involvement of key tumor-infiltrating cells, the mechanistic aspects, and prognostic biomarkers. We also cover recent advancements in therapeutic strategies.

CAR T-cells for colorectal cancer immunotherapy: Ready to go?

Chimeric antigen receptor (CAR) T-cells represent a new genetically engineered cell-based immunotherapy tool against cancer. The use of CAR T-cells has revolutionized the therapeutic approach for hematological malignancies. Unfortunately, there is a long way to go before this treatment can be developed for solid tumors, including colorectal cancer. CAR T-cell therapy for colorectal cancer is still in its early stages, and clinical data are scarce. Major limitations of this therapy include high toxicity, relapses, and an impermeable tumor microenvironment for CAR T-cell therapy in colorectal cancer. In this review, we summarize current knowledge, highlight challenges, and discuss perspectives regarding CAR T-cell therapy in colorectal cancer.

Aggressive, early resistant and relapsed mantle cell lymphoma distinct extrinsic microenvironment highlighted by transcriptome analysis

Immunotherapy strategies relying on innate or adaptive immune components are increasingly used in onco-haematology. However, little is known about the infiltrated lymph nodes (LN) or bone marrow (BM) landscape of mantle cell lymphoma (MCL). The original transcriptomic approach of reverse transcriptase multiplex ligation-dependent probe amplification (RT-MLPA) was applied here to explore the expression of 24 genes of interest in MCL at diagnosis (21 LN and 15 BM) or relapse (18 LN). This allowed us to identify that at baseline, samples from MCL patients with an aggressive morphology (i.e. blastoid or pleomorphic) or a high proliferative profile, displayed significantly higher monocyte/macrophage-associated transcripts (CD14 and CD163) in LN and BM. Regarding T-cells, aggressive MCL forms had significantly lower amounts of LN CD3E transcripts, yet an increased expression of cytotoxic markers in LN (CD8) and BM (CD94). A very high-risk group with early treatment resistance displayed, at diagnosis, high proliferation (KI67) and high macrophages and cytotoxic transcript levels. Post-immunochemotherapy relapsed samples revealed lower levels of T- and natural killer-cells markers, while monocyte/macrophage markers remained similar to diagnosis. This study suggests that rapid analysis of MCL microenvironment transcriptome signatures by RT-MLPA could allow for an early distinction of patient subgroups candidates for adapted treatment strategies.

Sources of Cancer Neoantigens beyond Single-Nucleotide Variants

The success of checkpoint blockade therapy against cancer has unequivocally shown that cancer cells can be effectively recognized by the immune system and eliminated. However, the identity of the cancer antigens that elicit protective immunity remains to be fully explored. Over the last decade, most of the focus has been on somatic mutations derived from non-synonymous single-nucleotide variants (SNVs) and small insertion/deletion mutations (indels) that accumulate during cancer progression. Mutated peptides can be presented on MHC molecules and give rise to novel antigens or neoantigens, which have been shown to induce potent anti-tumor immune responses. A limitation with SNV-neoantigens is that they are patient-specific and their accurate prediction is critical for the development of effective immunotherapies. In addition, cancer types with low mutation burden may not display sufficient high-quality [SNV/small indels] neoantigens to alone stimulate effective T cell responses. Accumulating evidence suggests the existence of alternative sources of cancer neoantigens, such as gene fusions, alternative splicing variants, post-translational modifications, and transposable elements, which may be attractive novel targets for immunotherapy. In this review, we describe the recent technological advances in the identification of these novel sources of neoantigens, the experimental evidence for their presentation on MHC molecules and their immunogenicity, as well as the current clinical development stage of immunotherapy targeting these neoantigens.

CD8 T cell function and cross-reactivity explored by stepwise increased peptide-HLA versus TCR affinity

Recruitment and activation of CD8 T cells occur through specific triggering of T cell receptor (TCR) by peptide-bound human leucocyte antigen (HLA) ligands. Within the generated trimeric TCR-peptide:HLA complex, the molecular binding affinities between peptide and HLA, and between TCR and peptide:HLA both impact T cell functional outcomes. However, how their individual and combined effects modulate immunogenicity and overall T cell responsiveness has not been investigated systematically. Here, we established two panels of human tumor peptide variants differing in their affinity to HLA. For precise characterization, we developed the “blue peptide assay”, an upgraded cell-based approach to measure the peptide:HLA affinity. These peptide variants were then used to investigate the cross-reactivity of tumor antigen-specific CD8 T cell clonotypes derived from blood of cancer patients after vaccination with either the native or an affinity-optimized Melan-A/MART-1 epitope, or isolated from tumor infiltrated lymph nodes (TILNs). Vaccines containing the native tumor epitope generated T cells with better functionality, and superior cross-reactivity against potential low affinity escape epitopes, as compared to T cells induced by vaccines containing an HLA affinity-optimized epitope. Comparatively, Melan-A/MART-1-specific TILN cells displayed functional and cross-reactive profiles that were heterogeneous and clonotype-dependent. Finally, we took advantage of a collection of T cells expressing affinity-optimized NY-ESO-1-specific TCRs to interrogate the individual and combined impact of peptide:HLA and TCR-pHLA affinities on overall CD8 T cell responses. We found profound and distinct effects of both biophysical parameters, with additive contributions and absence of hierarchical dominance. Altogether, the biological impact of peptide:HLA and TCR-pHLA affinities on T cell responses was carefully dissected in two antigenic systems, frequently targeted in human cancer immunotherapy. Our technology and stepwise comparison open new insights into the rational design and selection of vaccine-associated tumor-specific epitopes and highlight the functional and cross-reactivity profiles that endow T cells with best tumor control capacity.

Virus-Based Immuno-Oncology Models

Immunotherapy has been extensively explored in recent years with encouraging results in selected types of cancer. Such success aroused interest in the expansion of such indications, requiring a deep understanding of the complex role of the immune system in carcinogenesis. The definition of hot vs. cold tumors and the role of the tumor microenvironment enlightened the once obscure understanding of low response rates of solid tumors to immune check point inhibitors. Although the major scope found in the literature focuses on the T cell modulation, the innate immune system is also a promising oncolytic tool. The unveiling of the tumor immunosuppressive pathways, lead to the development of combined targeted therapies in an attempt to increase immune infiltration capability. Oncolytic viruses have been explored in different scenarios, in combination with various chemotherapeutic drugs and, more recently, with immune check point inhibitors. Moreover, oncolytic viruses may be engineered to express tumor specific pro-inflammatory cytokines, antibodies, and antigens to enhance immunologic response or block immunosuppressive mechanisms. Development of preclinical models capable to replicate the human immunologic response is one of the major challenges faced by these studies. A thorough understanding of immunotherapy and oncolytic viruses’ mechanics is paramount to develop reliable preclinical models with higher chances of successful clinical therapy application. Thus, in this article, we review current concepts in cancer immunotherapy including the inherent and synthetic mechanisms of immunologic enhancement utilizing oncolytic viruses, immune targeting, and available preclinical animal models, their advantages, and limitations.

Peptide-dependent tuning of major histocompatibility complex motional properties and the consequences for cellular immunity

T cell receptors (TCRs) and other receptors of the immune system recognize peptides presented by class I or class II major histocompatibility complex (MHC) proteins. Although we generally distinguish between the MHC protein and its peptide, at an atomic level the two form a structural composite, which allows peptides to influence MHC properties and vice versa. One consequence is the peptide-dependent tuning of MHC structural dynamics, which contributes to protein structural adaptability and influences how receptors identify and bind targets. Peptide-dependent tuning of MHC protein dynamics can impact processes such as antigenicity, TCR cross-reactivity, and T cell repertoire selection. Motional tuning extends beyond the binding groove, influencing peptide selection and exchange, as well as interactions with other immune receptors. Here, we review recent findings showing how peptides can affect the dynamic and adaptable nature of MHC proteins. We highlight consequences for immunity and demonstrate how MHC proteins have evolved to be highly sensitive dynamic reporters, with broad immunological consequences.

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.

Therapeutic Vaccines Targeting Neoantigens to Induce T-Cell Immunity against Cancers

Cancer immunotherapy has achieved multiple clinical benefits and has become an indispensable component of cancer treatment. Targeting tumor-specific antigens, also known as neoantigens, plays a crucial role in cancer immunotherapy. T cells of adaptive immunity that recognize neoantigens, but do not induce unwanted off-target effects, have demonstrated high efficacy and low side effects in cancer immunotherapy. Tumor neoantigens derived from accumulated genetic instability can be characterized using emerging technologies, such as high-throughput sequencing, bioinformatics, predictive algorithms, mass-spectrometry analyses, and immunogenicity validation. Neoepitopes with a higher affinity for major histocompatibility complexes can be identified and further applied to the field of cancer vaccines. Therapeutic vaccines composed of tumor lysates or cells and DNA, mRNA, or peptides of neoantigens have revoked adaptive immunity to kill cancer cells in clinical trials. Broad clinical applicability of these therapeutic cancer vaccines has emerged. In this review, we discuss recent progress in neoantigen identification and applications for cancer vaccines and the results of ongoing trials.

The Importance of Being Presented: Target Validation by Immunopeptidomics for Epitope-Specific Immunotherapies

Presentation of tumor-specific or tumor-associated peptides by HLA class I molecules to CD8+ T cells is the foundation of epitope-centric cancer immunotherapies. While often in silico HLA binding predictions or in vitro immunogenicity assays are utilized to select candidates, mass spectrometry-based immunopeptidomics is currently the only method providing a direct proof of actual cell surface presentation. Despite much progress in the last decade, identification of such HLA-presented peptides remains challenging. Here we review typical workflows and current developments in the field of immunopeptidomics, highlight the challenges which remain to be solved and emphasize the importance of direct target validation for clinical immunotherapy development.

Agents of cancer immunosurveillance: HSPs and dsDNA

Tumor immunosurveillance requires tumor cell-derived molecules to initiate responses through corresponding receptors on antigen presenting cells (APCs) and a specific effector response designed to eliminate the emerging tumor cells. This is supported by evidence from immunodeficient individuals and experimental animals. Recent discoveries suggest that adjuvanticity of tumor-derived heat shock proteins (HSPs) and double-stranded DNA (dsDNA) are necessary for tumor-specific immunity. There is also the obligatory early transfer of tumor antigens to APCs. We argue that tumor-derived HSPs deliver sufficient chaperoned antigen for cross-priming within the quantitative limits set by nascent tumors. In contrast to late-stage tumors, we are only just beginning to understand the unique interactions of the immune system with precancerous/nascent neoplastic cells, which is important for improved cancer prevention measures.

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.

NeoScore Integrates Characteristics of the Neoantigen:MHC Class I Interaction and Expression to Accurately Prioritize Immunogenic Neoantigens

Accurate prioritization of immunogenic neoantigens is key to developing personalized cancer vaccines and distinguishing those patients likely to respond to immune checkpoint inhibition. However, there is no consensus regarding which characteristics best predict neoantigen immunogenicity, and no model to date has both high sensitivity and specificity and a significant association with survival in response to immunotherapy. We address these challenges in the prioritization of immunogenic neoantigens by (1) identifying which neoantigen characteristics best predict immunogenicity; (2) integrating these characteristics into an immunogenicity score, the NeoScore; and (3) demonstrating a significant association of the NeoScore with survival in response to immune checkpoint inhibition. One thousand random and evenly split combinations of immunogenic and nonimmunogenic neoantigens from a validated dataset were analyzed using a regularized regression model for characteristic selection. The selected characteristics, the dissociation constant and binding stability of the neoantigen:MHC class I complex and expression of the mutated gene in the tumor, were integrated into the NeoScore. A web application is provided for calculation of the NeoScore. The NeoScore results in improved, or equivalent, performance in four test datasets as measured by sensitivity, specificity, and area under the receiver operator characteristics curve compared with previous models. Among cutaneous melanoma patients treated with immune checkpoint inhibition, a high maximum NeoScore was associated with improved survival. Overall, the NeoScore has the potential to improve neoantigen prioritization for the development of personalized vaccines and contribute to the determination of which patients are likely to respond to immunotherapy.

Cancer Neoantigens: Challenges and Future Directions for Prediction, Prioritization, and Validation

Prioritization of immunogenic neoantigens is key to enhancing cancer immunotherapy through the development of personalized vaccines, adoptive T cell therapy, and the prediction of response to immune checkpoint inhibition. Neoantigens are tumor-specific proteins that allow the immune system to recognize and destroy a tumor. Cancer immunotherapies, such as personalized cancer vaccines, adoptive T cell therapy, and immune checkpoint inhibition, rely on an understanding of the patient-specific neoantigen profile in order to guide personalized therapeutic strategies. Genomic approaches to predicting and prioritizing immunogenic neoantigens are rapidly expanding, raising new opportunities to advance these tools and enhance their clinical relevance. Predicting neoantigens requires acquisition of high-quality samples and sequencing data, followed by variant calling and variant annotation. Subsequently, prioritizing which of these neoantigens may elicit a tumor-specific immune response requires application and integration of tools to predict the expression, processing, binding, and recognition potentials of the neoantigen. Finally, improvement of the computational tools is held in constant tension with the availability of datasets with validated immunogenic neoantigens. The goal of this review article is to summarize the current knowledge and limitations in neoantigen prediction, prioritization, and validation and propose future directions that will improve personalized cancer treatment.

Biomarkers of response to PD-1 pathway blockade

The binding of T cell immune checkpoint proteins programmed death 1 (PD-1) and cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) to their ligands allows immune evasion by tumours. The development of therapeutic antibodies, termed checkpoint inhibitors, that bind these molecules or their ligands, has provided a means to release this brake on the host anti-tumour immune response. However, these drugs are costly, are associated with potentially severe side effects, and only benefit a small subset of patients. It is therefore important to identify biomarkers that discriminate between responders and non-responders. This review discusses the determinants for a successful response to antibodies that bind PD-1 or its ligand PD-L1, dividing them into markers found in the tumour biopsy and those in non-tumour samples. It provides an update on the established predictive biomarkers (tumour PD-L1 expression, tumour mismatch repair deficiency and tumour mutational burden) and assesses the evidence for new potential biomarkers.

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.

Peptides for Vaccine Development

This review discusses peptide epitopes used as antigens in the development of vaccines in clinical trials as well as future vaccine candidates. It covers peptides used in potential immunotherapies for infectious diseases including SARS-CoV-2, influenza, hepatitis B and C, HIV, malaria, and others. In addition, peptides for cancer vaccines that target examples of overexpressed proteins are summarized, including human epidermal growth factor receptor 2 (HER-2), mucin 1 (MUC1), folate receptor, and others. The uses of peptides to target cancers caused by infective agents, for example, cervical cancer caused by human papilloma virus (HPV), are also discussed. This review also provides an overview of model peptide epitopes used to stimulate non-specific immune responses, and of self-adjuvanting peptides, as well as the influence of other adjuvants on peptide formulations. As highlighted in this review, several peptide immunotherapies are in advanced clinical trials as vaccines, and there is great potential for future therapies due the specificity of the response that can be achieved using peptide epitopes.

What’s next in cancer immunotherapy? - The promise and challenges of neoantigen vaccination

The process of tumorigenesis leaves a series of indelible genetic changes in tumor cells, that when expressed, have the potential to be tumor-specific immune targets. Neoantigen vaccines that capitalize on this potential immunogenicity have shown efficacy in preclinical models and have now entered clinical trials. Here we discuss the status of personalized neoantigen vaccines and the current major challenges to this nascent field. In particular, we focus on the types of antigens that can be targeted by vaccination and on the role that preexisting immunosuppression, and in particular T-cell exhaustion, will play in the development of effective cancer vaccines.

GNIFdb: a neoantigen intrinsic feature database for glioma

ABSTRACT

Neoantigens are mutation-containing immunogenic peptides from tumor cells. Neoantigen intrinsic features are neoantigens' sequence-associated features characterized by different amino acid descriptors and physical-chemical properties, which have a crucial function in prioritization of neoantigens with immunogenic potentials and predicting patients with better survival. Different intrinsic features might have functions to varying degrees in evaluating neoantigens' potentials of immunogenicity. Identification and comparison of intrinsic features among neoantigens are particularly important for developing neoantigen-based personalized immunotherapy. However, there is still no public repository to host the intrinsic features of neoantigens. Therefore, we developed GNIFdb, a glioma neoantigen intrinsic feature database specifically designed for hosting, exploring and visualizing neoantigen and intrinsic features. The database provides a comprehensive repository of computationally predicted Human leukocyte antigen class I (HLA-I) restricted neoantigens and their intrinsic features; a systematic annotation of neoantigens including sequence, neoantigen-associated mutation, gene expression, glioma prognosis, HLA-I subtype and binding affinity between neoantigens and HLA-I; and a genome browser to visualize them in an interactive manner. It represents a valuable resource for the neoantigen research community and is publicly available at http://www.oncoimmunobank.cn/index.php.

DATABASE URL

http://www.oncoimmunobank.cn/index.php.

T Cell Recognition of Tumor Neoantigens and Insights Into T Cell Immunotherapy

In cancer, non-synonymous DNA base changes alter protein sequence and produce neoantigens that are detected by the immune system. For immune detection, neoantigens must first be presented on class I or II human leukocyte antigens (HLA) followed by recognition by peptide-specific receptors, exemplified by the T-cell receptor (TCR). Detection of neoantigens represents a unique challenge to the immune system due to their high similarity with endogenous 'self' proteins. Here, we review insights into how TCRs detect neoantigens from structural studies and delineate two broad mechanistic categories: 1) recognition of mutated 'self' peptides and 2) recognition of novel 'non-self' peptides generated through anchor residue modifications. While mutated 'self' peptides differ only by a single amino acid from an existing 'self' epitope, mutations that form anchor residues generate an entirely new epitope, hitherto unknown to the immune system. We review recent structural studies that highlight these structurally distinct mechanisms and discuss how they may lead to differential anti-tumor immune responses. We discuss how T cells specific for neoantigens derived from anchor mutations can be of high affinity and provide insights to their use in adoptive T cell transfer-based immunotherapy.

Neoantigen cancer vaccine augments anti-CTLA-4 efficacy

Immune checkpoint inhibitors (ICI) based on anti-CTLA-4 (αCTLA-4) and anti-PD1 (αPD1) are being tested in combination with different therapeutic approaches including other immunotherapies such as neoantigen cancer vaccines (NCV). Here we explored, in two cancer murine models, different therapeutic combinations of ICI with personalized DNA vaccines expressing neoantigens and delivered by electroporation (EP). Anti-cancer efficacy was evaluated using vaccines with or without CD4 epitopes. Therapeutic DNA vaccines showed synergistic effects in different therapeutic protocols including established large tumors. Flow cytometry (FC) was utilized to measure CD8, CD4, Treg, and switched B cells as well as neoantigen-specific immune responses, which were also measured by IFN-γ ELIspot. Immune responses were augmented in combination with αCTLA4 but not with αPD1 in the MC38 tumor-bearing mice, significantly impacting tumor growth. Similarly, neoantigen-specific T cell immune responses were enhanced in combined treatment with αCTLA-4 in the CT26 tumor model where large tumors regressed in all mice, while monotherapy with αCTLA-4 was less efficacious. In line with previous evidence, we observed an increased switched B cells in the spleen of mice treated with αCTLA-4 alone or in combination with NCV. These results support the use of NCV delivered by DNA-EP with αCTLA-4 and suggest a new combined therapy for clinical testing.

Structural insights into the interactions and epigenetic functions of human nucleic acid repair protein ALKBH6

Human AlkB homolog 6, ALKBH6, plays key roles in nucleic acid damage repair and tumor therapy. However, no precise structural and functional information are available for this protein. In this study, we determined atomic resolution crystal structures of human holo-ALKBH6 and its complex with ligands. AlkB members bind nucleic acids by NRLs (nucleotide recognition lids, also called Flips), which can recognize DNA/RNA and flip methylated lesions. We found that ALKBH6 has unusual Flip1 and Flip2 domains, distinct from other AlkB family members both in sequence and conformation. Moreover, we show that its unique Flip3 domain has multiple unreported functions, such as discriminating against double-stranded nucleic acids, blocking the active center, binding other proteins, and in suppressing tumor growth. Structural analyses and substrate screening reveal how ALKBH6 discriminates between different types of nucleic acids and may also function as a nucleic acid demethylase. Structure-based interacting partner screening not only uncovered an unidentified interaction of transcription repressor ZMYND11 and ALKBH6 in tumor suppression but also revealed cross talk between histone modification and nucleic acid modification in epigenetic regulation. Taken together, these results shed light on the molecular mechanism underlying ALKBH6-associated nucleic acid damage repair and tumor therapy.

[Research Progress of Immunotherapy Biomarkers for Non-small Cell Lung Cancer]

Lung cancer is one of the most prevalent malignancies with the highest morbidity and mortality rates worldwide. In recent years, with the development of immune-oncology research and several therapeutic antibodies have reach the clinic, many breakthroughs have been made in immunotherapy. The advent of immunotherapy has revolutionized the treatment of NSCLC, but the response and durable clinical benefit are only observed in a small subset of patients. Therefore, strategies to screen the potential beneficial population and improve the efficacy of immunotherapy remain an essential topic. In the current article, the author review the biomarkers that have potential to better predict responders to immunotherapy and to provide ideas for the clinical application of immunotherapy.
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Beyond Sequencing: Prioritizing and Delivering Neoantigens for Cancer Vaccines

Neoantigens are tumor-specific proteins and peptides that can be highly immunogenic. Immune-mediated tumor rejection is strongly associated with cytotoxic responses to neoantigen-derived peptides in noncovalent association with self-HLA molecules. Neoantigen-based therapies, such as adoptive T cell transfer, have shown the potential to induce remission of treatment-resistant metastatic disease in select patients. Cancer vaccines are similarly designed to elicit or amplify antigen-specific T cell populations and stimulate directed antitumor immunity, but the selection and prioritization of the neoantigens remains a challenge. Bioinformatic algorithms can predict tumor neoantigens from somatic mutations, insertion-deletions, and other aberrant peptide products, but this often leads to hundreds of potential neoepitopes, all unique for that tumor. Selecting neoantigens for cancer vaccines is complicated by the technical challenges of neoepitope discovery, the diversity of HLA molecules, and intratumoral heterogeneity of passenger mutations leading to immune escape. Despite strong preclinical evidence, few neoantigen cancer vaccines tested in vivo have generated epitope-specific T cell populations, suggesting suboptimal immune system activation. In this chapter, we review factors affecting the prioritization and delivery of candidate neoantigens in the design of therapeutic and preventive cancer vaccines and consider synergism with standard chemotherapies.

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.

Tumor-Infiltrating Lymphocytes and Their Role in Solid Tumor Progression

Tumor-infiltrating lymphocytes (TIL) are an important component of the tumor environment. Their role in tumor growth and progression has been debated for decades. Today, emphasis has shifted to beneficial effects of TIL for the host and to therapies optimizing the benefits by reducing immune suppression in the tumor microenvironment. Evidence indicates that when TILs are present in the tumor as dense aggregates of activated immune cells, tumor prognosis and responses to therapy are favorable. Gene signatures and protein profiling of TIL at the population and single-cell levels provide clues not only about their phenotype and numbers but also about TIL potential functions in the tumor. Correlations of the TIL data with clinicopathological tumor characteristics, clinical outcome, and patients' survival indicate that TILs exert influence on the disease progression, especially in colorectal carcinomas and breast cancer. At the same time, the recognition that TIL signatures vary with time and cancer progression has initiated investigations of TIL as potential prognostic biomarkers. Multiple mechanisms are utilized by tumors to subvert the host immune system. The balance between pro- and antitumor responses of TIL largely depends on the tumor microenvironment, which is unique in each cancer patient. This balance is orchestrated by the tumor and thus is shifted toward the promotion of tumor growth. Changes occurring in TIL during tumor progression appear to serve as a measure of tumor aggressiveness and potentially provide a key to selecting therapeutic strategies and inform about prognosis.

In silico tools and databases for designing cancer immunotherapy

Immunotherapy is a rapidly growing therapy for cancer which have numerous benefits over conventional treatments like surgery, chemotherapy, and radiation. Overall survival of cancer patients has improved significantly due to the use of immunotherapy. It acts as a novel pillar for treating different malignancies from their primary to the metastatic stage. Recent preferments in high-throughput sequencing and computational immunology leads to the development of targeted immunotherapy for precision oncology. In the last few decades, several computational methods and resources have been developed for designing immunotherapy against cancer. In this review, we have summarized cancer-associated genomic, transcriptomic, and mutation profile repositories. We have also enlisted in silico methods for the prediction of vaccine candidates, HLA binders, cytokines inducing peptides, and potential neoepitopes. Of note, we have incorporated the most important bioinformatics pipelines and resources for the designing of cancer immunotherapy. Moreover, to facilitate the scientific community, we have developed a web portal entitled ImmCancer (https://webs.iiitd.edu.in/raghava/immcancer/), comprises cancer immunotherapy tools and repositories.

A machine learning model for ranking candidate HLA class I neoantigens based on known neoepitopes from multiple human tumor types

Tumor neoepitopes presented by major histocompatibility complex (MHC) class I are recognized by tumor-infiltrating lymphocytes (TIL) and are targeted by adoptive T-cell therapies. Identifying which mutant neoepitopes from tumor cells are capable of recognition by T cells can assist in the development of tumor-specific, cell-based therapies and can shed light on antitumor responses. Here, we generate a ranking algorithm for class I candidate neoepitopes by using next-generation sequencing data and a dataset of 185 neoepitopes that are recognized by HLA class I-restricted TIL from individuals with metastatic cancer. Random forest model analysis showed that the inclusion of multiple factors impacting epitope presentation and recognition increased output sensitivity and specificity compared to the use of predicted HLA binding alone. The ranking score output provides a set of class I candidate neoantigens that may serve as therapeutic targets and provides a tool to facilitate in vitro and in vivo studies aimed at the development of more effective immunotherapies.

Know thy immune self and non-self: Proteomics informs on the expanse of self and non-self, and how and where they arise

T cells play an important role in the adaptive immune response to a variety of infections and cancers. Initiation of a T cell mediated immune response requires antigen recognition in a process termed MHC (major histocompatibility complex) restri ction. A T cell antigen is a composite structure made up of a peptide fragment bound within the antigen-binding groove of an MHC-encoded class I or class II molecule. Insight into the precise composition and biology of self and non-self immunopeptidomes is essential to harness T cell mediated immunity to prevent, treat, or cure infectious diseases and cancers. T cell antigen discovery is an arduous task! The pioneering work in the early 1990s has made large-scale T cell antigen discovery possible. Thus, advancements in mass spectrometry coupled with proteomics and genomics technologies make possible T cell antigen discovery with ease, accuracy, and sensitivity. Yet we have only begun to understand the breadth and the depth of self and non-self immunopeptidomes because the molecular biology of the cell continues to surprise us with new secrets directly related to the source, and the processing and presentation of MHC ligands. Focused on MHC class I molecules, this review, therefore, provides a brief historic account of T cell antigen discovery and, against a backdrop of key advances in molecular cell biologic processes, elaborates on how proteogenomics approaches have revolutionised the field.

Computational cancer neoantigen prediction: current status and recent advances

Over the last few decades, immunotherapy has shown significant therapeutic efficacy in a broad range of cancer types. Antitumor immune responses are contingent on the recognition of tumor-specific antigens, which are termed neoantigens. Tumor neoantigens are ideal targets for immunotherapy since they can be recognized as non-self antigens by the host immune system and thus are able to elicit an antitumor T-cell response. There are an increasing number of studies that highlight the importance of tumor neoantigens in immunoediting and in the sensitivity to immune checkpoint blockade. Therefore, one of the most fundamental tasks in the field of immuno-oncology research is the identification of patient-specific neoantigens. To this end, a plethora of computational approaches have been developed in order to predict tumor-specific aberrant peptides and quantify their likelihood of binding to patients' human leukocyte antigen molecules in order to be recognized by T cells. In this review, we systematically summarize and present the most recent advances in computational neoantigen prediction, and discuss the challenges and novel methods that are being developed to resolve them.

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Tumors have the ability to acquire immune escape mechanisms.

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Tumor-specific aberrant peptides (neoantigens) can elicit an immune response by the host immune system.

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The identification of neoantigens is one of the most fundamental tasks in the field of immuno-oncology research.

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A plethora of computational approaches have been developed in order to predict patient-specificneoantigens.

Reversion analysis reveals the in vivo immunogenicity of a poorly MHC I-binding cancer neoepitope

High-affinity MHC I-peptide interactions are considered essential for immunogenicity. However, some neo-epitopes with low affinity for MHC I have been reported to elicit CD8 T cell dependent tumor rejection in immunization-challenge studies. Here we show in a mouse model that a neo-epitope that poorly binds to MHC I is able to enhance the immunogenicity of a tumor in the absence of immunization. Fibrosarcoma cells with a naturally occurring mutation are edited to their wild type counterpart; the mutation is then re-introduced in order to obtain a cell line that is genetically identical to the wild type except for the neo-epitope-encoding mutation. Upon transplantation into syngeneic mice, all three cell lines form tumors that are infiltrated with activated T cells. However, lymphocytes from the two tumors that harbor the mutation show significantly stronger transcriptional signatures of cytotoxicity and TCR engagement, and induce greater breadth of TCR reactivity than those of the wild type tumors. Structural modeling of the neo-epitope peptide/MHC I pairs suggests increased hydrophobicity of the neo-epitope surface, consistent with higher TCR reactivity. These results confirm the in vivo immunogenicity of low affinity or ‘non-binding’ epitopes that do not follow the canonical concept of MHC I-peptide recognition.

The immunogenicity of peptides is believed to be determined by their high-affinity binding to MHC I. Here authors show that low-affinity MHC I-peptide interactions are also able to trigger robust T cell response and anti-tumour immunity in vivo.

T Cell Epitope Prediction and Its Application to Immunotherapy

T cells play a crucial role in controlling and driving the immune response with their ability to discriminate peptides derived from healthy as well as pathogenic proteins. In this review, we focus on the currently available computational tools for epitope prediction, with a particular focus on tools aimed at identifying neoepitopes, i.e. cancer-specific peptides and their potential for use in immunotherapy for cancer treatment. This review will cover how these tools work, what kind of data they use, as well as pros and cons in their respective applications.

Understanding the role of potential pathways and its components including hypoxia and immune system in case of oral cancer

There are a few biological functions or phenomenon which are universally associated with majority of the cancers and hypoxia and immune systems are among them. Hypoxia often occurs in most of the cancers which helps the cells in adapting different responses with respect to the normal cells which may be the activation of signaling pathways which regulate proliferation, angiogenesis, and cell death. Similar to it, immune signaling pathways are known to play critical roles in cancers. Moreover, there are a number of genes which are known to be associated with these hypoxia and immune system and appear to direct affect the tumor growth and propagations. Cancer is among the leading cause of death and oral cancer is the tenth-leading cause due to cancer death. In this study, we were mainly interested to understand the impact of alteration in the expression of hypoxia and immune system-related genes and their contribution to head and neck squamous cell carcinoma. Moreover, we have collected the genes associated with hypoxia and immune system from the literatures. In this work, we have performed meta-analysis of the gene and microRNA expression and mutational datasets obtained from public database for different grades of tumor in case of oral cancer. Based on our results, we conclude that the critical pathways which dominantly enriched are associated with metabolism, cell cycle, immune system and based on the survival analysis of the hypoxic genes, we observe that the potential genes associated with head and neck squamous cell carcinoma and its progression are STC2, PGK1, P4HA1, HK1, SPIB, ANXA5, SERPINE1, HGF, PFKM, TGFB1, L1CAM, ELK4, EHF, and CDK2.

Large-scale analysis of SARS-CoV-2 spike-glycoprotein mutants demonstrates the need for continuous screening of virus isolates

Due to the widespread of the COVID-19 pandemic, the SARS-CoV-2 genome is evolving in diverse human populations. Several studies already reported different strains and an increase in the mutation rate. Particularly, mutations in SARS-CoV-2 spike-glycoprotein are of great interest as it mediates infection in human and recently approved mRNA vaccines are designed to induce immune responses against it. We analyzed 1,036,030 SARS-CoV-2 genome assemblies and 30,806 NGS datasets from GISAID and European Nucleotide Archive (ENA) focusing on non-synonymous mutations in the spike protein. Only around 2.5% of the samples contained the wild-type spike protein with no variation from the reference. Among the spike protein mutants, we confirmed a low mutation rate exhibiting less than 10 non-synonymous mutations in 99.6% of the analyzed sequences, but the mean and median number of spike protein mutations per sample increased over time. 5,472 distinct variants were found in total. The majority of the observed variants were recurrent, but only 21 and 14 recurrent variants were found in at least 1% of the mutant genome assemblies and NGS samples, respectively. Further, we found high-confidence subclonal variants in about 2.6% of the NGS data sets with mutant spike protein, which might indicate co-infection with various SARS-CoV-2 strains and/or intra-host evolution. Lastly, some variants might have an effect on antibody binding or T-cell recognition. These findings demonstrate the continuous importance of monitoring SARS-CoV-2 sequences for an early detection of variants that require adaptations in preventive and therapeutic strategies.

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

Identification of neoepitopes that are effective in cancer therapy is a major challenge in creating cancer vaccines. Here, using an entirely unbiased approach, we queried all possible neoepitopes in a mouse cancer model and asked which of those are effective in mediating tumor rejection and, independently, in eliciting a measurable CD8 response. This analysis uncovered a large trove of effective anticancer neoepitopes that have strikingly different properties from conventional epitopes and suggested an algorithm to predict them. It also revealed that our current methods of prediction discard the overwhelming majority of true anticancer neoepitopes. These results from a single mouse model were validated in another antigenically distinct mouse cancer model and are consistent with data reported in human studies. Structural modeling showed how the MHC I-presented neoepitopes had an altered conformation, higher stability, or increased exposure to T cell receptors as compared with the unmutated counterparts. T cells elicited by the active neoepitopes identified here demonstrated a stem-like early dysfunctional phenotype associated with effective responses against viruses and tumors of transgenic mice. These abundant anticancer neoepitopes, which have not been tested in human studies thus far, can be exploited for generation of personalized human cancer vaccines.

Probing T-cell response by sequence-based probabilistic modeling

With the increasing ability to use high-throughput next-generation sequencing to quantify the diversity of the human T cell receptor (TCR) repertoire, the ability to use TCR sequences to infer antigen-specificity could greatly aid potential diagnostics and therapeutics. Here, we use a machine-learning approach known as Restricted Boltzmann Machine to develop a sequence-based inference approach to identify antigen-specific TCRs. Our approach combines probabilistic models of TCR sequences with clone abundance information to extract TCR sequence motifs central to an antigen-specific response. We use this model to identify patient personalized TCR motifs that respond to individual tumor and infectious disease antigens, and to accurately discriminate specific from non-specific responses. Furthermore, the hidden structure of the model results in an interpretable representation space where TCRs responding to the same antigen cluster, correctly discriminating the response of TCR to different viral epitopes. The model can be used to identify condition specific responding TCRs. We focus on the examples of TCRs reactive to candidate neoantigens and selected epitopes in experiments of stimulated TCR clone expansion.

Negative trade-off between neoantigen repertoire breadth and the specificity of HLA-I molecules shapes antitumor immunity

Human leukocyte antigen class I (HLA-I) genes shape our immune response against pathogens and cancer. Certain HLA-I variants can bind a wider range of peptides than others, a feature that could be favorable against a range of viral diseases. However, the implications of this phenomenon on cancer immune response are unknown. Here we quantified peptide repertoire breadth (or promiscuity) of a representative set of HLA-I alleles and found that patients with cancer who were carrying HLA-I alleles with high peptide-binding promiscuity have significantly worse prognosis after immune checkpoint inhibition. This can be explained by a reduced capacity of the immune system to discriminate tumor neopeptides from self-peptides when patients carry highly promiscuous HLA-I variants, shifting the regulation of tumor-infiltrating T cells from activation to tolerance. In summary, HLA-I peptide-binding specificity shapes neopeptide immunogenicity and the self-immunopeptidome repertoire in an antagonistic manner, and could underlie a negative trade-off between antitumor immunity and genetic susceptibility to viral infections.

Identification and Targeting of Mutant Peptide Neoantigens in Cancer Immunotherapy

In recent decades, adoptive cell transfer and checkpoint blockade therapies have revolutionized immunotherapeutic approaches to cancer treatment. Advances in whole exome/genome sequencing and bioinformatic detection of tumour-specific genetic variations and the amino acid sequence alterations they induce have revealed that T cell mediated anti-tumour immunity is substantially directed at mutated peptide sequences, and the identification and therapeutic targeting of patient-specific mutated peptide antigens now represents an exciting and rapidly progressing frontier of personalized medicine in the treatment of cancer. This review outlines the historical identification and validation of mutated peptide neoantigens as a target of the immune system, and the technical development of bioinformatic and experimental strategies for detecting, confirming and prioritizing both patient-specific or "private" and frequently occurring, shared "public" neoantigenic targets. Further, we examine the range of therapeutic modalities that have demonstrated preclinical and clinical anti-tumour efficacy through specifically targeting neoantigens, including adoptive T cell transfer, checkpoint blockade and neoantigen vaccination.

Neoantigen Controversies

Next-generation sequencing technologies have revolutionized our ability to catalog the landscape of somatic mutations in tumor genomes. These mutations can sometimes create so-called neoantigens, which allow the immune system to detect and eliminate tumor cells. However, efforts that stimulate the immune system to eliminate tumors based on their molecular differences have had less success than has been hoped for, and there are conflicting reports about the role of neoantigens in the success of this approach. Here we review some of the conflicting evidence in the literature and highlight key aspects of the tumor-immune interface that are emerging as major determinants of whether mutation-derived neoantigens will contribute to an immunotherapy response. Accounting for these factors is expected to improve success rates of future immunotherapy approaches.

Cancer Vaccines: Promising Therapeutics or an Unattainable Dream

The advent of cancer immunotherapy has revolutionized the field of cancer treatment and offers cancer patients new hope. Although this therapy has proved highly successful for some patients, its efficacy is not all encompassing and several cancer types do not respond. Cancer vaccines offer an alternate approach to promote anti-tumor immunity that differ in their mode of action from antibody-based therapies. Cancer vaccines serve to balance the equilibrium of the crosstalk between the tumor cells and the host immune system. Recent advances in understanding the nature of tumor-mediated tolerogenicity and antigen presentation has aided in the identification of tumor antigens that have the potential to enhance anti-tumor immunity. Cancer vaccines can either be prophylactic (preventative) or therapeutic (curative). An exciting option for therapeutic vaccines is the emergence of personalized vaccines, which are tailor-made and specific for tumor type and individual patient. This review summarizes the current standing of the most promising vaccine strategies with respect to their development and clinical efficacy. We also discuss prospects for future development of stem cell-based prophylactic vaccines.

Challenges targeting cancer neoantigens in 2021: a systematic literature review

Introduction: Cancer neoantigens represent important targets of cancer immunotherapy. The goal of cancer neoantigen vaccines is to induce neoantigen-specific immune responses and antitumor immunity while minimizing the potential for autoimmune toxicity. Advances in sequencing technologies, neoantigen prediction algorithms, and other technologies have dramatically improved the ability to identify and prioritize cancer neoantigens. Unfortunately, results from preclinical studies and early phase clinical trials highlight important challenges to the successful clinical translation of neoantigen cancer vaccines.Areas covered: In this review, we provide an overview of current strategies for the identification and prioritization of cancer neoantigens with a particular emphasis on the two most common strategies used for neoantigen identification: (1) direct identification of peptide ligands eluted from peptide-MHC complexes, and (2) next-generation sequencing combined with neoantigen prediction algorithms. We highlight the limitations of current neoantigen prediction pipelines, and discuss broader challenges associated with cancer neoantigen vaccines including tumor purity/heterogeneity and the immunosuppressive tumor microenvironment.Expert opinion: Despite current limitations, neoantigen prediction is likely to improve rapidly based on advances in sequencing, machine learning, and information sharing. The successful development of robust cancer neoantigen prediction strategies is likely to have a significant impact, with the potential to facilitate cancer neoantigen vaccine design.