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

Shared neoantigens for cancer immunotherapy

Exploration of neoantigens holds the potential to be productive in immuno-oncotherapy. Among tumor-specific antigens, neoantigens result from genetic instability that gives rise to non-synonymous somatic mutations, highly specific to tumor cells. In addition to point mutations, gene rearrangements, indels leading to frameshifts, chromosomal translocations or inversions that may lead to fusion proteins, alternative mRNA splicing, and integration of genetic material of oncogenic viruses into the host genome provide consistent sources of neoantigens that are absent in healthy tissues. Out of these alterations, 2%-3% may generate T cell neoepitopes, possibly detectable by TCRs. Neoantigens are absent in healthy tissues and are thus at low risk of triggering autoimmunity. In addition, the host lymphocytes have not been rendered tolerant toward them and it is possible to induce immune responses against them. Here, we overview the two categories of neoantigens, i.e., private and shared, and their use in immuno-oncotherapy in selected pre-clinical and clinical studies. The vast majority of commonly occurring tumor-specific mutations are cancer causing and are permanently expressed by all malignant tumor cells, preventing the latter from escaping vaccine-induced anti-neoantigen immunity. The use of public neoantigens combined with efficient vaccine platforms can provide non-personalized "off-the-shelf" therapeutic vaccine candidates for broad-spectrum immunotherapy purposes.

Shared neoantigens' atlas for off-the-shelf cancer vaccine development

BACKGROUND

We have recently described that the most prevalent 100 mutations identified in human cancers, both single nucleotide variations (SNVs) and InDels, generate a handful number of shared mutated neoantigens (SNV and InDel-NeoAgs) in association with 5 HLA-A and 7 B haplotypes.

METHODS

In the present study, we expanded such analysis to 50 haplotypes in the three MHC class I loci (10 HLA-A, 27 HLA-B and 13 HLA-C), including all the mutated proteins identified in at least 5% of cancer patients.

RESULTS

Overall, the extended analysis identified 15 SNV-NeoAgs and 55 InDel-NeoAgs with a significant affinity improvement over the corresponding wt (DAI > 10). These targetable shared NeoAgs are prevalently derived from PIK3CAH1047R (6/15 SNV-NeoAgs) and LARP4BT163Hfs (30/55 InDel-NeoAgs). From the HLA perspective, the HLA-A*33:03 is associated with the largest number of SNV-NeoAgs (4/15 NeoAgs) and the HLA-B*58:01 is associated with the largest number of InDel-NeoAgs (16/55 NeoAgs). According to the distribution of each HLA haplotype in at least 10% of the regional populations, therapeutic cancer vaccines based on mutated shared SNV and InDel-NeoAgs, might be developed for COAD, STAD and UCEC cancers, with a global coverage, and for PAAD and UVM, with a regional coverage.

CONCLUSIONS

This represents the first in-depth analysis for the identification of a specific repertoire of shared mutated NeoAgs, most of which never reported before. Such shared SNV and InDel-NeoAgs are indispensable for the development of "off-the-shelf" cancer vaccines targeting a relevant percentage of cancers in a significant percentage of cancer patients worldwide.

The Landmark Series: Therapeutic Cancer Vaccine Strategies for Cold Tumors

Immunologically cold tumors present a significant challenge in cancer treatment due to their limited baseline immune infiltration and resistance to immunotherapy. Cancer vaccines offer a promising strategy to overcome this barrier by introducing high-quality, tumor-relevant antigens that can stimulate an effective anti-tumor immune response. Therapeutic cancer vaccines are being explored in the neoadjuvant, adjuvant, and minimal residual disease contexts to enhance immune activation and promote immune cell infiltration and function, with the goal to eradicate malignant cells and improve patient survival. Critical hurdles remain in optimizing antigen selection, determining the most effective vaccine formulations, and defining the ideal clinical setting for vaccine use. Moreover, rational combinations of cancer vaccines with other immune modulators (e.g., adjuvants, immune checkpoint inhibitors, and cytokines) may hold the key to enhancing vaccine efficacy and expanding therapeutic options for difficult-to-treat malignancies. This review examines current advancements in cancer vaccines and their utilization for immunologically cold tumors in the perioperative setting, highlighting ongoing challenges and future directions in this evolving field.

A frameshift-generated cancer neoepitope that controls tumor burden in prophylaxis as well as therapy

Insertion or deletion of one or two base pairs within a coding region causes a frameshift, which has the potential to generate neoepitopes (InDel-generated neoepitopes) that lack a self-counterpart and are entirely novel. Despite the obvious appeal of InDel-generated neoepitopes, and the demonstration of such candidate neoepitopes that can elicit a CD8 T-cell response, no InDel-generated neoepitopes that actually control tumors in vivo have been reported thus far. Here, in a mouse colon carcinoma line, we identify 11 InDels, only one of which generates a neoepitope that elicits tumor control in vivo in models of prophylaxis as well as therapy. Although this neoepitope has no self-counterpart, it has a low affinity (IC50 33,937.60 nM) for its MHC I allele. Despite its low affinity for MHC I, this neoepitope elicits antitumor activity in vivo through CD8 T cells. Furthermore, CD8 T cells elicited by this InDel-generated neoepitope, like the neoepitopes created by point mutations, show notably less exhaustion than classical immunogenic epitopes. Ironically, this InDel-generated neoepitope follows the same rules as noted for most of the tumor control-mediating neoepitopes generated by point mutations that have a poor affinity for MHC I alleles.

Unraveling the power of NAP-CNB's machine learning-enhanced tumor neoantigen prediction

In this study, we present a proof-of-concept classical vaccination experiment that validates the in silico identification of tumor neoantigens (TNAs) using a machine learning-based platform called NAP-CNB. Unlike other TNA predictors, NAP-CNB leverages RNA-seq data to consider the relative expression of neoantigens in tumors. Our experiments show the efficacy of NAP-CNB. Predicted TNAs elicited potent antitumor responses in mice following classical vaccination protocols. Notably, optimal antitumor activity was observed when targeting the antigen with higher expression in the tumor, which was not the most immunogenic. Additionally, the vaccination combining different neoantigens resulted in vastly improved responses compared to each one individually, showing the worth of multiantigen-based approaches. These findings validate NAP-CNB as an innovative TNA identification platform and make a substantial contribution to advancing the next generation of personalized immunotherapies.

Comparative performance analysis of neoepitope prediction algorithms in head and neck cancer

Background

Mutations in cancer cells can result in the production of neoepitopes that can be recognized by T cells and trigger an immune response. A reliable pipeline to identify such immunogenic neoepitopes for a given tumor would be beneficial for the design of cancer immunotherapies. Current methods, such as the pipeline proposed by the Tumor Neoantigen Selection Alliance (TESLA), aim to select short peptides with the highest likelihood to be MHC-I restricted minimal epitopes. Typically, only a small percentage of these predicted epitopes are recognized by T cells when tested experimentally. This is particularly problematic as the limited amount of sample available from patients that are acutely sick restricts the number of peptides that can be tested in practice. This led our group to develop an in-house pipeline termed Identify-Prioritize-Validate (IPV) that identifies long peptides that cover both CD4 and CD8 epitopes.

Methods

Here, we systematically compared how IPV performs compared to the TESLA pipeline. Patient peripheral blood mononuclear cells were cultured in vitro with their corresponding candidate peptides, and immune recognition was measured using cytokine-secretion assays.

Results

The IPV pipeline consistently outperformed the TESLA pipeline in predicting neoepitopes that elicited an immune response in our assay. This was primarily due to the inclusion of longer peptides in IPV compared to TESLA.

Conclusions

Our work underscores the improved predictive ability of IPV in comparison to TESLA in this assay system and highlights the need to clearly define which experimental metrics are used to evaluate bioinformatic epitope predictions.

Individualized Neoantigen-Directed Melanoma Therapy

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

Comprehensive epitope mutational scan database enables accurate T cell receptor cross-reactivity prediction

Predicting T cell receptor (TCR) activation is challenging due to the lack of both unbiased benchmarking datasets and computational methods that are sensitive to small mutations to a peptide. To address these challenges, we curated a comprehensive database, called BATCAVE, encompassing complete single amino acid mutational assays of more than 22,000 TCR-peptide pairs, centered around 25 immunogenic human and mouse epitopes, across both major histocompatibility complex classes, against 151 TCRs. We then present an interpretable Bayesian model, called BATMAN, that can predict the set of peptides that activates a TCR. We also developed an active learning version of BATMAN, which can efficiently learn the binding profile of a novel TCR by selecting an informative yet small number of peptides to assay. When validated on our database, BATMAN outperforms existing methods and reveals important biochemical predictors of TCR-peptide interactions. Finally, we demonstrate the broad applicability of BATMAN, including for predicting off-target effects for TCR-based therapies and polyclonal T cell responses.

AI Model for Predicting Anti-PD1 Response in Melanoma Using Multi-Omics Biomarkers

BACKGROUND

Immune checkpoint inhibitors (ICIs) have demonstrated significantly improved clinical efficacy in a minority of patients with advanced melanoma, whereas non-responders potentially suffer from severe side effects and delays in other treatment options. Predicting the response to anti-PD1 treatment in melanoma remains a challenge because the current FDA-approved gold standard, the nonsynonymous tumor mutation burden (nsTMB), offers limited accuracy.

METHODS

In this study, we developed a multi-omics-based machine learning model that integrates genomic and transcriptomic biomarkers to predict the response to anti-PD1 treatment in patients with advanced melanoma. We employed least absolute shrinkage and selection operator (LASSO) regression with 49 biomarkers extracted from tumor-normal whole-exome and RNA sequencing as input features. The performance of the multi-omics AI model was thoroughly compared to that of nsTMB alone and to models that use only genomic or transcriptomic biomarkers.

RESULTS

We used publicly available DNA and RNA-seq datasets of melanoma patients for the training and validation of our model, forming a meta-cohort of 449 patients for which the outcome was recorded as a RECIST score. The model substantially improved the prediction of anti-PD1 outcomes compared to nsTMB alone, with an ROC AUC of 0.7 in the training set and an ROC AUC of 0.64 in the test set. Using SHAP values, we demonstrated the explainability of the model's predictions on a per-sample basis.

CONCLUSIONS

We demonstrated that models using only RNA-seq or multi-omics biomarkers outperformed nsTMB in predicting the response of melanoma patients to ICI. Furthermore, our machine learning approach improves clinical usability by providing explanations of its predictions on a per-patient basis. Our findings underscore the utility of multi-omics data for selecting patients for treatment with anti-PD1 drugs. However, to train clinical-grade AI models for routine applications, prospective studies collecting larger melanoma cohorts with consistent application of exome and RNA sequencing are required.

Low-avidity T cells drive endogenous tumor immunity in mice and humans

T cells recognize neoepitope peptide-major histocompatibility complex class I on cancer cells. The strength (or avidity) of the T cell receptor-peptide-major histocompatibility complex class I interaction is a critical variable in immune control of cancers. Here, we analyze neoepitope-specific CD8 cells of distinct avidities and show that low-avidity T cells are the sole mediators of cancer control in mice and are solely responsive to checkpoint blockade in mice and humans. High-avidity T cells are ineffective and immune-suppressive. The mechanistic basis of these differences lies in the higher exhaustion status of high-avidity cells. High-avidity T cells have a distinct transcriptomic profile that is used here to calculate an 'avidity score', which we then use for in silico identification of low-avidity and high-avidity T cells in mice and humans. Surprisingly, CD8+ T cells with identical T cell receptors exhibit wide variation in avidities, suggesting an additional level of regulation of T cell activity. Aside from providing a better understanding of endogenous T cell responses to cancer, these findings might instruct future immunotherapy strategies.

Unveiling immunogenic characteristics and neoantigens in endometrial cancer with POLE hotspot mutations for improved immunotherapy

Background

Immunotherapy, especially with the use of immune checkpoint inhibitors, has demonstrated efficacy for a variety of malignant tumors. However, the potential of immunotherapy for endometrial cancer (EC) with POLE mutations remains underexplored.

Methods

We utilized multiple databases and clinical specimens to investigate the immunogenicity profiles of EC patients carrying POLE mutations. One particular hotspot mutation POLEP286R was identified and further studied. Consequently, by constructing human leukocyte antigen (HLA) tetramers and incubating them with patients' peripheral blood mononuclear cells (PBMCs), T cells capable of recognizing the POLEP286R mutation were sorted for further transcriptomic, proteomic and T-cell receptor (TCR) sequencing analyses and for an organoid EC model.

Results

Tumor- and immune-related pathways were shown to be activated in the POLEP286R mutant group. Importantly, by using an organoid model of EC, we further confirmed the antitumor potential of T cells that were specific to the POLEP286R mutation.

Conclusions

Our study uncovers the pronounced immunogenicity of POLE-mutant EC and characterizes neoantigens that are unique to the POLEP286R mutation, thus providing a promising new immunotherapeutic strategy for EC.

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

The inherent antigen cross-reactivity of the T cell receptor (TCR) is balanced by high specificity. Surprisingly, TCR specificity often manifests in ways not easily interpreted from static structures. Here we show that TCR discrimination between an HLA-A*03:01 (HLA-A3)-restricted public neoantigen and its wild-type (WT) counterpart emerges from distinct motions within the HLA-A3 peptide binding groove that vary with the identity of the peptide's first primary anchor. These motions create a dynamic gate that, in the presence of the WT peptide, impedes a large conformational change required for TCR binding. The neoantigen is insusceptible to this limiting dynamic, and, with the gate open, upon TCR binding the central tryptophan can transit underneath the peptide backbone to the opposing side of the HLA-A3 peptide binding groove. Our findings thus reveal a novel mechanism driving TCR specificity for a cancer neoantigen that is rooted in the dynamic and allosteric nature of peptide/MHC-I binding grooves, with implications for resolving long-standing and often confounding questions about T cell specificity.

Level of Expression of MHCI-Presented Neoepitopes Influences Tumor Rejection by Neoantigen-Specific CD8+ T Cells

Neoantigen-targeted therapy holds an array of benefits for cancer immunotherapy, but the identification of peptide targets with tumor rejection capacity remains a limitation. To better define the criteria dictating tumor rejection potential, we examined the capacity of high-magnitude T-cell responses induced toward several distinct neoantigen targets to regress MC38 tumors. Despite their demonstrated immunogenicity, vaccine-induced T-cell responses were unable to regress established MC38 tumors or prevent tumor engraftment in a prophylactic setting. Although unable to kill tumor cells, T cells showed robust killing capacity toward neoantigen peptide-loaded cells. Tumor-cell killing was rescued by saturation of target peptide-loaded MHCs on the cell surface. Overall, this study demonstrates a pivotal role for target protein expression levels in modulating the tumor rejection capacity of neoantigens. Thus, inclusion of this metric, in addition to immunogenicity analysis, may benefit antigen prediction techniques to ensure the full antitumor effect of cancer vaccines.

The Evolving T Cell Receptor Recognition Code: The Rules Are More Like Guidelines

αβ T cell receptor (TCR) recognition of peptide-MHC complexes lies at the core of adaptive immunity, balancing specificity and cross-reactivity to facilitate effective antigen discrimination. Early structural studies established basic frameworks helpful for understanding and contextualizing TCR recognition and features such as peptide specificity and MHC restriction. However, the growing TCR structural database and studies launched from structural work continue to reveal exceptions to common assumptions and simplifications derived from earlier work. Here we explore our evolving understanding of TCR recognition, illustrating how structural and biophysical investigations regularly uncover complex phenomena that push against paradigms and expand our understanding of how TCRs bind to and discriminate between peptide/MHC complexes. We discuss the implications of these findings for basic, translational, and predictive immunology, including the challenges in accounting for the inherent adaptability, flexibility, and occasional biophysical sloppiness that characterize TCR recognition.

Relevance of mutation-derived neoantigens and non-classical antigens for anticancer therapies

Efficacy of cancer immunotherapies relies on correct recognition of tumor antigens by lymphocytes, eliciting thus functional responses capable of eliminating tumor cells. Therefore, important efforts have been carried out in antigen identification, with the aim of understanding mechanisms of response to immunotherapy and to design safer and more efficient strategies. In addition to classical tumor-associated antigens identified during the last decades, implementation of next-generation sequencing methodologies is enabling the identification of neoantigens (neoAgs) arising from mutations, leading to the development of new neoAg-directed therapies. Moreover, there are numerous non-classical tumor antigens originated from other sources and identified by new methodologies. Here, we review the relevance of neoAgs in different immunotherapies and the results obtained by applying neoAg-based strategies. In addition, the different types of non-classical tumor antigens and the best approaches for their identification are described. This will help to increase the spectrum of targetable molecules useful in cancer immunotherapies.

Probiotic neoantigen delivery vectors for precision cancer immunotherapy

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

Anti-cancer immune effect of human colorectal cancer neoantigen peptide based on MHC class I molecular affinity screening

Background

Tumor antigen peptide vaccines have shown remarkable efficacy, safety, and reliability in recent studies. However, the screening process for immunopotent antigenic peptides is cumbersome, limiting their widespread application. Identifying neoantigen peptides that can effectively trigger an immune response is crucial for personalized cancer treatment.

Methods

Whole exome sequencing was performed on patient-derived colon cancer cells to predict 9-amino-acid (9aa) neoantigen peptides. In vitro simulation of endogenous antigen presentation by antigen-presenting cells (dendritic cells) to CD8+ T cells was conducted, aiming to activate the CD8+ immune response to the predicted antigens. The immunological effects of each neoantigen were assessed using flow cytometry and ELISpot assays, while the relationship between neoantigen immunogenicity and MHC molecular affinity was examined.

Results

1. Next-generation sequencing (NGS) predicted 9-amino acid (9aa) neoantigen peptides for subsequent immunological analysis.2. Higher mDC Levels in Experimental Group: CD11c+CD83+ mature dendritic cells (mDCs) were 96.6% in the experimental group, compared to 0.051% in the control group. CD80 fluorescence intensity was also significantly higher in the experimental group, confirming a greater mDC presence.3. Neoantigen Peptides Promote CD4+, CD8+ T, and NK Cell Proliferation: After 14 days, flow cytometry showed higher percentages of CD4+ T (37.41% vs 7.8%), CD8+ T (16.67% vs 14.63%), and NK cells (33.09% vs 7.81%) in the experimental group, indicating that the neoantigen peptides induced proliferation of CD4+, CD8+ T cells, and NK cells. 4. The results, analyzed using two-way ANOVA, showed that the standardized T-value for HLA molecular affinity variation in the 1-4 range (Group B) was significantly higher than for ≤1 (Group A, p < 0.0001) and >4 (Group C, p < 0.05). Regarding HLA-allele genotypes, HLA-Type 1 had a significantly higher standardized T-value than HLA-Type 2 (p < 0.05) and HLA-Type 3 (p < 0.0001). HLA-Type 1 was identified as the allele associated with the highest T-value.

Conclusion

1. The most immunogenic neoantigens typically exhibit an MHC molecular affinity variation between 1 and 4, indicating that stronger immunogenicity correlates with higher MHC molecular affinity variation. 2. Each patient's HLA molecules were classified into Types 1, 2, and 3, with Type 1 showing the highest binding capacity for neoantigens. Our findings indicate that the most immunogenic neoantigens were associated with HLA Type 1. 3. Neoantigen peptides were shown to activate the proliferation of both CD8+ T-cells and induce proliferation of CD4+ T-cells and NK cells. 4. Variation in MHC molecular affinity and HLA neoantigen genotype are anticipated to serve as valuable variables for screening highly immunogenic neoantigens, facilitating more efficient preparation of effective polypeptide tumor vaccines.

Peptide-Based Therapeutics in Cancer Therapy

A monster called cancer is still one of the most challenging human problems and one of the leading causes of death in the world. Different types of treatment methods are used for cancer therapy; however, there are challenges such as high cost and harmful side effects in using these methods. Recent years have witnessed a surge in the development of therapeutic peptides for a wide range of diseases, notably cancer. Peptides are preferred over antibiotics, radiation therapy, and chemotherapy in the treatment of cancer due to a number of aspects, including flexibility, easy modification, low immunogenicity, and inexpensive cost of production. The use of therapeutic peptides in cancer treatment is a novel and intriguing strategy. These peptides provide excellent prospects for targeted drug delivery because of their high selectivity, specificity, small dimensions, good biocompatibility, and simplicity of modification. Target specificity and minimal toxicity are benefits of therapeutic peptides. Additionally, peptides can be used to design antigens or adjuvants for vaccine development. Here, types of therapeutic peptides for cancer therapy will be discussed, such as peptide-based cancer vaccines and tumor-targeting peptides (TTP) and cell-penetrating peptides (CPP).

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

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

Nanovaccines: Immunogenic tumor antigens, targeted delivery, and combination therapy to enhance cancer immunotherapy

Nanovaccines have been designed to overcome the limitations associated with conventional vaccines. Effective delivery methods such as engineered carriers or smart nanoparticles (NPs) are critical requisites for inducing self-tolerance and optimizing vaccine immunogenicity with minimum side effects. NPs can be used as adjuvants, immunogens, or nanocarriers to develop nanovaccines for efficient antigen delivery. Multiloaded nanovaccines carrying multiple tumor antigens along with immunostimulants can effectively increase immunity against tumor cells. They can be biologically engineered to boost interactions with dendritic cells and to allow a gradual and constant antigen release. Modifying NPs surface properties, using high-density lipoprotein-mimicking nanodiscs, and developing nano-based artificial antigen-presenting cells such as dendritic cell-derived-exosomes are amongst the new developed technologies to enhance antigen-presentation and immune reactions against tumor cells. The present review provides an overview on the different perspectives, improvements, and barriers of successful clinical application of current cancer therapeutic and vaccination options. The immunomodulatory effects of different types of nanovaccines and the nanoparticles incorporated into their structure are described. The advantages of using nanovaccines to prevent and treat common illnesses such as AIDS, malaria, cancer and tuberculosis are discussed. Further, potential paths to develop optimal cancer vaccines are described. Given the immunosuppressive characteristics of both cancer cells and the tumor microenvironment, applying immunomodulators and immune checkpoint inhibitors in combination with other conventional anticancer therapies are necessary to boost the effectiveness of the immune response.

Spatial intra-tumour heterogeneity and treatment-induced genomic evolution in oesophageal adenocarcinoma: implications for prognosis and therapy

BACKGROUND

Oesophageal adenocarcinoma (OAC) is a highly heterogeneous cancer with poor survival. Standard curative treatment is chemotherapy with or without radiotherapy followed by oesophagectomy. Genomic heterogeneity is a feature of OAC and has been linked to treatment resistance.

METHODS

Whole-genome sequencing data from 59 treatment-naïve and 18 post-treatment samples from 29 OAC patients was analysed. Twenty-seven of these were enrolled in the DOCTOR trial, sponsored by the Australasian Gastro-Intestinal Trials Group. Two biopsies from each treatment-naïve tumour were assessed to define 'shared' (between both samples) and 'private' (present in one sample) mutations.

RESULTS

Mutational signatures SBS2/13 (APOBEC) and SBS3 (BRCA) were almost exclusively detected in private mutation populations of treatment-naïve tumours. Patients presenting these signatures had significantly worse disease specific survival. Furthermore, mutational signatures associated with platinum-based chemotherapy treatment as well as high platinum enrichment scores were only detected in post-treatment samples. Additionally, clones with high putative neoantigen binding scores were detected in some treatment-naïve samples suggesting immunoediting of clones.

CONCLUSIONS

This study demonstrates the high intra-tumour heterogeneity in OAC, as well as indicators for treatment-induced changes during tumour evolution. Intra-tumour heterogeneity remains a problem for successful treatment strategies in OAC.

RNA vaccines for cancer: Principles to practice

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

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

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

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 Antigens beyond the Human Exome

With the advent of immunotherapeutics, a new era in the combat against cancer has begun. Particularly promising are neo-epitope-targeted therapies as the expression of neo-antigens is tumor-specific. In turn, this allows the selective targeting and killing of cancer cells whilst healthy cells remain largely unaffected. So far, many advances have been made in the development of treatment options which are tailored to the individual neo-epitope repertoire. The next big step is the achievement of efficacious "off-the-shelf" immunotherapies. For this, shared neo-epitopes propose an optimal target. Given the tremendous potential, a thorough understanding of the underlying mechanisms which lead to the formation of neo-antigens is of fundamental importance. Here, we review the various processes which result in the formation of neo-epitopes. Broadly, the origin of neo-epitopes can be categorized into three groups: canonical, noncanonical, and viral neo-epitopes. For the canonical neo-antigens that arise in direct consequence of somatic mutations, we summarize past and recent findings. Beyond that, our main focus is put on the discussion of noncanonical and viral neo-epitopes as we believe that targeting those provides an encouraging perspective to shape the future of cancer immunotherapeutics.

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.

Characterizing and forecasting neoantigens-resulting from MUC mutations in COAD

BACKGROUND

The treatment for colon adenocarcinoma (COAD) faces challenges in terms of immunotherapy effectiveness due to multiple factors. Because of the high tumor specificity and immunogenicity, neoantigen has been considered a pivotal target for cancer immunotherapy. Therefore, this study aims to identify and predict the potential tumor antigens of MUC somatic mutations (MUCmut) in COAD.

METHODS

Three databases of TCGA, TIMER2.0, and cBioPortal were used for a detailed evaluation of the association between MUCmut and multi-factors like tumor mutation burden (TMB), microsatellite instability (MSI), prognosis, and the tumor microenvironment within the context of total 2242 COAD patients. Next, TSNAdb and the differential agretopicity index (DAI) were utilized to predict high-confidence neopeptides for MUCmut based on 531 COAD patients' genomic information. DAI was calculated by subtraction of its predicted HLA binding affinity of the MUCmut peptide from the corresponding wild-type peptide.

RESULTS

The top six mutation frequencies (14 to 2.9%) were from MUC16, MUC17, MUC5B, MUC2, MUC4 and MUC6. COAD patients with MUC16 and MUC4 mutations had longer DFS and PFS. However, patients with MUC13 and MUC20 mutations had shorter OS. Patients with the mutation of MUC16, MUC5B, MUC2, MUC4, and MUC6 exhibited higher TMB and MSI. Moreover, these mutations from the MUC family were associated with the infiltration of diverse lymphocyte cells and the expression of immune checkpoint genes. Through TSNAdb 1.0/NetMHCpan v2.8, 452 single nucleotide variants (SNVs) of MUCmut peptides were identified. Moreover, through TSNAdb2.0/NetMHCpan v4.0, 57 SNVs, 1 Q-frame shift (TS), and 157 short insertions/deletions (INDELs) of MUCmut were identified. Finally, 10 high-confidence neopeptides of MUCmut were predicted by DAI.

CONCLUSIONS

Together, our findings establish the immunogenicity and therapeutic potential of mutant MUC family-derived neoantigens. Through combining the tools of TSNAdb and DAI, a group of novel MUCmut neoantigens were identified as potential targets for immunotherapy.

Transfer learning improves pMHC kinetic stability and immunogenicity predictions

The cellular immune response comprises several processes, with the most notable ones being the binding of the peptide to the Major Histocompability Complex (MHC), the peptide-MHC (pMHC) presentation to the surface of the cell, and the recognition of the pMHC by the T-Cell Receptor. Identifying the most potent peptide targets for MHC binding, presentation and T-cell recognition is vital for developing peptide-based vaccines and T-cell-based immunotherapies. Data-driven tools that predict each of these steps have been developed, and the availability of mass spectrometry (MS) datasets has facilitated the development of accurate Machine Learning (ML) methods for class-I pMHC binding prediction. However, the accuracy of ML-based tools for pMHC kinetic stability prediction and peptide immunogenicity prediction is uncertain, as stability and immunogenicity datasets are not abundant. Here, we use transfer learning techniques to improve stability and immunogenicity predictions, by taking advantage of a large number of binding affinity and MS datasets. The resulting models, TLStab and TLImm, exhibit comparable or better performance than state-of-the-art approaches on different stability and immunogenicity test sets respectively. Our approach demonstrates the promise of learning from the task of peptide binding to improve predictions on downstream tasks. The source code of TLStab and TLImm is publicly available at https://github.com/KavrakiLab/TL-MHC.

A large-scale study of peptide features defining immunogenicity of cancer neo-epitopes

Accurate prediction of immunogenicity for neo-epitopes arising from a cancer associated mutation is a crucial step in many bioinformatics pipelines that predict outcome of checkpoint blockade treatments or that aim to design personalised cancer immunotherapies and vaccines. In this study, we performed a comprehensive analysis of peptide features relevant for prediction of immunogenicity using the Cancer Epitope Database and Analysis Resource (CEDAR), a curated database of cancer epitopes with experimentally validated immunogenicity annotations from peer-reviewed publications. The developed model, ICERFIRE (ICore-based Ensemble Random Forest for neo-epitope Immunogenicity pREdiction), extracts the predicted ICORE from the full neo-epitope as input, i.e. the nested peptide with the highest predicted major histocompatibility complex (MHC) binding potential combined with its predicted likelihood of antigen presentation (%Rank). Key additional features integrated into the model include assessment of the BLOSUM mutation score of the neo-epitope, and antigen expression levels of the wild-type counterpart which is often reflecting a neo-epitope's abundance. We demonstrate improved and robust performance of ICERFIRE over existing immunogenicity and epitope prediction models, both in cross-validation and on external validation datasets.

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.

Translational bioinformatics approach to combat cardiovascular disease and cancers

Bioinformatics is an interconnected subject of science dealing with diverse fields including biology, chemistry, physics, statistics, mathematics, and computer science as the key fields to answer complicated physiological problems. Key intention of bioinformatics is to store, analyze, organize, and retrieve essential information about genome, proteome, transcriptome, metabolome, as well as organisms to investigate the biological system along with its dynamics, if any. The outcome of bioinformatics depends on the type, quantity, and quality of the raw data provided and the algorithm employed to analyze the same. Despite several approved medicines available, cardiovascular disorders (CVDs) and cancers comprises of the two leading causes of human deaths. Understanding the unknown facts of both these non-communicable disorders is inevitable to discover new pathways, find new drug targets, and eventually newer drugs to combat them successfully. Since, all these goals involve complex investigation and handling of various types of macro- and small- molecules of the human body, bioinformatics plays a key role in such processes. Results from such investigation has direct human application and thus we call this filed as translational bioinformatics. Current book chapter thus deals with diverse scope and applications of this translational bioinformatics to find cure, diagnosis, and understanding the mechanisms of CVDs and cancers. Developing complex yet small or long algorithms to address such problems is very common in translational bioinformatics. Structure-based drug discovery or AI-guided invention of novel antibodies that too with super-high accuracy, speed, and involvement of considerably low amount of investment are some of the astonishing features of the translational bioinformatics and its applications in the fields of CVDs and cancers.

Stimulating T cell responses against patient-derived breast cancer cells with neoantigen peptide-loaded peripheral blood mononuclear cells

Breast cancer stands as a formidable global health challenge for women. While neoantigens exhibit efficacy in activating T cells specific to cancer and instigating anti-tumor immune responses, the accuracy of neoantigen prediction remains suboptimal. In this study, we identified neoantigens from the patient-derived breast cancer cells, PC-B-142CA and PC-B-148CA cells, utilizing whole-genome and RNA sequencing. The pVAC-Seq pipeline was employed, with minor modification incorporating criteria (1) binding affinity of mutant (MT) peptide with HLA (IC50 MT) ≤ 500 nm in 3 of 5 algorithms and (2) IC50 wild type (WT)/MT > 1. Sequencing results unveiled 2513 and 3490 somatic mutations, and 646 and 652 non-synonymous mutations in PC-B-142CA and PC-B-148CA, respectively. We selected the top 3 neoantigens to perform molecular dynamic simulation and synthesized 9-12 amino acid neoantigen peptides, which were then pulsed onto healthy donor peripheral blood mononuclear cells (PBMCs). Results demonstrated that T cells activated by ADGRL1E274K, PARP1E619K, and SEC14L2R43Q peptides identified from PC-B-142CA exhibited significantly increased production of interferon-gamma (IFN-γ), while PARP1E619K and SEC14L2R43Q peptides induced the expression of CD107a on T cells. The % tumor cell lysis was notably enhanced by T cells activated with MT peptides across all three healthy donors. Moreover, ALKBH6V83M and GAAI823T peptides from PC-B-148CA remarkably stimulated IFN-γ- and CD107a-positive T cells, displaying high cell-killing activity against target cancer cells. In summary, our findings underscore the successful identification of neoantigens with anti-tumor T cell functions and highlight the potential of personalized neoantigens as a promising avenue for breast cancer treatment.

ImmuneMirror: A machine learning-based integrative pipeline and web server for neoantigen prediction

Neoantigens are derived from somatic mutations in the tumors but are absent in normal tissues. Emerging evidence suggests that neoantigens can stimulate tumor-specific T-cell-mediated antitumor immune responses, and therefore are potential immunotherapeutic targets. We developed ImmuneMirror as a stand-alone open-source pipeline and a web server incorporating a balanced random forest model for neoantigen prediction and prioritization. The prediction model was trained and tested using known immunogenic neopeptides collected from 19 published studies. The area under the curve of our trained model was 0.87 based on the testing data. We applied ImmuneMirror to the whole-exome sequencing and RNA sequencing data obtained from gastrointestinal tract cancers including 805 tumors from colorectal cancer (CRC), esophageal squamous cell carcinoma (ESCC) and hepatocellular carcinoma patients. We discovered a subgroup of microsatellite instability-high (MSI-H) CRC patients with a low neoantigen load but a high tumor mutation burden (> 10 mutations per Mbp). Although the efficacy of PD-1 blockade has been demonstrated in advanced MSI-H patients, almost half of such patients do not respond well. Our study identified a subset of MSI-H patients who may not benefit from this treatment with lower neoantigen load for major histocompatibility complex I (P < 0.0001) and II (P = 0.0008) molecules, respectively. Additionally, the neopeptide YMCNSSCMGV-TP53G245V, derived from a hotspot mutation restricted by HLA-A02, was identified as a potential actionable target in ESCC. This is so far the largest study to comprehensively evaluate neoantigen prediction models using experimentally validated neopeptides. Our results demonstrate the reliability and effectiveness of ImmuneMirror for neoantigen prediction.

Historical perspective and future directions: computational science in immuno-oncology

Immuno-oncology holds promise for transforming patient care having achieved durable clinical response rates across a variety of advanced and metastatic cancers. Despite these achievements, only a minority of patients respond to immunotherapy, underscoring the importance of elucidating molecular mechanisms responsible for response and resistance to inform the development and selection of treatments. Breakthroughs in molecular sequencing technologies have led to the generation of an immense amount of genomic and transcriptomic sequencing data that can be mined to uncover complex tumor-immune interactions using computational tools. In this review, we discuss existing and emerging computational methods that contextualize the composition and functional state of the tumor microenvironment, infer the reactivity and clonal dynamics from reconstructed immune cell receptor repertoires, and predict the antigenic landscape for immune cell recognition. We further describe the advantage of multi-omics analyses for capturing multidimensional relationships and artificial intelligence techniques for integrating omics data with histopathological and radiological images to encapsulate patterns of treatment response and tumor-immune biology. Finally, we discuss key challenges impeding their widespread use and clinical application and conclude with future perspectives. We are hopeful that this review will both serve as a guide for prospective researchers seeking to use existing tools for scientific discoveries and inspire the optimization or development of novel tools to enhance precision, ultimately expediting advancements in immunotherapy that improve patient survival and quality of life.

Refining PD-1/PD-L1 assessment for biomarker-guided immunotherapy: A review

Anti-programmed cell death ligand 1 (anti-PD-L1)  immunotherapy is an increasingly crucial in cancer treatment. To date, the Federal Drug Administration (FDA) has approved four PD-L1 immunohistochemistry (IHC) staining protocols, commercially available in the form of "kits", facilitating testing for PD-L1 expression. These kits comprise four PD-L1 antibodies on two separate IHC platforms, each utilizing distinct, non-interchangeable scoring systems. Several factors, including tumor heterogeneity and the size of the tissue specimens assessed, can lead to PD-L1 status misclassification, potentially hindering the initiation of therapy. Therefore, the development of more accurate predictive biomarkers to distinguish between responders and non-responders prior to anti-PD-1/PD-L1 therapy warrants further research. Achieving this goal necessitates refining sampling criteria, enhancing current methods of PD-L1 detection, and deepening our understanding of the impact of additional biomarkers. In this article, we review potential solutions to improve the predictive accuracy of PD-L1 assessment in order to more precisely anticipate patients' responses to anti-PD-1/PD-L1 therapy, monitor disease progression and predict clinical outcomes.

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.

Structural basis for T cell recognition of cancer neoantigens and implications for predicting neoepitope immunogenicity

Adoptive cell therapy (ACT) with tumor-specific T cells has been shown to mediate durable cancer regression. Tumor-specific T cells are also the basis of other therapies, notably cancer vaccines. The main target of tumor-specific T cells are neoantigens resulting from mutations in self-antigens over the course of malignant transformation. The detection of neoantigens presents a major challenge to T cells because of their high structural similarity to self-antigens, and the need to avoid autoimmunity. How different a neoantigen must be from its wild-type parent for it to induce a T cell response is poorly understood. Here we review recent structural and biophysical studies of T cell receptor (TCR) recognition of shared cancer neoantigens derived from oncogenes, including p53R175H, KRASG12D, KRASG12V, HHATp8F, and PIK3CAH1047L. These studies have revealed that, in some cases, the oncogenic mutation improves antigen presentation by strengthening peptide-MHC binding. In other cases, the mutation is detected by direct interactions with TCR, or by energetically driven or other indirect strategies not requiring direct TCR contacts with the mutation. We also review antibodies designed to recognize peptide-MHC on cell surfaces (TCR-mimic antibodies) as an alternative to TCRs for targeting cancer neoantigens. Finally, we review recent computational advances in this area, including efforts to predict neoepitope immunogenicity and how these efforts may be advanced by structural information on peptide-MHC binding and peptide-MHC recognition by TCRs.

Multiple instance learning to predict immune checkpoint blockade efficacy using neoantigen candidates

Previous studies showed that the neoantigen candidate load is an imperfect predictor of immune checkpoint blockade (ICB) efficacy. Further studies provided evidence that the response to ICB is also affected by the qualitative properties of a few or even single candidates, limiting the predictive power based on candidate quantity alone. Here, we predict ICB efficacy based on neoantigen candidates and their neoantigen features in the context of the mutation type, using Multiple-Instance Learning via Embedded Instance Selection (MILES). Multiple instance learning is a type of supervised machine learning that classifies labeled bags that are formed by a set of unlabeled instances. MILES performed better compared with neoantigen candidate load alone for low-abundant fusion genes in renal cell carcinoma. Our findings suggest that MILES is an appropriate method to predict the efficacy of ICB therapy based on neoantigen candidates without requiring direct T cell response information.

Machine learning methods and harmonized datasets improve immunogenic neoantigen prediction

The accurate selection of neoantigens that bind to class I human leukocyte antigen (HLA) and are recognized by autologous T cells is a crucial step in many cancer immunotherapy pipelines. We reprocessed whole-exome sequencing and RNA sequencing (RNA-seq) data from 120 cancer patients from two external large-scale neoantigen immunogenicity screening assays combined with an in-house dataset of 11 patients and identified 46,017 somatic single-nucleotide variant mutations and 1,781,445 neo-peptides, of which 212 mutations and 178 neo-peptides were immunogenic. Beyond features commonly used for neoantigen prioritization, factors such as the location of neo-peptides within protein HLA presentation hotspots, binding promiscuity, and the role of the mutated gene in oncogenicity were predictive for immunogenicity. The classifiers accurately predicted neoantigen immunogenicity across datasets and improved their ranking by up to 30%. Besides insights into machine learning methods for neoantigen ranking, we have provided homogenized datasets valuable for developing and benchmarking companion algorithms for neoantigen-based immunotherapies.

Cancer cell-intrinsic mechanisms driving acquired immune tolerance

Immune evasion is a hallmark of cancer, enabling tumors to survive contact with the host immune system and evade the cycle of immune recognition and destruction. Here, we review the current understanding of the cancer cell-intrinsic factors driving immune evasion. We focus on T cells as key effectors of anti-cancer immunity and argue that cancer cells evade immune destruction by gaining control over pathways that usually serve to maintain physiological tolerance to self. Using this framework, we place recent mechanistic advances in the understanding of cancer immune evasion into broad categories of control over T cell localization, antigen recognition, and acquisition of optimal effector function. We discuss the redundancy in the pathways involved and identify knowledge gaps that must be overcome to better target immune evasion, including the need for better, routinely available tools that incorporate the growing understanding of evasion mechanisms to stratify patients for therapy and trials.

Aggregation-prone peptides from within a non-self-protein homoaggregate are preferred for MHC association: Historical overview

New technologies assist re-evaluation of hypotheses on generation of immune cell repertoires and distinctions of self from non-self. Findings include positive correlations between peptide propensities to aggregate and their binding to major histocompatibility complex (MHC) proteins. This recalls the hypothesis that foreign proteins may homoaggregate in host cytosols prior to releasing their peptides (p) to form pMHC complexes. Clues to this included aggregation-related phenomena associated with infections (rouleaux formation, pyrexia, certain brain diseases). By virtue of 'promiscuous' gene expression by thymic presenting cells - perhaps adapted from earlier evolving gonadal mechanisms - developing T cells monitor surface pMHC clusterings. This evaluates intracellular concentrations of the corresponding proteins, and hence, following Burnet's two signal principle, degrees of self-reactivity. After positive selection in the thymic cortex for reactivity with 'near-self', high-level pMHC clustering suffices in the medulla for negatively selection. Following Burnet's principle, in the periphery low-level clustering suffices for T cell stimulation and high-level clustering again provokes negative selection (immunological tolerance). For evolving intracellular pathogens, fine-tuned polymorphisms of their host species have limited to 'near-self' some mimicking adaptations. It is proposed that while entire pathogen proteins may have evolved to minimize their aggregability, the greater aggregability of their peptides remains partially hidden within. Two-step proofreading mechanisms in prospective hosts select proteins containing aggregable peptide for the generation of pMHC clusters at the surface of presenting cells. Through mutations, some proteins of pathogens and cancer cells tend to converge towards the host 'near-self' that its T cells have auditioned to address.

A T cell receptor targeting a recurrent driver mutation in FLT3 mediates elimination of primary human acute myeloid leukemia in vivo

Acute myeloid leukemia (AML), the most frequent leukemia in adults, is driven by recurrent somatically acquired genetic lesions in a restricted number of genes. Treatment with tyrosine kinase inhibitors has demonstrated that targeting of prevalent FMS-related receptor tyrosine kinase 3 (FLT3) gain-of-function mutations can provide significant survival benefits for patients, although the efficacy of FLT3 inhibitors in eliminating FLT3-mutated clones is variable. We identified a T cell receptor (TCR) reactive to the recurrent D835Y driver mutation in the FLT3 tyrosine kinase domain (TCRFLT3D/Y). TCRFLT3D/Y-redirected T cells selectively eliminated primary human AML cells harboring the FLT3D835Y mutation in vitro and in vivo. TCRFLT3D/Y cells rejected both CD34+ and CD34- AML in mice engrafted with primary leukemia from patients, reaching minimal residual disease-negative levels, and eliminated primary CD34+ AML leukemia-propagating cells in vivo. Thus, T cells targeting a single shared mutation can provide efficient immunotherapy toward selective elimination of clonally involved primary AML cells in vivo.

Clusterin Expression in Colorectal Carcinomas

Colorectal cancer is the third most diagnosed cancer, behind only breast and lung cancer. In terms of overall mortality, it ranks second due to, among other factors, problems with screening programs, which means that one of the factors that directly impacts survival and treatment success is early detection of the disease. Clusterin (CLU) is a molecular chaperone that has been linked to tumorigenesis, cancer progression and resistance to anticancer treatments, which has made it a promising drug target. However, it is still necessary to continue this line of research and to adjust the situations in which its use is more favorable. The aim of this paper is to review the current genetic knowledge on the role of CLU in tumorigenesis and cancer progression in general, and discuss its possible use as a therapeutic target in colorectal cancer.

The impact of mutational clonality in predicting the response to immune checkpoint inhibitors in advanced urothelial cancer

Immune checkpoint inhibitors (ICI) have revolutionized cancer treatment and can result in complete remissions even at advanced stages of the disease. However, only a small fraction of patients respond to the treatment. To better understand which factors drive clinical benefit, we have generated whole exome and RNA sequencing data from 27 advanced urothelial carcinoma patients treated with anti-PD-(L)1 monoclonal antibodies. We assessed the influence on the response of non-synonymous mutations (tumor mutational burden or TMB), clonal and subclonal mutations, neoantigen load and various gene expression markers. We found that although TMB is significantly associated with response, this effect can be mostly explained by clonal mutations, present in all cancer cells. This trend was validated in an additional cohort. Additionally, we found that responders with few clonal mutations had abnormally high levels of T and B cell immune markers, suggesting that a high immune cell infiltration signature could be a better predictive biomarker for this subset of patients. Our results support the idea that highly clonal cancers are more likely to respond to ICI and suggest that non-additive effects of different signatures should be considered for predictive models.

A robust deep learning workflow to predict CD8 + T-cell epitopes

BACKGROUND

T-cells play a crucial role in the adaptive immune system by triggering responses against cancer cells and pathogens, while maintaining tolerance against self-antigens, which has sparked interest in the development of various T-cell-focused immunotherapies. However, the identification of antigens recognised by T-cells is low-throughput and laborious. To overcome some of these limitations, computational methods for predicting CD8 + T-cell epitopes have emerged. Despite recent developments, most immunogenicity algorithms struggle to learn features of peptide immunogenicity from small datasets, suffer from HLA bias and are unable to reliably predict pathology-specific CD8 + T-cell epitopes.

METHODS

We developed TRAP (T-cell recognition potential of HLA-I presented peptides), a robust deep learning workflow for predicting CD8 + T-cell epitopes from MHC-I presented pathogenic and self-peptides. TRAP uses transfer learning, deep learning architecture and MHC binding information to make context-specific predictions of CD8 + T-cell epitopes. TRAP also detects low-confidence predictions for peptides that differ significantly from those in the training datasets to abstain from making incorrect predictions. To estimate the immunogenicity of pathogenic peptides with low-confidence predictions, we further developed a novel metric, RSAT (relative similarity to autoantigens and tumour-associated antigens), as a complementary to 'dissimilarity to self' from cancer studies.

RESULTS

TRAP was used to identify epitopes from glioblastoma patients as well as SARS-CoV-2 peptides, and it outperformed other algorithms in both cancer and pathogenic settings. TRAP was especially effective at extracting immunogenicity-associated properties from restricted data of emerging pathogens and translating them onto related species, as well as minimising the loss of likely epitopes in imbalanced datasets. We also demonstrated that the novel metric termed RSAT was able to estimate immunogenic of pathogenic peptides of various lengths and species. TRAP implementation is available at: https://github.com/ChloeHJ/TRAP .

CONCLUSIONS

This study presents a novel computational workflow for accurately predicting CD8 + T-cell epitopes to foster a better understanding of antigen-specific T-cell response and the development of effective clinical therapeutics.

BMX-A and BMX-S: Accessible cell-free methods to estimate peptide-MHC-I affinity and stability

The affinity and stability of peptide binding to Major Histocompatibility Complex Class I (MHC-I) molecules are fundamental parameters that underpin the specificity and magnitude of CD8+ T cell responses. These parameters can be estimated in some cases by computational tools, but experimental validation remains valuable, especially for stability. Methods to measure peptide binding can be broadly categorised into either cell-based assays using TAP-deficient cell lines such as RMA/S, or cell-free strategies, such as peptide competition-binding assays and surface plasmon resonance. Cell-based assays are subject to confounding biological activity, including peptide trimming by peptidases and dilution of peptide-loaded MHC-I on the surface of cells through cell division. Current cell-free methods require in-house production and purification of MHC-I. In this study, we present the development of new cell-free assays to estimate the relative affinity and dissociation kinetics of peptide binding to MHC-I. These assays, which we have called BMX-A (relative affinity) and BMX-S (kinetic stability), are reliable, scalable and accessible, in that they use off-the-shelf commercial reagents and standard flow cytometry techniques.

Decoupled neoantigen cross-presentation by dendritic cells limits anti-tumor immunity against tumors with heterogeneous neoantigen expression

Cancer immunotherapies, in particular checkpoint blockade immunotherapy (CBT), can induce control of cancer growth, with a fraction of patients experiencing durable responses. However, the majority of patients currently do not respond to CBT and the molecular determinants of resistance have not been fully elucidated. Mounting clinical evidence suggests that the clonal status of neoantigens (NeoAg) impacts the anti-tumor T cell response. High intratumor heterogeneity (ITH), where the majority of NeoAgs are expressed subclonally, is correlated with poor clinical response to CBT and poor infiltration with tumor-reactive T cells. However, the mechanism by which ITH blunts tumor-reactive T cells is unclear. We developed a transplantable murine lung cancer model to characterize the immune response against a defined set of NeoAgs expressed either clonally or subclonally to model low or high ITH, respectively. Here we show that clonal expression of a weakly immunogenic NeoAg with a relatively strong NeoAg increased the immunogenicity of tumors with low but not high ITH. Mechanistically we determined that clonal NeoAg expression allowed cross-presenting dendritic cells to acquire and present both NeoAgs. Dual NeoAg presentation by dendritic cells was associated with a more mature DC phenotype and a higher stimulatory capacity. These data suggest that clonal NeoAg expression can induce more potent anti-tumor responses due to more stimulatory dendritic cell:T cell interactions. Therapeutic vaccination targeting subclonally expressed NeoAgs could be used to boost anti-tumor T cell responses.

Unraveling tumor specific neoantigen immunogenicity prediction: a comprehensive analysis

Introduction

Identification of tumor specific neoantigen (TSN) immunogenicity is crucial to develop peptide/mRNA based anti-tumoral vaccines and/or adoptive T-cell immunotherapies; thus, accurate in-silico classification/prioritization proves critical for cost-effective clinical applications. Several methods were proposed as TSNs immunogenicity predictors; however, comprehensive performance comparison is still lacking due to the absence of well documented and adequate TSN databases.

Methods

Here, by developing a new curated database having 199 TSNs with experimentally-validated MHC-I presentation and positive/negative immune response (ITSNdb), sixteen metrics were evaluated as immunogenicity predictors. In addition, by using a dataset emulating patient derived TSNs and immunotherapy cohorts containing predicted TSNs for tumor neoantigen burden (TNB) with outcome association, the metrics were evaluated as TSNs prioritizers and as immunotherapy response biomarkers.

Results

Our results show high performance variability among methods, highlighting the need for substantial improvement. Deep learning predictors were top ranked on ITSNdb but show discrepancy on validation databases. In overall, current predicted TNB did not outperform existing biomarkers.

Conclusion

Recommendations for their clinical application and the ITSNdb are presented to promote development and comparison of computational TSNs immunogenicity predictors.