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

A Computational Strategy for the Rapid Identification and Ranking of Patient-Specific T Cell Receptors Bound to Neoantigens

T cell receptor (TCR) recognition of a peptide-major histocompatibility complex (pMHC) is crucial for adaptive immune response. The identification of therapeutically relevant TCR-pMHC protein pairs is a bottleneck in the implementation of TCR-based immunotherapies. The ability to computationally design TCRs to target a specific pMHC requires automated integration of next-generation sequencing, protein-protein structure prediction, molecular dynamics, and TCR ranking. A pipeline to evaluate patient-specific, sequence-based TCRs to a target pMHC is presented. Using the three most frequently expressed TCRs from 16 colorectal cancer patients, the protein-protein structure of the TCRs to the target CEA peptide-MHC is predicted using Modeller and ColabFold. TCR-pMHC structures are compared using automated equilibration and successive analysis. ColabFold generated configurations require an ≈2.5× reduction in equilibration time of TCR-pMHC structures compared to Modeller. The structural differences between Modeller and ColabFold are demonstrated by root mean square deviation (≈0.20 nm) between clusters of equilibrated configurations, which impact the number of hydrogen bonds and Lennard-Jones contacts between the TCR and pMHC. TCR ranking criteria that may prioritize TCRs for evaluation of in vitro immunogenicity are identified, and this ranking is validated by comparing to state-of-the-art machine learning-based methods trained to predict the probability of TCR-pMHC binding.

Neoantigen-specific stimulation of tumor-infiltrating lymphocytes enables effective TCR isolation and expansion while preserving stem-like memory phenotypes

BACKGROUND

Tumor-infiltrating lymphocytes (TILs) targeting neoantigens can effectively treat a selected set of metastatic solid cancers. However, harnessing TILs for cancer treatments remains challenging because neoantigen-reactive T cells are often rare and exhausted, and ex vivo expansion can further reduce their frequencies. This complicates the identification of neoantigen-reactive T-cell receptors (TCRs) and the development of TIL products with high reactivity for patient treatment.

METHODS

We tested whether TILs could be in vitro stimulated against neoantigens to achieve selective expansion of neoantigen-reactive TILs. Given their prevalence, mutant p53 or RAS were studied as models of human neoantigens. An in vitro stimulation method, termed "NeoExpand", was developed to provide neoantigen-specific stimulation to TILs. 25 consecutive patient TILs from tumors harboring p53 or RAS mutations were subjected to NeoExpand.

RESULTS

We show that neoantigenic stimulation achieved selective expansion of neoantigen-reactive TILs and broadened the neoantigen-reactive CD4+ and CD8+ TIL clonal repertoire. This allowed the effective isolation of novel neoantigen-reactive TCRs. Out of the 25 consecutive TIL samples, neoantigenic stimulation enabled the identification of 16 unique reactivities and 42 TCRs, while conventional TIL expansion identified 9 reactivities and 14 TCRs. Single-cell transcriptome analysis revealed that neoantigenic stimulation increased neoantigen-reactive TILs with stem-like memory phenotypes expressing IL-7R, CD62L, and KLF2. Furthermore, neoantigenic stimulation improved the in vivo antitumor efficacy of TILs relative to the conventional OKT3-induced rapid TIL expansion in p53-mutated or KRAS-mutated xenograft mouse models.

CONCLUSIONS

Taken together, neoantigenic stimulation of TILs selectively expands neoantigen-reactive TILs by frequencies and by their clonal repertoire. NeoExpand led to improved phenotypes and functions of neoantigen-reactive TILs. Our data warrant its clinical evaluation.

TRIAL REGISTRATION NUMBER

NCT00068003, NCT01174121, and NCT03412877.

In silico design of high-affinity antigenic peptides for HLA-B44

Cancer cell-killing by CD8+ T cells demands effective tumor antigen presentation by human leukocyte antigen class I (HLA-I) molecules. Screening and designing highly immunogenic neoantigens require quantitative computations to reliably predict HLA-peptide binding affinities. Here, with all-atom molecular dynamics (MD) simulations and free energy perturbation (FEP) methods, we design a collection of antigenic peptide candidates through in silico mutagenesis studies on immunogenic neoantigens, yielding enhanced binding affinities to HLA-B*44:02. In-depth structural dissection shows that introducing positively charged residues such as arginine to position 6 or lysine to position 7 of the candidates triggers conformational shifts in both peptides and the antigen-binding groove of the HLA, following the "induced-fit" mechanism. Enhancement in binding affinities compared to the wild-type was found in three out of five mutated candidates. The HLA pocket, capable of accommodating positively charged residues in positions from 5 to 7, is designated as the "dynamic pocket". Taken together, we showcase an effective structure-based binding affinity optimization framework for antigenic peptides of HLA-B*44:02 and underscore the importance of dynamic nature of the antigen-binding groove in concert with the anchoring motifs. This work provides structural insights for rational design of favorable HLA-peptide bindings and future developments in neoantigen-based therapeutics.

Facts and Hopes in Using Omics to Advance Combined Immunotherapy Strategies

The field of oncology has been transformed by immune checkpoint inhibitors (ICI) and other immune-based agents; however, many patients do not receive a durable benefit. While biomarker assessments from pivotal ICI trials have uncovered certain mechanisms of resistance, results thus far have only scraped the surface. Mechanisms of resistance are as complex as the tumor microenvironment (TME) itself, and the development of effective therapeutic strategies will only be possible by building accurate models of the tumor-immune interface. With advancement of multi-omic technologies, high-resolution characterization of the TME is now possible. In addition to sequencing of bulk tumor, single-cell transcriptomic, proteomic, and epigenomic data as well as T-cell receptor profiling can now be simultaneously measured and compared between responders and nonresponders to ICI. Spatial sequencing and imaging platforms have further expanded the dimensionality of existing technologies. Rapid advancements in computation and data sharing strategies enable development of biologically interpretable machine learning models to integrate data from high-resolution, multi-omic platforms. These models catalyze the identification of resistance mechanisms and predictors of benefit in ICI-treated patients, providing scientific foundation for novel clinical trials. Moving forward, we propose a framework by which in silico screening, functional validation, and clinical trial biomarker assessment can be used for the advancement of combined immunotherapy strategies.

T-Cell-Based Platform for Functional Screening of T-Cell Receptors Identified in Single-Cell RNA Sequencing Data Sets of Tumor-Infiltrating T-Cells

The advent of single-cell RNA sequencing (scRNAseq) has enabled in-depth gene expression analysis of several thousand cells isolated from tissues. We recently reported the application of scRNAseq toward the dissection of the tumor-infiltrating T-cell repertoire in human pancreatic cancer samples. In this study, we demonstrated that combined whole transcriptome and T-cell receptor (TCR) sequencing provides an effective way to identify tumor-reactive TCR clonotypes on the basis of gene expression signatures. An important aspect in this respect was the experimental validation of TCR-mediated anti-tumor reactivity by means of an in vitro functional assay, which is the subject of the present protocol. This assay involves the transient transfection of mRNA gene constructs encoding TCRα/β pairs into a well-defined human T-cell line, followed by co-cultivation with the tumor cells of interest and detection of T-cell activation by flow cytometry. Due to the high transfectability and the low background reactivity of the mock-transfected T-cell line to a wide variety of tumor cells, this assay offers a highly robust and versatile platform for the functional screening of large numbers of TCR clonotypes as identified in scRNAseq data sets. Whereas the assay was initially developed to test TCRs of human origin, it was more recently also applied successfully for the screening of TCRs of murine origin. Key features • Efficient functional screening of-and discrimination between-TCRs isolated from tumor-reactive vs. bystander T-cell clones. • Applicable to TCRs from CD8+ and CD4+ tumor-infiltrating T-cells originating from patient-derived tumor samples and syngeneic mouse tumor models. • Rapid flow cytometric detection of T-cell activation by means of TNFα and CD107a expression after a 5 h T-cell/tumor cell co-cultivation.

Advancing nanotechnology for neoantigen-based cancer theranostics

Neoantigens play a pivotal role in the field of tumour therapy, encompassing the stimulation of anti-tumour immune response and the enhancement of tumour targeting capability. Nonetheless, numerous factors directly influence the effectiveness of neoantigens in bolstering anti-tumour immune responses, including neoantigen quantity and specificity, uptake rates by antigen-presenting cells (APCs), residence duration within the tumour microenvironment (TME), and their ability to facilitate the maturation of APCs for immune response activation. Nanotechnology assumes a significant role in several aspects, including facilitating neoantigen release, promoting neoantigen delivery to antigen-presenting cells, augmenting neoantigen uptake by dendritic cells, shielding neoantigens from protease degradation, and optimizing interactions between neoantigens and the immune system. Consequently, the development of nanotechnology synergistically enhances the efficacy of neoantigens in cancer theranostics. In this review, we provide an overview of neoantigen sources, the mechanisms of neoantigen-induced immune responses, and the evolution of precision neoantigen-based nanomedicine. This encompasses various therapeutic modalities, such as neoantigen-based immunotherapy, phototherapy, radiotherapy, chemotherapy, chemodynamic therapy, and other strategies tailored to augment precision in cancer therapeutics. We also discuss the current challenges and prospects in the application of neoantigen-based precision nanomedicine, aiming to expedite its clinical translation.

TripHLApan: predicting HLA molecules binding peptides based on triple coding matrix and transfer learning

Human leukocyte antigen (HLA) recognizes foreign threats and triggers immune responses by presenting peptides to T cells. Computationally modeling the binding patterns between peptide and HLA is very important for the development of tumor vaccines. However, it is still a big challenge to accurately predict HLA molecules binding peptides. In this paper, we develop a new model TripHLApan for predicting HLA molecules binding peptides by integrating triple coding matrix, BiGRU + Attention models, and transfer learning strategy. We have found the main interaction site regions between HLA molecules and peptides, as well as the correlation between HLA encoding and binding motifs. Based on the discovery, we make the preprocessing and coding closer to the natural biological process. Besides, due to the input being based on multiple types of features and the attention module focused on the BiGRU hidden layer, TripHLApan has learned more sequence level binding information. The application of transfer learning strategies ensures the accuracy of prediction results under special lengths (peptides in length 8) and model scalability with the data explosion. Compared with the current optimal models, TripHLApan exhibits strong predictive performance in various prediction environments with different positive and negative sample ratios. In addition, we validate the superiority and scalability of TripHLApan's predictive performance using additional latest data sets, ablation experiments and binding reconstitution ability in the samples of a melanoma patient. The results show that TripHLApan is a powerful tool for predicting the binding of HLA-I and HLA-II molecular peptides for the synthesis of tumor vaccines. TripHLApan is publicly available at https://github.com/CSUBioGroup/TripHLApan.git.

AutoPepVax, a Novel Machine-Learning-Based Program for Vaccine Design: Application to a Pan-Cancer Vaccine Targeting EGFR Missense Mutations

The current epitope selection methods for peptide vaccines often rely on epitope binding affinity predictions, prompting the need for the development of more sophisticated in silico methods to determine immunologically relevant epitopes. Here, we developed AutoPepVax to expedite and improve the in silico epitope selection for peptide vaccine design. AutoPepVax is a novel program that automatically identifies non-toxic and non-allergenic epitopes capable of inducing tumor-infiltrating lymphocytes by considering various epitope characteristics. AutoPepVax employs random forest classification and linear regression machine-learning-based models, which are trained with datasets derived from tumor samples. AutoPepVax, along with documentation on how to run the program, is freely available on GitHub. We used AutoPepVax to design a pan-cancer peptide vaccine targeting epidermal growth factor receptor (EGFR) missense mutations commonly found in lung adenocarcinoma (LUAD), colorectal adenocarcinoma (CRAD), glioblastoma multiforme (GBM), and head and neck squamous cell carcinoma (HNSCC). These mutations have been previously targeted in clinical trials for EGFR-specific peptide vaccines in GBM and LUAD, and they show promise but lack demonstrated clinical efficacy. Using AutoPepVax, our analysis of 96 EGFR mutations identified 368 potential MHC-I-restricted epitope-HLA pairs from 49,113 candidates and 430 potential MHC-II-restricted pairs from 168,669 candidates. Notably, 19 mutations presented viable epitopes for MHC I and II restrictions. To evaluate the potential impact of a pan-cancer vaccine composed of these epitopes, we used our program, PCOptim, to curate a minimal list of epitopes with optimal population coverage. The world population coverage of our list ranged from 81.8% to 98.5% for MHC Class II and Class I epitopes, respectively. From our list of epitopes, we constructed 3D epitope-MHC models for six MHC-I-restricted and four MHC-II-restricted epitopes, demonstrating their epitope binding potential and interaction with T-cell receptors. AutoPepVax's comprehensive approach to in silico epitope selection addresses vaccine safety, efficacy, and broad applicability. Future studies aim to validate the AutoPepVax-designed vaccines with murine tumor models that harbor the studied mutations.

Informing immunotherapy with multi-omics driven machine learning

Progress in sequencing technologies and clinical experiments has revolutionized immunotherapy on solid and hematologic malignancies. However, the benefits of immunotherapy are limited to specific patient subsets, posing challenges for broader application. To improve its effectiveness, identifying biomarkers that can predict patient response is crucial. Machine learning (ML) play a pivotal role in harnessing multi-omic cancer datasets and unlocking new insights into immunotherapy. This review provides an overview of cutting-edge ML models applied in omics data for immunotherapy analysis, including immunotherapy response prediction and immunotherapy-relevant tumor microenvironment identification. We elucidate how ML leverages diverse data types to identify significant biomarkers, enhance our understanding of immunotherapy mechanisms, and optimize decision-making process. Additionally, we discuss current limitations and challenges of ML in this rapidly evolving field. Finally, we outline future directions aimed at overcoming these barriers and improving the efficiency of ML in immunotherapy research.

Challenges in developing personalized neoantigen cancer vaccines

The recent success of cancer immunotherapies has highlighted the benefit of harnessing the immune system for cancer treatment. Vaccines have a long history of promoting immunity to pathogens and, consequently, vaccines targeting cancer neoantigens have been championed as a tool to direct and amplify immune responses against tumours while sparing healthy tissue. In recent years, extensive preclinical research and more than one hundred clinical trials have tested different strategies of neoantigen discovery and vaccine formulations. However, despite the enthusiasm for neoantigen vaccines, proof of unequivocal efficacy has remained beyond reach for the majority of clinical trials. In this Review, we focus on the key obstacles pertaining to vaccine design and tumour environment that remain to be overcome in order to unleash the true potential of neoantigen vaccines in cancer therapy.

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.

Key oncogenic signaling pathways affecting tumor-infiltrating lymphocytes infiltration in hepatocellular carcinoma: basic principles and recent advances

The incidence of hepatocellular carcinoma (HCC) ranks first among primary liver cancers, and its mortality rate exhibits a consistent annual increase. The treatment of HCC has witnessed a significant surge in recent years, with the emergence of targeted immune therapy as an adjunct to early surgical resection. Adoptive cell therapy (ACT) using tumor-infiltrating lymphocytes (TIL) has shown promising results in other types of solid tumors. This article aims to provide a comprehensive overview of the intricate interactions between different types of TILs and their impact on HCC, elucidate strategies for targeting neoantigens through TILs, and address the challenges encountered in TIL therapies along with potential solutions. Furthermore, this article specifically examines the impact of oncogenic signaling pathways activation within the HCC tumor microenvironment on the infiltration dynamics of TILs. Additionally, a concise overview is provided regarding TIL preparation techniques and an update on clinical trials investigating TIL-based immunotherapy in solid tumors.

Antigen cross-presentation by dendritic cells: A critical axis in cancer immunotherapy

Dendritic cells (DCs) are professional antigen-presenting cells that play a key role in shaping adaptive immunity. DCs have a unique ability to sample their environment, capture and process exogenous antigens into peptides that are then loaded onto major histocompatibility complex class I molecules for presentation to CD8+ T cells. This process, called cross-presentation, is essential for initiating and regulating CD8+ T cell responses against tumors and intracellular pathogens. In this review, we will discuss the role of DCs in cancer immunity, the molecular mechanisms underlying antigen cross-presentation by DCs, the immunosuppressive factors that limit the efficiency of this process in cancer, and approaches to overcome DC dysfunction and therapeutically promote antitumoral immunity.

Engineering Heterogeneous Tumor Models for Biomedical Applications

Tumor tissue engineering holds great promise for replicating the physiological and behavioral characteristics of tumors in vitro. Advances in this field have led to new opportunities for studying the tumor microenvironment and exploring potential anti-cancer therapeutics. However, the main obstacle to the widespread adoption of tumor models is the poor understanding and insufficient reconstruction of tumor heterogeneity. In this review, the current progress of engineering heterogeneous tumor models is discussed. First, the major components of tumor heterogeneity are summarized, which encompasses various signaling pathways, cell proliferations, and spatial configurations. Then, contemporary approaches are elucidated in tumor engineering that are guided by fundamental principles of tumor biology, and the potential of a bottom-up approach in tumor engineering is highlighted. Additionally, the characterization approaches and biomedical applications of tumor models are discussed, emphasizing the significant role of engineered tumor models in scientific research and clinical trials. Lastly, the challenges of heterogeneous tumor models in promoting oncology research and tumor therapy are described and key directions for future research are provided.

Neoantigen cancer vaccines: a new star on the horizon

Immunotherapy represents a promising strategy for cancer treatment that utilizes immune cells or drugs to activate the patient's own immune system and eliminate cancer cells. One of the most exciting advances within this field is the targeting of neoantigens, which are peptides derived from non-synonymous somatic mutations that are found exclusively within cancer cells and absent in normal cells. Although neoantigen-based therapeutic vaccines have not received approval for standard cancer treatment, early clinical trials have yielded encouraging outcomes as standalone monotherapy or when combined with checkpoint inhibitors. Progress made in high-throughput sequencing and bioinformatics have greatly facilitated the precise and efficient identification of neoantigens. Consequently, personalized neoantigen-based vaccines tailored to each patient have been developed that are capable of eliciting a robust and long-lasting immune response which effectively eliminates tumors and prevents recurrences. This review provides a concise overview consolidating the latest clinical advances in neoantigen-based therapeutic vaccines, and also discusses challenges and future perspectives for this innovative approach, particularly emphasizing the potential of neoantigen-based therapeutic vaccines to enhance clinical efficacy against advanced solid tumors.

Systems immunology spanning tumors, lymph nodes, and periphery

The immune system defines a complex network of tissues and cell types that orchestrate responses across the body in a dynamic manner. The local and systemic interactions between immune and cancer cells contribute to disease progression. Lymphocytes are activated in lymph nodes, traffic through the periphery, and impact cancer progression through their interactions with tumor cells. As a result, therapeutic response and resistance are mediated across tissues, and a comprehensive understanding of lymphocyte dynamics requires a systems-level approach. In this review, we highlight experimental and computational methods that can leverage the study of leukocyte trafficking through an immunomics lens and reveal how adaptive immunity shapes cancer.

Broadening the horizon: potential applications of CAR-T cells beyond current indications

Engineering immune cells to treat hematological malignancies has been a major focus of research since the first resounding successes of CAR-T-cell therapies in B-ALL. Several diseases can now be treated in highly therapy-refractory or relapsed conditions. Currently, a number of CD19- or BCMA-specific CAR-T-cell therapies are approved for acute lymphoblastic leukemia (ALL), diffuse large B-cell lymphoma (DLBCL), mantle cell lymphoma (MCL), multiple myeloma (MM), and follicular lymphoma (FL). The implementation of these therapies has significantly improved patient outcome and survival even in cases with previously very poor prognosis. In this comprehensive review, we present the current state of research, recent innovations, and the applications of CAR-T-cell therapy in a selected group of hematologic malignancies. We focus on B- and T-cell malignancies, including the entities of cutaneous and peripheral T-cell lymphoma (T-ALL, PTCL, CTCL), acute myeloid leukemia (AML), chronic myeloid leukemia (CML), chronic lymphocytic leukemia (CLL), classical Hodgkin-Lymphoma (HL), Burkitt-Lymphoma (BL), hairy cell leukemia (HCL), and Waldenström's macroglobulinemia (WM). While these diseases are highly heterogenous, we highlight several similarly used approaches (combination with established therapeutics, target depletion on healthy cells), targets used in multiple diseases (CD30, CD38, TRBC1/2), and unique features that require individualized approaches. Furthermore, we focus on current limitations of CAR-T-cell therapy in individual diseases and entities such as immunocompromising tumor microenvironment (TME), risk of on-target-off-tumor effects, and differences in the occurrence of adverse events. Finally, we present an outlook into novel innovations in CAR-T-cell engineering like the use of artificial intelligence and the future role of CAR-T cells in therapy regimens in everyday clinical practice.

Unsupervised and supervised AI on molecular dynamics simulations reveals complex characteristics of HLA-A2-peptide immunogenicity

Immunologic recognition of peptide antigens bound to class I major histocompatibility complex (MHC) molecules is essential to both novel immunotherapeutic development and human health at large. Current methods for predicting antigen peptide immunogenicity rely primarily on simple sequence representations, which allow for some understanding of immunogenic features but provide inadequate consideration of the full scale of molecular mechanisms tied to peptide recognition. We here characterize contributions that unsupervised and supervised artificial intelligence (AI) methods can make toward understanding and predicting MHC(HLA-A2)-peptide complex immunogenicity when applied to large ensembles of molecular dynamics simulations. We first show that an unsupervised AI method allows us to identify subtle features that drive immunogenicity differences between a cancer neoantigen and its wild-type peptide counterpart. Next, we demonstrate that a supervised AI method for class I MHC(HLA-A2)-peptide complex classification significantly outperforms a sequence model on small datasets corrected for trivial sequence correlations. Furthermore, we show that both unsupervised and supervised approaches reveal determinants of immunogenicity based on time-dependent molecular fluctuations and anchor position dynamics outside the MHC binding groove. We discuss implications of these structural and dynamic immunogenicity correlates for the induction of T cell responses and therapeutic T cell receptor design.

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.

SIGANEO: Similarity network with GAN enhancement for immunogenic neoepitope prediction

Target selection of the personalized cancer neoantigen vaccine, which is highly dependent on computational prediction algorithms, is crucial for its clinical efficacy. Due to the limited number of experimentally validated immunogenic neoepitopes as well as the complexity of neoantigens in eliciting T cell response, the accuracy of neoepitope immunogenicity prediction methods requires persistent efforts for improvement. We present a deep learning framework for neoepitope immunogenicity prediction - SIGANEO by integrating GAN-like network with similarity network to address issues of missing values and limited data concerning neoantigen prediction. This framework exhibits superior performance over competing machine-learning-based neoantigen prediction algorithms over an independent test dataset from TESLA consortium. Particularly for the clinical setting of neoantigen vaccine where only the top 10 and 20 predictions are selected for vaccine production, SIGANEO achieves significantly better accuracy for predicting experimentally validated neoepitopes. Our work demonstrates that deep learning techniques can greatly boost the accuracy of target identification for cancer neoantigen vaccine.

Methods behind neoantigen prediction for personalized anticancer vaccines

Next to conventional cancer therapies, immunotherapies such as immune checkpoint inhibitors have broadened the cancer treatment landscape over the past decades. Recent advances in next generation sequencing and bioinformatics technologies have made it possible to identify a patient's own immunogenic neoantigens. These cancer neoantigens serve as important targets for personalized immunotherapy which has the benefit of being more active and effective in targeting cancer cells. This paper is a step-by-step guide discussing the different analyses and challenges encountered during in-silico neoantigen prediction. The protocol describes all the tools and steps required for the identification of immunogenic neoantigens.

The peptide woods are lovely, dark and deep: Hunting for novel cancer antigens

Harnessing the patient's immune system to control a tumor is a proven avenue for cancer therapy. T cell therapies as well as therapeutic vaccines, which target specific antigens of interest, are being explored as treatments in conjunction with immune checkpoint blockade. For these therapies, selecting the best suited antigens is crucial. Most of the focus has thus far been on neoantigens that arise from tumor-specific somatic mutations. Although there is clear evidence that T-cell responses against mutated neoantigens are protective, the large majority of these mutations are not immunogenic. In addition, most somatic mutations are unique to each individual patient and their targeting requires the development of individualized approaches. Therefore, novel antigen types are needed to broaden the scope of such treatments. We review high throughput approaches for discovering novel tumor antigens and some of the key challenges associated with their detection, and discuss considerations when selecting tumor antigens to target in the clinic.

The immunopeptidome landscape associated with T cell infiltration, inflammation and immune editing in lung cancer

One key barrier to improving efficacy of personalized cancer immunotherapies that are dependent on the tumor antigenic landscape remains patient stratification. Although patients with CD3⁺CD8⁺ T cell-inflamed tumors typically show better response to immune checkpoint inhibitors, it is still unknown whether the immunopeptidome repertoire presented in highly inflamed and noninflamed tumors is substantially different. We surveyed 61 tumor regions and adjacent nonmalignant lung tissues from 8 patients with lung cancer and performed deep antigen discovery combining immunopeptidomics, genomics, bulk and spatial transcriptomics, and explored the heterogeneous expression and presentation of tumor (neo)antigens. In the present study, we associated diverse immune cell populations with the immunopeptidome and found a relatively higher frequency of predicted neoantigens located within HLA-I presentation hotspots in CD3⁺CD8⁺ T cell-excluded tumors. We associated such neoantigens with immune recognition, supporting their involvement in immune editing. This could have implications for the choice of combination therapies tailored to the patient's mutanome and immune microenvironment.

Genetic immune escape landscape in primary and metastatic cancer

Studies have characterized the immune escape landscape across primary tumors. However, whether late-stage metastatic tumors present differences in genetic immune escape (GIE) prevalence and dynamics remains unclear. We performed a pan-cancer characterization of GIE prevalence across six immune escape pathways in 6,319 uniformly processed tumor samples. To address the complexity of the HLA-I locus in the germline and in tumors, we developed LILAC, an open-source integrative framework. One in four tumors harbors GIE alterations, with high mechanistic and frequency variability across cancer types. GIE prevalence is generally consistent between primary and metastatic tumors. We reveal that GIE alterations are selected for in tumor evolution and focal loss of heterozygosity of HLA-I tends to eliminate the HLA allele, presenting the largest neoepitope repertoire. Finally, high mutational burden tumors showed a tendency toward focal loss of heterozygosity of HLA-I as the immune evasion mechanism, whereas, in hypermutated tumors, other immune evasion strategies prevail.

Artificial intelligence in cancer immunotherapy: Applications in neoantigen recognition, antibody design and immunotherapy response prediction

Cancer immunotherapy is a method of controlling and eliminating tumors by reactivating the body's cancer-immunity cycle and restoring its antitumor immune response. The increased availability of data, combined with advancements in high-performance computing and innovative artificial intelligence (AI) technology, has resulted in a rise in the use of AI in oncology research. State-of-the-art AI models for functional classification and prediction in immunotherapy research are increasingly used to support laboratory-based experiments. This review offers a glimpse of the current AI applications in immunotherapy, including neoantigen recognition, antibody design, and prediction of immunotherapy response. Advancing in this direction will result in more robust predictive models for developing better targets, drugs, and treatments, and these advancements will eventually make their way into the clinical setting, pushing AI forward in the field of precision oncology.

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.

Challenges in neoantigen-directed therapeutics

A fundamental prerequisite for the efficacy of cancer immunotherapy is the presence of functional, antigen-specific T cells within the tumor. Neoantigen-directed therapy is a promising strategy that aims at targeting the host's immune response against tumor-specific antigens, thereby eradicating cancer cells. Initial forays have been made in clinical environments utilizing vaccines and adoptive cell therapy; however, many challenges lie ahead. We provide an in-depth overview of the current state of the field with an emphasis on in silico neoantigen discovery and the clinical aspects that need to be addressed to unlock the full potential of this therapy.

The role of antigen expression in shaping the repertoire of HLA presented ligands

Human leukocyte antigen (HLA) presentation of peptides is a prerequisite of T cell immune activation. The understanding of the rules defining this event has large implications for our knowledge of basic immunology and for the rational design of immuno-therapeutics and vaccines. Historically, most of the available prediction methods have been solely focused on the information related to antigen processing and presentation. Recent work has, however, demonstrated that method performance can be boosted by integrating information related to antigen abundance. Here we expand on these later findings and develop an extended version of NetMHCpan, called NetMHCpanExp, integrating information on antigen abundance from RNA-Seq experiments. In line with earlier works, the model demonstrates improved performance for both HLA ligand and cancer neoantigen epitope prediction. Optimal results are obtained by use of sample-specific abundance information but also reference datasets can be applied with a limited performance drop. The developed tool is available at https://services.healthtech.dtu.dk/service.php?NetMHCpanExp-1.0.

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.

Neoantigen Identification and Response to Adoptive Cell Transfer in Anti-PD-1 Naïve and Experienced Patients with Metastatic Melanoma

PURPOSE

Immune checkpoint blockade (ICB) agents and adoptive cell transfer (ACT) of tumor-infiltrating lymphocytes (TIL) are prominent immunotherapies used for the treatment of advanced melanoma. Both therapies rely on activation of lymphocytes that target shared tumor antigens or neoantigens. Recent analysis of patients with metastatic melanoma who underwent treatment with TIL ACT at the NCI demonstrated decreased responses in patients previously treated with anti-PD-1 agents. We aimed to find a basis for the difference in response rates between anti-PD-1 naïve and experienced patients.

PATIENTS AND METHODS

We examined the tumor mutational burden (TMB) of resected tumors and the repertoire of neoantigens targeted by autologous TIL in a cohort of 112 anti-PD-1 naïve and 69 anti-PD-1 experienced patients.

RESULTS

Anti-PD-1 naïve patients were found to possess tumors with higher TMBs (352.0 vs. 213.5, P = 0.005) and received TIL reactive with more neoantigens (2 vs. 1, P = 0.003) compared with anti-PD-1 experienced patients. Among patients treated with TIL ACT, TMB and number of neoantigens identified were higher in ACT responders than ACT nonresponders in both anti-PD-1 naïve and experienced patients. Among patients with comparable TMBs and predicted neoantigen loads, treatment products administered to anti-PD-1 naïve patients were more likely to contain T cells reactive against neoantigens than treatment products for anti-PD-1 experienced patients (2.5 vs. 1, P = 0.02).

CONCLUSIONS

These results indicate that decreases in TMB and targeted neoantigens partially account for the difference in response to ACT and that additional factors likely influence responses in these patients. See related commentary by Blass and Ott, p. 2980.

Neoantigen-Specific T Cells in Adoptive Cell Therapy

ABSTRACT

The holy grail of cancer therapeutics is the destruction of cancer cells while avoiding harm to normal cells. Cancer is unique from normal tissues because of the presence of somatic mutations that accumulate during tumorigenesis. Some nonsynonymous mutations can give rise to mutated peptide antigens (hereafter referred to as neoantigens) that can be specifically recognized by T cells. Thus, the immunological targeting of neoantigens represents a safe and promising strategy to treat patients with cancer. This article reviews the clinical application of adoptive cell therapy targeting neoantigens in patients with epithelial cancers.

A Computational Algorithm to Assess the Physiochemical Determinants of T Cell Receptor Dissociation Kinetics

The rational design of T Cell Receptors (TCRs) for immunotherapy has stagnated due to a limited understanding of the dynamic physiochemical features of the TCR that elicit an immunogenic response. The physiochemical features of the TCR-peptide major histocompatibility complex (pMHC) bond dictate bond lifetime which, in turn, correlates with immunogenicity. Here, we: i) characterize the force-dependent dissociation kinetics of the bond between a TCR and a set of pMHC ligands using Steered Molecular Dynamics (SMD); and ii) implement a machine learning algorithm to identify which physiochemical features of the TCR govern dissociation kinetics. Our results demonstrate that the total number of hydrogen bonds between the CDR2β-MHC⍺(β), CDR1α-Peptide, and CDR3β-Peptide are critical features that determine bond lifetime.

Editorial overview: Vaccines: Reinvigorating therapeutic cancer vaccines

Lessons learned from the rapid deployment of vaccines during the COVID-19 pandemic are reinvigorating the cancer vaccine field. Using delivery platforms including mRNA and synthetic long peptides, recent clinical trials have demonstrated that cancer vaccines are safe, feasible, and can be associated with the generation of antigen-specific memory T cells and, in some cases, durable clinical responses. Despite these advances, fundamental questions remain regarding the optimal delivery platforms and antigen targets to use in cancer vaccines. Ongoing and future studies that harness advances in the identification of novel sources of antigens, the prediction of immunogenic antigens, and the use of single-cell technologies to profile antigen-specific T cells will hopefully reveal correlates with clinical outcomes and provide a mechanistic basis for future progress.

Adoptive tumor infiltrating lymphocyte transfer as personalized immunotherapy

Cancer is a leading cause of death worldwide and, despite new targeted therapies and immunotherapies, a large group of patients fail to respond to therapy or progress after initial response, which brings the need for additional treatment options. Manipulating the immune system using a variety of approaches has been explored for the past years with successful results. Sustained progress has been made to understand the T cell-mediated anti-tumor responses counteracting the tumorigenesis process. The T-lymphocyte pool, especially its capacity for antigen-directed cytotoxicity, has become a central focus for engaging the immune system in defeating cancer. The adoptive cell transfer of autologous tumor-infiltrating lymphocytes has been used in humans for over 30 years to treat metastatic melanoma. In this review, we provide a brief history of ACT-TIL and discuss the current state of ACT-TIL clinical development in solid tumors. We also discuss how key advances in understanding genetic intratumor heterogeneity, to accurately identify neoantigens, and new strategies designed to overcome T-cell exhaustion and tumor immunosuppression have improved the efficacy of the TIL-therapy infusion. Characteristics of the TIL products will be discussed, as well as new strategies, including the selective expansion of specific fractions from the cell product or the genetic manipulation of T cells for improving the in-vivo survival and functionality. In summary, this review outlines the potential of ACT-TIL as a personalized approach for epithelial tumors and continued discoveries are making it increasingly more effective against other types of cancers.

A phenotypic signature that identifies neoantigen-reactive T cells in fresh human lung cancers

A common theme across multiple successful immunotherapies for cancer is the recognition of tumor-specific mutations (neoantigens) by T cells. The rapid discovery of such antigen responses could lead to improved therapies through the adoptive transfer of T cells engineered to express neoantigen-reactive T cell receptors (TCRs). Here, through CITE-seq (cellular indexing of transcriptomes and epitopes by sequencing) and TCR-seq of non-small cell lung cancer (NSCLC) tumor-infiltrating lymphocytes (TILs), we develop a neoantigen-reactive T cell signature based on clonotype frequency and CD39 protein and CXCL13 mRNA expression. Screening of TCRs selected by the signature allows us to identify neoantigen-reactive TCRs with a success rate of 45% for CD8+ and 66% for CD4+ T cells. Because of the small number of samples analyzed (4 patients), generalizability remains to be tested. However, this approach can enable the quick identification of neoantigen-reactive TCRs and expedite the engineering of personalized neoantigen-reactive T cells for therapy.

Therapeutic cancer vaccines revamping: technology advancements and pitfalls

Cancer vaccines (CVs) represent a long-sought therapeutic and prophylactic immunotherapy strategy to obtain antigen (Ag)-specific T-cell responses and potentially achieve long-term clinical benefit. However, historically, most CV clinical trials have resulted in disappointing outcomes, despite promising signs of immunogenicity across most formulations. In the past decade, technological advances regarding vaccine delivery platforms, tools for immunogenomic profiling, and Ag/epitope selection have occurred. Consequently, the ability of CVs to induce tumor-specific and, in some cases, remarkable clinical responses have been observed in early-phase clinical trials. It is notable that the record-breaking speed of vaccine development in response to the coronavirus disease-2019 pandemic mainly relied on manufacturing infrastructures and technological platforms already developed for CVs. In turn, research, clinical data, and infrastructures put in place for the severe acute respiratory syndrome coronavirus 2 pandemic can further speed CV development processes. This review outlines the main technological advancements as well as major issues to tackle in the development of CVs. Possible applications for unmet clinical needs will be described, putting into perspective the future of cancer vaccinology.

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

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