Machine learning methods and harmonized datasets improve immunogenic neoantigen prediction

Harnessing nanoengineered CAR-T cell strategies to advance solid tumor immunotherapy

The efficacy and safety of chimeric antigen receptor (CAR) T cell therapy is still inconclusive in solid tumor treatment. Recently, nanotechnology has emerged as a potent strategy to reshape CAR-T cell therapy with promising outcomes. This review aims to discuss the significant potential of nano-engineered CAR-T cell therapy in addressing existing challenges, including CAR-T cell engineering evolution, tumor microenvironment (TME) modulation, and precise CAR-T cell therapy (precise targeting, monitoring, and activation), under the main consideration of clinical translation. It also focuses on the growing trend of technological convergence within this domain, such as mRNA therapeutics, organoids, neoantigen, and artificial intelligence. Moreover, safety management of nanomedicine is seriously emphasized to facilitate clinical translation.

Driver mutation landscape of acute myeloid leukemia provides insights for neoantigen-based immunotherapy

Acute myeloid leukemia (AML) has lagged in benefiting from immunotherapies, primarily due to the scarcity of actionable AML-specific antigens. Driver mutations represent promising immunogenic targets, but a comprehensive characterization of the AML neoantigen landscape and their impact on patient outcomes and the AML immune microenvironment remain unclear. Herein, we conducted matched DNA and RNA sequencing on 304 AML patients and extensively integrated data from additional ∼2,500 AML cases, identifying 49 driver genes, notably characterized by a significant proportion of insertions and deletions (indels). Neoantigen analysis showed that indels triggered a higher abundance of neoantigens both in quantity and quality compared to single nucleotide variants (SNVs) and gene fusions. By integrating peptide features pertinent to neoantigen presentation and T cell recognition, we developed two robust models of epitope immunogenicity that significantly enriched immunogenic neoepitopes. We validated 30 neoantigens through in vitro direct binding assays of predicted peptides to MHC proteins and confirmed the immunogenicity of 20 neoantigens using interferon-γ ELISpot and tetramer assays. Moreover, we demonstrated that patients with higher neoantigen loads, derived from driver mutations, exhibited poor clinical outcomes and an IFN-driven adaptive immune response, which was associated with immune suppression and tumor evasion. Through deconvolution of large-scale bulk transcriptomes, integration of single-cell RNA sequencing and multiparametric flow cytometry, we confirmed a strong association between neoantigen load and CD8+ T cell exhaustion. This study provides a comprehensive landscape of AML neoantigens derived from driver mutations, offering putative immunogenic targets and emphasizing the need for strategies to revitalize the immunosuppressive milieu.

Machine learning-enhanced immunopeptidomics applied to T-cell epitope discovery for COVID-19 vaccines

Next-generation T-cell-directed vaccines for COVID-19 focus on establishing lasting T-cell immunity against current and emerging SARS-CoV-2 variants. Precise identification of conserved T-cell epitopes is critical for designing effective vaccines. Here we introduce a comprehensive computational framework incorporating a machine learning algorithm-MHCvalidator-to enhance mass spectrometry-based immunopeptidomics sensitivity. MHCvalidator identifies unique T-cell epitopes presented by the B7 supertype, including an epitope from a + 1-frameshift in a truncated Spike antigen, supported by ribosome profiling. Analysis of 100,512 COVID-19 patient proteomes shows Spike antigen truncation in 0.85% of cases, revealing frameshifted viral antigens at the population level. Our EpiTrack pipeline tracks global mutations of MHCvalidator-identified CD8 + T-cell epitopes from the BNT162b4 vaccine. While most vaccine epitopes remain globally conserved, an immunodominant A*01-associated epitope mutates in Delta and Omicron variants. This work highlights SARS-CoV-2 antigenic features and emphasizes the importance of continuous adaptation in T-cell vaccine development.

Neoantigen architectures define immunogenicity and drive immune evasion of tumors with heterogenous neoantigen expression

BACKGROUND

Intratumoral heterogeneity (ITH) and subclonal antigen expression blunt antitumor immunity and are associated with poor responses to immune-checkpoint blockade immunotherapy (ICB) in patients with cancer. The underlying mechanisms however thus far remained elusive, preventing the design of novel treatment approaches for patients with high ITH tumors.

METHODS

We developed a mouse model of lung adenocarcinoma with defined expression of different neoantigens (NeoAg), enabling us to analyze how these impact antitumor T-cell immunity and to study underlying mechanisms. Data from a large cancer patient cohort was used to study whether NeoAg architecture characteristics found to define tumor immunogenicity in our mouse models are linked to ICB responses in patients with cancer.

RESULTS

We demonstrate that concurrent expression and clonality define NeoAg architectures which determine the immunogenicity of individual NeoAg and drive immune evasion of tumors with heterogenous NeoAg expression. Mechanistically, we identified concerted interplays between concurrent T-cell responses induced by cross-presenting dendritic cells (cDC1) mirroring the tumor NeoAg architecture during T-cell priming in the lymph node. Depending on the characteristics and clonality of respective NeoAg, this interplay mutually benefited concurrent T-cell responses or led to competition between T-cell responses to different NeoAg. In tumors with heterogenous NeoAg expression, NeoAg architecture-induced suppression of T-cell responses against branches of the tumor drove immune evasion and caused resistance to ICB. Therapeutic RNA-based vaccination targeting immune-suppressed T-cell responses synergized with ICB to enable control of tumors with subclonal NeoAg expression. A pan-cancer clinical data analysis indicated that competition and synergy between T-cell responses define responsiveness to ICB in patients with cancer.

CONCLUSIONS

NeoAg architectures modulate the immunogenicity of NeoAg and tumors by dictating the interplay between concurrent T-cell responses mediated by cDC1. Impaired induction of T-cell responses supports immune evasion in tumors with heterogenous NeoAg expression but is amenable to NeoAg architecture-informed vaccination, which in combination with ICB portrays a promising treatment approach for patients with tumors exhibiting high ITH.

Neoantigen prioritization based on antigen processing and presentation

Somatic mutations in tumor cells give rise to mutant proteins, fragments of which are often presented by MHC and serve as neoantigens. Neoantigens are tumor-specific and not expressed in healthy tissues, making them attractive targets for T-cell-based cancer immunotherapy. On the other hand, since most somatic mutations differ from patient to patient, neoantigen-targeted immunotherapy is personalized medicine and requires their identification in each patient. Computational algorithms and machine learning methods have been developed to prioritize neoantigen candidates. In fact, since the number of clinically relevant neoantigens present in a patient is generally limited, this process is like finding a needle in a haystack. Nevertheless, MHC presentation of neoantigens is not random but follows certain rules, and the efficiency of neoantigen detection may be further improved with technological innovations. In this review, we discuss current approaches to the detection of clinically relevant neoantigens, with a focus on antigen processing and presentation.

Advances in mRNA LNP-Based Cancer Vaccines: Mechanisms, Formulation Aspects, Challenges, and Future Directions

After the COVID-19 pandemic, mRNA-based vaccines have emerged as a revolutionary technology in immunization and vaccination. These vaccines have shown remarkable efficacy against the virus and opened up avenues for their possible application in other diseases. This has renewed interest and investment in mRNA vaccine research and development, attracting the scientific community to explore all its other applications beyond infectious diseases. Recently, researchers have focused on the possibility of adapting this vaccination approach to cancer immunotherapy. While there is a huge potential, challenges still remain in the design and optimization of the synthetic mRNA molecules and the lipid nanoparticle delivery system required to ensure the adequate elicitation of the immune response and the successful eradication of tumors. This review points out the basic mechanisms of mRNA-LNP vaccines in cancer immunotherapy and recent approaches in mRNA vaccine design. This review displays the current mRNA modifications and lipid nanoparticle components and how these factors affect vaccine efficacy. Furthermore, this review discusses the future directions and clinical applications of mRNA-LNP vaccines in cancer treatment.

The T cell receptor β chain repertoire of tumor infiltrating lymphocytes improves neoantigen prediction and prioritization

In the realm of cancer immunotherapy, the meticulous selection of neoantigens plays a fundamental role in enhancing personalized treatments. Traditionally, this selection process has heavily relied on predicting the binding of peptides to human leukocyte antigens (pHLA). Nevertheless, this approach often overlooks the dynamic interaction between tumor cells and the immune system. In response to this limitation, we have developed an innovative prediction algorithm rooted in machine learning, integrating T cell receptor β chain (TCRβ) profiling data from colorectal cancer (CRC) patients for a more precise neoantigen prioritization. TCRβ sequencing was conducted to profile the TCR repertoire of tumor-infiltrating lymphocytes (TILs) from 28 CRC patients. The data unveiled both intra-tumor and inter-patient heterogeneity in the TCRβ repertoires of CRC patients, likely resulting from the stochastic utilization of V and J segments in response to neoantigens. Our novel combined model integrates pHLA binding information with pHLA-TCR binding to prioritize neoantigens, resulting in heightened specificity and sensitivity compared to models using individual features alone. The efficacy of our proposed model was corroborated through ELISpot assays on long peptides, performed on four CRC patients. These assays demonstrated that neoantigen candidates prioritized by our combined model outperformed predictions made by the established tool NetMHCpan. This comprehensive assessment underscores the significance of integrating pHLA binding with pHLA-TCR binding analysis for more effective immunotherapeutic strategies.

A comprehensive proteogenomic pipeline for neoantigen discovery to advance personalized cancer immunotherapy

The accurate identification and prioritization of antigenic peptides is crucial for the development of personalized cancer immunotherapies. Publicly available pipelines to predict clinical neoantigens do not allow direct integration of mass spectrometry immunopeptidomics data, which can uncover antigenic peptides derived from various canonical and noncanonical sources. To address this, we present an end-to-end clinical proteogenomic pipeline, called NeoDisc, that combines state-of-the-art publicly available and in-house software for immunopeptidomics, genomics and transcriptomics with in silico tools for the identification, prediction and prioritization of tumor-specific and immunogenic antigens from multiple sources, including neoantigens, viral antigens, high-confidence tumor-specific antigens and tumor-specific noncanonical antigens. We demonstrate the superiority of NeoDisc in accurately prioritizing immunogenic neoantigens over recent prioritization pipelines. We showcase the various features offered by NeoDisc that enable both rule-based and machine-learning approaches for personalized antigen discovery and neoantigen cancer vaccine design. Additionally, we demonstrate how NeoDisc's multiomics integration identifies defects in the cellular antigen presentation machinery, which influence the heterogeneous tumor antigenic landscape.

Predicting Antigen-Specificities of Orphan T Cell Receptors from Cancer Patients with TCRpcDist

Approaches to analyze and cluster T-cell receptor (TCR) repertoires to reflect antigen specificity are critical for the diagnosis and prognosis of immune-related diseases and the development of personalized therapies. Sequence-based approaches showed success but remain restrictive, especially when the amount of experimental data used for the training is scarce. Structure-based approaches which represent powerful alternatives, notably to optimize TCRs affinity toward specific epitopes, show limitations for large-scale predictions. To handle these challenges, TCRpcDist is presented, a 3D-based approach that calculates similarities between TCRs using a metric related to the physico-chemical properties of the loop residues predicted to interact with the epitope. By exploiting private and public datasets and comparing TCRpcDist with competing approaches, it is demonstrated that TCRpcDist can accurately identify groups of TCRs that are likely to bind the same epitopes. Importantly, the ability of TCRpcDist is experimentally validated to determine antigen specificities (neoantigens and tumor-associated antigens) of orphan tumor-infiltrating lymphocytes (TILs) in cancer patients. TCRpcDist is thus a promising approach to support TCR repertoire analysis and TCR deorphanization for individualized treatments including cancer immunotherapies.

Leveraging artificial intelligence in vaccine development: A narrative review

Vaccine development stands as a cornerstone of public health efforts, pivotal in curbing infectious diseases and reducing global morbidity and mortality. However, traditional vaccine development methods are often time-consuming, costly, and inefficient. The advent of artificial intelligence (AI) has ushered in a new era in vaccine design, offering unprecedented opportunities to expedite the process. This narrative review explores the role of AI in vaccine development, focusing on antigen selection, epitope prediction, adjuvant identification, and optimization strategies. AI algorithms, including machine learning and deep learning, leverage genomic data, protein structures, and immune system interactions to predict antigenic epitopes, assess immunogenicity, and prioritize antigens for experimentation. Furthermore, AI-driven approaches facilitate the rational design of immunogens and the identification of novel adjuvant candidates with optimal safety and efficacy profiles. Challenges such as data heterogeneity, model interpretability, and regulatory considerations must be addressed to realize the full potential of AI in vaccine development. Integrating emerging technologies, such as single-cell omics and synthetic biology, promises to enhance vaccine design precision and scalability. This review underscores the transformative impact of AI on vaccine development and highlights the need for interdisciplinary collaborations and regulatory harmonization to accelerate the delivery of safe and effective vaccines against infectious diseases.

Microbiota-induced inflammatory responses in bladder tumors promote epithelial-mesenchymal transition and enhanced immune infiltration

The intratumoral microbiota can modulate the tumor immune microenvironment (TIME); however, the underlying mechanism by which intratumoral microbiota influences the TIME in urothelial carcinoma of the bladder (UCB) remains unclear. To address this, we collected samples from 402 patients with UCB, including paired host transcriptome and tumor microbiome data, from The Cancer Genome Atlas (TCGA). We found that the intratumoral microbiome profiles were significantly correlated with the expression pattern of epithelial-mesenchymal transition (EMT)-related genes. Furthermore, we detected that the genera Lachnoclostridium and Sutterella in tumors could indirectly promote the EMT program by inducing an inflammatory response. Moreover, the inflammatory response induced by these two intratumoral bacteria further enhanced intratumoral immune infiltration, affecting patient survival and response to immunotherapy. In addition, an independent immunotherapy cohort of 348 patients with bladder cancer was used to validate our results. Collectively, our study elucidates the potential mechanism by which the intratumoral microbiota influences the TIME of UCB and provides a new guiding strategy for the targeted therapy of UCB.NEW & NOTEWORTHY The intratumoral microbiota may mediate the bladder tumor inflammatory response, thereby promoting the epithelial-mesenchymal transition program and influencing tumor immune infiltration.

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.

Immunogenicity of Non-Mutated Ovarian Cancer-Specific Antigens

Epithelial ovarian cancer (EOC) has not significantly benefited from advances in immunotherapy, mainly because of the lack of well-defined actionable antigen targets. Using proteogenomic analyses of primary EOC tumors, we previously identified 91 aberrantly expressed tumor-specific antigens (TSAs) originating from unmutated genomic sequences. Most of these TSAs derive from non-exonic regions, and their expression results from cancer-specific epigenetic changes. The present study aimed to evaluate the immunogenicity of 48 TSAs selected according to two criteria: presentation by highly prevalent HLA allotypes and expression in a significant fraction of EOC tumors. Using targeted mass spectrometry analyses, we found that pulsing with synthetic TSA peptides leads to a high-level presentation on dendritic cells. TSA abundance correlated with the predicted binding affinity to the HLA allotype. We stimulated naïve CD8 T cells from healthy blood donors with TSA-pulsed dendritic cells and assessed their expansion with two assays: MHC-peptide tetramer staining and TCR Vβ CDR3 sequencing. We report that these TSAs can expand sizeable populations of CD8 T cells and, therefore, represent attractive targets for EOC immunotherapy.

Tumor-Derived Antigenic Peptides as Potential Cancer Vaccines

Peptide antigens derived from tumors have been observed to elicit protective immune responses, categorized as either tumor-associated antigens (TAAs) or tumor-specific antigens (TSAs). Subunit cancer vaccines incorporating these antigens have shown promise in inducing protective immune responses, leading to cancer prevention or eradication. Over recent years, peptide-based cancer vaccines have gained popularity as a treatment modality and are often combined with other forms of cancer therapy. Several clinical trials have explored the safety and efficacy of peptide-based cancer vaccines, with promising outcomes. Advancements in techniques such as whole-exome sequencing, next-generation sequencing, and in silico methods have facilitated the identification of antigens, making it increasingly feasible. Furthermore, the development of novel delivery methods and a deeper understanding of tumor immune evasion mechanisms have heightened the interest in these vaccines among researchers. This article provides an overview of novel insights regarding advancements in the field of peptide-based vaccines as a promising therapeutic avenue for cancer treatment. It summarizes existing computational methods for tumor neoantigen prediction, ongoing clinical trials involving peptide-based cancer vaccines, and recent studies on human vaccination experiments.

Emerging potential of immunopeptidomics by mass spectrometry in cancer immunotherapy

With significant advances in analytical technologies, research in the field of cancer immunotherapy, such as adoptive T cell therapy, cancer vaccine, and immune checkpoint blockade (ICB), is currently gaining tremendous momentum. Since the efficacy of cancer immunotherapy is recognized only by a minority of patients, more potent tumor-specific antigens (TSAs, also known as neoantigens) and predictive markers for treatment response are of great interest. In cancer immunity, immunopeptides, presented by human leukocyte antigen (HLA) class I, play a role as initiating mediators of immunogenicity. The latest advancement in the interdisciplinary multiomics approach has rapidly enlightened us about the identity of the "dark matter" of cancer and the associated immunopeptides. In this field, mass spectrometry (MS) is a viable option to select because of the naturally processed and actually presented TSA candidates in order to grasp the whole picture of the immunopeptidome. In the past few years the search space has been enlarged by the multiomics approach, the sensitivity of mass spectrometers has been improved, and deep/machine-learning-supported peptide search algorithms have taken immunopeptidomics to the next level. In this review, along with the introduction of key technical advancements in immunopeptidomics, the potential and further directions of immunopeptidomics will be reviewed from the perspective of cancer immunotherapy.

Untranslated regions (UTRs) are a potential novel source of neoantigens for personalised immunotherapy

Background

Neoantigens, mutated tumour-specific antigens, are key targets of anti-tumour immunity during checkpoint inhibitor (CPI) treatment. Their identification is fundamental to designing neoantigen-directed therapy. Non-canonical neoantigens arising from the untranslated regions (UTR) of the genome are an overlooked source of immunogenic neoantigens. Here, we describe the landscape of UTR-derived neoantigens and release a computational tool, PrimeCUTR, to predict UTR neoantigens generated by start-gain and stop-loss mutations.

Methods

We applied PrimeCUTR to a whole genome sequencing dataset of pre-treatment tumour samples from CPI-treated patients (n = 341). Cancer immunopeptidomic datasets were interrogated to identify MHC class I presentation of UTR neoantigens.

Results

Start-gain neoantigens were predicted in 72.7% of patients, while stop-loss mutations were found in 19.3% of patients. While UTR neoantigens only accounted 2.6% of total predicted neoantigen burden, they contributed 12.4% of neoantigens with high dissimilarity to self-proteome. More start-gain neoantigens were found in CPI responders, but this relationship was not significant when correcting for tumour mutational burden. While most UTR neoantigens are private, we identified two recurrent start-gain mutations in melanoma. Using immunopeptidomic datasets, we identify two distinct MHC class I-presented UTR neoantigens: one from a recurrent start-gain mutation in melanoma, and one private to Jurkat cells.

Conclusion

PrimeCUTR is a novel tool which complements existing neoantigen discovery approaches and has potential to increase the detection yield of neoantigens in personalised therapeutics, particularly for neoantigens with high dissimilarity to self. Further studies are warranted to confirm the expression and immunogenicity of UTR neoantigens.

A functional identification platform reveals frequent, spontaneous neoantigen-specific T cell responses in patients with cancer

The clinical impact of tumor-specific neoantigens as both immunotherapeutic targets and biomarkers has been impeded by the lack of efficient methods for their identification and validation from routine samples. We have developed a platform that combines bioinformatic analysis of tumor exomes and transcriptional data with functional testing of autologous peripheral blood mononuclear cells (PBMCs) to simultaneously identify and validate neoantigens recognized by naturally primed CD4+ and CD8+ T cell responses across a range of tumor types and mutational burdens. The method features a human leukocyte antigen (HLA)-agnostic bioinformatic algorithm that prioritizes mutations recognized by patient PBMCs at a greater than 40% positive predictive value followed by a short-term in vitro functional assay, which allows interrogation of 50 to 75 expressed mutations from a single 50-ml blood sample. Neoantigens validated by this method include both driver and passenger mutations, and this method identified neoantigens that would not have been otherwise detected using an in silico prediction approach. These findings reveal an efficient approach to systematically validate clinically actionable neoantigens and the T cell receptors that recognize them and demonstrate that patients across a variety of human cancers have a diverse repertoire of neoantigen-specific T cells.

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.

Trade-offs inside the black box of neoantigen prediction

Success of precision neoantigen-based immunotherapies hinges on the selection of immunogenic neoantigens, yet currently neither large-scale datasets nor streamlined methods are available to achieve this goal. Müller et al. present a large experimental dataset resource along with machine learning-based models to classify immunogenic neoantigens.