Identifying neoantigens for use in immunotherapy

Targeting Neoantigens in Pancreatic Ductal Adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is the most common type of pancreatic cancer and is currently the third leading cause of cancer-related death in the United States after lung and colon cancer. PDAC is estimated to be the second leading cause of cancer-related death by 2030. The diagnosis at a late stage is the underlying cause for higher mortality and poor prognosis after surgery. Treatment resistance to chemotherapy and immunotherapy results in recurrence after surgery and poor prognosis. Neoantigen burden and CD8+ T-cell infiltration are associated with clinical outcomes in PDAC and paucity of neoantigen-reactive tumor-infiltrating lymphocytes may be the underlying cause for treatment resistance for immunotherapy. This suggests a need to identify additional neoantigens and therapies targeting these neoantigens to improve clinical outcomes in PDAC. In this review, we focus on describing the pathophysiology, current treatment strategies, and treatment resistance in PDAC followed by the need to target neoantigens in PDAC.

MONET: a database for prediction of neoantigens derived from microsatellite loci

Background

Microsatellite instability (MSI) secondary to mismatch repair (MMR) deficiency is characterized by insertions and deletions (indels) in short DNA sequences across the genome. These indels can generate neoantigens, which are ideal targets for precision immune interception. However, current neoantigen databases lack information on neoantigens arising from coding microsatellites. To address this gap, we introduce The MicrOsatellite Neoantigen Discovery Tool (MONET).

Method

MONET identifies potential mutated tumor-specific neoantigens (neoAgs) by predicting frameshift mutations in coding microsatellite sequences of the human genome. Then MONET annotates these neoAgs with key features such as binding affinity, stability, expression, frequency, and potential pathogenicity using established algorithms, tools, and public databases. A user-friendly web interface (https://monet.mdanderson.org/) facilitates access to these predictions.

Results

MONET predicts over 4 million and 15 million Class I and Class II potential frameshift neoAgs, respectively. Compared to existing databases, MONET demonstrates superior coverage (>85% vs. <25%) using a set of experimentally validated neoAgs.

Conclusion

MONET is a freely available, user-friendly web tool that leverages publicly available resources to identify neoAgs derived from microsatellite loci. This systems biology approach empowers researchers in the field of precision immune interception.

Sliding Window INteraction Grammar (SWING): a generalized interaction language model for peptide and protein interactions

The explosion of sequence data has allowed the rapid growth of protein language models (pLMs). pLMs have now been employed in many frameworks including variant-effect and peptide-specificity prediction. Traditionally, for protein-protein or peptide-protein interactions (PPIs), corresponding sequences are either co-embedded followed by post-hoc integration or the sequences are concatenated prior to embedding. Interestingly, no method utilizes a language representation of the interaction itself. We developed an interaction LM (iLM), which uses a novel language to represent interactions between protein/peptide sequences. Sliding Window Interaction Grammar (SWING) leverages differences in amino acid properties to generate an interaction vocabulary. This vocabulary is the input into a LM followed by a supervised prediction step where the LM's representations are used as features. SWING was first applied to predicting peptide:MHC (pMHC) interactions. SWING was not only successful at generating Class I and Class II models that have comparable prediction to state-of-the-art approaches, but the unique Mixed Class model was also successful at jointly predicting both classes. Further, the SWING model trained only on Class I alleles was predictive for Class II, a complex prediction task not attempted by any existing approach. For de novo data, using only Class I or Class II data, SWING also accurately predicted Class II pMHC interactions in murine models of SLE (MRL/lpr model) and T1D (NOD model), that were validated experimentally. To further evaluate SWING's generalizability, we tested its ability to predict the disruption of specific protein-protein interactions by missense mutations. Although modern methods like AlphaMissense and ESM1b can predict interfaces and variant effects/pathogenicity per mutation, they are unable to predict interaction-specific disruptions. SWING was successful at accurately predicting the impact of both Mendelian mutations and population variants on PPIs. This is the first generalizable approach that can accurately predict interaction-specific disruptions by missense mutations with only sequence information. Overall, SWING is a first-in-class generalizable zero-shot iLM that learns the language of PPIs.

ALDRESS: A Retrospective Pilot Study to Develop a Pharmacological Causality Algorithm for Drug Reaction with Eosinophilia and Systemic Symptoms (DRESS)

Background: The drug reaction with eosinophilia and systemic symptoms (DRESS) syndrome represents a severe form of drug hypersensitivity reaction characterized by significant morbidity, mortality, and long-term sequelae, coupled with limited therapeutic avenues. Accurate identification of the causative drug(s) is paramount for acute management, exploration of safe therapeutic alternatives, and prevention of future occurrences. However, the absence of a standardized diagnostic test and a specific causality algorithm tailored to DRESS poses a significant challenge in its clinical management. Methods: We conducted a retrospective case-control study involving 37 DRESS patients to validate a novel causality algorithm, the ALDRESS, designed explicitly for this syndrome, comparing it against the current standard algorithm, SEFV. Results: The ALDRESS algorithm showcased superior performance, exhibiting an 85.7% sensitivity and 93% specificity with comparable negative predictive values (80.6% vs. 97%). Notably, the ALDRESS algorithm yielded a substantially higher positive predictive value (75%) compared to SEFV (51.40%), achieving an overall accuracy rate of 92%. Conclusions: Our findings underscore the efficacy of the ALDRESS algorithm in accurately attributing causality to drugs implicated in DRESS syndrome. However, further validation studies involving larger, diverse cohorts are warranted to consolidate its clinical utility and broaden its applicability. This study lays the groundwork for a refined causality assessment tool, promising advancements in the diagnosis and management of DRESS syndrome.

Assessing TCR identity, knock-in efficiency, and potency for individualized TCR-T cell therapy

Advances in mass spectrometry, genome sequencing techniques, and bioinformatic strategies have accelerated the discovery of cancer-specific neoantigens. Tumors express multiple immunogenic neoantigens, and neoantigen-specific T cell receptors (TCRs) can be identified in peripheral blood's mononuclear cells in cancer patients. Therefore, individualized TCR-based therapies are a promising approach whereby multiple neoantigen-specific TCRs can be selected in each patient, potentially leading to a highly effective treatment for cancer patients. We developed three multiplex analytical assays to determine the quality attributes of the TCR-T cell drug product with a mixture of five engineered TCRs. The identity of each TCR was determined by two NGS-based methods, Illumina MiSeq and PacBio platforms. This approach not only confirms the expected TCR sequences but also differentiates them by their variable regions. The five individual TCR and total TCR knock-in efficiencies were measured by droplet digital PCR using specific reverse primers. A potency assay based on transfection of antigen-encoding-RNA was developed to assess the dose-dependent activation of T cells for each TCR by measuring the surface activation marker CD137 expression and cytokine secretion. This work provides new assays to characterize individualized TCR-T cell products and insights into quality attributes for the control strategy.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Harnessing epithelial-mesenchymal plasticity to boost cancer immunotherapy

Immune checkpoint blockade (ICB) therapy is a powerful option for cancer treatment. Despite demonstrable progress, most patients fail to respond or achieve durable responses due to primary or acquired ICB resistance. Recently, tumor epithelial-to-mesenchymal plasticity (EMP) was identified as a critical determinant in regulating immune escape and immunotherapy resistance in cancer. In this review, we summarize the emerging role of tumor EMP in ICB resistance and the tumor-intrinsic or extrinsic mechanisms by which tumors exploit EMP to achieve immunosuppression and immune escape. We discuss strategies to modulate tumor EMP to alleviate immune resistance and to enhance the efficiency of ICB therapy. Our discussion provides new prospects to enhance the ICB response for therapeutic gain in cancer patients.

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.

Tumor heterogeneity: preclinical models, emerging technologies, and future applications

Heterogeneity describes the differences among cancer cells within and between tumors. It refers to cancer cells describing variations in morphology, transcriptional profiles, metabolism, and metastatic potential. More recently, the field has included the characterization of the tumor immune microenvironment and the depiction of the dynamics underlying the cellular interactions promoting the tumor ecosystem evolution. Heterogeneity has been found in most tumors representing one of the most challenging behaviors in cancer ecosystems. As one of the critical factors impairing the long-term efficacy of solid tumor therapy, heterogeneity leads to tumor resistance, more aggressive metastasizing, and recurrence. We review the role of the main models and the emerging single-cell and spatial genomic technologies in our understanding of tumor heterogeneity, its contribution to lethal cancer outcomes, and the physiological challenges to consider in designing cancer therapies. We highlight how tumor cells dynamically evolve because of the interactions within the tumor immune microenvironment and how to leverage this to unleash immune recognition through immunotherapy. A multidisciplinary approach grounded in novel bioinformatic and computational tools will allow reaching the integrated, multilayered knowledge of tumor heterogeneity required to implement personalized, more efficient therapies urgently required for cancer patients.

A B-cell or a key player? The different roles of B-cells and antibodies in melanoma

The B‐cell system plays an important role in the melanoma immune response; however, consensus has yet to be reached in many facets. Here, we comprehensively review human studies only, due to fundamental differences in the humoral response with animal models. Tumour‐infiltrating B‐cells are associated with contradictory prognostic values, reflecting a lack of agreement between studies on cell subset classification and differences in the markers used, particularly the common use of a single marker not differentiating multiple subsets. Tertiary lymphoid structures (TLS) organise T‐cells and B‐cells within tumours to generate a local anti‐tumour response and TLS presence associates with improved survival in response to immune checkpoint blockade, in late‐stage disease. Autoantibody production is increased in melanoma patients and has been proposed as biomarkers for diagnosis, prognosis and treatment/toxicity response; however, no consistent targets are yet identified. The function of antibodies in an anti‐tumour response is determined by its isotype and subclass; IgG₄ is immune‐suppressive and robustly correlate with poor patient survival in melanoma. We conclude that the current B‐cell literature needs careful interpretation based on the methods used and that we need a consensus of markers to define B‐cells and associated lymphoid organs. Furthermore, future studies need to not only examine antibody targets, but also isotypes when considering functional roles.

MHCRoBERTa: pan-specific peptide-MHC class I binding prediction through transfer learning with label-agnostic protein sequences

Predicting the binding of peptide and major histocompatibility complex (MHC) plays a vital role in immunotherapy for cancer. The success of Alphafold of applying natural language processing (NLP) algorithms in protein secondary struction prediction has inspired us to explore the possibility of NLP methods in predicting peptide-MHC class I binding. Based on the above motivations, we propose the MHCRoBERTa method, RoBERTa pre-training approach, for predicting the binding affinity between type I MHC and peptides. Analysis of the results on benchmark dataset demonstrates that MHCRoBERTa can outperform other state-of-art prediction methods with an increase of the Spearman rank correlation coefficient (SRCC) value. Notably, our model gave a significant improvement on IC50 value. Our method has achieved SRCC value and AUC value as 0.785 and 0.817, respectively. Our SRCC value is 14.3% higher than NetMHCpan3.0 (the second highest SRCC value on pan-specific) and is 3% higher than MHCflurry (the second highest SRCC value on all methods). The AUC value is also better than any other pan-specific methods. Moreover, we visualize the multi-head self-attention for the token representation across the layers and heads by this method. Through the analysis of the representation of each layer and head, we can show whether the model has learned the syntax and semantics necessary to perform the prediction task well. All these results demonstrate that our model can accurately predict the peptide-MHC class I binding affinity and that MHCRoBERTa is a powerful tool for screening potential neoantigens for cancer immunotherapy. MHCRoBERTa is available as an open source software at github (https://github.com/FuxuWang/MHCRoBERTa).

Neoantigen: A Promising Target for the Immunotherapy of Colorectal Cancer

At present, there are various treatment strategies for colorectal cancer, including surgery, chemotherapy, radiotherapy, and targeted therapy. In recent years, with the continuous development of immunotherapy, immune checkpoint inhibitors (ICIs) can significantly improve the treatment of advanced colorectal cancer patients with high levels of microsatellite instability. In addition to ICIs, neoantigens, as a class of tumor-specific antigens (TSA), are regarded as new immunotherapy targets for many cancer species and are being explored for antitumor therapy. Immunotherapy strategies based on neoantigens include tumor vaccines and adoptive cell therapy (ACT). These methods aim to eliminate tumor cells by enhancing the immune response of host T-cells to neoantigens. In addition, for MSS colorectal cancer, such “cold tumors” with low mutation rates and stable microsatellites are not sensitive to ICIs, whereas neoantigens could provide a promising immunotherapeutic avenue. In this review, we summarized the current status of colorectal cancer neoantigen prediction and current clinical trials of neoantigens and discussed the difficulties and limitations of neoantigens-based therapies for the treatment of CRC.

Neoantigens and their potential applications in tumor immunotherapy

The incidence of malignant tumors is increasing, the majority of which are associated with high morbidity and mortality rates worldwide. The traditional treatment method for malignant tumors is surgery, coupled with radiotherapy or chemotherapy. However, these therapeutic strategies are frequently accompanied with adverse side effects. Over recent decades, tumor immunotherapy shown promise in demonstrating notable efficacy for the treatment of cancer. With the development of sequencing technology and bioinformatics algorithms, neoantigens have become compelling targets for cancer immunotherapy due to high levels of immunogenicity. In addition, neoantigen-based vaccines have demonstrated potential for cancer therapy, primarily by augmenting T-cell responses. Neoantigens have also been shown to be effective in immune checkpoint blockade therapy. Therefore, neoantigens may serve to be predictive biomarkers and synergistic treatment targets in cancer immunotherapy. The aim of the present review was to provide an overview of the recent progress in the classification, screening and clinical application of neoantigens for cancer therapy.

Development of neoantigens: from identification in cancer cells to application in cancer vaccines

Introduction: The discovery of neoantigens as mutated proteins specifically expressed in tumor cells but not in normal cells has led to improved cancer vaccines. Targeting neoantigens can induce anti-tumor T-cell responses to destroy tumors without damaging healthy cells. Extensive advances in genome sequencing technology and bioinformatics analysis have made it possible to discover and design effective neoantigens for use in therapeutic cancer vaccines. Neoantigens-based therapeutic personalized vaccines have shown promising results in cancer immunotherapy.Areas covered: We discuss the types of cancer neoantigens that can be recognized by the immune system in this review. We also summarize the detection, identification, and design of neoantigens and their appliction in developing cancer vaccines. Finally, clinical trials of neoantigen-based vaccines, their advantages, and their limitations are reviewed. From 2015 to 2020, the authors conducted a literature search of controlled randomized trials and laboratory investigations that that focused on neoantigens, their use in the design of various types of cancer vaccines.Expert opinion: Neoantigens are cancer cell-specific antigens, which their expression leads to the immune stimulation against tumor cells. The identification and delivery of specific neoantigens to antigen-presenting cells (APCs) with the help of anti-cancer vaccines promise novel and more effective cancer treatments.

Challenges and next steps in the advancement of immunotherapy: summary of the 2018 and 2020 National Cancer Institute workshops on cell-based immunotherapy for solid tumors

Cell-based immunotherapies have had remarkable success in the clinic, specifically in the treatment of hematologic malignancies. However, these strategies have had limited efficacy in patients with solid tumors. To better understand the challenges involved, the National Cancer Institute (NCI) convened an initial workshop with immuno-oncology thought leaders in December 2018 and a follow-up workshop in December 2020. The goals of the NCI workshops on cell-based immunotherapy for solid tumors were to discuss the current state of the field of cell-based immunotherapy, obtain insights into critical knowledge gaps, and identify ways in which NCI could facilitate progress. At both meetings, subjects emphasized four main types of challenges in further developing cell-based immunotherapy for patients with solid tumors: scientific, technical, clinical, and regulatory. The scientific barriers include selecting appropriate targets, ensuring adequate trafficking of cell therapy products to tumor sites, overcoming the immunosuppressive tumor microenvironment, and identifying appropriate models for these investigations. While mouse models may provide some useful data, the majority of those that are commonly used are immunodeficient and unable to fully recapitulate the immune response in patients. There is therefore a need for enhanced support of small early-phase human clinical studies, preferably with adaptive trial designs, to provide proof of concept for novel cell therapy approaches. Furthermore, the requirements for manufacturing, shipping, and distributing cell-based therapies present technical challenges and regulatory questions, which many research institutions are not equipped to address. Overall, workshop subjects identified key areas where NCI support might help the research community in driving forward innovation and clinical utility: 1) provide focused research support on topics such as tumor target selection, immune cell fitness and persistence, cell trafficking, and the immunosuppressive tumor microenvironment; 2) support the rapid translation of preclinical findings into proof of concept clinical testing, harmonize clinical trial regimens, and facilitate early trial data sharing (including negative results); 3) expand manufacturing support for cell therapies, including vectors and reagents, and provide training programs for technical staff; and 4) develop and share standard operating procedures for cell handling and analytical assays, and work with the Food and Drug Administration to harmonize product characterization specifications.

In vitro induction of neoantigen-specific T cells in myelodysplastic syndrome, a disease with low mutational burden

Therapies that utilize immune checkpoint inhibition work by leveraging mutation-derived neoantigens and have shown greater clinical efficacy in tumors with higher mutational burden. Whether tumors with a low mutational burden are susceptible to neoantigen-targeted therapy has not been fully addressed. To examine the feasibility of neoantigen-specific adoptive T-cell therapy, the authors studied the T-cell response against somatic variants in five patients with myelodysplastic syndrome (MDS), a malignancy with a very low tumor mutational burden. DNA and RNA from tumor (CD34⁺) and normal (CD3⁺) cells isolated from the patients' blood were sequenced to predict patient-specific MDS neopeptides. Neopeptides representing the somatic variants were used to induce and expand autologous T cells ex vivo, and these were systematically tested in killing assays to determine the proportion of neopeptides yielding neoantigen-specific T cells. The authors identified a total of 32 somatic variants (four to eight per patient) and found that 21 (66%) induced a peptide-specific T-cell response and 19 (59%) induced a T-cell response capable of killing autologous tumor cells. Of the 32 somatic variants, 11 (34%) induced a CD4⁺ response and 11 (34%) induced a CD8⁺ response that killed the tumor. These results indicate that in vitro induction of neoantigen-specific T cells is feasible for tumors with very low mutational burden and that this approach warrants investigation as a therapeutic option for such patients.

Cancer-associated mutations in endometriosis: shedding light on the pathogenesis and pathophysiology

BACKGROUND

Endometriosis is a benign gynaecological disease. Thus, it came as a complete surprise when it was reported recently that the majority of deep endometriosis lesions harbour somatic mutations and a sizeable portion of them contain known cancer-associated mutations (CAMs). Four more studies have since been published, all demonstrating the existence of CAMs in different subtypes of endometriosis. While the field is still evolving, the confirmation of CAMs has raised many questions that were previously overlooked.

OBJECTIVE AND RATIONALE

A comprehensive overview of CAMs in endometriosis has been produced. In addition, with the recently emerged understanding of the natural history of endometriotic lesions as well as CAMs in normal and apparently healthy tissues, this review attempts to address the following questions: Why has there been such a wild discrepancy in reported mutation frequencies? Why does ectopic endometrium have a higher mutation rate than that of eutopic endometrium? Would the presence of CAMs in endometriotic lesions increase the risk of cancer to the bearers? Why do endometriotic epithelial cells have much higher mutation frequencies than their stromal counterpart? What clinical implications, if any, do the CAMs have for the bearers? Do these CAMs tell us anything about the pathogenesis and/or pathophysiology of endometriosis?

SEARCH METHODS

The PubMed database was searched, from its inception to September 2019, for all papers in English using the term 'endometriosis and CAM', 'endometriosis and cancer-driver mutation', 'somatic mutations', 'fibrosis', 'fibrosis and epigenetic', 'CAMs and tumorigenesis', 'somatic mutation and normal tissues', 'oestrogen receptor and fibrosis', 'oxidative stress and fibrosis', 'ARID1A mutation', and 'Kirsten rat sarcoma mutation and therapeutics'. All retrieved papers were read and, when relevant, incorporated into the review results.

OUTCOMES

Seven papers that identified CAMs in endometriosis using various sequencing methods were retrieved, and their results were somewhat different. Yet, it is apparent that those using microdissection techniques and more accurate sequencing methods found more CAMs, echoing recent discoveries that apparently healthy tissues also harbour CAMs as a result of the replicative aging process. Hence endometriotic lesions, irrespective of subtype, if left intact, would generate CAMs as part of replicative aging, oxidative stress and perhaps other factors yet to be identified and, in some rare cases, develop cancer. The published data still are unable to paint a clear picture on pathogenesis of endometriosis. However, since endometriotic epithelial cells have a higher turnover than their stromal counterpart due to cyclic bleeding, and since the endometriotic stromal component can be formed by refresh influx of mesenchymal cells through epithelial-mesenchymal transition, endothelial-mesenchymal transition, mesothelial-mesenchymal transition and other processes as well as recruitment of bone-marrow-derived stem cells and outflow due to smooth muscle metaplasia, endometriotic epithelial cells have much higher mutation frequencies than their stromal counterpart. The epithelial and stromal cellular components develop in a dependent and co-evolving manner. Genes involved in CAMs are likely to be active players in lesional fibrogenesis, and hyperestrogenism and oxidative stress are likely drivers of both CAMs and fibrogenesis. Finally, endometriotic lesions harbouring CAMs would conceivably be more refractory to medical treatment, due, in no small part, to their high fibrotic content and reduced vascularity and cellularity.

WIDER IMPLICATIONS

The accumulating data on CAMs in endometriosis have shed new light on the pathogenesis and pathophysiology of endometriosis. They also suggest new challenges in management. The distinct yet co-evolving developmental trajectories of endometriotic stroma and epithelium underscore the importance of the lesional microenvironment and ever-changing cellular identity. Mutational profiling of normal endometrium from women of different ages and reproductive history is needed in order to gain a deeper understanding of the pathogenesis. Moreover, one area that has conspicuously received scant attention is the epigenetic landscape of ectopic, eutopic and normal endometrium.

Inducing multiple antibodies to treat squamous cell esophageal carcinoma

BACKGROUND

The positive response and the clinical usefulness of 14 serum antibodies in patients with esophageal squamous cell carcinoma (ESCC) were examined in this study. The Cancer Genome Atlas (TCGA) was used to investigate the frequency of gene expressions, mutations, and amplification of these 14 antigens and also the possible effects of antibody induction.

METHODS

Blood serum derived from 85 patients with ESCC was collected and analyzed for the 14 antibodies using ELISA. The prognosis between positive and negative antibodies were then compared. The antibody panel included LGALS1, HCA25a, HCC-22-5, and HSP70.

RESULTS

Patient serum was positive for all antibodies, except VEGF, with the positive rates ranging from 1.18 to 10.59%. Positive rates for LGALS1, HCA25a, HCC-22-5, and HSP70 were > 10%. TCGA data revealed that all antigen-related genes had little or no mutation or amplification, and hence an increase in gene expression affected antibody induction. The positive results from the panel accounted for the positive rate comparable to the combination of CEA and SCC. No significant association was observed between the presence of antibodies and disease prognosis.

CONCLUSIONS

The detection rates of LGALS1, HCA25a, HCC-22-5, and HSP70 were 10% higher in patients with ESCC. Gene overexpression may be involved in such antibody production. These four antibodies were applied as a panel in comparison with conventional tumor markers. Moreover, it was confirmed that the combination of this panel and the conventional tumor markers significantly improved the positive rate.

Engineered Cell-Membrane-Coated Nanoparticles Directly Present Tumor Antigens to Promote Anticancer Immunity

The recent success of immunotherapies has highlighted the power of leveraging the immune system in the fight against cancer. In order for most immune-based therapies to succeed, T cell subsets with the correct tumor-targeting specificities must be mobilized. When such specificities are lacking, providing the immune system with tumor antigen material for processing and presentation is a common strategy for stimulating antigen-specific T cell populations. While straightforward in principle, experience has shown that manipulation of the antigen presentation process can be incredibly complex, necessitating sophisticated strategies that are difficult to translate. Herein, the design of a biomimetic nanoparticle platform is reported that can be used to directly stimulate T cells without the need for professional antigen-presenting cells. The nanoparticles are fabricated using a cell membrane coating derived from cancer cells engineered to express a co-stimulatory marker. Combined with the peptide epitopes naturally presented on the membrane surface, the final formulation contains the necessary signals to promote tumor antigen-specific immune responses, priming T cells that can be used to control tumor growth. The reported approach represents an emerging strategy that can be used to develop multiantigenic, personalized cancer immunotherapies.

Engineered Cell-Membrane-Coated Nanoparticles Directly Present Tumor Antigens to Promote Anticancer Immunity

The recent success of immunotherapies has highlighted the power of leveraging the immune system in the fight against cancer. In order for most immune-based therapies to succeed, T cell subsets with the correct tumor-targeting specificities must be mobilized. When such specificities are lacking, providing the immune system with tumor antigen material for processing and presentation is a common strategy for stimulating antigen-specific T cell populations. While straightforward in principle, experience has shown that manipulation of the antigen presentation process can be incredibly complex, necessitating sophisticated strategies that are difficult to translate. Herein, the design of a biomimetic nanoparticle platform is reported that can be used to directly stimulate T cells without the need for professional antigen-presenting cells. The nanoparticles are fabricated using a cell membrane coating derived from cancer cells engineered to express a co-stimulatory marker. Combined with the peptide epitopes naturally presented on the membrane surface, the final formulation contains the necessary signals to promote tumor antigen-specific immune responses, priming T cells that can be used to control tumor growth. The reported approach represents an emerging strategy that can be used to develop multiantigenic, personalized cancer immunotherapies.

Myeloid-Derived Suppressor Cells as a Therapeutic Target for Cancer

The emergence of immunotherapy has been an astounding breakthrough in cancer treatments. In particular, immune checkpoint inhibitors, targeting PD-1 and CTLA-4, have shown remarkable therapeutic outcomes. However, response rates from immunotherapy have been reported to be varied, with some having pronounced success and others with minimal to no clinical benefit. An important aspect associated with this discrepancy in patient response is the immune-suppressive effects elicited by the tumour microenvironment (TME). Immune suppression plays a pivotal role in regulating cancer progression, metastasis, and reducing immunotherapy success. Most notably, myeloid-derived suppressor cells (MDSC), a heterogeneous population of immature myeloid cells, have potent mechanisms to inhibit T-cell and NK-cell activity to promote tumour growth, development of the pre-metastatic niche, and contribute to resistance to immunotherapy. Accumulating research indicates that MDSC can be a therapeutic target to alleviate their pro-tumourigenic functions and immunosuppressive activities to bolster the efficacy of checkpoint inhibitors. In this review, we provide an overview of the general immunotherapeutic approaches and discuss the characterisation, expansion, and activities of MDSCs with the current treatments used to target them either as a single therapeutic target or synergistically in combination with immunotherapy.

Immunosurveillance and Immunoediting of Lung Cancer: Current Perspectives and Challenges

The immune system plays a dual role in tumor evolution-it can identify and control nascent tumor cells in a process called immunosurveillance and can promote tumor progression through immunosuppression via various mechanisms. Thus, bilateral host-protective and tumor-promoting actions of immunity are integrated as cancer immunoediting. In this decade, immune checkpoint inhibitors, specifically programmed cell death 1 (PD-1) pathway inhibitors, have changed the treatment paradigm of advanced non-small cell lung cancer (NSCLC). These agents are approved for the treatment of patients with NSCLC and demonstrate impressive clinical activity and durable responses in some patients. However, for many NSCLC patients, the efficacy of immune checkpoint inhibitors is limited. To optimize the full utility of the immune system for eradicating cancer, a broader understanding of cancer immunosurveillance and immunoediting is essential. In this review, we discuss the fundamental knowledge of the phenomena and provide an overview of the next-generation immunotherapies in the pipeline.

Novel Immunotherapeutic Approaches in Head and Neck Cancer

Unresectable recurrent or metastatic head and neck cancer is an incurable disease with survival of approximately 12 months. Head and neck tumors exhibit numerous derangements in the tumor microenvironment that aid in immune evasion and may serve as targets for future therapies. Pembrolizumab is now approved as a first line therapy. Despite the promise of currently approved immunotherapies there continues to be low response rates and additional strategies are needed. Here, alterations in the immune microenvironment and current therapeutic strategies are reviewed with a focus on novel immunologic approaches.

Tumor antigens heterogeneity and immune response-targeting neoantigens in breast cancer

Breast cancer is both the most common type of cancer and the most frequent cause of cancer mortality in women, mainly because of its heterogeneity and limited immunogenicity. The aim of specific active cancer immunotherapy is to stimulate the host's immune response against cancer cells directly using a vaccine platform carrying one or more tumor antigens. In particular, the ideal tumor antigen should be able to elicit T cell and B cell responses, be specific for the tumor and be expressed at high levels on cancer cells. Neoantigens are ideal targets for immunotherapy because they are exclusive to individual patient's tumors, are absent in healthy tissues and are not subject to immune tolerance mechanisms. Thus, neoantigens should generate a specific reaction towards tumors since they constitute the largest fraction of targets of tumor-infiltrating T cells. In this review, we describe the technologies used for neoantigen discovery, the heterogeneity of neoantigens in breast cancer and recent studies of breast cancer immunotherapy targeting neoantigens.

Protecting Tumors by Preventing Human Papilloma Virus Antigen Presentation: Insights from Emerging Bioinformatics Algorithms

Recent developments in bioinformatics technologies have led to advances in our understanding of how oncogenic viruses such as the human papilloma virus drive cancer progression and evade the host immune system. Here, we focus our review on understanding how these emerging bioinformatics technologies influence our understanding of how human papilloma virus (HPV) drives immune escape in cancers of the head and neck, and how these new informatics approaches may be generally applicable to other virally driven cancers. Indeed, these tools enable researchers to put existing data from genome wide association studies, in which high risk alleles have been identified, in the context of our current understanding of cellular processes regulating neoantigen presentation. In the future, these new bioinformatics approaches are highly likely to influence precision medicine-based decision making for the use of immunotherapies in virally driven cancers.

Tumor neoantigens: from basic research to clinical applications

Tumor neoantigen is the truly foreign protein and entirely absent from normal human organs/tissues. It could be specifically recognized by neoantigen-specific T cell receptors (TCRs) in the context of major histocompatibility complexes (MHCs) molecules. Emerging evidence has suggested that neoantigens play a critical role in tumor-specific T cell-mediated antitumor immune response and successful cancer immunotherapies. From a theoretical perspective, neoantigen is an ideal immunotherapy target because they are distinguished from germline and could be recognized as non-self by the host immune system. Neoantigen-based therapeutic personalized vaccines and adoptive T cell transfer have shown promising preliminary results. Furthermore, recent studies suggested the significant role of neoantigen in immune escape, immunoediting, and sensitivity to immune checkpoint inhibitors. In this review, we systematically summarize the recent advances of understanding and identification of tumor-specific neoantigens and its role on current cancer immunotherapies. We also discuss the ongoing development of strategies based on neoantigens and its future clinical applications.

Radiation as an In Situ Auto-Vaccination: Current Perspectives and Challenges

Radiotherapy is generally considered to be a local treatment, but there have been reports of rare cases demonstrating abscopal effects in which antitumor effects have been observed in cancer lesions other than the irradiated site. This result is more likely to occur when immune checkpoint inhibitors are used in addition to radiotherapy. Certain radiation-induced chemokines and cytokines have immune-enhancing effects. Immune checkpoint inhibitors may strengthen these effects by stimulating antigen-presenting cells and effector cytotoxic T cells. To date, there is no consensus regarding the applicability of the abscopal effect in the clinical setting, including optimal methods for combining immune checkpoint inhibitors and irradiation. In this review, we highlight the evidence for interactions between cancer immunotherapy and radiotherapy and discuss the potential of such interactions for use in designing novel combination therapies.