Best practices for bioinformatic characterization of neoantigens for clinical utility

Advances in mRNA vaccine therapy for breast cancer research

Breast cancer represents the most prevalent cancer among women globally, constituting approximately 30% of newly diagnosed female malignancies and serving as the second leading cause of cancer-related mortality, accounting for 11.6% of deaths. Despite notable advancements in survival rates and quality of life for breast cancer patients over recent decades-achieved through interventions such as surgery, chemotherapy, radiotherapy, and endocrine therapy-there remains an urgent need for novel therapeutic strategies. This necessity arises from challenges associated with recurrence, metastasis, and drug resistance. The COVID-19 pandemic has accelerated the development of Messenger RNA (mRNA) vaccines at an unprecedented pace, and as a novel form of precision immunotherapy, mRNA vaccines are increasingly being recognized for their potential in cancer treatment. mRNA vaccines efficiently produce antigens within the cytoplasm, specifically activating the immune system to target tumor cells while minimizing the risk of T-cell tolerance. Therefore, mRNA vaccines have emerged as a promising approach in cancer immunotherapy. This review systematically examines the principles, mechanisms, advantages, key targets, and recent progress in mRNA vaccine therapy for breast cancer. Furthermore, it discusses current challenges and suggests potential directions for future research.

Key genes altered in glioblastoma based on bioinformatics (Review)

Glioblastoma multiforme (GBM) is an aggressive brain tumor with poor prognosis. Recent advancements in bioinformatics have contributed to uncovering the genetic alterations that underlie the development and progression of GBM. Analysis of extensive genomic data led to the identification of significant pathways involved in GBM, such as the PI3K/AKT/mTOR and Ras/Raf/MEK/ERK signaling pathways, alongside key genes such as EGFR, TP53 and TERT. These findings have enhanced our understanding of GBM biology and led to the identification of new therapeutic targets. Bioinformatics has become an indispensable tool in pinpointing the genetic modifications that drive GBM, paving the way for innovative treatment strategies. This approach not only aids in comprehending the complexities of GBM but also holds promise for improving outcomes in patients suffering from this devastating disease. The ongoing integration of bioinformatics in GBM research continues to be vital for advancing therapeutic options.

Neoantigen-based immunotherapy: advancing precision medicine in cancer and glioblastoma treatment through discovery and innovation

Neoantigen-based immunotherapy has emerged as a transformative approach in cancer treatment, offering precision medicine strategies that target tumor-specific antigens derived from genetic, transcriptomic, and proteomic alterations unique to cancer cells. These neoantigens serve as highly specific targets for personalized therapies, promising more effective and tailored treatments. The aim of this article is to explore the advances in neoantigen-based therapies, highlighting successful treatments such as vaccines, tumor-infiltrating lymphocyte (TIL) therapy, T-cell receptor-engineered T cells therapy (TCR-T), and chimeric antigen receptor T cells therapy (CAR-T), particularly in cancer types like glioblastoma (GBM). Advances in technologies such as next-generation sequencing, RNA-based platforms, and CRISPR gene editing have accelerated the identification and validation of neoantigens, moving them closer to clinical application. Despite promising results, challenges such as tumor heterogeneity, immune evasion, and resistance mechanisms persist. The integration of AI-driven tools and multi-omic data has refined neoantigen discovery, while combination therapies are being developed to address issues like immune suppression and scalability. Additionally, the article discusses the ongoing development of personalized immunotherapies targeting tumor mutations, emphasizing the need for continued collaboration between computational and experimental approaches. Ultimately, the integration of cutting-edge technologies in neoantigen research holds the potential to revolutionize cancer care, offering hope for more effective and targeted treatments.

The therapeutic effect of mRNA vaccines in glioma: a comprehensive review

INTRODUCTION

Glioma is the most common primary brain tumor, with glioblastoma being the most lethal type due to its heterogeneous and invasive nature of the cancer. Current therapies have low curative success and are limited to surgery, radiotherapy, and chemotherapy. More than 50% of patients become resistant to chemotherapy, and tumor recurrence occurs in most patients following an initial course of therapy. Therefore, developing novel, effective strategies for glioma treatment is essential. Cancer vaccines are novel therapies that demonstrate advantages over conventional methods and, therefore, may be promising options for treating glioma.

AREAS COVERED

This article provided a critical review of pre-clinical and clinical studies that explored appropriate tumor antigen candidates for developing mRNA vaccines and discussed their clinical application in glioma patients. Medline database, PubMed, and ClinicalTrials.gov were searched for glioma vaccine studies published before 2025 using related keywords.

EXPERT OPINION

mRNA vaccines are promising strategies for treating glioma because they are efficient, cost-beneficial, and have lower side effects than other types such as peptide or DNA-based vaccines.

Advancing personalized immunotherapy for melanoma: Integrating immunoinformatics in multi-epitope vaccine development, neoantigen identification via NGS, and immune simulation evaluation

The use of cancer vaccines represents a promising avenue in cancer immunotherapy. Advances in next-generation sequencing (NGS) technology, coupled with the development of sophisticated analysis tools, have enabled the identification of somatic mutations by comparing genetic sequences between normal and tumor samples. Tumor neoantigens, derived from these mutations, have emerged as potential candidates for therapeutic cancer vaccines. In this study, raw NGS data from two melanoma patients (NCI_3903 and NCI_3998) were analyzed using publicly available SRA datasets from NCBI to identify patient-specific neoantigens. A comprehensive pipeline was employed to select candidate peptides based on their antigenicity, immunogenicity, physicochemical properties, and toxicity profiles. These validated epitopes were utilized to design multi-epitope chimeric vaccines tailored to each patient. Peptide linkers were employed to connect the epitopes, ensuring optimal vaccine structure and function. The two-dimensional (2D) and three-dimensional (3D) structures of the chimeric vaccines were predicted and refined to ensure structural stability and immunogenicity. Furthermore, molecular docking simulations were conducted to evaluate the binding interactions between the vaccine chimeras and the HLA class I receptors, confirming their potential to elicit a robust immune response. This work highlights a personalized approach to cancer vaccine development, demonstrating the feasibility of utilizing neoantigen-based immunoinformatics pipelines to design patient-specific therapeutic vaccines for melanoma.

Advancements and challenges in personalized neoantigen-based cancer vaccines

Advancements in personalized neoantigen-based cancer vaccines are ushering in a new era in oncology, targeting unique genetic alterations within tumors to enhance treatment precision and efficacy. Neoantigens, specific to cancer cells and absent in normal tissues, are at the heart of these vaccines, promising to direct the immune system specifically against the tumor, thereby maximizing therapeutic efficacy while minimizing side effects. The identification of neoantigens through genomic and proteomic technologies is central to developing these vaccines, allowing for the precise mapping of a tumor's mutational landscape. Despite advancements, accurately predicting which neoantigens will elicit strong immune responses remains challenging due to tumor variability and the complexity of immune system interactions. This necessitates further refinement of bioinformatics tools and predictive models. Moreover, the efficacy of these vaccines heavily depends on innovative delivery methods that enhance neoantigen presentation to the immune system. Techniques like encapsulating neoantigens in lipid nanoparticles and using viral vectors are critical for improving vaccine stability and delivery. Additionally, these vaccines contribute towards achieving Sustainable Development Goal 3.8, promoting universal health coverage by advancing access to safe and effective cancer treatments. This review delves into the potential of neoantigen-based vaccines to transform cancer treatment, examining both revolutionary advancements and the ongoing challenges they face.

Frontline pemetrexed and cisplatin based-chemotherapy combined with NRT promoted the antitumor in a mouse model of lung carcinoma

The efficacy of neoantigen-reactive T cells (NRT) therapy in solid tumors, encompassing aspects such as infiltration, recognition, cytotoxicity, and enduring persistence, is notably influenced by the immunological microenvironment. This study endeavors to investigate whether the co-administration of pemetrexed and cisplatin augments the therapeutic efficacy of NRT therapy in lung cancer. Neoantigens were predicted using a comprehensive analysis of mutation data from Lewis lung carcinoma cells and mouse tail tissues. The immunogenicity of NRT cells was assessed through flow cytometry and IFN-γ ELISpot assays. A mouse model of NSCLC was used to investigate the anti-tumor effects of NRT combined with chemotherapy. The combination of NRT cells and chemotherapy significantly inhibited tumor growth in a mouse model, increased CD3+/CD137+ T cells to promote IFN-γ secretion from NRT cells, and up-regulated the levels of inflammatory cytokine proteins including IFN-γ, TNF, IL-6 and IL-10. Immunofluorescence analysis confirmed increased T-cell infiltration in tumor tissues without adverse effects on vital organs. In addition, transcriptome analyses indicated that the tumor microenvironment was altered to favor M1-like macrophages with an increased M1/M2 ratio, creating a pro-inflammatory environment. The integration of NRT with frontline chemotherapy for lung cancer could yield profoundly ideal therapeutic outcomes.

Integration of proteomics profiling data to facilitate discovery of cancer neoantigens: a survey

Cancer neoantigens are peptides that originate from alterations in the genome, transcriptome, or proteome. These peptides can elicit cancer-specific T-cell recognition, making them potential candidates for cancer vaccines. The rapid advancement of proteomics technology holds tremendous potential for identifying these neoantigens. Here, we provided an up-to-date survey about database-based search methods and de novo peptide sequencing approaches in proteomics, and we also compared these methods to recommend reliable analytical tools for neoantigen identification. Unlike previous surveys on mass spectrometry-based neoantigen discovery, this survey summarizes the key advancements in de novo peptide sequencing approaches that utilize artificial intelligence. From a comparative study on a dataset of the HepG2 cell line and nine mixed hepatocellular carcinoma proteomics samples, we demonstrated the potential of proteomics for the identification of cancer neoantigens and conducted comparisons of the existing methods to illustrate their limits. Understanding these limits, we suggested a novel workflow for neoantigen discovery as perspectives.

Recent progress in cancer vaccines and nanovaccines

Vaccine science, nanotechnology, and immunotherapy are at the forefront of cancer treatment strategies, each offering significant potential for enhancing tumor-specific immunity and establishing long-lasting immune memory to prevent tumor recurrence. Despite the promise of these personalized and precision-based anti-cancer approaches, challenges such as immunosuppression, suboptimal immune activation, and T-cell exhaustion continue to hinder their effectiveness. The limited clinical success of cancer vaccines often stems from difficulties in identifying effective antigens, efficiently targeting immune cells, lymphoid organs, and the tumor microenvironment, overcoming immune evasion, enhancing immunogenicity, and avoiding lysosomal degradation. However, numerous studies have demonstrated that integrating nanotechnology with immunotherapeutic strategies in vaccine development can overcome these challenges, leading to potent antitumor immune responses and significant progress in the field. This review highlights the critical components of cancer vaccine and nanovaccine strategies for immunomodulatory antitumor therapy. It covers general vaccine strategies, types of vaccines, antigen forms, nanovaccine platforms, challenges faced, potential solutions, and key findings from preclinical and clinical studies, along with future perspectives. To fully unlock the potential of cancer vaccines and nanovaccines, precise immunological monitoring during early-phase trials is essential. This approach will help identify and address obstacles, ultimately expanding the available options for patients who are resistant to conventional cancer immunotherapies.

Biomimetic Nanoparticle Based Targeted mRNA Vaccine Delivery as a Novel Therapy for Glioblastoma Multiforme

The prognosis for patients with glioblastoma multiforme (GBM), an aggressive and deadly brain tumor, is poor due to the limited therapeutic options available. Biomimetic nanoparticles have emerged as a promising vehicle for targeted mRNA vaccine delivery, thanks to recent advances in nanotechnology. This presents a novel treatment method for GBM. This review explores the potential of using biomimetic nanoparticles to improve the specificity and effectiveness of mRNA vaccine against GBM. These nanoparticles can evade immune detection, cross the blood-brain barrier, & deliver mRNA directly to glioma cells by mimicking natural biological structures. This allows glioma cells to produce tumor-specific antigens that trigger strong immune responses against the tumor. This review discusses biomimetic nanoparticle design strategies, which are critical for optimizing transport and ensuring targeted action. These tactics include surface functionalization and encapsulation techniques. It also highlights the ongoing preclinical research and clinical trials that demonstrate the therapeutic advantages and challenges of this strategy. Biomimetic nanoparticles for mRNA vaccine delivery represent a new frontier in GBM treatment, which could impact the management of this deadly disease and improve patient outcomes by integrating cutting-edge nanotechnology with immunotherapy.

Manufacturing and Financial Evaluation of Peptide-Based Neoantigen Cancer Vaccines for Triple-Negative Breast Cancer in the United Kingdom: Opportunities and Challenges

This review evaluates the financial burden of current treatments for triple-negative breast cancer (TNBC) and projects potential financial scenarios to assess the feasibility of introducing a peptide-based neoantigen cancer vaccine (NCV) targeting the disease, using the UK as a healthcare system model. TNBC, the most aggressive breast cancer subtype, is associated with poor prognosis, worsened by the lack of personalised treatment options. Neoantigen cancer vaccine therapies present a personalised alternative with the potential to enhance T-cell responses independently of genetic factors, unlike approved immunotherapies for TNBC. Through a systematic literature review, the underlying science and manufacturing processes of NCVs are explored, the direct medical costs of existing TNBC treatments are enumerated, and two contrasting pricing scenarios for NCV clinical adoption are evaluated. The findings indicate that limited immunogenicity is the main scientific barrier to NCV clinical advancement, alongside production inefficiencies. Financial analysis shows that the UK spends approximately GBP 230 million annually on TNBC treatments, ranging from GBP 2200 to GBP 54,000 per patient. A best-case pricing model involving government-sponsored NCV therapy appears financially viable, while a worst-case, privately funded model exceeds the National Institute for Health and Care Excellence (NICE) cost thresholds. This study concludes that while NCVs show potential clinical benefits for TNBC, uncertainties about their standalone efficacy make their widespread adoption in the UK unlikely without further clinical research.

Establishment of a novel tumor neoantigen prediction tool for personalized vaccine design

The personalized neoantigen nanovaccine (PNVAC) platform for patients with gastric cancer we established previously exhibited promising anti-tumor immunoreaction. However, limited by the ability of traditional neoantigen prediction tools, a portion of epitopes failed to induce specific immune response. In order to filter out more neoantigens to optimize our PNVAC platform, we develop a novel neoantigen prediction model, NUCC. This prediction tool trained through a deep learning approach exhibits better neoantigen prediction performance than other prediction tools, not only in two independent epitope datasets, but also in a totally new epitope dataset we construct from scratch, including 25 patients with advance gastric cancer and 150 candidate mutant peptides, 13 of which prove to be neoantigen by immunogenicity test in vitro. Our work lay the foundation for the improvement of our PNVAC platform for gastric cancer in the future.

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

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

CapHLA: a comprehensive tool to predict peptide presentation and binding to HLA class I and class II

Human leukocyte antigen class I (HLA-I) and class II (HLA-II) proteins play an essential role in epitope binding and presentation to initiate an immune response. Accurate prediction of peptide-HLA (pHLA) binding and presentation is critical for developing effective immunotherapies. However, current tools can predict antigens exclusively for pHLA-I or pHLA-II, but not both; have constraints on peptide length; and commonly show unsatisfactory predictive accuracy. Here, we developed a convolution and attention-based model, CapHLA, trained with eluted ligand and binding affinity mass spectrometry data, to predict peptide presentation probability (PB) and binding affinities (BA) for HLA-I and HLA-II. In comparison with 11 other methods, CapHLA consistently showed improved performance in predicting pHLA BA and PB, particularly in HLA-II and non-classical peptide length datasets. Using CapHLA PB and BA predictions in combination with antigen expression level (EP) from transcriptomic data, we developed a neoantigen quality model for predicting immunotherapy response. In analyses of clinical response among 276 cancer patients given immunotherapy and overall survival in 7228 cancer patients, our neoantigen quality model outperformed other genetics-based models in predicting response to checkpoint inhibitors and patient prognosis. This study provides a versatile neoantigen screening tool, illustrating the prognostic value of neoantigen quality.

pVACview: an interactive visualization tool for efficient neoantigen prioritization and selection

BACKGROUND

Neoantigen-targeting therapies including personalized vaccines have shown promise in the treatment of cancers, particularly when used in combination with checkpoint blockade therapy. At least 100 clinical trials involving these therapies have been initiated globally. Accurate identification and prioritization of neoantigens is crucial for designing these trials, predicting treatment response, and understanding mechanisms of resistance. With the advent of massively parallel DNA and RNA sequencing technologies, it is now possible to computationally predict neoantigens based on patient-specific variant information. However, numerous factors must be considered when prioritizing neoantigens for use in personalized therapies. Complexities such as alternative transcript annotations, various binding, presentation and immunogenicity prediction algorithms, and variable peptide lengths/registers all potentially impact the neoantigen selection process. There has been a rapid development of computational tools that attempt to account for these complexities. While these tools generate numerous algorithmic predictions for neoantigen characterization, results from these pipelines are difficult to navigate and require extensive knowledge of the underlying tools for accurate interpretation. This often leads to over-simplification of pipeline outputs to make them tractable, for example, limiting prediction to a single RNA isoform or only summarizing the top ranked of many possible peptide candidates. In addition to variant detection, gene expression, and predicted peptide binding affinities, recent studies have also demonstrated the importance of mutation location, allele-specific anchor locations, and variation of T-cell response to long versus short peptides. Due to the intricate nature and number of salient neoantigen features, presenting all relevant information to facilitate candidate selection for downstream applications is a difficult challenge that current tools fail to address.

RESULTS

We have created pVACview, the first interactive tool designed to aid in the prioritization and selection of neoantigen candidates for personalized neoantigen therapies including cancer vaccines. pVACview has a user-friendly and intuitive interface where users can upload, explore, select, and export their neoantigen candidates. The tool allows users to visualize candidates at multiple levels of detail including variant, transcript, peptide, and algorithm prediction information.

CONCLUSIONS

pVACview will allow researchers to analyze and prioritize neoantigen candidates with greater efficiency and accuracy in basic and translational settings. The application is available as part of the pVACtools software at pvactools.org and as an online server at pvacview.org.

Molecular targets and strategies in the development of nucleic acid cancer vaccines: from shared to personalized antigens

Recent breakthroughs in cancer immunotherapies have emphasized the importance of harnessing the immune system for treating cancer. Vaccines, which have traditionally been used to promote protective immunity against pathogens, are now being explored as a method to target cancer neoantigens. Over the past few years, extensive preclinical research and more than a hundred clinical trials have been dedicated to investigating various approaches to neoantigen discovery and vaccine formulations, encouraging development of personalized medicine. Nucleic acids (DNA and mRNA) have become particularly promising platform for the development of these cancer immunotherapies. This shift towards nucleic acid-based personalized vaccines has been facilitated by advancements in molecular techniques for identifying neoantigens, antigen prediction methodologies, and the development of new vaccine platforms. Generating these personalized vaccines involves a comprehensive pipeline that includes sequencing of patient tumor samples, data analysis for antigen prediction, and tailored vaccine manufacturing. In this review, we will discuss the various shared and personalized antigens used for cancer vaccine development and introduce strategies for identifying neoantigens through the characterization of gene mutation, transcription, translation and post translational modifications associated with oncogenesis. In addition, we will focus on the most up-to-date nucleic acid vaccine platforms, discuss the limitations of cancer vaccines as well as provide potential solutions, and raise key clinical and technical considerations in vaccine development.

Tumour mutational burden: clinical utility, challenges and emerging improvements

Tumour mutational burden (TMB), defined as the total number of somatic non-synonymous mutations present within the cancer genome, varies across and within cancer types. A first wave of retrospective and prospective research identified TMB as a predictive biomarker of response to immune-checkpoint inhibitors and culminated in the disease-agnostic approval of pembrolizumab for patients with TMB-high tumours based on data from the Keynote-158 trial. Although the applicability of outcomes from this trial to all cancer types and the optimal thresholds for TMB are yet to be ascertained, research into TMB is advancing along three principal avenues: enhancement of TMB assessments through rigorous quality control measures within the laboratory process, including the mitigation of confounding factors such as limited panel scope and low tumour purity; refinement of the traditional TMB framework through the incorporation of innovative concepts such as clonal, persistent or HLA-corrected TMB, tumour neoantigen load and mutational signatures; and integration of TMB with established and emerging biomarkers such as PD-L1 expression, microsatellite instability, immune gene expression profiles and the tumour immune contexture. Given its pivotal functions in both the pathogenesis of cancer and the ability of the immune system to recognize tumours, a profound comprehension of the foundational principles and the continued evolution of TMB are of paramount relevance for the field of oncology.

Engineered T cells for Colorectal Cancer

Colorectal cancer (CRC) is a major contributor to global cancer incidence and mortality. Conventional treatments have limitations; hence, innovative approaches are imperative. Recent advancements in cancer research have led to the development of personalized targeted therapies and immunotherapies. Immunotherapy, in particular, T cell-based therapies, exhibited to be promising in enhancing cancer treatment outcomes. This review focuses on the landscape of engineered T cells as a potential option for the treatment of CRC. It highlights the approaches, challenges and current advancements in this field. As the understanding of molecular mechanisms increases, engineered T cells hold great potential in revolutionizing cancer treatment. To fully explore their safety efficacy in improving patient outcomes, further research and clinical trials are necessary.

Revamping Hepatocellular Carcinoma Immunotherapy: The Advent of Microbial Neoantigen Vaccines

Immunotherapy has revolutionized the treatment paradigm for hepatocellular carcinoma (HCC). However, its efficacy varies significantly with each patient's genetic composition and the complex interactions with their microbiome, both of which are pivotal in shaping anti-tumor immunity. The emergence of microbial neoantigens, a novel class of tumor vaccines, heralds a transformative shift in HCC therapy. This review explores the untapped potential of microbial neoantigens as innovative tumor vaccines, poised to redefine current HCC treatment modalities. For instance, neoantigens derived from the microbiome have demonstrated the capacity to enhance anti-tumor immunity in colorectal cancer, suggesting similar applications in HCC. By harnessing these unique neoantigens, we propose a framework for a personalized immunotherapeutic response, aiming to deliver a more precise and potent treatment strategy for HCC. Leveraging these neoantigens could significantly advance personalized medicine, potentially revolutionizing patient outcomes in HCC therapy.

Neoantigen landscape supports feasibility of personalized cancer vaccine for follicular lymphoma

ABSTRACT

Personalized cancer vaccines designed to target neoantigens represent a promising new treatment paradigm in oncology. In contrast to classical idiotype vaccines, we hypothesized that "polyvalent" vaccines could be engineered for the personalized treatment of follicular lymphoma (FL) using neoantigen discovery by combined whole-exome sequencing (WES) and RNA sequencing (RNA-seq). Fifty-eight tumor samples from 57 patients with FL underwent WES and RNA-seq. Somatic and B-cell clonotype neoantigens were predicted and filtered to identify high-quality neoantigens. B-cell clonality was determined by the alignment of B-cell receptor (BCR) CDR3 regions from RNA-seq data, grouping at the protein level, and comparison with the BCR repertoire from healthy individuals using RNA-seq data. An average of 52 somatic mutations per patient (range, 2-172) were identified, and ≥2 (median, 15) high-quality neoantigens were predicted for 56 of 58 FL samples. The predicted neoantigen peptides were composed of missense mutations (77%), indels (9%), gene fusions (3%), and BCR sequences (11%). Building off of these preclinical analyses, we initiated a pilot clinical trial using personalized neoantigen vaccination combined with PD-1 blockade in patients with relapsed or refractory FL (#NCT03121677). Synthetic long peptide vaccines targeting predicted high-quality neoantigens were successfully synthesized for and administered to all 4 patients enrolled. Initial results demonstrate feasibility, safety, and potential immunologic and clinical responses. Our study suggests that a genomics-driven personalized cancer vaccine strategy is feasible for patients with FL, and this may overcome prior challenges in the field. This trial was registered at www.ClinicalTrials.gov as #NCT03121677.

Cancer Nanovaccines: Nanomaterials and Clinical Perspectives

Cancer nanovaccines represent a promising frontier in cancer immunotherapy, utilizing nanotechnology to augment traditional vaccine efficacy. This review comprehensively examines the current state-of-the-art in cancer nanovaccine development, elucidating innovative strategies and technologies employed in their design. It explores both preclinical and clinical advancements, emphasizing key studies demonstrating their potential to elicit robust anti-tumor immune responses. The study encompasses various facets, including integrating biomaterial-based nanocarriers for antigen delivery, adjuvant selection, and the impact of nanoscale properties on vaccine performance. Detailed insights into the complex interplay between the tumor microenvironment and nanovaccine responses are provided, highlighting challenges and opportunities in optimizing therapeutic outcomes. Additionally, the study presents a thorough analysis of ongoing clinical trials, presenting a snapshot of the current clinical landscape. By curating the latest scientific findings and clinical developments, this study aims to serve as a comprehensive resource for researchers and clinicians engaged in advancing cancer immunotherapy. Integrating nanotechnology into vaccine design holds immense promise for revolutionizing cancer treatment paradigms, and this review provides a timely update on the evolving landscape of cancer nanovaccines.

Integrating Multisector Molecular Characterization into Personalized Peptide Vaccine Design for Patients with Newly Diagnosed Glioblastoma

PURPOSE

Outcomes for patients with glioblastoma (GBM) remain poor despite multimodality treatment with surgery, radiation, and chemotherapy. There are few immunotherapy options due to the lack of tumor immunogenicity. Several clinical trials have reported promising results with cancer vaccines. To date, studies have used data from a single tumor site to identify targetable antigens, but this approach limits the antigen pool and is antithetical to the heterogeneity of GBM. We have implemented multisector sequencing to increase the pool of neoantigens across the GBM genomic landscape that can be incorporated into personalized peptide vaccines called NeoVax.

PATIENTS AND METHODS

In this study, we report the findings of four patients enrolled onto the NeoVax clinical trial (NCT0342209).

RESULTS

Immune reactivity to NeoVax neoantigens was assessed in peripheral blood mononuclear cells pre- and post-NeoVax for patients 1 to 3 using IFNγ-ELISPOT assay. A statistically significant increase in IFNγ producing T cells at the post-NeoVax time point for several neoantigens was observed. Furthermore, a post-NeoVax tumor biopsy was obtained from patient 3 and, upon evaluation, revealed evidence of infiltrating, clonally expanded T cells.

CONCLUSIONS

Collectively, our findings suggest that NeoVax stimulated the expansion of neoantigen-specific effector T cells and provide encouraging results to aid in the development of future neoantigen vaccine-based clinical trials in patients with GBM. Herein, we demonstrate the feasibility of incorporating multisector sampling in cancer vaccine design and provide information on the clinical applicability of clonality, distribution, and immunogenicity of the neoantigen landscape in patients with GBM.

Public neoantigens in breast cancer immunotherapy (Review)

Among women globally, breast cancer is the most prevalent cancer and the leading cause of cancer‑related death. Interestingly, though genetic mutations contribute to the disease, <15% of women diagnosed with breast cancer have a family history of the disease, suggesting a prevalence of sporadic genetic mutations in breast cancer development. In the rapidly rising field of cancer genomics, neoantigen‑based immunotherapy has come to the fore. The investigation of novel proteins arising from unique somatic mutations or neoantigens have opened a new pathway for both individualized and public cancer treatments. Because they are shared among individuals with similar genetic changes, public neoantigens provide an opportunity for 'off‑the‑shelf' anticancer therapies, potentially extending the benefits to a wider patient group. The present review aimed to highlight the role of shared or public neoantigens as therapeutic targets for patients with breast cancer, emphasizing common hotspot mutations of certain genes identified in breast cancer. The clinical utilization of public neoantigen‑based therapies for breast cancer treatment were also discussed.

pVACview: an interactive visualization tool for efficient neoantigen prioritization and selection

Background

Neoantigen targeting therapies including personalized vaccines have shown promise in the treatment of cancers, particularly when used in combination with checkpoint blockade therapy. At least 100 clinical trials involving these therapies are underway globally. Accurate identification and prioritization of neoantigens is highly relevant to designing these trials, predicting treatment response, and understanding mechanisms of resistance. With the advent of massively parallel DNA and RNA sequencing technologies, it is now possible to computationally predict neoantigens based on patient-specific variant information. However, numerous factors must be considered when prioritizing neoantigens for use in personalized therapies. Complexities such as alternative transcript annotations, various binding, presentation and immunogenicity prediction algorithms, and variable peptide lengths/registers all potentially impact the neoantigen selection process. There has been a rapid development of computational tools that attempt to account for these complexities. While these tools generate numerous algorithmic predictions for neoantigen characterization, results from these pipelines are difficult to navigate and require extensive knowledge of the underlying tools for accurate interpretation. This often leads to over-simplification of pipeline outputs to make them tractable, for example limiting prediction to a single RNA isoform or only summarizing the top ranked of many possible peptide candidates. In addition to variant detection, gene expression and predicted peptide binding affinities, recent studies have also demonstrated the importance of mutation location, allele-specific anchor locations, and variation of T-cell response to long versus short peptides. Due to the intricate nature and number of salient neoantigen features, presenting all relevant information to facilitate candidate selection for downstream applications is a difficult challenge that current tools fail to address.

Results

We have created pVACview, the first interactive tool designed to aid in the prioritization and selection of neoantigen candidates for personalized neoantigen therapies including cancer vaccines. pVACview has a user-friendly and intuitive interface where users can upload, explore, select and export their neoantigen candidates. The tool allows users to visualize candidates across three different levels, including variant, transcript and peptide information.

Conclusions

pVACview will allow researchers to analyze and prioritize neoantigen candidates with greater efficiency and accuracy in basic and translational settings The application is available as part of the pVACtools pipeline at pvactools.org and as an online server at pvacview.org.

Targeting Neoantigens in Cancer: Possibilities and Opportunities in Breast Cancer

As one of the most prevalent forms of cancer worldwide, breast cancer has garnered significant attention within the clinical research setting. While traditional treatment employs a multidisciplinary approach including a variety of therapies such as chemotherapy, hormone therapy, and even surgery, researchers have since directed their attention to the budding role of neoantigens. Neoantigens are defined as tumor-specific antigens that result from a multitude of genetic alterations, the most prevalent of which is the single nucleotide variant. As a result of their foreign nature, neoantigens elicit immune responses upon presentation by Major Histocompatibility Complexes I and II followed by recognition by T cell receptors. Previously, researchers have been able to utilize these immunogenic properties and manufacture neoantigen-specific T-cells and neoantigen vaccines. Within the context of breast cancer, biomarkers such as tumor protein 53 (TP53), Survivin, Partner and Localizer of BRCA2 (PALB2), and protein tyrosine phosphatase receptor T (PTPRT) display exceeding potential to serve as neoantigens. However, despite their seemingly limitless potential, neoantigens must overcome various obstacles if they are to be fairly distributed to patients. For instance, a prolonged period between the identification of a neoantigen and the dispersal of treatment poses a serious risk within the context of breast cancer. Regardless of these current obstacles, it appears highly promising that future research into neoantigens will make an everlasting impact on the health outcomes within the realm of breast cancer. The purpose of this literature review is to comprehensively discuss the etiology of various forms of breast cancer and current treatment modalities followed by the significance of neoantigens in cancer therapeutics and their application to breast cancer. Further, we have discussed the limitations, future directions, and the role of transcriptomics in neoantigen identification and personalized medicine. The concepts discussed in the original and review articles were included in this review article.

The Neo-Open Reading Frame Peptides That Comprise the Tumor Framome Are a Rich Source of Neoantigens for Cancer Immunotherapy

Identification of immunogenic cancer neoantigens as targets for therapy is challenging. Here, we integrate the whole-genome and long-read transcript sequencing of cancers to identify the collection of neo-open reading frame peptides (NOP) expressed in tumors. We termed this collection of NOPs the tumor framome. NOPs represent tumor-specific peptides that are different from wild-type proteins and may be strongly immunogenic. We describe a class of hidden NOPs that derive from structural genomic variants involving an upstream protein coding gene driving expression and translation of noncoding regions of the genome downstream of a rearrangement breakpoint, i.e., where no gene annotation or evidence for transcription exists. The entire collection of NOPs represents a vast number of possible neoantigens particularly in tumors with many structural genomic variants and a low number of missense mutations. We show that NOPs are immunogenic and epitopes derived from NOPs can bind to MHC class I molecules. Finally, we provide evidence for the presence of memory T cells specific for hidden NOPs in peripheral blood from a patient with lung cancer. This work highlights NOPs as a major source of possible neoantigens for personalized cancer immunotherapy and provides a rationale for analyzing the complete cancer genome and transcriptome as a basis for the detection of NOPs.

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

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

NeoAgDT: optimization of personal neoantigen vaccine composition by digital twin simulation of a cancer cell population

MOTIVATION

Neoantigen vaccines make use of tumor-specific mutations to enable the patient's immune system to recognize and eliminate cancer. Selecting vaccine elements, however, is a complex task which needs to take into account not only the underlying antigen presentation pathway but also tumor heterogeneity.

RESULTS

Here, we present NeoAgDT, a two-step approach consisting of: (i) simulating individual cancer cells to create a digital twin of the patient's tumor cell population and (ii) optimizing the vaccine composition by integer linear programming based on this digital twin. NeoAgDT shows improved selection of experimentally validated neoantigens over ranking-based approaches in a study of seven patients.

AVAILABILITY AND IMPLEMENTATION

The NeoAgDT code is published on Github: https://github.com/nec-research/neoagdt.

Estrogen Receptor Mutations as Novel Targets for Immunotherapy in Metastatic Estrogen Receptor-positive Breast Cancer

Estrogen receptor-positive (ER+) breast cancer is not considered immunogenic and, to date, has been proven resistant to immunotherapy. Endocrine therapy remains the cornerstone of treatment for ER+ breast cancers. However, constitutively activating mutations in the estrogen receptor alpha (ESR1) gene can emerge during treatment, rendering tumors resistant to endocrine therapy. Although these mutations represent a pathway of resistance, they also represent a potential source of neoepitopes that can be targeted by immunotherapy. In this study, we investigated ESR1 mutations as novel targets for breast cancer immunotherapy. Using machine learning algorithms, we identified ESR1-derived peptides predicted to form stable complexes with HLA-A*0201. We then validated the binding affinity and stability of the top predicted peptides through in vitro binding and dissociation assays and showed that these peptides bind HLA-A*0201 with high affinity and stability. Using tetramer assays, we confirmed the presence and expansion potential of antigen-specific CTLs from healthy female donors. Finally, using in vitro cytotoxicity assays, we showed the lysis of peptide-pulsed targets and breast cancer cells expressing common ESR1 mutations by expanded antigen-specific CTLs. Ultimately, we identified five peptides derived from the three most common ESR1 mutations (D538G, Y537S, and E380Q) and their associated wild-type peptides, which were the most immunogenic. Overall, these data confirm the immunogenicity of epitopes derived from ESR1 and highlight the potential of these peptides to be targeted by novel immunotherapy strategies.

SIGNIFICANCE

Estrogen receptor (ESR1) mutations have emerged as a key factor in endocrine therapy resistance. We identified and validated five novel, immunogenic ESR1-derived peptides that could be targeted through vaccine-based immunotherapy.

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

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

Evaluating NetMHCpan performance on non-European HLA alleles not present in training data

Bias in neural network model training datasets has been observed to decrease prediction accuracy for groups underrepresented in training data. Thus, investigating the composition of training datasets used in machine learning models with healthcare applications is vital to ensure equity. Two such machine learning models are NetMHCpan-4.1 and NetMHCIIpan-4.0, used to predict antigen binding scores to major histocompatibility complex class I and II molecules, respectively. As antigen presentation is a critical step in mounting the adaptive immune response, previous work has used these or similar predictions models in a broad array of applications, from explaining asymptomatic viral infection to cancer neoantigen prediction. However, these models have also been shown to be biased toward hydrophobic peptides, suggesting the network could also contain other sources of bias. Here, we report the composition of the networks' training datasets are heavily biased toward European Caucasian individuals and against Asian and Pacific Islander individuals. We test the ability of NetMHCpan-4.1 and NetMHCpan-4.0 to distinguish true binders from randomly generated peptides on alleles not included in the training datasets. Unexpectedly, we fail to find evidence that the disparities in training data lead to a meaningful difference in prediction quality for alleles not present in the training data. We attempt to explain this result by mapping the HLA sequence space to determine the sequence diversity of the training dataset. Furthermore, we link the residues which have the greatest impact on NetMHCpan predictions to structural features for three alleles (HLA-A*34:01, HLA-C*04:03, HLA-DRB1*12:02).

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.

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.

Integration of organoids in peptide drug discovery: Rise of the high‐throughput screening

Organoids are three‐dimensional cell aggregates with near‐physiologic cell behaviors and can undergo long‐term expansion in vitro. They are amenable to high‐throughput drug screening processes, which renders them a viable preclinical model for drug development. The procedure of organoid‐based high‐throughput screening has been extensively employed to discover small‐molecule drugs, encompassing the steps of generating organoids, examining efficient drugs in organoid cultures, and data assessment. Compared to small molecules, peptides are more straightforward to synthesize, can be modified chemically, and demonstrate high target specificity and low cytotoxicity. Therefore, they have emerged as promising carriers to deliver drugs to disease‐associated targets and could be efficient therapeutic drugs for various diseases. To date, organoids have been used to evaluate the efficacy of certain peptide agents; however, no organoid‐based high‐throughput screening of peptide drugs has been reported. Given the advantages of peptide drugs, there is an urgent need to establish organoid‐based peptide high‐throughput screening platforms. In this review, we discuss the typical approach of screening small‐molecular drugs with the use of organoid cultures, as well as provide an overview of the studies that have incorporated organoids in peptide research. Drawing on the knowledge from small molecular screens, we explore the difficulties and potential avenues for creating new platforms to identify peptide agents using organoid models.

Leading the pack: Best practices in comparative canine cancer genomics to inform human oncology

Pet dogs develop spontaneous cancers at a rate estimated to be five times higher than that of humans, providing a unique opportunity to study disease biology and evaluate novel therapeutic strategies in a model system that possesses an intact immune system and mirrors key aspects of human cancer biology. Despite decades of interest, effective utilization of pet dog cancers has been hindered by a limited repertoire of necessary cellular and molecular reagents for both in vitro and in vivo studies, as well as a dearth of information regarding the genomic landscape of these cancers. Recently, many of these critical gaps have been addressed through the generation of a highly annotated canine reference genome, the creation of several tools necessary for multi-omic analysis of canine tumours, and the development of a centralized repository for key genomic and associated clinical information from canine cancer patients, the Integrated Canine Data Commons. Together, these advances have catalysed multidisciplinary efforts designed to integrate the study of pet dog cancers more effectively into the translational continuum, with the ultimate goal of improving human outcomes. The current review summarizes this recent progress and provides a guide to resources and tools available for comparative study of pet dog cancers.

Recent advances in immunopeptidomic-based tumor neoantigen discovery

The role of aberrantly expressed proteins in tumors in driving immune-mediated control of cancer has been well documented for more than five decades. Today, we know that both aberrantly expressed normal proteins as well as mutant proteins (neoantigens) can function as tumor antigens in both humans and mice. Next-generation sequencing (NGS) and high-resolution mass spectrometry (MS) technologies have made significant advances since the early 2010s, enabling detection of rare but clinically relevant neoantigens recognized by T cells. MS profiling of tumor-specific immunopeptidomes remains the most direct method to identify mutant peptides bound to cellular MHC. However, the need for use of large numbers of cells or significant amounts of tumor tissue to achieve neoantigen detection has historically limited the application of MS. Newer, more sensitive MS technologies have recently demonstrated the capacities to detect neoantigens from fewer cells. Here, we highlight recent advancements in immunopeptidomics-based characterization of tumor-specific neoantigens. Various tumor antigen categories and neoantigen identification approaches are also discussed. Furthermore, we summarize recent reports that achieved successful tumor neoantigen detection by MS using a variety of starting materials, MS acquisition modes, and novel ion mobility devices.

Personalised neoantigen-based therapy in colorectal cancer

Colorectal cancer (CRC) has become one of the most common tumours with high morbidity, mortality and distinctive evolution mechanism. The neoantigens arising from the somatic mutations have become considerable treatment targets in the management of CRC. As cancer-specific aberrant peptides, neoantigens can trigger the robust host immune response and exert anti-tumour effects while minimising the emergence of adverse events commonly associated with alternative therapeutic regimens. In this review, we summarised the mechanism, generation, identification and prognostic significance of neoantigens, as well as therapeutic strategies challenges of neoantigen-based therapy in CRC. The evidence suggests that the establishment of personalised neoantigen-based therapy holds great promise as an effective treatment approach for patients with CRC.

mRNA-based vaccines and therapeutics: an in-depth survey of current and upcoming clinical applications

mRNA-based drugs have tremendous potential as clinical treatments, however, a major challenge in realizing this drug class will promise to develop methods for safely delivering the bioactive agents with high efficiency and without activating the immune system. With regard to mRNA vaccines, researchers have modified the mRNA structure to enhance its stability and promote systemic tolerance of antigenic presentation in non-inflammatory contexts. Still, delivery of naked modified mRNAs is inefficient and results in low levels of antigen protein production. As such, lipid nanoparticles have been utilized to improve delivery and protect the mRNA cargo from extracellular degradation. This advance was a major milestone in the development of mRNA vaccines and dispelled skepticism about the potential of this technology to yield clinically approved medicines. Following the resounding success of mRNA vaccines for COVID-19, many other mRNA-based drugs have been proposed for the treatment of a variety of diseases. This review begins with a discussion of mRNA modifications and delivery vehicles, as well as the factors that influence administration routes. Then, we summarize the potential applications of mRNA-based drugs and discuss further key points pertaining to preclinical and clinical development of mRNA drugs targeting a wide range of diseases. Finally, we discuss the latest market trends and future applications of mRNA-based drugs.

[Research Progress of Lung Cancer Vaccines]

With the development of medical technology, tumor vaccines as a novel precise immunotherapy approach have gradually received attention in clinical applications. Against the backdrop of the global corona virus disease 2019 (COVID-19) outbreak, vaccine technology has further advanced. Depending on the types of antigens, tumor vaccines can be divided into whole-cell vaccines, peptide vaccines, messenger ribonucleic acid (mRNA) vaccines, recombinant virus vaccines, etc. Although some tumor vaccines have been marketed and achieved certain therapeutic effects, the results of tumor vaccines in clinical trials have been unsatisfactory in the past period. With the maturation of next-generation sequencing (NGS) technology and the continuous development of bioinformatics, dynamic monitoring of the entire process of tumor subpopulation development has become a reality, which has laid a solid foundation for personalized, neoantigen-centered therapeutic tumor vaccines. This article reviews the recent developments of tumor vaccines of different types, starts with lung cancer and summarizes the achievements of tumor vaccines in clinical applications, and provides an outlook for the future development of antigen-centered tumor vaccines.
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Neoantigen identification: Technological advances and challenges

Neoantigens have emerged as promising targets for cutting-edge immunotherapies, such as cancer vaccines and adoptive cell therapy. These neoantigens are unique to tumors and arise exclusively from somatic mutations or non-genomic aberrations in tumor proteins. They encompass a wide range of alterations, including genomic mutations, post-transcriptomic variants, and viral oncoproteins. With the advancements in technology, the identification of immunogenic neoantigens has seen rapid progress, raising new opportunities for enhancing their clinical significance. Prediction of neoantigens necessitates the acquisition of high-quality samples and sequencing data, followed by mutation calling. Subsequently, the pipeline involves integrating various tools that can predict the expression, processing, binding, and recognition potential of neoantigens. However, the continuous improvement of computational tools is constrained by the availability of datasets which contain validated immunogenic neoantigens. This review article aims to provide a comprehensive summary of the current knowledge as well as limitations in neoantigen prediction and validation. Additionally, it delves into the origin and biological role of neoantigens, offering a deeper understanding of their significance in the field of cancer immunotherapy. This article thus seeks to contribute to the ongoing efforts to harness neoantigens as powerful weapons in the fight against cancer.

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

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

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.

Improvement in neoantigen prediction via integration of RNA sequencing data for variant calling

Introduction

Neoantigen-based immunotherapy has emerged as a promising strategy for improving the life expectancy of cancer patients. This therapeutic approach heavily relies on accurate identification of cancer mutations using DNA sequencing (DNAseq) data. However, current workflows tend to provide a large number of neoantigen candidates, of which only a limited number elicit efficient and immunogenic T-cell responses suitable for downstream clinical evaluation. To overcome this limitation and increase the number of high-quality immunogenic neoantigens, we propose integrating RNA sequencing (RNAseq) data into the mutation identification step in the neoantigen prediction workflow.

Methods

In this study, we characterize the mutation profiles identified from DNAseq and/or RNAseq data in tumor tissues of 25 patients with colorectal cancer (CRC). Immunogenicity was then validated by ELISpot assay using long synthesis peptides (sLP).

Results

We detected only 22.4% of variants shared between the two methods. In contrast, RNAseq-derived variants displayed unique features of affinity and immunogenicity. We further established that neoantigen candidates identified by RNAseq data significantly increased the number of highly immunogenic neoantigens (confirmed by ELISpot) that would otherwise be overlooked if relying solely on DNAseq data.

Discussion

This integrative approach holds great potential for improving the selection of neoantigens for personalized cancer immunotherapy, ultimately leading to enhanced treatment outcomes and improved survival rates for cancer patients.

Immunotherapy for Colorectal Cancer with High Microsatellite Instability: The Ongoing Search for Biomarkers

Microsatellite instability (MSI) is a biological condition associated with inflamed tumors, high tumor mutational burden (TMB), and responses to immune checkpoint inhibitors. In colorectal cancer (CRC), MSI tumors are found in 5% of patients in the metastatic setting and 15% in early-stage disease. Following the impressive clinical activity of immune checkpoint inhibitors in the metastatic setting, associated with deep and long-lasting responses, the development of immune checkpoint inhibitors has expanded to early-stage disease. Several phase II trials have demonstrated a high rate of pathological complete responses, with some patients even spared from surgery. However, in both settings, not all patients respond and some responses are short, emphasizing the importance of the ongoing search for accurate biomarkers. While various biomarkers of response have been evaluated in the context of MSI CRC, including B2M and JAK1/2 mutations, TMB, WNT pathway mutations, and Lynch syndrome, with mixed results, liver metastases have been associated with a lack of activity in such strategies. To improve patient selection and treatment outcomes, further research is required to identify additional biomarkers and refine existing ones. This will allow for the development of personalized treatment approaches and the integration of novel therapeutic strategies for MSI CRC patients with liver metastases.

Antigenic targets in clear cell renal cell carcinoma

Immune checkpoint inhibitors (ICIs) have transformed the management of advanced renal cell carcinoma (RCC), but most patients still do not receive a long-term benefit from these therapies, and many experience off-target, immune-related adverse effects. RCC is also different from many other ICI-responsive tumors, as it has only a modest mutation burden, and total neoantigen load does not correlate with ICI response. In order to improve the efficacy and safety of immunotherapies for RCC, it is therefore critical to identify the antigens that are targeted in effective anti-tumor immunity. In this review, we describe the potential classes of target antigens, and provide examples of previous and ongoing efforts to investigate and target antigens in RCC, with a focus on clear cell histology. Ultimately, we believe that a concerted antigen discovery effort in RCC will enable an improved understanding of response and resistance to current therapies, and lay a foundation for the future development of "precision" antigen-directed immunotherapies.

Rethinking Antigen Source: Cancer Vaccines Based on Whole Tumor Cell/tissue Lysate or Whole Tumor Cell

Cancer immunotherapies have improved human health, and one among the important technologies for cancer immunotherapy is cancer vaccine. Antigens are the most important components in cancer vaccines. Generally, antigens in cancer vaccines can be divided into two categories: pre-defined antigens and unidentified antigens. Although, cancer vaccines loaded with predefined antigens are commonly used, cancer vaccine loaded with mixed unidentified antigens, especially whole cancer cells or cancer cell lysates, is a very promising approach, and such vaccine can obviate some limitations in cancer vaccines. Their advantages include, but are not limited to, the inclusion of pan-spectra (all or most kinds of) antigens, inducing pan-clones specific T cells, and overcoming the heterogeneity of cancer cells. In this review, the recent advances in cancer vaccines based on whole-tumor antigens, either based on whole cancer cells or whole cancer cell lysates, are summarized. In terms of whole cancer cell lysates, the focus is on applying whole water-soluble cell lysates as antigens. Recently, utilizing the whole cancer cell lysates as antigens in cancer vaccines has become feasible. Considering that pre-determined antigen-based cancer vaccines (mainly peptide-based or mRNA-based) have various limitations, developing cancer vaccines based on whole-tumor antigens is a promising alternative.

Adoptive cell therapy for cancer treatment

Adoptive cell therapy (ACT) is a rapidly growing anti-cancer strategy that has shown promise in treating various cancer types. The concept of ACT involves activating patients' own immune cells ex vivo and then transferring them back to the patients to recognize and eliminate cancer cells. Currently, the commonly used ACT includes tumor-infiltrating lymphocytes (TILs), genetically engineered immune cells, and dendritic cells (DCs) vaccines. With the advancement of cell culture and genetic engineering techniques, ACT has been used in clinics to treat malignant hematological diseases and many new ACT-based regimens are in different stages of clinical trials. Here, representative ACT approaches are introduced and the opportunities and challenges for clinical translation of ACT are discussed.

Targeting LSD1 in tumor immunotherapy: rationale, challenges and potential

Lysine-specific demethylase 1 (LSD1) is an enzyme that removes lysine methylation marks from nucleosome histone tails and plays an important role in cancer initiation, progression, metastasis, and recurrence. Recent research shows that LSD1 regulates tumor cells and immune cells through multiple upstream and downstream pathways, enabling tumor cells to adapt to the tumor microenvironment (TME). As a potential anti-tumor treatment strategy, immunotherapy has developed rapidly in the past few years. However, most patients have a low response rate to available immune checkpoint inhibitors (ICIs), including anti-PD-(L)1 therapy and CAR-T cell therapy, due to a broad array of immunosuppressive mechanisms. Notably, inhibition of LSD1 turns "cold tumors" into "hot tumors" and subsequently enhances tumor cell sensitivity to ICIs. This review focuses on recent advances in LSD1 and tumor immunity and discusses a potential therapeutic strategy for combining LSD1 inhibition with immunotherapy.

Usefulness of Docking and Molecular Dynamics in Selecting Tumor Neoantigens to Design Personalized Cancer Vaccines: A Proof of Concept

Personalized cancer vaccines based on neoantigens are a new and promising treatment for cancer; however, there are still multiple unresolved challenges to using this type of immunotherapy. Among these, the effective identification of immunogenic neoantigens stands out, since the in silico tools used generate a significant portion of false positives. Inclusion of molecular simulation techniques can refine the results these tools produce. In this work, we explored docking and molecular dynamics to study the association between the stability of peptide-HLA complexes and their immunogenicity, using as a proof of concept two HLA-A2-restricted neoantigens that were already evaluated in vitro. The results obtained were in accordance with the in vitro immunogenicity, since the immunogenic neoantigen ASTN1 remained bound at both ends to the HLA-A2 molecule. Additionally, molecular dynamic simulation suggests that position 1 of the peptide has a more relevant role in stabilizing the N-terminus than previously proposed. Likewise, the mutations may have a "delocalized" effect on the peptide-HLA interaction, which means that the mutated amino acid influences the intensity of the interactions of distant amino acids of the peptide with the HLA. These findings allow us to propose the inclusion of molecular simulation techniques to improve the identification of neoantigens for cancer vaccines.

Computational prediction of MHC anchor locations guides neoantigen identification and prioritization

Neoantigens are tumor-specific peptide sequences resulting from sources such as somatic DNA mutations. Upon loading onto major histocompatibility complex (MHC) molecules, they can trigger recognition by T cells. Accurate neoantigen identification is thus critical for both designing cancer vaccines and predicting response to immunotherapies. Neoantigen identification and prioritization relies on correctly predicting whether the presenting peptide sequence can successfully induce an immune response. Because most somatic mutations are single-nucleotide variants, changes between wild-type and mutated peptides are typically subtle and require cautious interpretation. A potentially underappreciated variable in neoantigen prediction pipelines is the mutation position within the peptide relative to its anchor positions for the patient's specific MHC molecules. Whereas a subset of peptide positions are presented to the T cell receptor for recognition, others are responsible for anchoring to the MHC, making these positional considerations critical for predicting T cell responses. We computationally predicted anchor positions for different peptide lengths for 328 common HLA alleles and identified unique anchoring patterns among them. Analysis of 923 tumor samples shows that 6 to 38% of neoantigen candidates are potentially misclassified and can be rescued using allele-specific knowledge of anchor positions. A subset of anchor results were orthogonally validated using protein crystallography structures. Representative anchor trends were experimentally validated using peptide-MHC stability assays and competition binding assays. By incorporating our anchor prediction results into neoantigen prediction pipelines, we hope to formalize, streamline, and improve the identification process for relevant clinical studies.