TSNAdb: A Database for Tumor-specific Neoantigens from Immunogenomics Data Analysis

Pan-cancer proteogenomics expands the landscape of therapeutic targets

Fewer than 200 proteins are targeted by cancer drugs approved by the Food and Drug Administration (FDA). We integrate Clinical Proteomic Tumor Analysis Consortium (CPTAC) proteogenomics data from 1,043 patients across 10 cancer types with additional public datasets to identify potential therapeutic targets. Pan-cancer analysis of 2,863 druggable proteins reveals a wide abundance range and identifies biological factors that affect mRNA-protein correlation. Integration of proteomic data from tumors and genetic screen data from cell lines identifies protein overexpression- or hyperactivation-driven druggable dependencies, enabling accurate predictions of effective drug targets. Proteogenomic identification of synthetic lethality provides a strategy to target tumor suppressor gene loss. Combining proteogenomic analysis and MHC binding prediction prioritizes mutant KRAS peptides as promising public neoantigens. Computational identification of shared tumor-associated antigens followed by experimental confirmation nominates peptides as immunotherapy targets. These analyses, summarized at https://targets.linkedomics.org, form a comprehensive landscape of protein and peptide targets for companion diagnostics, drug repurposing, and therapy development.

Transformers meets neoantigen detection: a systematic literature review

Cancer immunology offers a new alternative to traditional cancer treatments, such as radiotherapy and chemotherapy. One notable alternative is the development of personalized vaccines based on cancer neoantigens. Moreover, Transformers are considered a revolutionary development in artificial intelligence with a significant impact on natural language processing (NLP) tasks and have been utilized in proteomics studies in recent years. In this context, we conducted a systematic literature review to investigate how Transformers are applied in each stage of the neoantigen detection process. Additionally, we mapped current pipelines and examined the results of clinical trials involving cancer vaccines.

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.

Proteo-genomic characterization of cirrhosis-associated liver cancers reveals potential subtypes and therapeutic targets

BACKGROUND

This study aimed to identify potential subtypes of hepatocellular carcinoma (HCC) associated with cirrhosis and to investigate key markers using bioinformatic analysis of gene expression datasets-0.

METHODS

Three data sets (GSE17548, GSE56140, and GSE87630) were extracted from the Gene Expression Omnibus (GEO) database and normalized using the Limma package in R. Principal component analysis (PCA) and cluster analysis was performed to examine data distribution and identify subtypes. Differential gene expression analysis was performed using the Limma software package. Protein-protein interaction analysis and functional annotation were performed using the STRING database and Cytoscape software. Important signaling pathways and processes were identified using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway Analysis.

RESULTS

The analysis revealed different subtypes of HCC associated with cirrhosis and identified several key genes, including CCNB2, MCM4, and CDC20, with strong binding power and prognostic value. Functional annotation indicated involvement in cell cycle regulation and metabolic pathways. ROC analysis showed high sensitivity and specificity of these genes in predicting HCC prognosis.

CONCLUSION

These results suggest that CCNB2, MCM4, and CDC20 may serve as potential biomarkers for predicting HCC prognosis in patients with cirrhosis and provide insights into the molecular mechanisms of HCC progression.

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.

Tumor-Derived Antigenic Peptides as Potential Cancer Vaccines

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

Conserved methylation signatures associate with the tumor immune microenvironment and immunotherapy response

BACKGROUND

Aberrant DNA methylation is a major characteristic of cancer genomes. It remains unclear which biological processes determine epigenetic reprogramming and how these processes influence the variants in the cancer methylome, which can further impact cancer phenotypes.

METHODS

We performed pairwise permutations of 381,900 loci in 569 paired DNA methylation profiles of cancer tissue and matched normal tissue from The Cancer Genome Atlas (TCGA) and defined conserved differentially methylated positions (DMPs) based on the resulting null distribution. Then, we derived independent methylation signatures from 2,465 cancer-only methylation profiles from the TCGA and 241 cell line-based methylation profiles from the Genomics of Drug Sensitivity in Cancer (GDSC) cohort using nonnegative matrix factorization (NMF). We correlated DNA methylation signatures with various clinical and biological features, including age, survival, cancer stage, tumor immune microenvironment factors, and immunotherapy response. We inferred the determinant genes of these methylation signatures by integrating genomic and transcriptomic data and evaluated the impact of these signatures on cancer phenotypes in independent bulk and single-cell RNA/methylome cohorts.

RESULTS

We identified 7,364 differentially methylated positions (2,969 Hyper-DMPs and 4,395 Hypo-DMPs) in nine cancer types from the TCGA. We subsequently retrieved three highly conserved, independent methylation signatures (Hyper-MS1, Hypo-MS1, and Hypo-MS4) from cancer tissues and cell lines based on these Hyper and Hypo-DMPs. Our data suggested that Hypo-MS4 activity predicts poor survival and is associated with immunotherapy response and distant tumor metastasis, and Hypo-MS4 activity is related to TP53 mutation and FOXA1 binding specificity. In addition, we demonstrated a correlation between the activities of Hypo-MS4 in cancer cells and the fractions of regulatory CD4 + T cells with the expression levels of immunological genes in the tumor immune microenvironment.

CONCLUSIONS

Our findings demonstrated that the methylation signatures of distinct biological processes are associated with immune activity in the cancer microenvironment and predict immunotherapy response.

Characterizing and forecasting neoantigens-resulting from MUC mutations in COAD

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Immunoactivity of a hybrid membrane biosurface on nanoparticles: enhancing interactions with dendritic cells to augment anti-tumor immune responses

Nano-biointerfaces play a pivotal role in determining the functionality of engineered therapeutic nanoparticles, particularly in the context of designing nanovaccines to effectively activate immune cells for cancer immunotherapy. Unlike involving chemical reactions by conventional surface decorating strategies, cell membrane-coating technology offers a straightforward approach to endow nanoparticles with natural biosurfaces, enabling them to mimic and integrate into the intricate biosystems of the body to interact with specific cells under physiological conditions. In this study, cell membranes, in a hybrid formulation, derived from cancer and activated macrophage cells were found to enhance the interaction of nanoparticles (HMP) with dendritic cells (DCs) and T cells among the mixed immune cells from lymph nodes (LNs), which could be leveraged in the development of nanovaccines for anti-tumor therapy. After loading with an adjuvant (R837), the nanoparticles coated with a hybrid membrane (HMPR) demonstrated effectiveness in priming DCs both in vitro and in vivo, resulting in amplified anti-tumor immune responses compared to those of nanoparticles coated with a single type of membrane or those lacking a membrane coating. The elevated immunoactivity of nanoparticles achieved by incorporating a hybrid membrane biosurface provides us a more profound comprehension of the nano-immune interaction, which may significantly benefit the development of bioactive nanomaterials for advanced therapy.

COSMIC-based mutation database enhances identification efficiency of HLA-I immunopeptidome

BACKGROUND

Neoantigens have emerged as a promising area of focus in tumor immunotherapy, with several established strategies aiming to enhance their identification. Human leukocyte antigen class I molecules (HLA-I), which present intracellular immunopeptides to T cells, provide an ideal source for identifying neoantigens. However, solely relying on a mutation database generated through commonly used whole exome sequencing (WES) for the identification of HLA-I immunopeptides, may result in potential neoantigens being missed due to limitations in sequencing depth and sample quality.

METHOD

In this study, we constructed and evaluated an extended database for neoantigen identification, based on COSMIC mutation database. This study utilized mass spectrometry-based proteogenomic profiling to identify the HLA-I immunopeptidome enriched from HepG2 cell. HepG2 WES-based and the COSMIC-based mutation database were generated and utilized to identify HepG2-specific mutant immunopeptides.

RESULT

The results demonstrated that COSMIC-based database identified 5 immunopeptides compared to only 1 mutant peptide identified by HepG2 WES-based database, indicating its effectiveness in identifying mutant immunopeptides. Furthermore, HLA-I affinity of the mutant immunopeptides was evaluated through NetMHCpan and peptide-docking modeling to validate their binding to HLA-I molecules, demonstrating the potential of mutant peptides identified by the COSMIC-based database as neoantigens.

CONCLUSION

Utilizing the COSMIC-based mutation database is a more efficient strategy for identifying mutant peptides from HLA-I immunopeptidome without significantly increasing the false positive rate. HepG2 specific WES-based database may exclude certain mutant peptides due to WES sequencing depth or sample heterogeneity. The COSMIC-based database can effectively uncover potential neoantigens within the HLA-I immunopeptidomes.

In Silico: Predicting Intrinsic Features of HLA Class-I Restricted Neoantigens

Mutation-containing immunogenic peptides from tumor cells, also named as neoantigens, have various amino acid descriptors and physical-chemical properties characterized intrinsic features, which are useful in prioritizing the immunogenicity potentials of neoantigens and predicting patients' survival. Here, we describe a glioma neoantigen intrinsic feature database, GNIFdb, that hosts computationally predicted HLA-I restricted neoantigens of gliomas, their intrinsic features, and the tools for calculating intrinsic features and predicting overall survival of gliomas. We illustrate the application of GNIFdb in searching for possible neoantigen candidates from ATF6 that plays important roles in tumor growth and resistance to radiotherapy in glioblastoma. We also demonstrate the application of intrinsic feature associated tools in GNIFdb to predict the overall survival of primary IDH wild-type glioblastoma.

Neo-Antigen-Reactive T Cells Immunotherapy for Colorectal Cancer: A More Personalized Cancer Therapy Approach

Colorectal cancer (CRC) is the second most common malignancy in women and the third most frequent cancer in men. Evidence has revealed that the survival of patients with metastatic CRC is very low, between one and three years. Neoantigens are known proteins encoded by mutations in tumor cells. It is theorized that recognizing neoantigens by T cells leads to T cell activation and further antitumor responses. Neoantigen-reactive T cells (NRTs) are designed against the mentioned neoantigens expressed by tumor cells. NRTs selectively kill tumor cells without damage to non-cancerous cells. Identifying patient-specific and high immunogen neoantigens is important in NRT immunotherapy of patients with CRC. However, the main challenges are the side effects and preparation of NRTs, as well as the effectiveness of these cells in vivo. This review summarized the properties of neoantigens as well as the preparation and therapeutic outcomes of NRTs for the treatment of CRC.

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.

Unraveling tumor specific neoantigen immunogenicity prediction: a comprehensive analysis

Introduction

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

Methods

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

Results

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

Conclusion

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

CrypticProteinDB: an integrated database of proteome and immunopeptidome derived non-canonical cancer proteins

Translated non-canonical proteins derived from noncoding regions or alternative open reading frames (ORFs) can contribute to critical and diverse cellular processes. In the context of cancer, they also represent an under-appreciated source of targets for cancer immunotherapy through their tumor-enriched expression or by harboring somatic mutations that produce neoantigens. Here, we introduce the largest integration and proteogenomic analysis of novel peptides to assess the prevalence of non-canonical ORFs (ncORFs) in more than 900 patient proteomes and 26 immunopeptidome datasets across 14 cancer types. The integrative proteogenomic analysis of whole-cell proteomes and immunopeptidomes revealed peptide support for a nonredundant set of 9760 upstream, downstream, and out-of-frame ncORFs in protein coding genes and 12811 in noncoding RNAs. Notably, 6486 ncORFs were derived from differentially expressed genes and 340 were ubiquitously translated across eight or more cancers. The analysis also led to the discovery of thirty-four epitopes and eight neoantigens from non-canonical proteins in two cohorts as novel cancer immunotargets. Collectively, our analysis integrated both bottom-up proteogenomic and targeted peptide validation to illustrate the prevalence of translated non-canonical proteins in cancer and to provide a resource for the prioritization of novel proteins supported by proteomic, immunopeptidomic, genomic and transcriptomic data, available at https://www.maherlab.com/crypticproteindb.

epiTCR: a highly sensitive predictor for TCR-peptide binding

MOTIVATION

Predicting the binding between T-cell receptor (TCR) and peptide presented by human leucocyte antigen molecule is a highly challenging task and a key bottleneck in the development of immunotherapy. Existing prediction tools, despite exhibiting good performance on the datasets they were built with, suffer from low true positive rates when used to predict epitopes capable of eliciting T-cell responses in patients. Therefore, an improved tool for TCR-peptide prediction built upon a large dataset combining existing publicly available data is still needed.

RESULTS

We collected data from five public databases (IEDB, TBAdb, VDJdb, McPAS-TCR, and 10X) to form a dataset of >3 million TCR-peptide pairs, 3.27% of which were binding interactions. We proposed epiTCR, a Random Forest-based method dedicated to predicting the TCR-peptide interactions. epiTCR used simple input of TCR CDR3β sequences and antigen sequences, which are encoded by flattened BLOSUM62. epiTCR performed with area under the curve (0.98) and higher sensitivity (0.94) than other existing tools (NetTCR, Imrex, ATM-TCR, and pMTnet), while maintaining comparable prediction specificity (0.9). We identified seven epitopes that contributed to 98.67% of false positives predicted by epiTCR and exerted similar effects on other tools. We also demonstrated a considerable influence of peptide sequences on prediction, highlighting the need for more diverse peptides in a more balanced dataset. In conclusion, epiTCR is among the most well-performing tools, thanks to the use of combined data from public sources and its use will contribute to the quest in identifying neoantigens for precision cancer immunotherapy.

AVAILABILITY AND IMPLEMENTATION

epiTCR is available on GitHub (https://github.com/ddiem-ri-4D/epiTCR).

Advancing CAR T cell therapy through the use of multidimensional omics data

Despite the notable success of chimeric antigen receptor (CAR) T cell therapies in the treatment of certain haematological malignancies, challenges remain in optimizing CAR designs and cell products, improving response rates, extending the durability of remissions, reducing toxicity and broadening the utility of this therapeutic modality to other cancer types. Data from multidimensional omics analyses, including genomics, epigenomics, transcriptomics, T cell receptor-repertoire profiling, proteomics, metabolomics and/or microbiomics, provide unique opportunities to dissect the complex and dynamic multifactorial phenotypes, processes and responses of CAR T cells as well as to discover novel tumour targets and pathways of resistance. In this Review, we summarize the multidimensional cellular and molecular profiling technologies that have been used to advance our mechanistic understanding of CAR T cell therapies. In addition, we discuss current applications and potential strategies leveraging multi-omics data to identify optimal target antigens and other molecular features that could be exploited to enhance the antitumour activity and minimize the toxicity of CAR T cell therapy. Indeed, fully utilizing multi-omics data will provide new insights into the biology of CAR T cell therapy, further accelerate the development of products with improved efficacy and safety profiles, and enable clinicians to better predict and monitor patient responses.

TSNAdb v2.0: The Updated Version of Tumor-specific Neoantigen Database

In recent years, neoantigens have been recognized as ideal targets for tumor immunotherapy. With the development of neoantigen-based tumor immunotherapy, comprehensive neoantigen databases are urgently needed to meet the growing demand for clinical studies. We have built the tumor-specific neoantigen database (TSNAdb) previously, which has attracted much attention. In this study, we provide TSNAdb v2.0, an updated version of the TSNAdb. TSNAdb v2.0 offers several new features, including (1) adopting more stringent criteria for neoantigen identification, (2) providing predicted neoantigens derived from three types of somatic mutations, and (3) collecting experimentally validated neoantigens and dividing them according to the experimental level. TSNAdb v2.0 is freely available at https://pgx.zju.edu.cn/tsnadb/.

In silico designed mRNA vaccines targeting CA-125 neoantigen in breast and ovarian cancer

Somatic mutation-derived neoantigens are associated with patient survival in breast and ovarian cancer. These neoantigens are targets for cancer, as shown by the implementation of neoepitope peptides as cancer vaccines. The success of cost-effective multi-epitope mRNA vaccines against SARS-Cov-2 in the pandemic established a model for reverse vaccinology. In this study, we aimed to develop an in silico pipeline designing an mRNA vaccine of the CA-125 neoantigen against breast and ovarian cancer, respectively. Using immuno-bioinformatics tools, we predicted cytotoxic CD8+ T cell epitopes based on somatic mutation-driven neoantigens of CA-125 in breast or ovarian cancer, constructed a self-adjuvant mRNA vaccine with CD40L and MHC-I -targeting domain to enhance cross-presentation of neoepitopes by dendritic cells. With an in silico ImmSim algorithm, we estimated the immune responses post-immunization, showing IFN-γ and CD8+ T cell response. The strategy described in this study may be scaled up and implemented to design precision multi-epitope mRNA vaccines by targeting multiple neoantigens.

Designing neoantigen cancer vaccines, trials, and outcomes

Neoantigen vaccines are based on epitopes of antigenic parts of mutant proteins expressed in cancer cells. These highly immunogenic antigens may trigger the immune system to combat cancer cells. Improvements in sequencing technology and computational tools have resulted in several clinical trials of neoantigen vaccines on cancer patients. In this review, we have looked into the design of the vaccines which are undergoing several clinical trials. We have discussed the criteria, processes, and challenges associated with the design of neoantigens. We searched different databases to track the ongoing clinical trials and their reported outcomes. We observed, in several trials, the vaccines boost the immune system to combat the cancer cells while maintaining a reasonable margin of safety. Detection of neoantigens has led to the development of several databases. Adjuvants also play a catalytic role in improving the efficacy of the vaccine. Through this review, we can conclude that the efficacy of vaccines can make it a potential treatment across different types of cancers.

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.

Neoantigens: promising targets for cancer therapy

Recent advances in neoantigen research have accelerated the development and regulatory approval of tumor immunotherapies, including cancer vaccines, adoptive cell therapy and antibody-based therapies, especially for solid tumors. Neoantigens are newly formed antigens generated by tumor cells as a result of various tumor-specific alterations, such as genomic mutation, dysregulated RNA splicing, disordered post-translational modification, and integrated viral open reading frames. Neoantigens are recognized as non-self and trigger an immune response that is not subject to central and peripheral tolerance. The quick identification and prediction of tumor-specific neoantigens have been made possible by the advanced development of next-generation sequencing and bioinformatic technologies. Compared to tumor-associated antigens, the highly immunogenic and tumor-specific neoantigens provide emerging targets for personalized cancer immunotherapies, and serve as prospective predictors for tumor survival prognosis and immune checkpoint blockade responses. The development of cancer therapies will be aided by understanding the mechanism underlying neoantigen-induced anti-tumor immune response and by streamlining the process of neoantigen-based immunotherapies. This review provides an overview on the identification and characterization of neoantigens and outlines the clinical applications of prospective immunotherapeutic strategies based on neoantigens. We also explore their current status, inherent challenges, and clinical translation potential.

Prognosis and Characterization of Microenvironment in Cervical Cancer Influenced by Fatty Acid Metabolism-Related Genes

Increasing evidence suggests that diverse activation patterns of metabolic signalling pathways may lead to molecular diversity of cervical cancer (CC). But rare research focuses on the alternation of fatty acid metabolism (FAM) in CC. Therefore, we constructed and compared models based on the expression of FAM-related genes from the Cancer Genome Atlas by different machine learning algorithms. The most reliable model was built with 14 significant genes by LASSO-Cox regression, and the CC cohort was divided into low-/high-risk groups by the median of risk score. Then, a feasible nomogram was established and validated by C-index, calibration curve, net benefit, and decision curve analysis. Furthermore, the hub genes among differential expression genes were identified and the post-transcriptional and translational regulation networks were characterized. Moreover, the somatic mutation and copy number variation landscapes were depicted. Importantly, the specific mutation drivers and signatures of the FAM phenotypes were excavated. As a result, the high-risk samples were featured by activated de novo fatty acid synthesis, epithelial to mesenchymal transition, angiogenesis, and chronic inflammation response, which might be caused by mutations of oncogenic driver genes in RTK/RAS, PI3K, and NOTCH signalling pathways. Besides the hyperactivity of cytidine deaminase and deficiency of mismatch repair, the mutations of POLE might be partially responsible for the mutations in the high-risk group. Next, the antigenome including the neoantigen and cancer germline antigens was estimated. The decreasing expression of a series of cancer germline antigens was identified to be related to reduction of CD8 T cell infiltration in the high-risk group. Then, the comprehensive evaluation of connotations between the tumour microenvironment and FAM phenotypes demonstrated that the increasing risk score was related to the suppressive immune microenvironment. Finally, the prediction of therapy targets revealed that the patients with high risk might be sensitive to the RAF inhibitor AZ628. Our findings provide a novel insight for personalized treatment in CC.

TSNAD and TSNAdb: The Useful Toolkit for Clinical Application of Tumor-Specific Neoantigens

Tumor-specific neoantigens play important roles in tumor immunotherapy. How to predict neoantigens accurately and efficiently has attracted much attention. TSNAD is the first one-stop neoantigen prediction tool from next-generation sequencing data, and TSNAdb provides both predicted and validated neoantigens based on pan-cancer immunogenomics analyses. In this chapter, we describe the usage of TSNAD and TSNAdb for the clinical application of neoantigens. The latest version of TSNAD is available at https://pgx.zju.edu.cn/tsnad , and the latest version of TSNAdb is available at https://pgx.zju.edu.cn/tsnadb .

Characterization of genomic alterations and neoantigens and analysis of immune infiltration identified therapeutic and prognostic biomarkers in adenocarcinoma at the gastroesophageal junction

Objectives

Adenocarcinoma at the gastroesophageal junction (ACGEJ) refers to a malignant tumor that occurs at the esophagogastric junction. Despite some progress in targeted therapies for HER2, FGFR2, EGFR, MET, Claudin 18.2 and immune checkpoints in ACGEJ tumors, the 5-year survival rate of patients remains poor. Thus, it is urgent to explore genomic alterations and neoantigen characteristics of tumors and identify CD8+ T-cell infiltration-associated genes to find potential therapeutic targets and develop a risk model to predict ACGEJ patients’ overall survival (OS).

Methods

Whole-exome sequencing (WES) was performed on 55 paired samples from Chinese ACGEJ patients. Somatic mutations and copy number variations were detected by Strelka2 and FACETS, respectively. SigProfiler and SciClone were employed to decipher the mutation signature and clonal structure of each sample, respectively. Neoantigens were predicted using the MuPeXI pipeline. RNA sequencing (RNA-seq) data of ACGEJ samples from our previous studies and The Cancer Genome Atlas (TCGA) were used to identify genes significantly associated with CD8+ T-cell infiltration by weighted gene coexpression network analysis (WGCNA). To construct a risk model, we conducted LASSO and univariate and multivariate Cox regression analyses.

Results

Recurrent MAP2K7, RNF43 and RHOA mutations were found in ACGEJ tumors. The COSMIC signature SBS17 was associated with ACGEJ progression. CCNE1 and VEGFA were identified as putative CNV driver genes. PI3KCA and TP53 mutations conferred selective advantages to cancer cells. The Chinese ACGEJ patient neoantigen landscape was revealed for the first time, and 58 potential neoantigens common to TSNAdb and IEDB were identified. Compared with Siewert type II samples, Siewert type III samples had significant enrichment of the SBS17 signature, a lower TNFRSF14 copy number, a higher proportion of samples with complex clonal architecture and a higher neoantigen load. We identified 10 important CD8+ T-cell infiltration-related Hub genes (CCL5, CD2, CST7, GVINP1, GZMK, IL2RB, IKZF3, PLA2G2D, P2RY10 and ZAP70) as potential therapeutic targets from the RNA-seq data. Seven CD8+ T-cell infiltration-related genes (ADAM28, ASPH, CAMK2N1, F2R, STAP1, TP53INP2, ZC3H3) were selected to construct a prognostic model. Patients classified as high risk based on this model had significantly worse OS than low-risk patients, which was replicated in the TCGA-ACGEJ cohort.

Conclusions

This study provides new neoantigen-based immunotherapeutic targets for ACGEJ treatment and effective disease prognosis biomarkers.

Neoantigens and their clinical applications in human gastrointestinal cancers

Background

Tumor-specific neoantigens are ideal targets for cancer immunotherapy. As research findings have proved, neoantigen-specific T cell activity is immunotherapy’s most important determinant.

Main text

There is sufficient evidence showing the role of neoantigens in clinically successful immunotherapy, providing a justification for targeting. Because of the significance of the pre-existing anti-tumor immune response for the immune checkpoint inhibitor, it is believed that personalized neoantigen-based therapy may be an imperative approach for cancer therapy. Thus, intensive attention is given to strategies targeting neoantigens for the significant impact with other immunotherapies, such as the immune checkpoint inhibitor. Today, several algorithms are designed and optimized based on Next-Generation Sequencing and public databases, including dbPepNeo, TANTIGEN 2.0, Cancer Antigenic Peptide Database, NEPdb, and CEDAR databases for predicting neoantigens in silico that stimulates the development of T cell therapies, cancer vaccine, and other ongoing immunotherapy approaches.

Conclusions

In this review, we deliberated the current developments in understanding and recognition of the immunogenicity of newly found gastrointestinal neoantigens as well as their functions in immunotherapies and cancer detection. We also described how neoantigens are being developed and how they might be used in the treatment of GI malignancies.

The role of hypoxia-related genes in TACE-refractory hepatocellular carcinoma: Exploration of prognosis, immunological characteristics and drug resistance based on onco-multi-OMICS approach

Transcatheter arterial chemoembolization (TACE) is an effective treatment for hepatocellular carcinoma (HCC). During TACE, chemotherapeutic agents are locally infused into the tumor and simultaneously cause hypoxia in tumor cells. Importantly, the poor effect of TACE in some HCC patients has been shown to be related to dysregulated expression of hypoxia-related genes (HRGs). Therefore, we identified 33 HRGs associated with TACE (HRGTs) by differential analysis and characterized the mutational landscape of HRGTs. Among 586 HCC patients, two molecular subtypes reflecting survival status were identified by consistent clustering analysis based on 24 prognosis-associated HRGs. Comparing the transcriptomic difference of the above molecular subtypes, three molecular subtypes that could reflect changes in the immune microenvironment were then identified. Ultimately, four HRGTs (CTSO, MMP1, SPP1, TPX2) were identified based on machine learning approachs. Importantly, risk assessment can be performed for each patient by these genes. Based on the parameters of the risk model, we determined that high-risk patients have a more active immune microenvironment, indicating “hot tumor” status. And the Tumor Immune Dysfunction and Exclusion (TIDE), the Cancer Immunome Atlas (TCIA), and Genome of Drug Sensitivity in Cancer (GDSC) databases further demonstrated that high-risk patients have a positive response to immunotherapy and have lower IC50 values for drugs targeting cell cycle, PI3K/mTOR, WNT, and RTK related signaling pathways. Finally, single-cell level analysis revealed significant overexpression of CTSO, MMP1, SPP1, and TPX2 in malignant cell after PD-L1/CTLA-4 treatment. In conclusion, Onco-Multi-OMICS analysis showed that HRGs are potential biomarkers for patients with refractory TACE, and it provides a novel immunological perspective for developing personalized therapies.

Discovery and characterization of tumor antigens in hepatocellular carcinoma for mRNA vaccine development

BACKGROUND

mRNA vaccines are emerging as new targets for cancer immunotherapy. However, the potential tumor antigens for mRNA vaccine design in hepatocellular carcinoma (HCC) remain to be elucidated.

METHODS

Genetic and RNA-Seq data were obtained from TCGA and ICGC. Tumor-specific antigens (TSAs) were identified by differential expression, mutation status, HLA binding, antigen-presenting cell (APC) correlation, immune checkpoint (ICP) relevance and prognosis. Consensus clustering was used for patient classification. The molecular and immune status of TSAs and clustered patients, including prognostic ability, tumor microenvironment, tumor-related signature and tumor immune dysfunction and exclusion (TIDE), were further characterized.

RESULTS

Five dysregulated and mutated TSAs were identified in HCC (TSA5): FXYD6, JAM2, GALNT16, C7, and CCDC146. Seven immune gene modules and five immune subtypes (IS1-IS5) of HCC were identified. The immune subtypes and TSA5-related modules showed distinct molecular, cellular and clinical characteristics. According to our study, IS1 patients may be suitable for vaccination.

Structural aspects of chemical modifications in the MHC-restricted immunopeptidome; Implications for immune recognition

Significant advances in mass-spectroscopy (MS) have made it possible to investigate the cellular immunopeptidome, a large collection of MHC-associated epitopes presented on the surface of healthy, stressed and infected cells. These approaches have hitherto allowed the unambiguous identification of large cohorts of epitope sequences that are restricted to specific MHC class I and II molecules, enhancing our understanding of the quantities, qualities and origins of these peptide populations. Most importantly these analyses provide essential information about the immunopeptidome in responses to pathogens, autoimmunity and cancer, and will hopefully allow for future tailored individual therapies. Protein post-translational modifications (PTM) play a key role in cellular functions, and are essential for both maintaining cellular homeostasis and increasing the diversity of the proteome. A significant proportion of proteins is post-translationally modified, and thus a deeper understanding of the importance of PTM epitopes in immunopeptidomes is essential for a thorough and stringent understanding of these peptide populations. The aim of the present review is to provide a structural insight into the impact of PTM peptides on stability of MHC/peptide complexes, and how these may alter/modulate immune responses.

Functional landscapes of POLE and POLD1 mutations in checkpoint blockade-dependent antitumor immunity

Defects in pathways governing genomic fidelity have been linked to improved response to immune checkpoint blockade therapy (ICB). Pathogenic POLE/POLD1 mutations can cause hypermutation, yet how diverse mutations in POLE/POLD1 influence antitumor immunity following ICB is unclear. Here, we comprehensively determined the effect of POLE/POLD1 mutations in ICB and elucidated the mechanistic impact of these mutations on tumor immunity. Murine syngeneic tumors harboring Pole/Pold1 functional mutations displayed enhanced antitumor immunity and were sensitive to ICB. Patients with POLE/POLD1 mutated tumors harboring telltale mutational signatures respond better to ICB than patients harboring wild-type or signature-negative tumors. A mutant POLE/D1 function-associated signature-based model outperformed several traditional approaches for identifying POLE/POLD1 mutated patients that benefit from ICB. Strikingly, the spectrum of mutational signatures correlates with the biochemical features of neoantigens. Alterations that cause POLE/POLD1 function-associated signatures generate T cell receptor (TCR)-contact residues with increased hydrophobicity, potentially facilitating T cell recognition. Altogether, the functional landscapes of POLE/POLD1 mutations shape immunotherapy efficacy.

Identification of Metabolism-Related Gene-Based Subgroup in Prostate Cancer

Prostate cancer is still the main male health problem in the world. The role of metabolism in the occurrence and development of prostate cancer is becoming more and more obvious, but it is not clear. Here we firstly identified a metabolism-related gene-based subgroup in prostate cancer. We used metabolism-related genes to divide prostate cancer patients from The Cancer Genome Atlas into different clinical benefit populations, which was verified in the International Cancer Genome Consortium. After that, we analyzed the metabolic and immunological mechanisms of clinical beneficiaries from the aspects of functional analysis of differentially expressed genes, gene set variation analysis, tumor purity, tumor microenvironment, copy number variations, single-nucleotide polymorphism, and tumor-specific neoantigens. We identified 56 significant genes for non-negative matrix factorization after survival-related univariate regression analysis and identified three subgroups. Patients in subgroup 2 had better overall survival, disease-free interval, progression-free interval, and disease-specific survival. Functional analysis indicated that differentially expressed genes in subgroup 2 were enriched in the xenobiotic metabolic process and regulation of cell development. Moreover, the metabolism and tumor purity of subgroup 2 were higher than those of subgroup 1 and subgroup 3, whereas the composition of immune cells of subgroup 2 was lower than that of subgroup 1 and subgroup 3. The expression of major immune genes, such as CCL2, CD274, CD276, CD4, CTLA4, CXCR4, IL1A, IL6, LAG3, TGFB1, TNFRSF4, TNFRSF9, and PDCD1LG2, in subgroup 2 was almost significantly lower than that in subgroup 1 and subgroup 3, which is consistent with the results of tumor purity analysis. Finally, we identified that subgroup 2 had lower copy number variations, single-nucleotide polymorphism, and neoantigen mutation. Our systematic study established a metabolism-related gene-based subgroup to predict outcomes of prostate cancer patients, which may contribute to individual prevention and treatment.

Experimental Study of Potential CD8+ Trivalent Synthetic Peptides for Liver Cancer Vaccine Development Using Sprague Dawley Rat Models

Background. Liver cancer (LC) is the most devastating disease affecting a large set of populations in the world. The mortality due to LC is escalating, indicating the lack of effective therapeutic options. Immunotherapeutic agents may play an important role against cancer cells. As immune cells, especially T lymphocytes, which are part of cancer immunology, the design of vaccine candidates for cytotoxic T lymphocytes may be an effective strategy for curing liver cancer. Results. In our study, based on an immunoinformatics approach, we predicted potential T cell epitopes of MHC class I molecules using integrated steps of data retrieval, screening of antigenic proteins, functional analysis, peptide synthesis, and experimental in vivo investigations. We predicted the binding affinity of epitopes LLECADDRADLAKY, VSEHRIQDKDGLFY, and EYILSLEELVNGMY of LC membrane-bounded extracellular proteins including butyrophilin-like protein-2 (BTNL2), glypican-3 (GPC3), and serum albumin (ALB), respectively, with MHC class I molecules (allele: HLA-A $\ast$ 01:01). These T cell epitopes rely on the level of their binding energy and antigenic properties. We designed and constructed a trivalent immunogenic model by conjugating these epitopes with linkers to activate cytotoxic T cells. For validation, the nonspecific hematological assays showed a significant rise in the count of white blood cells ( $5\times {10}^9/\text{l}$ ), lymphocytes ( $13\times {10}^9/\text{l}$ ), and granulocytes ( $5\times {10}^9/\text{l}$ ) compared to the control after administration of trivalent peptides. Specific immunoassays including granzyme B and IgG ELISA exhibited the significant concentration of these effector molecules in blood serum, indicating the activity of cytotoxic T cells. Granzyme concentration increased to 1050 pg/ml at the second booster dose compared to the control (95 pg/ml), while the concentration of IgG raised to 6 g/l compared to the control (2 g/l). Conclusion. We concluded that a potential therapeutic trivalent vaccine can activate and modulate the immune system to cure liver cancer on the basis of significant outcomes of specific and nonspecific assays.

CAD v1.0: Cancer Antigens Database Platform for Cancer Antigen Algorithm Development and Information Exploration

Cancer vaccines have gradually attracted attention for their tremendous preclinical and clinical performance. With the development of next-generation sequencing technologies and related algorithms, pipelines based on sequencing and machine learning methods have become mainstream in cancer antigen prediction; of particular focus are neoantigens, mutation peptides that only exist in tumor cells that lack central tolerance and have fewer side effects. The rapid prediction and filtering of neoantigen peptides are crucial to the development of neoantigen-based cancer vaccines. However, due to the lack of verified neoantigen datasets and insufficient research on the properties of neoantigens, neoantigen prediction algorithms still need to be improved. Here, we recruited verified cancer antigen peptides and collected as much relevant peptide information as possible. Then, we discussed the role of each dataset for algorithm improvement in cancer antigen research, especially neoantigen prediction. A platform, Cancer Antigens Database (CAD, http://cad.bio-it.cn/), was designed to facilitate users to perform a complete exploration of cancer antigens online.

Multi-Level Analysis and Identification of Tumor Mutational Burden Genes across Cancer Types

Tumor mutational burden (TMB) is considered a potential biomarker for predicting the response and effect of immune checkpoint inhibitors (ICIs). However, there are still inconsistent standards of gene panels using next-generation sequencing and poor correlation between the TMB genes, immune cell infiltrating, and prognosis. We applied text-mining technology to construct specific TMB-associated gene panels cross various cancer types. As a case exploration, Pearson’s correlation between TMB genes and immune cell infiltrating was further analyzed in colorectal cancer. We then performed LASSO Cox regression to construct a prognosis predictive model and calculated the risk score of each sample for receiver operating characteristic (ROC) analysis. The results showed that the assessment of TMB gene panels performed well with fewer than 500 genes, highly mutated genes, and the inclusion of synonymous mutations and immune regulatory and drug-target genes. Moreover, the analysis of TMB differentially expressed genes (DEGs) suggested that JAKMIP1 was strongly correlated with the gene expression level of CD8⁺ T cell markers in colorectal cancer. Additionally, the prognosis predictive model based on 19 TMB DEGs reached AUCs of 0.836, 0.818, and 0.787 in 1-, 3-, and 5-year OS models, respectively (C-index: 0.810). In summary, the gene panel performed well and TMB DEGs showed great potential value in immune cell infiltration and in predicting survival.

GNIFdb: a neoantigen intrinsic feature database for glioma

ABSTRACT

Neoantigens are mutation-containing immunogenic peptides from tumor cells. Neoantigen intrinsic features are neoantigens' sequence-associated features characterized by different amino acid descriptors and physical-chemical properties, which have a crucial function in prioritization of neoantigens with immunogenic potentials and predicting patients with better survival. Different intrinsic features might have functions to varying degrees in evaluating neoantigens' potentials of immunogenicity. Identification and comparison of intrinsic features among neoantigens are particularly important for developing neoantigen-based personalized immunotherapy. However, there is still no public repository to host the intrinsic features of neoantigens. Therefore, we developed GNIFdb, a glioma neoantigen intrinsic feature database specifically designed for hosting, exploring and visualizing neoantigen and intrinsic features. The database provides a comprehensive repository of computationally predicted Human leukocyte antigen class I (HLA-I) restricted neoantigens and their intrinsic features; a systematic annotation of neoantigens including sequence, neoantigen-associated mutation, gene expression, glioma prognosis, HLA-I subtype and binding affinity between neoantigens and HLA-I; and a genome browser to visualize them in an interactive manner. It represents a valuable resource for the neoantigen research community and is publicly available at http://www.oncoimmunobank.cn/index.php.

DATABASE URL

http://www.oncoimmunobank.cn/index.php.

Clinical Significance and Immunometabolism Landscapes of a Novel Recurrence-Associated Lipid Metabolism Signature In Early-Stage Lung Adenocarcinoma: A Comprehensive Analysis

Background

The early-stage lung adenocarcinoma (LUAD) rate has increased with heightened public awareness and lung cancer screening implementation. Lipid metabolism abnormalities are associated with lung cancer initiation and progression. However, the comprehensive features and clinical significance of the immunometabolism landscape and lipid metabolism-related genes (LMRGs) in cancer recurrence for early-stage LUAD remain obscure.

Methods

LMRGs were extracted from Gene Set Enrichment Analysis (GSEA) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. Samples from The Cancer Genome Atlas (TCGA) were used as training cohort, and samples from four Gene Expression Omnibus (GEO) datasets were used as validation cohorts. The LUAD recurrence-associated LMRG molecular pattern and signature was constructed through unsupervised consensus clustering, time-dependent receiver operating characteristic (ROC), and least absolute shrinkage and selection operator (LASSO) analyses. Kaplan-Meier, ROC, and multivariate Cox regression analyses and prognostic meta-analysis were used to test the suitability and stability of the signature. We used Gene Ontology (GO), KEGG pathway, immune cell infiltration, chemotherapy response analyses, gene set variation analysis (GSVA), and GSEA to explore molecular mechanisms and immune landscapes related to the signature and the potential of the signature to predict immunotherapy or chemotherapy response.

Results

First, two LMRG molecular patterns were established, which showed diverse prognoses and immune infiltration statuses. Then, a 12-gene signature was identified, and a risk model was built. The signature remained an independent prognostic parameter in multivariate Cox regression and prognostic meta-analysis. In addition, this signature stratified patients into high- and low-risk groups with significantly different recurrence rates and was well validated in different clinical subgroups and several independent validation cohorts. The results of GO and KEGG analyses and GSEA showed that there were differences in multiple lipid metabolism, immune response, and drug metabolism pathways between the high- and low-risk groups. Further analyses revealed that the signature-based risk model was related to distinct immune cell proportions, immune checkpoint parameters, and immunotherapy and chemotherapy response, consistent with the GO, KEGG, and GSEA results.

Conclusions

This is the first lipid metabolism-based signature for predicting recurrence, and it could provide vital guidance to achieve optimized antitumor for immunotherapy or chemotherapy for early-stage LUAD.

Influence of T Cell-Mediated Immune Surveillance on Somatic Mutation Occurrences in Melanoma

Background

Neoantigens are presented on the cancer cell surface by peptide-restricted human leukocyte antigen (HLA) proteins and can subsequently activate cognate T cells. It has been hypothesized that the observed somatic mutations in tumors are shaped by immunosurveillance.

Methods

We investigated all somatic mutations identified in The Cancer Genome Atlas (TCGA) Skin Cutaneous Melanoma (SKCM) samples. By applying a computational algorithm, we calculated the binding affinity of the resulting neo-peptides and their corresponding wild-type peptides with the major histocompatibility complex (MHC) Class I complex. We then examined the relationship between binding affinity alterations and mutation frequency.

Results

Our results show that neoantigens derived from recurrent mutations tend to have lower binding affinities with the MHC Class I complex compared to peptides from non-recurrent mutations. Tumor samples harboring recurrent SKCM mutations exhibited lower immune infiltration levels, indicating a relatively colder immune microenvironment.

Conclusions

These results suggested that the occurrences of somatic mutations in melanoma have been shaped by immunosurveillance. Mutations that lead to neoantigens with high MHC class I binding affinity are more likely to be eliminated and thus are less likely to be present in tumors.

In silico tools and databases for designing cancer immunotherapy

Immunotherapy is a rapidly growing therapy for cancer which have numerous benefits over conventional treatments like surgery, chemotherapy, and radiation. Overall survival of cancer patients has improved significantly due to the use of immunotherapy. It acts as a novel pillar for treating different malignancies from their primary to the metastatic stage. Recent preferments in high-throughput sequencing and computational immunology leads to the development of targeted immunotherapy for precision oncology. In the last few decades, several computational methods and resources have been developed for designing immunotherapy against cancer. In this review, we have summarized cancer-associated genomic, transcriptomic, and mutation profile repositories. We have also enlisted in silico methods for the prediction of vaccine candidates, HLA binders, cytokines inducing peptides, and potential neoepitopes. Of note, we have incorporated the most important bioinformatics pipelines and resources for the designing of cancer immunotherapy. Moreover, to facilitate the scientific community, we have developed a web portal entitled ImmCancer (https://webs.iiitd.edu.in/raghava/immcancer/), comprises cancer immunotherapy tools and repositories.

Integrating single cell sequencing with a spatial quantitative systems pharmacology model spQSP for personalized prediction of triple-negative breast cancer immunotherapy response

Response to cancer immunotherapies depends on the complex and dynamic interactions between T cell recognition and killing of cancer cells that are counteracted through immunosuppressive pathways in the tumor microenvironment. Therefore, while measurements such as tumor mutational burden provide biomarkers to select patients for immunotherapy, they neither universally predict patient response nor implicate the mechanisms that underlie immunotherapy resistance. Recent advances in single-cell RNA sequencing technology measure cellular heterogeneity within cells of an individual tumor but have yet to realize the promise of predictive oncology. In addition to data, mechanistic multiscale computational models are developed to predict treatment response. Incorporating single-cell data from tumors to parameterize these computational models provides deeper insights into prediction of clinical outcome in individual patients. Here, we integrate whole-exome sequencing and scRNA-seq data from Triple-Negative Breast Cancer patients to model neoantigen burden in tumor cells as input to a spatial Quantitative System Pharmacology model. The model comprises a four-compartmental Quantitative System Pharmacology sub-model to represent a whole patient and a spatial agent-based sub-model to represent tumor volumes at the cellular scale. We use the high-throughput single-cell data to model the role of antigen burden and heterogeneity relative to the tumor microenvironment composition on predicted immunotherapy response. We demonstrate how this integrated modeling and single-cell analysis framework can be used to relate neoantigen heterogeneity to immunotherapy treatment outcomes. Our results demonstrate feasibility of merging single-cell data to initialize cell states in multiscale computational models such as the spQSP for personalized prediction of clinical outcomes to immunotherapy.

caAtlas: An immunopeptidome atlas of human cancer

Comprehensive characterization of tumor antigens is essential for the design of cancer immunotherapies, and mass spectrometry (MS)-based immunopeptidomics enables high-throughput identification of major histocompatibility complex (MHC)-bound peptide antigens in vivo. Here we construct an immunopeptidome atlas of human cancer through an extensive collection of 43 published immunopeptidomic datasets and standardized analysis of 81.6 million MS/MS spectra using an open search engine. Our analysis greatly expands the current knowledge of MHC-bound antigens, including an unprecedented characterization of post-translationally modified antigens and their cancer-association. We also perform systematic analysis of cancer-testis antigens, cancer-associated antigens, and neoantigens. We make all these data together with annotated MS/MS spectra supporting identification of each antigen in an easily browsable web portal named cancer antigen atlas (caAtlas). caAtlas provides a central resource for the selection and prioritization of MHC-bound peptides for in vitro HLA binding assay and immunogenicity testing, which will pave the way to eventual development of cancer immunotherapies.

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Extensive collection of 43 immunopeptidomic datasets with 1018 samples

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Standardized and rigorous identification of HLA-bound peptides, including PTM peptides

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Comprehensive annotation of CT antigens and cancer-associated antigens

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User-friendly data dissemination through the caAtlas web portal

Systematic discovery and validation of T cell targets directed against oncogenic KRAS mutations

Oncogenic mutations in KRAS can be recognized by T cells on specific class I human leukocyte antigen (HLA-I) molecules, leading to tumor control. To date, the discovery of T cell targets from KRAS mutations has relied on occasional T cell responses in patient samples or the use of transgenic mice. To overcome these limitations, we have developed a systematic target discovery and validation pipeline. We evaluate the presentation of mutant KRAS peptides on individual HLA-I molecules using targeted mass spectrometry and identify 13 unpublished KRASG12C/D/R/V mutation/HLA-I pairs and nine previously described pairs. We assess immunogenicity, generating T cell responses to nearly all targets. Using cytotoxicity assays, we demonstrate that KRAS-specific T cells and T cell receptors specifically recognize endogenous KRAS mutations. The discovery and validation of T cell targets from KRAS mutations demonstrate the potential for this pipeline to aid the development of immunotherapies for important cancer targets.

A novel immune subtype classification of ER-positive, PR-negative and HER2-negative breast cancer based on the genomic and transcriptomic landscape

BACKGROUND

The diversity and plasticity behind ER+/PR-/HER2- breast cancer have not been widely explored. It is essential to identify heterogeneous microenvironment phenotypes and investigate specific genomic events driving the formation of these phenotypes.

METHODS

Based on the immune-related gene expression profiles of 411 ER+/PR-/HER2- breast cancers in the METABRIC cohort, we used consensus clustering to identify heterogeneous immune subtypes and assessed their reproducibility in an independent meta-cohort including 135 patients collected from GEO database. We further analyzed the differences of cellular and molecular characteristics, and potential immune escape mechanism among immune subtypes. In addition, we constructed a transcriptional trajectory to visualize the distribution of individual patient.

RESULTS

Our analysis identified and validated five reproducible immune subtypes with distinct cellular and molecular characteristics, potential immune escape mechanisms, genomic drivers, as well as clinical outcomes. An immune-cold subtype, with the least amount of lymphocyte infiltration, had a poorer prognosis. By contrast, an immune-hot subtype, which demonstrated the highest infiltration of CD8+ T cells, DCs and NK cells, and elevated IFN-γ response, had a comparatively favorable prognosis. Other subtypes showed more diverse gene expression and immune infiltration patterns with distinct clinical outcomes. Finally, our analysis revealed a complex immune landscape consisting of both discrete cluster and continuous spectrum.

CONCLUSION

Overall, this study revealed five heterogeneous immune subtypes among ER+/PR-/HER2- breast cancer, also provided important implications for clinical translations.

TSNAD v2.0: A one-stop software solution for tumor-specific neoantigen detection

TSNAD is a one-stop software solution for predicting neoantigens from the whole genome/exome sequencing data of tumor-normal pairs. Here we present TSNAD v2.0 which provides several new features such as the function of RNA-Seq analysis including gene expression and gene fusion analysis, the support of different versions of the reference genome. Most importantly, we replace the NetMHCpan with DeepHLApan we developed previously, which considers both the binding between peptide and major histocompatibility complex (MHC) and the immunogenicity of the presented peptide-MHC complex (pMHC). TSNAD v2.0 achieves good performamce on a standard dataset. For better usage, we provide the Docker version and the web service of TSNAD v2.0. The source code of TSNAD v2.0 is freely available at https://github.com/jiujiezz/tsnad. And the web service of TSNAD v2.0 is available at http://biopharm.zju.edu.cn/tsnad/.

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.

CanImmunother: a manually curated database for identification of cancer immunotherapies associating with biomarkers, targets, and clinical effects

As immunotherapy is evolving into an essential armamentarium against cancers, numerous translational studies associated with relevant biomarkers, targets, and clinical effects have been reported in recent years. However, a large amount of associated experimental data remains unexplored due to the difficulty in accessibility and utilization. Here, we established a comprehensive high-quality database for cancer immunotherapy called CanImmunother (http://www.biomedical-web.com/cancerit/) through manual curation on 4515 publications. CanImmunother contains 3267 experimentally validated associations between 218 cancer sub-types across 34 body parts and 484 immunotherapies with 642 biomarkers, 108 targets, and 121 control therapies. Each association was manually curated by professional curators, incorporated with valuable annotation and cross references, and assigned with an association score for prioritization. To help clinicians and researchers in identifying and discovering better cancer immunotherapy and their respective biomarkers and targets, CanImmunother offers user-friendly web applications including search, browse, excel table, association prioritization, and network visualization. CanImmunother presents a landscape of experimental cancer immunotherapy association data, serving as a useful resource to improve our insight and to facilitate further discovery of advanced immunotherapy options for cancer patients.

Identifying Potential Neoantigens for Cervical Cancer Immunotherapy Using Comprehensive Genomic Variation Profiling of Cervical Intraepithelial Neoplasia and Cervical Cancer

Cervical cancer (CC) is one of the most common gynecological malignant tumors. The 5-year survival rate remains poor for the advanced and metastatic cervical cancer for the lack of effective treatments. Immunotherapy plays an important role in clinical tumor therapy. Neoantigens derived from tumor-specific somatic mutations are prospective targets for immunotherapy. Hence, the identification of new targets is of great significance for the treatment of advanced and metastatic cervical cancer. In this study, we performed whole-exome sequencing in 70 samples, including 25 cervical intraepithelial neoplasia (CINs) with corresponding blood samples and 10 CCs along with paired adjacent tissues to identify genomic variations and to find the potential neoantigens for CC immunotherapy. Using systematic bioinformatics pipeline, we found that C>T transitions were in both CINs and CCs. In contrast, the number of somatic mutations in CCs was significantly higher than those in CINs (t-test, P = 6.60E-04). Meanwhile, mutational signatures analysis revealed that signature 6 was detected in CIN2, CIN3, and CC, but not in CIN1, while signature 2 was only observed in CCs. Furthermore, PIK3CA, ARHGAP5 and ADGRB1 were identified as potential driver genes in this report, of which ADGRB1 was firstly reported in CC. Based on the genomic variation profiling of CINs and CCs, we identified 2586 potential neoantigens in these patients, of which 45 neoantigens were found in three neoantigen-related databases (TSNAdb, IEDB, and CTDatabase). Our current findings lay a solid foundation for the study of the pathogenesis of CC and the development of neoantigen-targeted immunotherapeutic measures.

Integrin β7 Inhibits Colorectal Cancer Pathogenesis via Maintaining Antitumor Immunity

Immune cell infiltration is important for predicting the clinical outcomes of colorectal cancer. Integrin β7 (ITGB7), which is expressed on the surface of leukocytes, plays an essential role in the homing of immune cells to gut-associated lymphoid tissue and facilitating the retention of lymphocytes in gut epithelium; however, its role in colorectal cancer pathogenesis is poorly explored. Here, we found that the number of β7⁺ cells decreased significantly in tumor tissue compared with adjacent normal tissue. β7 expression decreased in tumor-derived compared with normal tissue-derived CD8⁺ T cells. With bulk RNA expression data from public platforms, we demonstrated that higher ITGB7 expression correlated with longer patient survival, higher cytotoxic immune cell infiltration, lower somatic copy-number alterations, decreased mutation frequency of APC and TP53, and better response to immunotherapy. The possible cell-cell interactions mediated by ITGB7 and its ligands MAdCAM-1, VCAM-1, and CDH1 were investigated using public single-cell RNA sequencing data. ITGB7 deficiency led to exaggerated tumorigenesis and progression in both Apc^{min /+} spontaneous and MC38 orthotopic models of colorectal cancer, which could be due to a reduced infiltration of activated CD8⁺ T cells, effector memory CD8⁺ T cells, IFNγ⁺ CD8⁺ T cells, IFNγ⁺ natural killer cells, CD103⁺ dendritic cells, and other immune cell subsets that are essential players in antitumor immunity. In conclusion, our data revealed that ITGB7 could inhibit the tumorigenesis and progression of colorectal cancer by maintaining antitumor immunity.

Challenges targeting cancer neoantigens in 2021: a systematic literature review

Introduction: Cancer neoantigens represent important targets of cancer immunotherapy. The goal of cancer neoantigen vaccines is to induce neoantigen-specific immune responses and antitumor immunity while minimizing the potential for autoimmune toxicity. Advances in sequencing technologies, neoantigen prediction algorithms, and other technologies have dramatically improved the ability to identify and prioritize cancer neoantigens. Unfortunately, results from preclinical studies and early phase clinical trials highlight important challenges to the successful clinical translation of neoantigen cancer vaccines.Areas covered: In this review, we provide an overview of current strategies for the identification and prioritization of cancer neoantigens with a particular emphasis on the two most common strategies used for neoantigen identification: (1) direct identification of peptide ligands eluted from peptide-MHC complexes, and (2) next-generation sequencing combined with neoantigen prediction algorithms. We highlight the limitations of current neoantigen prediction pipelines, and discuss broader challenges associated with cancer neoantigen vaccines including tumor purity/heterogeneity and the immunosuppressive tumor microenvironment.Expert opinion: Despite current limitations, neoantigen prediction is likely to improve rapidly based on advances in sequencing, machine learning, and information sharing. The successful development of robust cancer neoantigen prediction strategies is likely to have a significant impact, with the potential to facilitate cancer neoantigen vaccine design.

Comprehensive analysis of a chemokine- and chemokine receptor family-based signature for patients with lung adenocarcinoma

The clinical significance and comprehensive features of chemokines and their receptors in lung adenocarcinoma (LUAD) have not been clarified. We aimed to characterize the expression profiles of chemokine and chemokine receptor family members and construct a chemokine- and chemokine receptor-based prognosis signature. A total of 1511 patients with LUAD from seven independent cohorts were included in the study. The training set collected from The Cancer Genome Atlas (TCGA) database containing 468 cases. The validation was performed on the basis of six different cohorts downloaded from Gene Expression Omnibus (GEO) database. A five-chemokine- and chemokine receptor-(CXCL2, CXCL13, CCL26, CCL20, CX3CR1) based prognosis signature was constructed with TCGA dataset using LASSO Cox regression and Cox proportional hazards regression analysis. A multivariate analysis verified that this signature was an independent prognostic factor. The predictive value of this signature was further verified by other six independent cohorts and multiple clinical subtypes. We performed immune cell infiltration analysis and biological pathway analysis which provided more insight into this signature-related immune and inflammatory landscape and clarified the intrinsic molecular mechanism by which this signature could be used to predict clinical prognosis. Furthermore, we explored the close relationship between this signature and tumor mutation burden (TMB), neoantigen burden, PD-1, PD-L1, CTLA4, TIDE score, T cell-inflamed score. This signature provided a robust prognostic biomarker for LUAD and could serve as a predictor for immunotherapy response, which may be used as an important supplement to immunotherapy to achieve individualized tumor treatment by optimizing the prognostic management and immunotherapy for patients with LUAD.