TSNAD: an integrated software for cancer somatic mutation and tumour-specific neoantigen detection

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.

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.

Biomedical Big Data Technologies, Applications, and Challenges for Precision Medicine: A Review

The explosive growth of biomedical Big Data presents both significant opportunities and challenges in the realm of knowledge discovery and translational applications within precision medicine. Efficient management, analysis, and interpretation of big data can pave the way for groundbreaking advancements in precision medicine. However, the unprecedented strides in the automated collection of large-scale molecular and clinical data have also introduced formidable challenges in terms of data analysis and interpretation, necessitating the development of novel computational approaches. Some potential challenges include the curse of dimensionality, data heterogeneity, missing data, class imbalance, and scalability issues. This overview article focuses on the recent progress and breakthroughs in the application of big data within precision medicine. Key aspects are summarized, including content, data sources, technologies, tools, challenges, and existing gaps. Nine fields-Datawarehouse and data management, electronic medical record, biomedical imaging informatics, Artificial intelligence-aided surgical design and surgery optimization, omics data, health monitoring data, knowledge graph, public health informatics, and security and privacy-are discussed.

Neoantigen-targeted TCR-engineered T cell immunotherapy: current advances and challenges

Adoptive cell therapy using T cell receptor-engineered T cells (TCR-T) is a promising approach for cancer therapy with an expectation of no significant side effects. In the human body, mature T cells are armed with an incredible diversity of T cell receptors (TCRs) that theoretically react to the variety of random mutations generated by tumor cells. The outcomes, however, of current clinical trials using TCR-T cell therapies are not very successful especially involving solid tumors. The therapy still faces numerous challenges in the efficient screening of tumor-specific antigens and their cognate TCRs. In this review, we first introduce TCR structure-based antigen recognition and signaling, then describe recent advances in neoantigens and their specific TCR screening technologies, and finally summarize ongoing clinical trials of TCR-T therapies against neoantigens. More importantly, we also present the current challenges of TCR-T cell-based immunotherapies, e.g., the safety of viral vectors, the mismatch of T cell receptor, the impediment of suppressive tumor microenvironment. Finally, we highlight new insights and directions for personalized TCR-T therapy.

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.

The Value of Microbes in Cancer Neoantigen Immunotherapy

Tumor neoantigens are widely used in cancer immunotherapy, and a growing body of research suggests that microbes play an important role in these neoantigen-based immunotherapeutic processes. The human body and its surrounding environment are filled with a large number of microbes that are in long-term interaction with the organism. The microbiota can modulate our immune system, help activate neoantigen-reactive T cells, and play a great role in the process of targeting tumor neoantigens for therapy. Recent studies have revealed the interconnection between microbes and neoantigens, which can cross-react with each other through molecular mimicry, providing theoretical guidance for more relevant studies. The current applications of microbes in immunotherapy against tumor neoantigens are mainly focused on cancer vaccine development and immunotherapy with immune checkpoint inhibitors. This article summarizes the related fields and suggests the importance of microbes in immunotherapy against neoantigens.

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/.

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.

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 .

Computational Biology Predicts the Efficacy of Tumor Immune Checkpoint Blockade

Tumor immunotherapy is considered as one of the most promising methods in cancer treatment in recent years. Immune checkpoint blockade (ICB) can activate immune cells to destroy tumors by relieving the inhibitory pathway of tumor cells to immune cells. In silico prediction of the ICB response is an important step toward achieving effective and personalized cancer immunotherapy. Although immune checkpoint inhibitors have shown exciting clinical effects in the treatment of many types of tumors, there are still some clinical problems in practical application, such as low response rate and large individualized differences. How to predict the efficacy of effective individualized immune checkpoint inhibitors for tumor patients based on specific biomarkers and computational models is one of the key issues in the immunotherapy of this kind of tumor. In our work, from the five levels of genome level, transcription level, epigenetic level, microbial taxonomy level, and the immune cell infiltration profile level, the biomarkers and in silico calculation methods that affect the efficacy of tumor immune checkpoint inhibitors are comprehensively summarized.

Therapeutic Cancer Vaccines—Antigen Discovery and Adjuvant Delivery Platforms

For decades, vaccines have played a significant role in protecting public and personal health against infectious diseases and proved their great potential in battling cancers as well. This review focused on the current progress of therapeutic subunit vaccines for cancer immunotherapy. Antigens and adjuvants are key components of vaccine formulations. We summarized several classes of tumor antigens and bioinformatic approaches of identification of tumor neoantigens. Pattern recognition receptor (PRR)-targeting adjuvants and their targeted delivery platforms have been extensively discussed. In addition, we emphasized the interplay between multiple adjuvants and their combined delivery for cancer immunotherapy.

ProGeo-Neo v2.0: A One-Stop Software for Neoantigen Prediction and Filtering Based on the Proteogenomics Strategy

A proteogenomics-based neoantigen prediction pipeline, namely ProGeo-neo, was previously developed by our team to predict neoantigens, allowing the identification of class-I major histocompatibility complex (MHC) binding peptides based on single-nucleotide variation (SNV) mutations. To improve it, we here present an updated pipeline, i.e., ProGeo-neo v2.0, in which a one-stop software solution was proposed to identify neoantigens based on the paired tumor-normal whole genome sequencing (WGS)/whole exome sequencing (WES) data in FASTQ format. Preferably, in ProGeo-neo v2.0, several new features are provided. In addition to the identification of MHC-I neoantigens, the new version supports the prediction of MHC class II-restricted neoantigens, i.e., peptides up to 30-mer in length. Moreover, the source of neoantigens has been expanded, allowing more candidate neoantigens to be identified, such as in-frame insertion-deletion (indels) mutations, frameshift mutations, and gene fusion analysis. In addition, we propose two more efficient screening approaches, including an in-group authentic neoantigen peptides database and two more stringent thresholds. The range of candidate peptides was effectively narrowed down to those that are more likely to elicit an immune response, providing a more meaningful reference for subsequent experimental validation. Compared to ProGeo-neo, the ProGeo-neo v2.0 performed well based on the same dataset, including updated functionality and improved accuracy.

Characterization of Somatic Mutations That Affect Neoantigens in Non-Small Cell Lung Cancer

Purpose

Immune checkpoint inhibitors (ICIs) have recently emerged as an important option for treating patients with advanced non-small cell lung cancer (NSCLC). Neoantigens are important biomarkers and potential immunotherapy targets that play important roles in the prognosis and treatment of patients with NSCLC. This study aimed to evaluate and characterize the relationships between somatic mutations and potential neoantigens in specimens from patients who underwent surgical treatment for NSCLC.

Patients and Methods

This prospective study evaluated specimens from patients with NSCLC who underwent surgical treatment at the Peking Union Medical College, China, from June 2019 to September 2019. Whole-exome sequencing was performed for tumor tissues and corresponding normal tissues. Candidate neoantigens were predicted using generative software, and the relationships between various mutation characteristics and number of neoantigens were evaluated.

Results

Neoantigen-related gene mutations were less frequent than mutations affecting the whole genome. Genes with high neoantigen burden had more types and higher frequencies of mutations. The number of candidate neoantigens was positively correlated with missense mutations, code shift insertions/deletions, split-site variations, and nonsense mutations. However, in the multiple linear regression analysis, only missense mutations were positively correlated with the number of neoantigens. The number of neoantigens was also positively correlated with base transversions (A>C/C>A, T>G/G>T, and C>G/G>C) and negatively correlated with base transitions (A>G/G>A and C>T/T>C).

Conclusion

The number of candidate neoantigens in NSCLC specimens was associated with mutation frequency, type of mutation, and type of base substitution.

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.

Computational cancer neoantigen prediction: current status and recent advances

Over the last few decades, immunotherapy has shown significant therapeutic efficacy in a broad range of cancer types. Antitumor immune responses are contingent on the recognition of tumor-specific antigens, which are termed neoantigens. Tumor neoantigens are ideal targets for immunotherapy since they can be recognized as non-self antigens by the host immune system and thus are able to elicit an antitumor T-cell response. There are an increasing number of studies that highlight the importance of tumor neoantigens in immunoediting and in the sensitivity to immune checkpoint blockade. Therefore, one of the most fundamental tasks in the field of immuno-oncology research is the identification of patient-specific neoantigens. To this end, a plethora of computational approaches have been developed in order to predict tumor-specific aberrant peptides and quantify their likelihood of binding to patients' human leukocyte antigen molecules in order to be recognized by T cells. In this review, we systematically summarize and present the most recent advances in computational neoantigen prediction, and discuss the challenges and novel methods that are being developed to resolve them.

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Tumors have the ability to acquire immune escape mechanisms.

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Tumor-specific aberrant peptides (neoantigens) can elicit an immune response by the host immune system.

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The identification of neoantigens is one of the most fundamental tasks in the field of immuno-oncology research.

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A plethora of computational approaches have been developed in order to predict patient-specificneoantigens.

T Cell Epitope Prediction and Its Application to Immunotherapy

T cells play a crucial role in controlling and driving the immune response with their ability to discriminate peptides derived from healthy as well as pathogenic proteins. In this review, we focus on the currently available computational tools for epitope prediction, with a particular focus on tools aimed at identifying neoepitopes, i.e. cancer-specific peptides and their potential for use in immunotherapy for cancer treatment. This review will cover how these tools work, what kind of data they use, as well as pros and cons in their respective applications.

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/.

Personalized Neoantigen-Pulsed DC Vaccines: Advances in Clinical Applications

In the past few decades, great progress has been made in the clinical application of dendritic cell (DC) vaccines loaded with personalized neoantigens. Personalized neoantigens are antigens arising from somatic mutations in cancers, with specificity to each patient. DC vaccines work based on the fundamental characteristics of DCs, which are professional antigen-presenting cells (APCs), responsible for the uptake, processing, and presentation of antigens to T cells to activate immune responses. Neoantigens can exert their antitumor effects only after they are taken up by APCs and presented to T cells. In recent years, neoantigen-based personalized tumor therapeutic vaccines have proven to be safe, immunogenic and feasible treatment strategies in patients with melanoma and glioblastoma that provide new hope in the treatment of cancer patients and a new approach to cure cancer. In addition, according to ClinicalTrials.gov, hundreds of registered DC vaccine trials are either completed or ongoing worldwide, of which 9 are in early phase I, 191 in phase I, 166 in phase II and 8 in phase III. Hundreds of clinical studies on therapeutic tumor vaccines globally have proven that DC vaccines are stable, reliable and very safe. However, in this process, many other factors still limit the effectiveness of the vaccine. This review will focus on the current research progress on personalized neoantigen-pulsed DC vaccines, their limitations and future research directions of DC vaccines loaded with neoantigens. This review aims to provide a better understanding of DCs biology and manipulation of activated DCs for DCs researchers to produce the next generation of highly efficient cancer vaccines for patients.

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.

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.

TP53 Targeted Deep Sequencing of Cell-Free DNA in Esophageal Squamous Cell Carcinoma Using Low-Quality Serum: Concordance with Tumor Mutation

Circulating cell-free DNA (cfDNA) is emerging as a potential tumor biomarker. CfDNA-based biomarkers may be applicable in tumors without an available non-invasive screening method among at-risk populations. Esophageal squamous cell carcinoma (ESCC) and residents of the Asian cancer belt are examples of those malignancies and populations. Previous epidemiological studies using cfDNA have pointed to the need for high volumes of good quality plasma (i.e., >1 mL plasma with 0 or 1 cycles of freeze-thaw) rather than archival serum, which is often the main available source of cfDNA in retrospective studies. Here, we have investigated the concordance of TP53 mutations in tumor tissue and cfDNA extracted from archival serum left-over from 42 cases and 39 matched controls (age, gender, residence) in a high-risk area of Northern Iran (Golestan). Deep sequencing of TP53 coding regions was complemented with a specialized variant caller (Needlestack). Overall, 23% to 31% of mutations were concordantly detected in tumor and serum cfDNA (based on two false discovery rate thresholds). Concordance was positively correlated with high cfDNA concentration, smoking history (p-value = 0.02) and mutations with a high potential of neoantigen formation (OR; 95%CI = 1.9 (1.11-3.29)), suggesting that tumor DNA release in the bloodstream might reflect the effects of immune and inflammatory context on tumor cell turnover. We identified TP53 mutations in five controls, one of whom was subsequently diagnosed with ESCC. Overall, the results showed that cfDNA mutations can be reliably identified by deep sequencing of archival serum, with a rate of success comparable to plasma. Nonetheless, 70% non-identifiable mutations among cancer patients and 12% mutation detection in controls are the main challenges in applying cfDNA to detect tumor-related variants when blindly targeting whole coding regions of the TP53 gene in ESCC.

A novel genomic classification system of gastric cancer via integrating multidimensional genomic characteristics

BACKGROUND

Gastric cancer (GC) is one of the leading causes of cancer deaths with high heterogeneity. There is currently a paucity of clinically applicable molecular classification system to guide precise medicine.

METHODS

A total of 70 Chinese patients with GC were included in this study and whole-exome sequencing was performed. Unsupervised clustering was undertaken to identify genomic subgroups, based on mutational signature, copy number variation, neoantigen, clonality, and essential genomic alterations. Subgroups were characterized by clinicopathological factors, molecular features, and prognosis.

RESULTS

We identified 32 significantly mutated genes (SMGs), including TP53, ARID1A, PIK3CA, CDH1, and RHOA. Of these, PREX2, PIEZO1, and FSIP2 have not been previously reported in GC. Using a novel genome-based classification method that integrated multidimensional genomic features, we categorized GC into four subtypes with distinct clinical phenotypes and prognosis. Subtype 1, which was predominantly Lauren intestinal type, harbored recurrent TP53 mutation and ERBB2 amplification, high tumor mutation burden (TMB)/tumor neoantigen burden (TNB), and intratumoral heterogeneity, with a liver metastasis tendency. Subtype 2 tended to occur at an elder age, accompanying with frequent TP53 and SYNE1 mutations, high TMB/TNB, and was associated with poor prognosis. Subtype 3 and subtype 4 included patients with mainly diffuse/mixed type tumors, high frequency of peritoneal metastasis, and genomical stability, whereas subtype 4 was associated with a favorable prognosis.

CONCLUSIONS

By integrating multidimensional genomic characteristics, we proposed a novel genomic classification system of GC associated with clinical phenotypes and provided a new insight to facilitate genome-guided risk stratification and disease management.

Semi-supervised learning for somatic variant calling and peptide identification in personalized cancer immunotherapy

BACKGROUND

Personalized cancer vaccines are emerging as one of the most promising approaches to immunotherapy of advanced cancers. However, only a small proportion of the neoepitopes generated by somatic DNA mutations in cancer cells lead to tumor rejection. Since it is impractical to experimentally assess all candidate neoepitopes prior to vaccination, developing accurate methods for predicting tumor-rejection mediating neoepitopes (TRMNs) is critical for enabling routine clinical use of cancer vaccines.

RESULTS

In this paper we introduce Positive-unlabeled Learning using AuTOml (PLATO), a general semi-supervised approach to improving accuracy of model-based classifiers. PLATO generates a set of high confidence positive calls by applying a stringent filter to model-based predictions, then rescores remaining candidates by using positive-unlabeled learning. To achieve robust performance on clinical samples with large patient-to-patient variation, PLATO further integrates AutoML hyper-parameter tuning, classification threshold selection based on spies, and support for bootstrapping.

CONCLUSIONS

Experimental results on real datasets demonstrate that PLATO has improved performance compared to model-based approaches for two key steps in TRMN prediction, namely somatic variant calling from exome sequencing data and peptide identification from MS/MS data.

Main Strategies for the Identification of Neoantigens

Genetic instability of tumors leads to the appearance of numerous tumor-specific somatic mutations that could potentially result in the production of mutated peptides that are presented on the cell surface by the MHC molecules. Peptides of this kind are commonly called neoantigens. Their presence on the cell surface specifically distinguishes tumors from healthy tissues. This feature makes neoantigens a promising target for immunotherapy. The rapid evolution of high-throughput genomics and proteomics makes it possible to implement these techniques in clinical practice. In particular, they provide useful tools for the investigation of neoantigens. The most valuable genomic approach to this problem is whole-exome sequencing coupled with RNA-seq. High-throughput mass-spectrometry is another option for direct identification of MHC-bound peptides, which is capable of revealing the entire MHC-bound peptidome. Finally, structure-based predictions could significantly improve the understanding of physicochemical and structural features that affect the immunogenicity of peptides. The development of pipelines combining such tools could improve the accuracy of the peptide selection process and decrease the required time. Here we present a review of the main existing approaches to investigating the neoantigens and suggest a possible ideal pipeline that takes into account all modern trends in the context of neoantigen discovery.

neoepiscope improves neoepitope prediction with multivariant phasing

MOTIVATION

The vast majority of tools for neoepitope prediction from DNA sequencing of complementary tumor and normal patient samples do not consider germline context or the potential for the co-occurrence of two or more somatic variants on the same mRNA transcript. Without consideration of these phenomena, existing approaches are likely to produce both false-positive and false-negative results, resulting in an inaccurate and incomplete picture of the cancer neoepitope landscape. We developed neoepiscope chiefly to address this issue for single nucleotide variants (SNVs) and insertions/deletions (indels).

RESULTS

Herein, we illustrate how germline and somatic variant phasing affects neoepitope prediction across multiple datasets. We estimate that up to ∼5% of neoepitopes arising from SNVs and indels may require variant phasing for their accurate assessment. neoepiscope is performant, flexible and supports several major histocompatibility complex binding affinity prediction tools.

AVAILABILITY AND IMPLEMENTATION

neoepiscope is available on GitHub at https://github.com/pdxgx/neoepiscope under the MIT license. Scripts for reproducing results described in the text are available at https://github.com/pdxgx/neoepiscope-paper under the MIT license. Additional data from this study, including summaries of variant phasing incidence and benchmarking wallclock times, are available in Supplementary Files 1, 2 and 3. Supplementary File 1 contains Supplementary Table 1, Supplementary Figures 1 and 2, and descriptions of Supplementary Tables 2-8. Supplementary File 2 contains Supplementary Tables 2-6 and 8. Supplementary File 3 contains Supplementary Table 7. Raw sequencing data used for the analyses in this manuscript are available from the Sequence Read Archive under accessions PRJNA278450, PRJNA312948, PRJNA307199, PRJNA343789, PRJNA357321, PRJNA293912, PRJNA369259, PRJNA305077, PRJNA306070, PRJNA82745 and PRJNA324705; from the European Genome-phenome Archive under accessions EGAD00001004352 and EGAD00001002731; and by direct request to the authors.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Mutational Landscape of Esophageal Squamous Cell Carcinoma in an Indian Cohort

Esophageal squamous cell carcinoma (ESCC) is the most common histological subtype of esophageal cancer in India. Cigarette smoking and chewing tobacco are known risk factors associated with ESCC. However, genomic alterations associated with ESCC in India are not well-characterized. In this study, we carried out exome sequencing to characterize the mutational landscape of ESCC tumors from subjects with a varied history of tobacco usage. Whole exome sequence analysis of ESCC from an Indian cohort revealed several genes that were mutated or had copy number changes. ESCC from tobacco chewers had a higher frequency of C:G > A:T transversions and 2-fold enrichment for mutation signature 4 compared to smokers and non-users of tobacco. Genes, such as TP53, CSMD3, SYNE1, PIK3CA, and NOTCH1 were found to be frequently mutated in Indian cohort. Mutually exclusive mutation patterns were observed in PIK3CA-NOTCH1, DNAH5-ZFHX4, MUC16-FAT1, and ZFHX4-NOTCH1 gene pairs. Recurrent amplifications were observed in 3q22-3q29, 11q13.3-q13.4, 7q22.1-q31.1, and 8q24 regions. Approximately 53% of tumors had genomic alterations in PIK3CA making this pathway a promising candidate for targeted therapy. In conclusion, we observe enrichment of mutation signature 4 in ESCC tumors from patients with a history of tobacco chewing. This is likely due to direct exposure of esophagus to tobacco carcinogens when it is chewed and swallowed. Genomic alterations were frequently observed in PIK3CA-AKT pathway members independent of the history of tobacco usage. PIK3CA pathway can be potentially targeted in ESCC which currently has no effective targeted therapeutic options.

ASNEO: Identification of personalized alternative splicing based neoantigens with RNA-seq

Cancer neoantigens have shown great potential in immunotherapy, while current software focuses on identifying neoantigens which are derived from SNVs, indels or gene fusions. Alternative splicing widely occurs in tumor samples and it has been proven to contribute to the generation of candidate neoantigens. Here we present ASNEO, which is an integrated computational pipeline for the identification of personalized Alternative Splicing based NEOantigens with RNA-seq. Our analyses showed that ASNEO could identify neopeptides which are presented by MHC I complex through mass spectrometry data validation. When ASNEO was applied to two immunotherapy-treated cohorts, we found that alternative splicing based neopeptides generally have a higher immune score than that of somatic neopeptides and alternative splicing based neopeptides could be a marker to predict patient survival pattern. Our identification of alternative splicing derived neopeptides would contribute to a more complete understanding of the tumor immune landscape. Prediction of patient-specific alternative splicing neopeptides has the potential to contribute to the development of personalized cancer vaccines.

Shared neoantigens: ideal targets for off-the-shelf cancer immunotherapy

Neoantigen, as an important member of tumor-specific antigens, has attracted a great deal of attention as a target for immunotherapy. Neoantigens are potential targets for personalized vaccines and adoptive cell transfer therapies. However, most of the neoantigen-targeted immunotherapies in the process are customized and costly. So, we are inclined to find shared neoantigens suitable for more patients. With the help of existing neoantigen prediction algorithms, we found that the most frequent shared neoantigens occurred in more than 1% of patients for 17 tumor types and the ten most frequent shared neoantigens covered approximately 50% of pancreatic cancer patients, providing a potential list of targets for off-the-shelf immunotherapy.

Enhanced Immunogenicity of Mitochondrial-Localized Proteins in Cancer Cells

Epitopes derived from mutated cancer proteins elicit strong antitumor T-cell responses that correlate with clinical efficacy in a proportion of patients. However, it remains unclear whether the subcellular localization of mutated proteins influences the efficiency of T-cell priming. To address this question, we compared the immunogenicity of NY-ESO-1 and OVA localized either in the cytosol or in mitochondria. We showed that tumors expressing mitochondrial-localized NY-ESO-1 and OVA proteins elicit significantdly higher frequencies of antigen-specific CD8+ T cells in vivo. We also demonstrated that this stronger immune response is dependent on the mitochondrial location of the antigenic proteins, which contributes to their higher steady-state amount, compared with cytosolic localized proteins. Consistent with these findings, we showed that injection of mitochondria purified from B16 melanoma cells can protect mice from a challenge with B16 cells, but not with irrelevant tumors. Finally, we extended these findings to cancer patients by demonstrating the presence of T-cell responses specific for mutated mitochondrial-localized proteins. These findings highlight the utility of prioritizing epitopes derived from mitochondrial-localized mutated proteins as targets for cancer vaccination strategies.

ProGeo-neo: a customized proteogenomic workflow for neoantigen prediction and selection

BACKGROUND

Neoantigens can be differentially recognized by T cell receptor (TCR) as these sequences are derived from mutant proteins and are unique to the tumor. The discovery of neoantigens is the first key step for tumor-specific antigen (TSA) based immunotherapy. Based on high-throughput tumor genomic analysis, each missense mutation can potentially give rise to multiple neopeptides, resulting in a vast total number, but only a small percentage of these peptides may achieve immune-dominant status with a given major histocompatibility complex (MHC) class I allele. Specific identification of immunogenic candidate neoantigens is consequently a major challenge. Currently almost all neoantigen prediction tools are based on genomics data.

RESULTS

Here we report the construction of proteogenomics prediction of neoantigen (ProGeo-neo) pipeline, which incorporates the following modules: mining tumor specific antigens from next-generation sequencing genomic and mRNA expression data, predicting the binding mutant peptides to class I MHC molecules by latest netMHCpan (v.4.0), verifying MHC-peptides by MaxQuant with mass spectrometry proteomics data searched against customized protein database, and checking potential immunogenicity of T-cell-recognization by additional screening methods. ProGeo-neo pipeline achieves proteogenomics strategy and the neopeptides identified were of much higher quality as compared to those identified using genomic data only.

CONCLUSIONS

The pipeline was constructed based on the genomics and proteomics data of Jurkat leukemia cell line but is generally applicable to other solid cancer research. With massively parallel sequencing and proteomics profiling increasing, this proteogenomics workflow should be useful for neoantigen oriented research and immunotherapy.

neoANT-HILL: an integrated tool for identification of potential neoantigens

Background

Cancer neoantigens have attracted great interest in immunotherapy due to their capacity to elicit antitumoral responses. These molecules arise from somatic mutations in cancer cells, resulting in alterations on the original protein. Neoantigens identification remains a challenging task due largely to a high rate of false-positives.

Results

We have developed an efficient and automated pipeline for the identification of potential neoantigens. neoANT-HILL integrates several immunogenomic analyses to improve neoantigen detection from Next Generation Sequence (NGS) data. The pipeline has been compiled in a pre-built Docker image such that minimal computational background is required for download and setup. NeoANT-HILL was applied in The Cancer Genome Atlas (TCGA) melanoma dataset and found several putative neoantigens including ones derived from the recurrent RAC1:P29S and SERPINB3:E250K mutations. neoANT-HILL was also used to identify potential neoantigens in RNA-Seq data with a high sensitivity and specificity.

Conclusion

neoANT-HILL is a user-friendly tool with a graphical interface that performs neoantigens prediction efficiently. neoANT-HILL is able to process multiple samples, provides several binding predictors, enables quantification of tumor-infiltrating immune cells and considers RNA-Seq data for identifying potential neoantigens. The software is available through github at https://github.com/neoanthill/neoANT-HILL.

DeepHLApan: A Deep Learning Approach for Neoantigen Prediction Considering Both HLA-Peptide Binding and Immunogenicity

Neoantigens play important roles in cancer immunotherapy. Current methods used for neoantigen prediction focus on the binding between human leukocyte antigens (HLAs) and peptides, which is insufficient for high-confidence neoantigen prediction. In this study, we apply deep learning techniques to predict neoantigens considering both the possibility of HLA-peptide binding (binding model) and the potential immunogenicity (immunogenicity model) of the peptide-HLA complex (pHLA). The binding model achieves comparable performance with other well-acknowledged tools on the latest Immune Epitope Database (IEDB) benchmark datasets and an independent mass spectrometry (MS) dataset. The immunogenicity model could significantly improve the prediction precision of neoantigens. The further application of our method to the mutations with pre-existing T-cell responses indicating its feasibility in clinical application. DeepHLApan is freely available at https://github.com/jiujiezz/deephlapan and http://biopharm.zju.edu.cn/deephlapan.

pTuneos: prioritizing tumor neoantigens from next-generation sequencing data

BACKGROUND

Cancer neoantigens are expressed only in cancer cells and presented on the tumor cell surface in complex with major histocompatibility complex (MHC) class I proteins for recognition by cytotoxic T cells. Accurate and rapid identification of neoantigens play a pivotal role in cancer immunotherapy. Although several in silico tools for neoantigen prediction have been presented, limitations of these tools exist.

RESULTS

We developed pTuneos, a computational pipeline for prioritizing tumor neoantigens from next-generation sequencing data. We tested the performance of pTuneos on the melanoma cancer vaccine cohort data and tumor-infiltrating lymphocyte (TIL)-recognized neopeptide data. pTuneos is able to predict the MHC presentation and T cell recognition ability of the candidate neoantigens, and the actual immunogenicity of single-nucleotide variant (SNV)-based neopeptides considering their natural processing and presentation, surpassing the existing tools with a comprehensive and quantitative benchmark of their neoantigen prioritization performance and running time. pTuneos was further tested on The Cancer Genome Atlas (TCGA) cohort data as well as the melanoma and non-small cell lung cancer (NSCLC) cohort data undergoing checkpoint blockade immunotherapy. The overall neoantigen immunogenicity score proposed by pTuneos is demonstrated to be a powerful and pan-cancer marker for survival prediction compared to traditional well-established biomarkers.

CONCLUSIONS

In summary, pTuneos provides the state-of-the-art one-stop and user-friendly solution for prioritizing SNV-based candidate neoepitopes, which could help to advance research on next-generation cancer immunotherapies and personalized cancer vaccines. pTuneos is available at https://github.com/bm2-lab/pTuneos , with a Docker version for quick deployment at https://cloud.docker.com/u/bm2lab/repository/docker/bm2lab/ptuneos .

Best practices for bioinformatic characterization of neoantigens for clinical utility

Neoantigens are newly formed peptides created from somatic mutations that are capable of inducing tumor-specific T cell recognition. Recently, researchers and clinicians have leveraged next generation sequencing technologies to identify neoantigens and to create personalized immunotherapies for cancer treatment. To create a personalized cancer vaccine, neoantigens must be computationally predicted from matched tumor-normal sequencing data, and then ranked according to their predicted capability in stimulating a T cell response. This candidate neoantigen prediction process involves multiple steps, including somatic mutation identification, HLA typing, peptide processing, and peptide-MHC binding prediction. The general workflow has been utilized for many preclinical and clinical trials, but there is no current consensus approach and few established best practices. In this article, we review recent discoveries, summarize the available computational tools, and provide analysis considerations for each step, including neoantigen prediction, prioritization, delivery, and validation methods. In addition to reviewing the current state of neoantigen analysis, we provide practical guidance, specific recommendations, and extensive discussion of critical concepts and points of confusion in the practice of neoantigen characterization for clinical use. Finally, we outline necessary areas of development, including the need to improve HLA class II typing accuracy, to expand software support for diverse neoantigen sources, and to incorporate clinical response data to improve neoantigen prediction algorithms. The ultimate goal of neoantigen characterization workflows is to create personalized vaccines that improve patient outcomes in diverse cancer types.

Alternative tumour-specific antigens

The study of tumour-specific antigens (TSAs) as targets for antitumour therapies has accelerated within the past decade. The most commonly studied class of TSAs are those derived from non-synonymous single-nucleotide variants (SNVs), or SNV neoantigens. However, to increase the repertoire of available therapeutic TSA targets, 'alternative TSAs', defined here as high-specificity tumour antigens arising from non-SNV genomic sources, have recently been evaluated. Among these alternative TSAs are antigens derived from mutational frameshifts, splice variants, gene fusions, endogenous retroelements and other processes. Unlike the patient-specific nature of SNV neoantigens, some alternative TSAs may have the advantage of being widely shared by multiple tumours, allowing for universal, off-the-shelf therapies. In this Opinion article, we will outline the biology, available computational tools, preclinical and/or clinical studies and relevant cancers for each alternative TSA class, as well as discuss both current challenges preventing the therapeutic application of alternative TSAs and potential solutions to aid in their clinical translation.

Neoantigens Derived from Recurrently Mutated Genes as Potential Immunotherapy Targets for Gastric Cancer

Neoantigens are optimal tumor-specific targets for T-cell based immunotherapy, especially for patients with “undruggable” mutated driver genes. T-cell immunotherapy can be a “universal” treatment for HLA genotype patients sharing same oncogenic mutations. To identify potential neoantigens for therapy in gastric cancer, 32 gastric cancer patients were enrolled in our study. Whole exome sequencing data from these patients was processed by TSNAD software to detect cancer somatic mutations and predict neoantigens. The somatic mutations between different patients suggested a high interpatient heterogeneity. C>A and C>T substitutions are common, suggesting an active nucleotide excision repair. The number of predicted neoantigens was significantly higher in patients at stage T1a compared to in patients at T2 or T4b. Six genes (PIK3CA, FAT4, BRCA2, GNAQ, LRP1B, and PREX2) were found as recurrently mutated driver genes in our study. Combining with highly frequent HLA alleles, several neoantigens derived from six recurrently mutated genes were considered as potential targets for further immunotherapy.

NeoPredPipe: high-throughput neoantigen prediction and recognition potential pipeline

BACKGROUND

Next generation sequencing has yielded an unparalleled means of quickly determining the molecular make-up of patient tumors. In conjunction with emerging, effective immunotherapeutics for a number of cancers, this rapid data generation necessitates a paired high-throughput means of predicting and assessing neoantigens from tumor variants that may stimulate immune response.

RESULTS

Here we offer NeoPredPipe (Neoantigen Prediction Pipeline) as a contiguous means of predicting putative neoantigens and their corresponding recognition potentials for both single and multi-region tumor samples. NeoPredPipe is able to quickly provide summary information for researchers, and clinicians alike, on predicted neoantigen burdens while providing high-level insights into tumor heterogeneity given somatic mutation calls and, optionally, patient HLA haplotypes. Given an example dataset we show how NeoPredPipe is able to rapidly provide insights into neoantigen heterogeneity, burden, and immune stimulation potential.

CONCLUSIONS

Through the integration of widely adopted tools for neoantigen discovery NeoPredPipe offers a contiguous means of processing single and multi-region sequence data. NeoPredPipe is user-friendly and adaptable for high-throughput performance. NeoPredPipe is freely available at https://github.com/MathOnco/NeoPredPipe .

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

Tumor-specific neoantigens have attracted much attention since they can be used as biomarkers to predict therapeutic effects of immune checkpoint blockade therapy and as potential targets for cancer immunotherapy. In this study, we developed a comprehensive tumor-specific neoantigen database (TSNAdb v1.0), based on pan-cancer immunogenomic analyses of somatic mutation data and human leukocyte antigen (HLA) allele information for 16 tumor types with 7748 tumor samples from The Cancer Genome Atlas (TCGA) and The Cancer Immunome Atlas (TCIA). We predicted binding affinities between mutant/wild-type peptides and HLA class I molecules by NetMHCpan v2.8/v4.0, and presented detailed information of 3,707,562/1,146,961 potential neoantigens generated by somatic mutations of all tumor samples. Moreover, we employed recurrent mutations in combination with highly frequent HLA alleles to predict potential shared neoantigens across tumor patients, which would facilitate the discovery of putative targets for neoantigen-based cancer immunotherapy. TSNAdb is freely available at http://biopharm.zju.edu.cn/tsnadb.

The perfect personalized cancer therapy: cancer vaccines against neoantigens

In the advent of Immune Checkpoint inhibitors (ICI) and of CAR-T adoptive T-cells, the new frontier in Oncology is Cancer Immunotherapy because of its ability to provide long term clinical benefit in metastatic disease in several solid and liquid tumor types. It is now clear that ICI acts by unmasking preexisting immune responses as well as by inducing de novo responses against tumor neoantigens. Thanks to theprogress made in genomics technologies and the evolution of bioinformatics, neoantigens represent ideal targets, due to their specific expression in cancer tissue and the potential lack of side effects. In this review, we discuss the promise of preclinical and clinical results with mutation-derived neoantigen cancer vaccines (NCVs) along with the current limitations from bioinformatics prediction to manufacturing an effective new therapeutic approach.

Population-level distribution and putative immunogenicity of cancer neoepitopes

Background

Tumor neoantigens are drivers of cancer immunotherapy response; however, current prediction tools produce many candidates requiring further prioritization. Additional filtration criteria and population-level understanding may assist with prioritization. Herein, we show neoepitope immunogenicity is related to measures of peptide novelty and report population-level behavior of these and other metrics.

Methods

We propose four peptide novelty metrics to refine predicted neoantigenicity: tumor vs. paired normal peptide binding affinity difference, tumor vs. paired normal peptide sequence similarity, tumor vs. closest human peptide sequence similarity, and tumor vs. closest microbial peptide sequence similarity. We apply these metrics to neoepitopes predicted from somatic missense mutations in The Cancer Genome Atlas (TCGA) and a cohort of melanoma patients, and to a group of peptides with neoepitope-specific immune response data using an extension of pVAC-Seq (Hundal et al., pVAC-Seq: a genome-guided in silico approach to identifying tumor neoantigens. Genome Med 8:11, 2016).

Results

We show neoepitope burden varies across TCGA diseases and HLA alleles, with surprisingly low repetition of neoepitope sequences across patients or neoepitope preferences among sets of HLA alleles. Only 20.3% of predicted neoepitopes across TCGA patients displayed novel binding change based on our binding affinity difference criteria. Similarity of amino acid sequence was typically high between paired tumor-normal epitopes, but in 24.6% of cases, neoepitopes were more similar to other human peptides, or bacterial (56.8% of cases) or viral peptides (15.5% of cases), than their paired normal counterparts. Applied to peptides with neoepitope-specific immune response, a linear model incorporating neoepitope binding affinity, protein sequence similarity between neoepitopes and their closest viral peptides, and paired binding affinity difference was able to predict immunogenicity (AUROC = 0.66).

Conclusions

Our proposed prioritization criteria emphasize neoepitope novelty and refine patient neoepitope predictions for focus on biologically meaningful candidate neoantigens. We have demonstrated that neoepitopes should be considered not only with respect to their paired normal epitope, but to the entire human proteome, and bacterial and viral peptides, with potential implications for neoepitope immunogenicity and personalized vaccines for cancer treatment. We conclude that putative neoantigens are highly variable across individuals as a function of cancer genetics and personalized HLA repertoire, while the overall behavior of filtration criteria reflects predictable patterns.

Electronic supplementary material

The online version of this article (10.1186/s12885-018-4325-6) contains supplementary material, which is available to authorized users.

Immunopharmacogenomics towards personalized cancer immunotherapy targeting neoantigens

Utilizing the host immune system to eradicate cancer cells has been the most investigated subject in the cancer research field in recent years. However, most of the studies have focused on highly variable responses from immunotherapies such as immune checkpoint inhibitors, from which the majority of patients experienced no or minimum clinical benefit. Advances in genomic sequencing technologies have improved our understanding of immunopharmacogenomics and allowed us to identify novel cancer-specific immune targets. Highly tumor-specific antigens, neoantigens, are generated by somatic mutations that are not present in normal cells. It is plausible that by targeting antigens with high tumor-specificity, such as neoantigens, the likelihood of toxic effects is very limited. However, understanding the interaction between neoantigens and the host immune system remains a significant challenge. This review focuses on the potential use of neoantigen-targeted immunotherapies in cancer treatment and the recent progress of different strategies in predicting, identifying, and validating neoantigens. Successful identification of highly tumor-specific antigens accelerates the development of personalized immunotherapy with no or minimum adverse effects and with a broader coverage of patients.

Immuno-oncology from the perspective of somatic evolution

The past years have witnessed significant success for cancer immunotherapies that activate a patient's immune system against their cancer cells. At the same time our understanding of the genetic changes driving tumor evolution have progressed dramatically. The study of cancer genomes has shown that tumors are best understood as cell populations governed by the rules of evolution, leading to the emergence and spread of cell lineages with pathogenic mutations. Moreover, somatic evolution can explain the acquisition of mutations conferring drug resistance in the ever-lasting battle for reaching even fitter cell states. Here, we review the current state of the art of somatic cancer evolution and mechanisms of immune control and escape. We also revisit the principles of immunotherapy from the perspective of somatic evolution and discuss the basic rules of resistance to immunotherapies as dictated by evolution.

Towards In Silico Prediction of the Immune-Checkpoint Blockade Response

Cancer immunotherapy with immune-checkpoint blockade (ICB) is considered a promising strategy for cancer treatment. Identifying predictive biomarkers and developing efficient computational models to predict the ICB response are important issues for successful immunotherapy. Here, we present a concise and intuitive survey of the computational issues for ICB response prediction, providing a summary of the available predictive biomarkers and building of one-stop machine-learning models that integrate biomarkers calculable from high-throughput sequencing (HTS) data. Several points for discussion are highlighted to inspire further research for improving ICB treatment. Continuing efforts are required to improve ICB response prediction and to identify novel predictive biomarkers by taking advantage of the rapid development of computational models and HTS techniques for effective and personalized cancer immunotherapy.

Identification of trunk mutations in gastric carcinoma: a case study

Background

Intratumor heterogeneity (ITH) poses an urgent challenge for cancer precision medicine because it can cause drug resistance against cancer target therapy and immunotherapy. The search for trunk mutations that are present in all cancer cells is therefore critical for each patient.

Case presentation

In this study, we aimed to evaluate the efficiency of multiregional sequencing for the identification of trunk mutations present in all regions of a tumor as a case study. We applied multiregional whole-exome sequencing (WES) to investigate the genetic heterogeneity and homogeneity of a case of gastric carcinoma. Approximately 83% of common missense mutations present in two samples and approximately 89% of common missense mutations present in three samples were trunk mutations. Notably, trunk mutations appeared to have higher variant allele frequencies (VAFs) than non-trunk mutations.

Conclusions

Our results indicate that small-scale multiregional sampling and subsequent screening of low VAF somatic mutations might be a cost-effective strategy for identifying the majority of trunk mutations in gastric carcinoma.

Electronic supplementary material

The online version of this article (doi:10.1186/s12920-017-0285-y) contains supplementary material, which is available to authorized users.