Best practices for bioinformatic characterization of neoantigens for clinical utility

Exploration and validation of hub genes in lung adenocarcinoma based on bioinformatics analysis

BACKGROUND

Genomic abnormality is a crucial factor for lung cancer development. This study used bioinformatics analysis to explore the hub genes involved in lung adenocarcinoma.

METHODS

The GeneCards, Comparative Toxicogenomics Database (CTD), and DISEASES databases were used to screen the genes associated with lung adenocarcinoma. The hub genes were then identified using WebGestalt. The Cancer Genome Atlas (TCGA), UALCAN, and the Human Protein Atlas (HPA) were used to validate the expression of hub genes. The predictive effects of hub genes on the risk of lung adenocarcinoma were evaluated using receiver operating characteristic (ROC) curve analysis. The Tumor-Immune System Interaction Database (TISIDB) was used to estimate the correlation between hub genes and immune infiltration.

RESULTS

A total of 21 genes were defined as common genes associated with lung adenocarcinoma, and from these, AKT1, CD44, and CDKN2A were identified as hub genes. Significant differences in the hub gene mRNA and protein expression were observed between lung adenocarcinoma samples and normal samples derived from the TCGA and UALCAN databases. The area under the ROC curve (AUC) for AKT1, CD44, and CDKN2A in predicting lung adenocarcinoma risk was 0.847, 0.880, and 0.805, respectively, with sensitivity of 89.8%, 93.2%, and 94.9%, respectively. TISIDB analysis indicated that AKT1, CD44, and CDKN2A expression had a strong relationship with immune infiltration in lung adenocarcinoma.

CONCLUSIONS

These hub genes, AKT1, CD44, and CDKN2A, may represent tumor biomarkers that may contribute to the understanding, diagnosis, and treatment of lung adenocarcinoma.

Research progress of neoantigens in gynecologic cancers

The incidence and mortality of gynecological cancers have increased over the past decade. In the absence of effective treatment strategies, many advanced patients develop resistance to conventional therapies and have poor prognosis. Neoantigens have emerged as a novel tumor-specific antigen (TSA) that arises from genomic mutations in tumor cells. With higher immunogenicity than tumor-associated antigens (TAA), they have no risk of developing autoimmune response, leading them an attractive candidate for tumor therapeutic vaccines. With the development of next-generation sequencing (NGS) technology, the identification of neoantigens has been gradually improved, and the scope of application of neoantigen vaccines has continued to expand. Combined with other therapies such as immune-checkpoint inhibitors (ICIs) or adoptive cell therapy (ACT), the application of neoantigen in gynecological cancers has extended to clinical practice. Here, we reviewed the preclinical and clinical studies of neoantigens in gynecological cancers.

Sources of Cancer Neoantigens beyond Single-Nucleotide Variants

The success of checkpoint blockade therapy against cancer has unequivocally shown that cancer cells can be effectively recognized by the immune system and eliminated. However, the identity of the cancer antigens that elicit protective immunity remains to be fully explored. Over the last decade, most of the focus has been on somatic mutations derived from non-synonymous single-nucleotide variants (SNVs) and small insertion/deletion mutations (indels) that accumulate during cancer progression. Mutated peptides can be presented on MHC molecules and give rise to novel antigens or neoantigens, which have been shown to induce potent anti-tumor immune responses. A limitation with SNV-neoantigens is that they are patient-specific and their accurate prediction is critical for the development of effective immunotherapies. In addition, cancer types with low mutation burden may not display sufficient high-quality [SNV/small indels] neoantigens to alone stimulate effective T cell responses. Accumulating evidence suggests the existence of alternative sources of cancer neoantigens, such as gene fusions, alternative splicing variants, post-translational modifications, and transposable elements, which may be attractive novel targets for immunotherapy. In this review, we describe the recent technological advances in the identification of these novel sources of neoantigens, the experimental evidence for their presentation on MHC molecules and their immunogenicity, as well as the current clinical development stage of immunotherapy targeting these neoantigens.

Clinical interpretation of whole-genome and whole-transcriptome sequencing for precision oncology

Whole-genome sequencing either alone or in combination with whole-transcriptome sequencing has started to be used to analyze clinical tumor samples to improve diagnosis, provide risk stratification, and select patient-specific therapies. Compared with current genomic testing strategies, largely focused on small number of genes tested individually or targeted panels, whole-genome and transcriptome sequencing (WGTS) provides novel opportunities to identify and report a potentially much larger number of actionable alterations with diagnostic, prognostic, and/or predictive impact. Such alterations include point mutations, indels, copy- number aberrations and structural variants, but also germline variants, fusion genes, noncoding alterations and mutational signatures. Nevertheless, these comprehensive tests are accompanied by many challenges ranging from the extent and diversity of sequence alterations detected by these methods to the complexity and limited existing standardization in interpreting them. We describe the challenges of WGTS interpretation and the opportunities with comprehensive genomic testing.

A synthetic DNA template for fast manufacturing of versatile single epitope mRNA

A flexible, affordable, and rapid vaccine platform is necessary to unlock the potential of personalized cancer vaccines in order to achieve full clinical efficiency. mRNA cancer vaccine manufacture relies on the rigid sequence design of multiepitope constructs produced by laborious bacterial cloning and time-consuming plasmid preparation. Here, we introduce a synthetic DNA template (SDT) assembly process, which allows cost- and time-efficient manufacturing of single (neo)epitope mRNA. We benchmarked SDT-derived mRNA against mRNA derived from a plasmid DNA template (PDT), showing that monocyte-derived dendritic cells (moDCs) electroporated with SDT-mRNA or PDT-mRNA, encoding HLA-I- or HLA-II-restricted (neo)epitopes, equally activated T cells that were modified to express the cognate T cell receptors. Furthermore, we validated the SDT-mRNA platform for neoepitope immunogenicity screening using the characterized HLA-A2-restricted neoepitope DHX40B and four new candidate HLA-A2-restricted melanoma neoepitopes. Finally, we compared SDT-mRNA with PDT-mRNA for vaccine development purposes. moDCs electroporated with mRNA encoding the HLA-A2-restricted, mutated Melan-A/Mart-1 epitope together with TriMix mRNA-generated high levels of functional Melan-A/Mart-1-specific CD8⁺ T cells. In conclusion, SDT single epitope mRNA can be manufactured in a more flexible, cost-efficient, and time-efficient way compared with PDT-mRNA, allowing prompt neoepitope immunogenicity screening, and might be exploited for the development of personalized cancer vaccines.

Neoantigen vaccine and neoantigen-specific cell adoptive transfer therapy in solid tumors: Challenges and future directions

The phenomenon of tumor hierarchy and genetic instability can be explained by the "two-hits theory" and results in the occurrence of many somatic mutations. The expression of nonsynonymous mutations results in the production of mutant proteins from tumor cells, namely tumor-specific antigens called neoantigens. Because neoantigens do not exist in healthy cells, they have the potential to stimulate antitumor immune responses by CD4+ and CD8+ T-cell activation without jeopardizing normal tissues. Immunotherapy has reshaped the cancer treatment paradigm in recent decades with the introduction of immune-checkpoint blockade therapy and transgenic T-cell receptor/chimeric antigen receptor T cells. However, these strategies performed poorly in solid tumors because of the obstacles of the immunosuppressive microenvironment caused by regulatory T cells and other suppressor cells. Therefore, other immunotherapeutic strategies are under development, such as personalized vaccines, to trigger de novo T-cell responses against neoantigens and lead to the amplification of tumor-specific T-cell subclones. Neoantigen epitope prediction algorithms have enabled the detection of neoantigens and the creation of tailored neoantigen vaccines as a result of the fast development of next-generation sequencing and cancer bioinformatics. Here we provide an overview of the current neoantigen cancer vaccines and adoptive T-cell transfer therapy with neoantigen-specific lymphocytes. We also discuss the challenges in developing neoantigen-targeted immunotherapeutic strategies for cancer.

A systemic pan-cancer analysis of MPZL3 as a potential prognostic biomarker and its correlation with immune infiltration and drug sensitivity in breast cancer

Background

This study aimed to analyze the role of myelin protein zero-like 3 (MPZL3), a single membrane glycoprotein, in prognosis, tumor immune infiltration, and drug susceptibility in human cancers.

Methods

Data regarding MPZL3 were extracted from the TCGA, GTEx, CellMiner, CCLE, TIMER, GSEA, and USCS Xena databases. The expression difference, survival outcomes, DNA methylation, tumor mutation burden (TMB), microsatellite instability (MSI), mismatch repair (MMR), tumor microenvironment (TME), immune cell infiltration, and drug sensitivity of MPZL3 were analyzed by R language software. Cell proliferation and drug sensitivity tests were applied to analyze the biological role of MPZL3 and drug sensitivities in breast cancer.

Results

MPZL3 was highly expressed in most cancer types and correlated with unfavorable survival outcomes in several cancers. TMB, MSI, MMR, DNA methylation, and RNA modification played a significant role in mediating MPZL3 dysregulation in cancers, and MPZL3 was closely linked to CD8+ T cells and CD4+ T immune infiltration. The MPML3 mRNA level was associated with protein secretion, the Notch signaling pathway, and heme metabolism. In addition, drug sensitivity analysis and validation also indicated that MPZL3 expression influenced the sensitivity of therapeutics targeting EGFR, ABL, FGFR, etc. Additionally, MPZL3 overexpression contributed to proliferation and drug sensitivity in different subtypes of breast cancer.

Conclusions

This study provides a comprehensive analysis and understanding of the oncogenic roles of the pan-cancer gene MPZL3 across different tumors, including breast cancer. MPZL3 could be a potential prognostic biomarker and therapeutic target for breast cancer.

Therapeutic Potential of Intrabodies for Cancer Immunotherapy: Current Status and Future Directions

Tumor cells are characterized by overexpressed tumor-associated antigens or mutated neoantigens, which are expressed on the cell surface or intracellularly. One strategy of cancer immunotherapy is to target cell-surface-expressed tumor-associated antigens (TAAs) with therapeutic antibodies. For targeting TAAs or neoantigens, adoptive T-cell therapies with activated autologous T cells from cancer patients transduced with novel recombinant TCRs or chimeric antigen receptors have been successfully applied. Many TAAs and most neoantigens are expressed in the cytoplasm or nucleus of tumor cells. As alternative to adoptive T-cell therapy, the mRNA of intracellular tumor antigens can be depleted by RNAi, the corresponding genes or proteins deleted by CRISPR-Cas or inactivated by kinase inhibitors or by intrabodies, respectively. Intrabodies are suitable to knockdown TAAs and neoantigens without off-target effects. RNA sequencing and proteome analysis of single tumor cells combined with computational methods is bringing forward the identification of new neoantigens for the selection of anti-cancer intrabodies, which can be easily performed using phage display antibody repertoires. For specifically delivering intrabodies into tumor cells, the usage of new capsid-modified adeno-associated viruses and lipid nanoparticles coupled with specific ligands to cell surface receptors can be used and might bring cancer intrabodies into the clinic.

Neoantigens in precision cancer immunotherapy: from identification to clinical applications

Immunotherapies targeting cancer neoantigens are safe, effective, and precise. Neoantigens can be identified mainly by genomic techniques such as next-generation sequencing and high-throughput single-cell sequencing; proteomic techniques such as mass spectrometry; and bioinformatics tools based on high-throughput sequencing data, mass spectrometry data, and biological databases. Neoantigen-related therapies are widely used in clinical practice and include neoantigen vaccines, neoantigen-specific CD8+ and CD4+ T cells, and neoantigen-pulsed dendritic cells. In addition, neoantigens can be used as biomarkers to assess immunotherapy response, resistance, and prognosis. Therapies based on neoantigens are an important and promising branch of cancer immunotherapy. Unremitting efforts are needed to unravel the comprehensive role of neoantigens in anti-tumor immunity and to extend their clinical application. This review aimed to summarize the progress in neoantigen research and to discuss its opportunities and challenges in precision cancer immunotherapy.

Considerations for personalized neoantigen vaccination in Malignant glioma

Malignant gliomas are the most common primary brain cancer diagnosed and still carry a poor prognosis despite aggressive multimodal management. Despite the continued advances in immunotherapy for other cancer types, however, there remain no FDA approved immunotherapies for cancers such as glioblastoma. OF the many approaches being explored, cancer vaccine programs are undergoing a renaissance due to the technological advances and personalized nature of their contemporary design. Neoantigen vaccines are a form of immunotherapy involving the use of DNA, mRNA, and proteins derived from non-synonymous mutations identified in patient tumor tissue samples to stimulate tumor-specific T-cell reactivity leading to enhance tumor targeting. In the last several years, the study of neoantigens as a therapeutic target has increased, with the routine workflow implementation of comprehensive next generation sequencing and in silico peptide binding prediction algorithms. Several neoantigen vaccine platforms are being evaluated in clinical trials for malignancies including melanoma, pancreatic cancer, breast cancer, lung cancer, and glioblastoma, among others. In this review, we will review the concept of neoantigen discovery using cancer immunogenomics approaches in glioblastoma and explore the disease-specific issues being addressed in the design of effective personalized cancer vaccine strategies.

Next-generation sequencing: unraveling genetic mechanisms that shape cancer immunotherapy efficacy

Immunity is governed by fundamental genetic processes. These processes shape the nature of immune cells and set the rules that dictate the myriad complex cellular interactions that power immune systems. Everything from the generation of T cell receptors and antibodies, control of epitope presentation, and recognition of pathogens by the immunoediting of cancer cells is, in large part, made possible by core genetic mechanisms and the cellular machinery that they encode. In the last decade, next-generation sequencing has been used to dissect the complexities of cancer immunity with potent effect. Sequencing of exomes and genomes has begun to reveal how the immune system recognizes “foreign” entities and distinguishes self from non-self, especially in the setting of cancer. High-throughput analyses of transcriptomes have revealed deep insights into how the tumor microenvironment affects immunotherapy efficacy. In this Review, we discuss how high-throughput sequencing has added to our understanding of how immune systems interact with cancer cells and how cancer immunotherapies work.

The Neoantigen Landscape of the Coding and Noncoding Cancer Genome Space

Tumor mutation burden (TMB) is a measure to predict patient responsiveness to immune checkpoint immunotherapy because with increased mutation frequency, the likelihood of a greater neoantigen burden is increased. Although neoantigen prediction tools exist, tumor neoantigen burden has not been adopted as a measure to predict immunotherapy response. With both measures, current guidelines are limited to the coding regions, but ectopic expression of sequences in the noncoding space may potentially be a source of neoantigens. A pan-cancer cohort of 574 advanced disease stage patients with whole genome and transcriptome sequencing was analyzed to report mutation burden and neoantigen counts within the coding and noncoding regions. The efficacy of tumor neoantigen burden, reported as tumor neoantigen count (TNC), including neoantigens derived from the expression of noncoding regions, compared with TMB as a predictor of response to immunotherapy for 80 patients who had received treatment, was evaluated. TMB was found to be the best predictor of response to immunotherapy, whereas expression-derived TNC from the noncoding regions did not improve prediction of response. Therefore, there is minimal benefit in extending the calculation of TNC to the noncoding space for the purposes of predicting response. However, it is likely that there is a wealth of neoantigens derived from the noncoding space that may impact patient outcomes and treatments.

Application of Organoids in Carcinogenesis Modeling and Tumor Vaccination

Organoids well recapitulate organ-specific functions from their tissue of origin and remain fundamental aspects of organogenesis. Organoids are widely applied in biomedical research, drug discovery, and regenerative medicine. There are various cultivated organoid systems induced by adult stem cells and pluripotent stem cells, or directly derived from primary tissues. Researchers have drawn inspiration by combination of organoid technology and tissue engineering to produce organoids with more physiological relevance and suitable for translational medicine. This review describes the value of applying organoids for tumorigenesis modeling and tumor vaccination. We summarize the application of organoids in tumor precision medicine. Extant challenges that need to be conquered to make this technology be more feasible and precise are discussed.

Neoantigen: A Promising Target for the Immunotherapy of Colorectal Cancer

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

Tumour mutational burden: an overview for pathologists

Cancer immunotherapy holds great promise and has shown durable responses in many patients; however, these responses are not uniform in all patients or all tumour streams. There is an ongoing clinical need for objective diagnostic biomarkers to identify patients that will respond to immunotherapies. Tumour mutational burden (TMB) is a diagnostic biomarker that can stratify cancer patients for response to immune checkpoint inhibitor therapies. It is commonly defined as the average number of somatic mutations per megabase in a tumour exome. Here we describe the TMB biomarker, how it is determined, its underlying molecular basis, the relationship to neoantigens and the issues around its clinical use. This overview is directed toward practising pathologists wishing to be informed of this predictive biomarker.

Landscape of Immunotherapy Options for Colorectal Cancer: Current Knowledge and Future Perspectives beyond Immune Checkpoint Blockade

Colorectal cancer is the third most prevalent malignancy in Western countries and a major cause of death despite recent improvements in screening programs and early detection methods. In the last decade, a growing effort has been put into better understanding how the immune system interacts with cancer cells. Even if treatments with immune checkpoint inhibitors (anti-PD1, anti-PD-L1, anti-CTLA4) were proven effective for several cancer types, the benefit for colorectal cancer patients is still limited. However, a subset of patients with deficient mismatch repair (dMMR)/microsatellite-instability-high (MSI-H) metastatic colorectal cancer has been observed to have a prolonged benefit to immune checkpoint inhibitors. As a result, pembrolizumab and nivolumab +/- ipilimumab recently obtained the Food and Drug Administration approval. This review aims to highlight the body of knowledge on immunotherapy in the colorectal cancer setting, discussing the potential mechanisms of resistance and future strategies to extend its use.

Beyond Sequencing: Prioritizing and Delivering Neoantigens for Cancer Vaccines

Neoantigens are tumor-specific proteins and peptides that can be highly immunogenic. Immune-mediated tumor rejection is strongly associated with cytotoxic responses to neoantigen-derived peptides in noncovalent association with self-HLA molecules. Neoantigen-based therapies, such as adoptive T cell transfer, have shown the potential to induce remission of treatment-resistant metastatic disease in select patients. Cancer vaccines are similarly designed to elicit or amplify antigen-specific T cell populations and stimulate directed antitumor immunity, but the selection and prioritization of the neoantigens remains a challenge. Bioinformatic algorithms can predict tumor neoantigens from somatic mutations, insertion-deletions, and other aberrant peptide products, but this often leads to hundreds of potential neoepitopes, all unique for that tumor. Selecting neoantigens for cancer vaccines is complicated by the technical challenges of neoepitope discovery, the diversity of HLA molecules, and intratumoral heterogeneity of passenger mutations leading to immune escape. Despite strong preclinical evidence, few neoantigen cancer vaccines tested in vivo have generated epitope-specific T cell populations, suggesting suboptimal immune system activation. In this chapter, we review factors affecting the prioritization and delivery of candidate neoantigens in the design of therapeutic and preventive cancer vaccines and consider synergism with standard chemotherapies.

Using population-specific add-on polymorphisms to improve genotype imputation in underrepresented populations

Genome-wide association studies rely on the statistical inference of untyped variants, called imputation, to increase the coverage of genotyping arrays. However, the results are often suboptimal in populations underrepresented in existing reference panels and array designs, since the selected single nucleotide polymorphisms (SNPs) may fail to capture population-specific haplotype structures, hence the full extent of common genetic variation. Here, we propose to sequence the full genomes of a small subset of an underrepresented study cohort to inform the selection of population-specific add-on tag SNPs and to generate an internal population-specific imputation reference panel, such that the remaining array-genotyped cohort could be more accurately imputed. Using a Tanzania-based cohort as a proof-of-concept, we demonstrate the validity of our approach by showing improvements in imputation accuracy after the addition of our designed add-on tags to the base H3Africa array.

Genome-wide association studies, which study the association between genetic variants and various phenotypes, typically rely on genotyping arrays. Only a small proportion of genetic variants within the genome are typed on genotyping arrays. Untyped variants are statistically inferred through a process known as genotype imputation, where correlations between variants (haplotypes) observed in external reference panels are leveraged to infer untyped variants in the study population. However, for study populations that are underrepresented in existing reference panels, the quality of imputation is often sub-optimal. This is because typed variants incorporated on existing genotyping arrays can be unsuitable for the study population, and haplotype structures can be different between the reference and the study population. Here, we illustrate an approach to select a custom set of population-specific typed variants to improve genotype imputation in such underrepresented populations.

Alternative Splicing in Myeloid Malignancies

Alternative RNA splicing (AS) is an essential physiologic function that diversifies the human proteome. AS also has a crucial role during cellular development. In fact, perturbations in RNA-splicing have been implicated in the development of several cancers, including myeloid malignancies. Splicing dysfunction can be independent of genetic lesions or appear as a direct consequence of mutations in components of the RNA-splicing machinery, such as in the case of mutations occurring in splicing factor genes (i.e., SF3B1, SRSF2, U2AF1) and their regulators. In addition, cancer cells exhibit marked gene expression alterations, including different usage of AS isoforms, possibly causing tissue-specific effects and perturbations of downstream pathways. This review summarizes several modalities leading to splicing diversity in myeloid malignancies.

Is There a Role for Immunotherapy in Central Nervous System Cancers?

Glioblastoma has emerged as an immunotherapy-refractory tumor based on negative phase III studies of anti-programmed cell death-1 therapy among newly diagnosed as well as recurrent patients. In addition, although much work on vaccine and cellular approaches is ongoing, therapeutic benefit with these approaches has been underwhelming. Much scientific insight into the multitiered layers of immunosuppression exploited by glioblastoma tumors is emerging that sheds light on the explanation for the disappointing results to date and highlights possible therapeutic avenues that may offer a better likelihood of therapeutic benefit for immune-based therapies.

Applying artificial intelligence for cancer immunotherapy

Artificial intelligence (AI) is a general term that refers to the use of a machine to imitate intelligent behavior for performing complex tasks with minimal human intervention, such as machine learning; this technology is revolutionizing and reshaping medicine. AI has considerable potential to perfect health-care systems in areas such as diagnostics, risk analysis, health information administration, lifestyle supervision, and virtual health assistance. In terms of immunotherapy, AI has been applied to the prediction of immunotherapy responses based on immune signatures, medical imaging and histological analysis. These features could also be highly useful in the management of cancer immunotherapy given their ever-increasing performance in improving diagnostic accuracy, optimizing treatment planning, predicting outcomes of care and reducing human resource costs. In this review, we present the details of AI and the current progression and state of the art in employing AI for cancer immunotherapy. Furthermore, we discuss the challenges, opportunities and corresponding strategies in applying the technology for widespread clinical deployment. Finally, we summarize the impact of AI on cancer immunotherapy and provide our perspectives about underlying applications of AI in the future.

Vaccines for Non-Viral Cancer Prevention

Cancer vaccines are a type of immune therapy that seeks to modulate the host’s immune system to induce durable and protective immune responses against cancer-related antigens. The little clinical success of therapeutic cancer vaccines is generally attributed to the immunosuppressive tumor microenvironment at late-stage diseases. The administration of cancer-preventive vaccination at early stages, such as pre-malignant lesions or even in healthy individuals at high cancer risk could increase clinical efficacy by potentiating immune surveillance and pre-existing specific immune responses, thus eliminating de novo appearing lesions or maintaining equilibrium. Indeed, research focus has begun to shift to these approaches and some of them are yielding encouraging outcomes.

Characterization of the Genomic and Immunological Diversity of Malignant Brain Tumors Through Multi-Sector Analysis

Despite some success in secondary brain metastases, targeted or immune-based therapies have shown limited efficacy against primary brain malignancies such as glioblastoma (GBM). While the intratumoral heterogeneity of GBM is implicated in treatment resistance, it remains unclear whether this diversity is observed within brain metastases and to what extent cancer-cell intrinsic heterogeneity sculpts the local immune microenvironment. Here, we profiled the immunogenomic state of 93 spatially distinct regions from 30 malignant brain tumors through whole exome, RNA, and TCR-sequencing. Our analyses identified differences between primary and secondary malignancies with gliomas displaying more spatial heterogeneity at the genomic and neoantigen level. Additionally, this spatial diversity was recapitulated in the distribution of T cell clones where some gliomas harbored highly expanded but spatially restricted clonotypes. This study defines the immunogenomic landscape across a cohort of malignant brain tumors and contains implications for the design of targeted and immune-based therapies against intracranial malignancies.

Implications of Antigen Selection on T Cell-Based Immunotherapy

Many immunotherapies rely on CD8+ effector T cells to recognize and kill cognate tumor cells. These T cell-based immunotherapies include adoptive cell therapy, such as CAR T cells or transgenic TCR T cells, and anti-cancer vaccines which expand endogenous T cell populations. Tumor mutation burden and the choice of antigen are among the most important aspects of T cell-based immunotherapies. Here, we highlight various classes of cancer antigens, including self, neojunction-derived, human endogenous retrovirus (HERV)-derived, and somatic nucleotide variant (SNV)-derived antigens, and consider their utility in T cell-based immunotherapies. We further discuss the respective anti-tumor/anti-self-properties that influence both the degree of immunotolerance and potential off-target effects associated with each antigen class.

Integrating Cancer Vaccines in the Standard-of-Care of Ovarian Cancer: Translating Preclinical Models to Human

Simple Summary

The overall survival of ovarian cancer (OC) remains poor for most patients. Despite incorporation of novel therapeutic agents such as bevacizumab and PARP inhibitors to OC standard-of-care, efficacy is only observed in a subset of patients. Cancer vaccination has demonstrated effectiveness in OC patients and could be considered for potential incorporation into OC standard-of-care. This review provides an overview of the different types of cancer vaccination strategies and discusses the use of murine OC tumor models to evaluate combinatorial regimens comprising cancer vaccines and OC standard-of-care.

Abstract

As the majority of ovarian cancer (OC) patients are diagnosed with metastatic disease, less than 40% will survive past 5 years after diagnosis. OC is characterized by a succession of remissions and recurrences. The most promising time point for immunotherapeutic interventions in OC is following debulking surgery. Accumulating evidence shows that T cells are important in OC; thus, cancer vaccines capable of eliciting antitumor T cells will be effective in OC treatment. In this review, we discuss different cancer vaccines and propose strategies for their incorporation into the OC standard-of-care regimens. Using the murine ID8 ovarian tumor model, we provide evidence that a cancer vaccine can be effectively combined with OC standard-of-care to achieve greater overall efficacy. We demonstrate several important similarities between the ID8 model and OC patients, in terms of response to immunotherapies, and the ID8 model can be an important tool for evaluating combinatorial regimens and clinical trial designs in OC. Other emerging models, including patient-derived xenograft and genetically engineered mouse models, are continuing to improve and can be useful for evaluating cancer vaccination therapies in the near future. Here, we provide a comprehensive review of the completed and current clinical trials evaluating cancer vaccines in OC.

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/.

The Cancer Epitope Database and Analysis Resource: A Blueprint for the Establishment of a New Bioinformatics Resource for Use by the Cancer Immunology Community

Recent years have witnessed a dramatic rise in interest towards cancer epitopes in general and particularly neoepitopes, antigens that are encoded by somatic mutations that arise as a consequence of tumorigenesis. There is also an interest in the specific T cell and B cell receptors recognizing these epitopes, as they have therapeutic applications. They can also aid in basic studies to infer the specificity of T cells or B cells characterized in bulk and single-cell sequencing data. The resurgence of interest in T cell and B cell epitopes emphasizes the need to catalog all cancer epitope-related data linked to the biological, immunological, and clinical contexts, and most importantly, making this information freely available to the scientific community in a user-friendly format. In parallel, there is also a need to develop resources for epitope prediction and analysis tools that provide researchers access to predictive strategies and provide objective evaluations of their performance. For example, such tools should enable researchers to identify epitopes that can be effectively used for immunotherapy or in defining biomarkers to predict the outcome of checkpoint blockade therapies. We present here a detailed vision, blueprint, and work plan for the development of a new resource, the Cancer Epitope Database and Analysis Resource (CEDAR). CEDAR will provide a freely accessible, comprehensive collection of cancer epitope and receptor data curated from the literature and provide easily accessible epitope and T cell/B cell target prediction and analysis tools. The curated cancer epitope data will provide a transparent benchmark dataset that can be used to assess how well prediction tools perform and to develop new prediction tools relevant to the cancer research community.

Identification and Targeting of Mutant Peptide Neoantigens in Cancer Immunotherapy

In recent decades, adoptive cell transfer and checkpoint blockade therapies have revolutionized immunotherapeutic approaches to cancer treatment. Advances in whole exome/genome sequencing and bioinformatic detection of tumour-specific genetic variations and the amino acid sequence alterations they induce have revealed that T cell mediated anti-tumour immunity is substantially directed at mutated peptide sequences, and the identification and therapeutic targeting of patient-specific mutated peptide antigens now represents an exciting and rapidly progressing frontier of personalized medicine in the treatment of cancer. This review outlines the historical identification and validation of mutated peptide neoantigens as a target of the immune system, and the technical development of bioinformatic and experimental strategies for detecting, confirming and prioritizing both patient-specific or "private" and frequently occurring, shared "public" neoantigenic targets. Further, we examine the range of therapeutic modalities that have demonstrated preclinical and clinical anti-tumour efficacy through specifically targeting neoantigens, including adoptive T cell transfer, checkpoint blockade and neoantigen vaccination.

Characterization of tumor mutation burden, PD-L1 and DNA repair genes to assess relationship to immune checkpoint inhibitors response in metastatic renal cell carcinoma

Background

Immune checkpoint inhibitors (ICIs) have expanded treatment options for metastatic renal cell carcinoma (mRCC); however, there are limited predictive biomarkers for response to ICIs in this indication, with programmed death-ligand 1 (PD-L1) status demonstrating little predictive utility in mRCC. While predictive of ICI response in other tumor types, the utility of tumor mutation burden (TMB) in mRCC is unclear. Here, we assess TMB, loss of antigen presentation genes and PD-L1 status correlated with outcomes to ICI treatment in mRCC.

Methods

Tumor samples from 34 patients with mRCC treated with ICI therapy at Duke Cancer Institute were retrospectively evaluated using Personal Genome Diagnostics elio tissue complete (RUO version), a tumor genomic profiling assay for somatic variants, TMB, microsatellite status and genomic status of antigen presentation genes. Tumor samples were also analyzed with the Dako 28-8 PD-L1 immunohistochemistry assay. Deidentified clinical information was extracted from the medical record, and tumor response was evaluated based on the Response Evaluation Criteria In Solid Tumors (RECIST) V.1.1 criteria.

Results

Patients were stratified by overall response following ICI therapy and designated as progressive disease (PD; n=18) or disease control groups (DC; n=16). TMB scores ranged from 0.36 to 12.24 mutations/Mb (mean 2.83 mutations/Mb) with no significant difference between the PD and DC groups (3.01 vs 2.63 mutations/Mb, respectively; p=0.7682). Interestingly, 33% of PD patients displayed loss of heterozygosity of major histocompatibility complex class I genes (LOH-MHC) vs 6% of DC patients. Nine of 34 samples were PD-L1-positive (4 in the PD group; 5 in the DC group), suggesting no correlation between PD-L1 expression and response to ICI therapy. Notably, the DC group displayed an enrichment of mutations in DNA repair genes (p=0.04), with 68.8% exhibiting at least one mutated homologous recombination repair (HRR)-related gene compared with only 38.9% of the PD group (p=0.03).

Conclusions

Overall, neither TMB nor PD-L1 correlated with ICI response and TMB was not significantly associated with PD-L1 expression. The higher incidence of LOH-MHC in PD group suggests that loss of antigen presentation may restrict response to ICIs. Separately, enrichment of HRR gene mutations in the DC group suggests potential utility in predicting ICI response and a potential therapeutic target, warranting future studies.

Synthetic multiepitope neoantigen DNA vaccine for personalized cancer immunotherapy

Neoantigen-based personalized vaccination has emerged as a viable method for tumor immunotherapy. Here we set up a DNA-based neoantigen vaccine platform with comprehensive identification of individual somatic mutations using whole-exome sequencing (WES) and RNA-seq, bioinformatic prediction of neo-epitopes, dendritic cell (DC)-based efficacy prevalidation of vaccine candidates, optimization of the DNA vaccine and its nanocarrier and adjuvant, and preparation of a liposome-encapsulated multiepitope DNA vaccine. The DNA vaccine was efficiently uptaken by DCs and induced effective immune response against mouse melanoma cells, leading to significant inhibition of melanoma tumor growth and reduction of lung metastasis in a mouse model. Numerous intratumoral infiltrated CD8+ T-cells with specific in vitro killing ability towards melanoma cells were identified. Our study offers evidence that a multiepitope neoantigen DNA vaccine in a nanocarrier can be exploited for personalized tumor immunotherapy and as a reliable prevalidation approach for rapid enrichment of effective neoantigens.

Neoantigen Controversies

Next-generation sequencing technologies have revolutionized our ability to catalog the landscape of somatic mutations in tumor genomes. These mutations can sometimes create so-called neoantigens, which allow the immune system to detect and eliminate tumor cells. However, efforts that stimulate the immune system to eliminate tumors based on their molecular differences have had less success than has been hoped for, and there are conflicting reports about the role of neoantigens in the success of this approach. Here we review some of the conflicting evidence in the literature and highlight key aspects of the tumor-immune interface that are emerging as major determinants of whether mutation-derived neoantigens will contribute to an immunotherapy response. Accounting for these factors is expected to improve success rates of future immunotherapy approaches.

Targeting Neoepitopes to Treat Solid Malignancies: Immunosurgery

Successful outcome of immune checkpoint blockade in patients with solid cancers is in part associated with a high tumor mutational burden (TMB) and the recognition of private neoantigens by T-cells. The quality and quantity of target recognition is determined by the repertoire of ‘neoepitope’-specific T-cell receptors (TCRs) in tumor-infiltrating lymphocytes (TIL), or peripheral T-cells. Interferon gamma (IFN-γ), produced by T-cells and other immune cells, is essential for controlling proliferation of transformed cells, induction of apoptosis and enhancing human leukocyte antigen (HLA) expression, thereby increasing immunogenicity of cancer cells. TCR αβ-dependent therapies should account for tumor heterogeneity and availability of the TCR repertoire capable of reacting to neoepitopes and functional HLA pathways. Immunogenic epitopes in the tumor-stroma may also be targeted to achieve tumor-containment by changing the immune-contexture in the tumor microenvironment (TME). Non protein-coding regions of the tumor-cell genome may also contain many aberrantly expressed, non-mutated tumor-associated antigens (TAAs) capable of eliciting productive anti-tumor immune responses. Whole-exome sequencing (WES) and/or RNA sequencing (RNA-Seq) of cancer tissue, combined with several layers of bioinformatic analysis is commonly used to predict possible neoepitopes present in clinical samples. At the ImmunoSurgery Unit of the Champalimaud Centre for the Unknown (CCU), a pipeline combining several tools is used for predicting private mutations from WES and RNA-Seq data followed by the construction of synthetic peptides tailored for immunological response assessment reflecting the patient’s tumor mutations, guided by MHC typing. Subsequent immunoassays allow the detection of differential IFN-γ production patterns associated with (intra-tumoral) spatiotemporal differences in TIL or peripheral T-cells versus TIL. These bioinformatics tools, in addition to histopathological assessment, immunological readouts from functional bioassays and deep T-cell ‘adaptome’ analyses, are expected to advance discovery and development of next-generation personalized precision medicine strategies to improve clinical outcomes in cancer in the context of i) anti-tumor vaccination strategies, ii) gauging mutation-reactive T-cell responses in biological therapies and iii) expansion of tumor-reactive T-cells for the cellular treatment of patients with cancer.

Immune deserts in head and neck squamous cell carcinoma: A review of challenges and opportunities for modulating the tumor immune microenvironment

Immunotherapy revolutionized cancer treatment but has yet to elicit durable responses in the majority of patients with head and neck squamous cell carcinoma (HNSCC). HNSCC is generally characterized by a high tumor mutational burden, which has translated to a large neoantigen load that could prime the immune system to recognize and eliminate malignant cells. Studies are increasingly showing, however, that HNSCC is an "immune desert" tumor that can hijack multiple parts of the tumor immunity cycle in order to evade immune recognition and suppress immune system activation. Herein we will review how HNSCC tumors modulate their architecture, cellular composition, and cytokine milieu to maximize immunosuppression; as well as relevant therapeutic opportunities and emerging issues facing the field of HNSCC immuno-oncology.

Immunogenomics in personalized cancer treatments

Recent advances in next-generation sequencing technologies have led to significant improvements in cancer genomic research and cancer treatment. Through the use of comprehensive cancer genome data, precision medicine has become more of a reality; albeit, at present, only ~10-15% of patients can benefit from current genomic testing practices. Improvements in cancer genome analyses have contributed to a better understanding of antitumor immunity and have provided solutions for targeting highly cancer-specific neoantigens generated from somatic mutations in individual patients. Since then, numerous studies have demonstrated the importance of neoantigens and neoantigen-reactive T cells in the tumor microenvironment and how their presence influences the beneficial responses associated with various cancer immunotherapies, including immune checkpoint inhibitor therapy. Indeed, cancer immunotherapies that explicitly target neoantigens specific to individual cancer patients would lead to the ultimate form of cancer precision medicine. For this to be realized, several issues would need to be overcome, including the accurate prediction and selection of neoantigens that can induce cytotoxic T cells in individual patients. The precise prediction of target neoantigens will likely accelerate the development of personalized immunotherapy including cancer vaccines and T-cell receptor-engineered T-cell therapy for patients with cancer.

T Cell Specificity: A Great Challenge in Chagas Disease

The CD4⁺ and CD8⁺ T cell immune response against T. cruzi, the parasite causing Chagas disease, are relevant for both parasite control and disease pathogenesis. Several studies have been focused on their phenotype and functionally, but only a few have drilled down to identify the parasite proteins that are processed and presented to these cells, especially to CD4⁺ T lymphocytes. Although approximately 10,000 proteins are encoded per haploid T. cruzi genome, fewer than 200 T cell epitopes from 49 T. cruzi proteins have been identified so far. In this context, a detailed knowledge of the specific targets of T cell memory response emerges as a prime tool for the conceptualization and development of prophylactic or therapeutic vaccines, an approach with great potential to prevent and treat this chronic disease. Here, we review the available information about this topic in a comprehensive manner and discuss the future challenges in the field.

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.

Mechanisms of Immune Escape and Resistance to Checkpoint Inhibitor Therapies in Mismatch Repair Deficient Metastatic Colorectal Cancers

Simple Summary

A subset of colorectal cancers (CRCs) is characterized by a mismatch repair deficiency that is frequently associated with microsatellite instability (MSI). The compromised DNA repair machinery leads to the accumulation of tumor neoantigens affecting the sensitivity of MSI metastatic CRC to immune checkpoint inhibitors (CPIs), both upfront and in later lines of treatment. However, up to 30% of MSI CRCs exhibit primary resistance to frontline immune based therapy, and an additional subset develops acquired resistance. Here, we first discuss the clinical and molecular features of MSI CRCs and then we review how the loss of antigenicity, immunogenicity, and a hostile tumor microenvironment could influence primary and acquired resistance to CPIs. Finally, we describe strategies to improve the outcome of MSI CRC patients upon CPI treatment.

Abstract

Immune checkpoint inhibitors (CPIs) represent an effective therapeutic strategy for several different types of solid tumors and are remarkably effective in mismatch repair deficient (MMRd) tumors, including colorectal cancer (CRC). The prevalent view is that the elevated and dynamic neoantigen burden associated with the mutator phenotype of MMRd fosters enhanced immune surveillance of these cancers. In addition, recent findings suggest that MMRd tumors have increased cytosolic DNA, which triggers the cGAS STING pathway, leading to interferon-mediated immune response. Unfortunately, approximately 30% of MMRd CRC exhibit primary resistance to CPIs, while a substantial fraction of tumors acquires resistance after an initial benefit. Profiling of clinical samples and preclinical studies suggests that alterations in the Wnt and the JAK-STAT signaling pathways are associated with refractoriness to CPIs. Intriguingly, mutations in the antigen presentation machinery, such as loss of MHC or Beta-2 microglobulin (B2M), are implicated in initial immune evasion but do not impair response to CPIs. In this review, we outline how understanding the mechanistic basis of immune evasion and CPI resistance in MMRd CRC provides the rationale for innovative strategies to increase the subset of patients benefiting from CPIs.

Biological controls for standardization and interpretation of adaptive immune receptor repertoire profiling

Use of adaptive immune receptor repertoire sequencing (AIRR-seq) has become widespread, providing new insights into the immune system with potential broad clinical and diagnostic applications. However, like many high-throughput technologies, it comes with several problems, and the AIRR Community was established to understand and help solve them. We, the AIRR Community's Biological Resources Working Group, have surveyed scientists about the need for standards and controls in generating and annotating AIRR-seq data. Here, we review the current status of AIRR-seq, provide the results of our survey, and based on them, offer recommendations for developing AIRR-seq standards and controls, including future work.

Predicting MHC I restricted T cell epitopes in mice with NAP-CNB, a novel online tool

Lack of a dedicated integrated pipeline for neoantigen discovery in mice hinders cancer immunotherapy research. Novel sequential approaches through recurrent neural networks can improve the accuracy of T-cell epitope binding affinity predictions in mice, and a simplified variant selection process can reduce operational requirements. We have developed a web server tool (NAP-CNB) for a full and automatic pipeline based on recurrent neural networks, to predict putative neoantigens from tumoral RNA sequencing reads. The developed software can estimate H-2 peptide ligands, with an AUC comparable or superior to state-of-the-art methods, directly from tumor samples. As a proof-of-concept, we used the B16 melanoma model to test the system’s predictive capabilities, and we report its putative neoantigens. NAP-CNB web server is freely available at http://biocomp.cnb.csic.es/NeoantigensApp/ with scripts and datasets accessible through the download section.

Obstacles and Coping Strategies of CAR-T Cell Immunotherapy in Solid Tumors

Chimeric antigen receptor (CAR) T-cell immunotherapy refers to an adoptive immunotherapy that has rapidly developed in recent years. It is a novel type of treatment that enables T cells to express specific CARs on their surface, then returns these T cells to tumor patients to kill the corresponding tumor cells. Significant strides in CAR-T cell immunotherapy against hematologic malignancies have elicited research interest among scholars in the treatment of solid tumors. Nonetheless, in contrast with the efficacy of CAR-T cell immunotherapy in the treatment of hematologic malignancies, its general efficacy against solid tumors is insignificant. This has been attributed to the complex biological characteristics of solid tumors. CAR-T cells play a better role in solid tumors, for instance by addressing obstacles including the lack of specific targets, inhibition of tumor microenvironment (TME), homing barriers of CAR-T cells, differentiation and depletion of CAR-T cells, inhibition of immune checkpoints, trogocytosis of CAR-T cells, tumor antigen heterogeneity, etc. This paper reviews the obstacles influencing the efficacy of CAR-T cell immunotherapy in solid tumors, their mechanism, and coping strategies, as well as economic restriction of CAR-T cell immunotherapy and its solutions. It aims to provide some references for researchers to better overcome the obstacles that affect the efficacy of CAR-T cells in solid tumors.

Nanomaterial-based delivery vehicles for therapeutic cancer vaccine development

Nanomaterial-based delivery vehicles such as lipid-based, polymer-based, inorganics-based, and bio-inspired vehicles often carry distinct and attractive advantages in the development of therapeutic cancer vaccines. Based on various delivery vehicles, specifically designed nanomaterials-based vaccines are highly advantageous in boosting therapeutic and prophylactic antitumor immunities. Specifically, therapeutic vaccines featuring unique properties have made major contributions to the enhancement of antigen immunogenicity, encapsulation efficiency, biocompatibility, and stability, as well as promoting antigen cross-presentation and specific CD8+ T cell responses. However, for clinical applications, tumor-associated antigen-derived vaccines could be an obstacle, involving immune tolerance and deficiency of tumor specificities, in achieving maximum therapeutic indices. However, when using bioinformatics predictions with emerging innovations of in silico tools, neoantigen-based therapeutic vaccines might become potent personalized vaccines for tumor treatments. In this review, we summarize the development of preclinical therapeutic cancer vaccines and the advancements of nanomaterial-based delivery vehicles for cancer immunotherapies, which provide the basis for a personalized vaccine delivery platform. Moreover, we review the existing challenges and future perspectives of nanomaterial-based personalized vaccines for novel tumor immunotherapies.

Making a Cold Tumor Hot: The Role of Vaccines in the Treatment of Glioblastoma

The use of immunotherapies for the treatment of brain tumors is a topic that has garnered considerable excitement in recent years. Discoveries such as the presence of a glymphatic system and immune surveillance in the central nervous system (CNS) have shattered the theory of immune privilege and opened up the possibility of treating CNS malignancies with immunotherapies. However, despite many immunotherapy clinical trials aimed at treating glioblastoma (GBM), very few have demonstrated a significant survival benefit. Several factors for this have been identified, one of which is that GBMs are immunologically "cold," implying that the cancer does not induce a strong T cell response. It is postulated that this is why clinical trials using an immune checkpoint inhibitor alone have not demonstrated efficacy. While it is well established that anti-cancer T cell responses can be facilitated by the presentation of tumor-specific antigens to the immune system, treatment-related death of GBM cells and subsequent release of molecules have not been shown to be sufficient to evoke an anti-tumor immune response effective enough to have a significant impact. To overcome this limitation, vaccines can be used to introduce exogenous antigens at higher concentrations to the immune system to induce strong tumor antigen-specific T cell responses. In this review, we will describe vaccination strategies that are under investigation to treat GBM; categorizing them based on their target antigens, form of antigens, vehicles used, and pairing with specific adjuvants. We will review the concept of vaccine therapy in combination with immune checkpoint inhibitors, as it is hypothesized that this approach may be more effective in overcoming the immunosuppressive milieu of GBM. Clinical trial design and the need for incorporating robust immune monitoring into future studies will also be discussed here. We believe that the integration of evolving technologies of vaccine development, delivery, and immune monitoring will further enhance the role of these therapies and will likely remain an important area of investigation for future treatment strategies for GBM patients.

Exploiting Tumor Neoantigens to Target Cancer Evolution: Current Challenges and Promising Therapeutic Approaches

Immunotherapeutic manipulation of the antitumor immune response offers an attractive strategy to target genomic instability in cancer. A subset of tumor-specific somatic mutations can be translated into immunogenic and HLA-bound epitopes called neoantigens, which can induce the activation of helper and cytotoxic T lymphocytes. However, cancer immunoediting and immunosuppressive mechanisms often allow tumors to evade immune recognition. Recent evidence also suggests that the tumor neoantigen landscape extends beyond epitopes originating from nonsynonymous single-nucleotide variants in the coding exome. Here we review emerging approaches for identifying, prioritizing, and immunologically targeting personalized neoantigens using polyvalent cancer vaccines and T-cell receptor gene therapy. SIGNIFICANCE: Several major challenges currently impede the clinical efficacy of neoantigen-directed immunotherapy, such as the relative infrequency of immunogenic neoantigens, suboptimal potency and priming of de novo tumor-specific T cells, and tumor cell-intrinsic and -extrinsic mechanisms of immune evasion. A deeper understanding of these biological barriers could help facilitate the development of effective and durable immunotherapy for any type of cancer, including immunologically "cold" tumors that are otherwise therapeutically resistant.

Emerging immunotherapies for malignant glioma: from immunogenomics to cell therapy

As immunotherapy assumes a central role in the management of many cancers, ongoing work is directed at understanding whether immune-based treatments will be successful in patients with glioblastoma (GBM). Despite several large studies conducted in the last several years, there remain no FDA-approved immunotherapies in this patient population. Nevertheless, there are a range of exciting new approaches being applied to GBM, all of which may not only allow us to develop new treatments but also help us understand fundamental features of the immune response in the central nervous system. In this review, we summarize new developments in the application of immune checkpoint blockade, from biomarker-driven patient selection to the timing of treatment. Moreover, we summarize novel work in personalized immune-oncology by reviewing work in cancer immunogenomics-driven neoantigen vaccine studies. Finally, we discuss cell therapy efforts by reviewing the current state of chimeric antigen receptor T-cell therapy.

Artificial Intelligence in Bulk and Single-Cell RNA-Sequencing Data to Foster Precision Oncology

Artificial intelligence, or the discipline of developing computational algorithms able to perform tasks that requires human intelligence, offers the opportunity to improve our idea and delivery of precision medicine. Here, we provide an overview of artificial intelligence approaches for the analysis of large-scale RNA-sequencing datasets in cancer. We present the major solutions to disentangle inter- and intra-tumor heterogeneity of transcriptome profiles for an effective improvement of patient management. We outline the contributions of learning algorithms to the needs of cancer genomics, from identifying rare cancer subtypes to personalizing therapeutic treatments.

Increased expression of peptides from non-coding genes in cancer proteomics datasets suggests potential tumor neoantigens

Neoantigen-based immunotherapy has yielded promising results in clinical trials. However, it is limited to tumor-specific mutations, and is often tailored to individual patients. Identifying suitable tumor-specific antigens is still a major challenge. Previous proteogenomics studies have identified peptides encoded by predicted non-coding sequences in human genome. To investigate whether tumors express specific peptides encoded by non-coding genes, we analyzed published proteomics data from five cancer types including 933 tumor samples and 275 matched normal samples and compared these to data from 31 different healthy human tissues. Our results reveal that many predicted non-coding genes such as DGCR9 and RHOXF1P3 encode peptides that are overexpressed in tumors compared to normal controls. Furthermore, from the non-coding genes-encoded peptides specifically detected in cancers, we predict a large number of “dark antigens” (neoantigens from non-coding genomic regions), which may provide an alternative source of neoantigens beyond standard tumor specific mutations.

Rong Xiang et al. analyze the expression of non-coding genes encoded peptides in publicly-available proteomics data from five cancer types and matched controls. They identify peptides from non-coding genes including DGCR9 and RHOXF1P3 that are upregulated in tumors compared to controls, suggesting that non-coding gene-encoded peptides may be a source of neoantigens in some cancers.

Neoantigen landscape in metastatic nasopharyngeal carcinoma

Background: Reportedly, nasopharyngeal carcinoma (NPC) patients with MHC I Class aberration are prone to poor survival outcomes, which indicates that the deficiency of tumor neoantigens might represent a mechanism of immune surveillance escape in NPC.

Methods: To clearly delineate the landscape of neoantigens in NPC, we performed DNA and RNA sequencing on paired primary tumor, regional lymph node metastasis and distant metastasis samples from 26 patients. Neoantigens were predicted using pVACseq pipeline. Subtype prediction model was built using random forest algorithm.

Results: Portraying the landscape of neoantigens in NPC for the first time, we found that the neoantigen load of NPC was above average compared to that of other cancers in The Cancer Genome Atlas program. While the quantity and quality of neoantigens were similar among primary tumor, regional lymph node metastasis and distant metastasis samples, neoantigen depletion was more severe in metastatic sites than in primary tumors. Upon tracking the clonality change of neoantigens, we found that neoantigen reduction occurred during metastasis. Building a subtype prediction model based on reported data, we observed that subtype I lacked T cells and suffered from severe neoantigen depletion, subtype II highly expressed immune checkpoint molecules and suffered from the least neoantigen depletion, and subtype III was heterogenous.

Conclusions: These results indicate that neoantigens are conducive to the guidance of clinical treatment, and personalized therapeutic vaccines for NPC deserve deeper basic and clinical investigations to make them feasible in the future.