pVACtools: A Computational Toolkit to Identify and Visualize Cancer Neoantigens

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.

A machine learning model for ranking candidate HLA class I neoantigens based on known neoepitopes from multiple human tumor types

Tumor neoepitopes presented by major histocompatibility complex (MHC) class I are recognized by tumor-infiltrating lymphocytes (TIL) and are targeted by adoptive T-cell therapies. Identifying which mutant neoepitopes from tumor cells are capable of recognition by T cells can assist in the development of tumor-specific, cell-based therapies and can shed light on antitumor responses. Here, we generate a ranking algorithm for class I candidate neoepitopes by using next-generation sequencing data and a dataset of 185 neoepitopes that are recognized by HLA class I-restricted TIL from individuals with metastatic cancer. Random forest model analysis showed that the inclusion of multiple factors impacting epitope presentation and recognition increased output sensitivity and specificity compared to the use of predicted HLA binding alone. The ranking score output provides a set of class I candidate neoantigens that may serve as therapeutic targets and provides a tool to facilitate in vitro and in vivo studies aimed at the development of more effective immunotherapies.

Novel Tumor-Specific Antigens for Immunotherapy Identified From Multi-omics Profiling in Thymic Carcinomas

Thymic carcinoma (TC) is the most aggressive thymic epithelial neoplasm. TC patients with microsatellite instability, whole-genome doubling, or alternative tumor-specific antigens from gene fusion are most likely to benefit from immunotherapies. However, due to the rarity of this disease, how to prioritize the putative biomarkers and what constitutes an optimal treatment regimen remains largely unknown. Therefore, we integrated genomic and transcriptomic analyses from TC patients and revealed that frameshift indels in KMT2C and CYLD frequently produce neoantigens. Moreover, a median of 3 fusion-derived neoantigens was predicted across affected patients, especially the CATSPERB-TC2N neoantigens that were recurrently predicted in TC patients. Lastly, potentially actionable alterations with early levels of evidence were uncovered and could be used for designing clinical trials. In summary, this study shed light on our understanding of tumorigenesis and presented new avenues for molecular characterization and immunotherapy in TC.

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.

In silico epitope prediction analyses highlight the potential for distracting antigen immunodominance with allogeneic cancer vaccines

Allogeneic cancer vaccines are designed to induce antitumor immune responses with the goal of impacting tumor growth. Typical allogeneic cancer vaccines are produced by expansion of established cancer cell lines, transfection with vectors encoding immunostimulatory cytokines, and lethal irradiation. More than 100 clinical trials have investigated the clinical benefit of allogeneic cancer vaccines in various cancer types. Results show limited therapeutic benefit in clinical trials and currently there are no FDA approved allogeneic cancer vaccines. We used recently developed bioinformatics tools including the pVAC-seq suite of software tools to analyze DNA/RNA sequencing data from the TCGA to examine the repertoire of antigens presented by a typical allogeneic cancer vaccine, and to simulate allogeneic cancer vaccine clinical trials. Specifically, for each simulated clinical trial we modeled the repertoire of antigens presented by allogeneic cancer vaccines consisting of three hypothetical cancer cell lines to 30 patients with the same cancer type. Simulations were repeated ten times for each cancer type. Each tumor sample in the vaccine and the vaccine recipient was subjected to HLA typing, differential expression analyses for tumor associated antigens (TAAs), germline variant calling, and neoantigen prediction. These analyses provided a robust, quantitative comparison between potentially beneficial TAAs and neoantigens versus distracting antigens present in the allogeneic cancer vaccines. We observe that distracting antigens greatly outnumber shared TAAs and neoantigens, providing one potential explanation for the lack of observed responses to allogeneic cancer vaccines. This analysis provides additional rationale for the redirection of efforts towards a personalized cancer vaccine approach.

Immuno-transcriptomic profiling of extracranial pediatric solid malignancies

We perform an immunogenomics analysis utilizing whole-transcriptome sequencing of 657 pediatric extracranial solid cancer samples representing 14 diagnoses, and additionally utilize transcriptomes of 131 pediatric cancer cell lines and 147 normal tissue samples for comparison. We describe patterns of infiltrating immune cells, T cell receptor (TCR) clonal expansion, and translationally relevant immune checkpoints. We find that tumor-infiltrating lymphocytes and TCR counts vary widely across cancer types and within each diagnosis, and notably are significantly predictive of survival in osteosarcoma patients. We identify potential cancer-specific immunotherapeutic targets for adoptive cell therapies including cell-surface proteins, tumor germline antigens, and lineage-specific transcription factors. Using an orthogonal immunopeptidomics approach, we find several potential immunotherapeutic targets in osteosarcoma and Ewing sarcoma and validated PRAME as a bona fide multi-pediatric cancer target. Importantly, this work provides a critical framework for immune targeting of extracranial solid tumors using parallel immuno-transcriptomic and -peptidomic approaches.

NeoFox: annotating neoantigen candidates with neoantigen features

SUMMARY

The detection and prediction of true neoantigens is of great importance for the field of cancer immunotherapy. Wesearched the literature for proposed neoantigen features and integrated them into a toolbox called NEOantigen Feature toolbOX (NeoFox). NeoFox is an easy-to-use Python package that enables the annotation of neoantigen candidates with 16 neoantigen features.

AVAILABILITY AND IMPLEMENTATION

NeoFox is freely available as an open source Python package released under the GNU General Public License (GPL) v3 license at https://github.com/TRON-Bioinformatics/neofox.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Commensal Bifidobacterium Strains Enhance the Efficacy of Neo-Epitope Based Cancer Vaccines

A large body of data both in animals and humans demonstrates that the gut microbiome plays a fundamental role in cancer immunity and in determining the efficacy of cancer immunotherapy. In this work, we have investigated whether and to what extent the gut microbiome can influence the antitumor activity of neo-epitope-based cancer vaccines in a BALB/c-CT26 cancer mouse model. Similarly to that observed in the C57BL/6-B16 model, Bifidobacterium administration per se has a beneficial effect on CT26 tumor inhibition. Furthermore, the combination of Bifidobacterium administration and vaccination resulted in a protection which was superior to vaccination alone and to Bifidobacterium administration alone, and correlated with an increase in the frequency of vaccine-specific T cells. The gut microbiome analysis by 16S rRNA gene sequencing and shotgun metagenomics showed that tumor challenge rapidly altered the microbiome population, with Muribaculaceae being enriched and Lachnospiraceae being reduced. Over time, the population of Muribaculaceae progressively reduced while the Lachnospiraceae population increased—a trend that appeared to be retarded by the oral administration of Bifidobacterium. Interestingly, in some Bacteroidales, Prevotella and Muribaculacee species we identified sequences highly homologous to immunogenic neo-epitopes of CT26 cells, supporting the possible role of “molecular mimicry” in anticancer immunity. Our data strengthen the importance of the microbiome in cancer immunity and suggests a microbiome-based strategy to potentiate neo-epitope-based cancer vaccines.

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.

High-affinity neoantigens correlate with better prognosis and trigger potent antihepatocellular carcinoma (HCC) activity by activating CD39+CD8+ T cells

OBJECTIVE

It remains controversial whether tumour mutational burden (TMB) or neoantigens are prognostic markers in hepatocellular carcinoma (HCC). This study aimed to define the function of TMB or neoantigens in antitumour immunotherapy.

DESIGN

Neoantigens of patients (n=56) were analysed by pVAC tools with major histocompatibility complex-1 (MHC-I) algorithms based on whole exome sequencing and neoantigens with mutant type IC₅₀ <50 nM were defined as high-affinity neoantigens (HANs). Patients were segregated into HAN-high/low groups by median of HAN value, and overall survival (OS) was analysed. Autologous organoid killing model was developed to clarify the antitumour activity of HANs.

RESULTS

The value of HAN showed a better correlation with OS (p=0.0199) than TMB (p=0.7505) or neoantigens (p=0.2297) in patients with HCC and positively correlated with the frequency of CD39⁺CD8⁺ tumour infiltrating lymphocytes (TILs). Furthermore, HAN-specific CD8⁺ T cells were identified in CD39⁺CD8⁺ TILs, which showed better antitumour activity in HAN-high versus HAN-low group. In addition, more effective HAN peptides were identified in HAN-high versus HAN-low group. Besides, flow cytometry data showed that in fresh tumour, CD39⁺PD-1^intCD8⁺ TILs displayed an effector phenotype and stronger antitumour activity in HAN-high versus HAN-low group. More importantly, patients in HAN-high versus HAN-low group showed a better prognosis after anti-PD-1 therapy.

CONCLUSIONS

Our study first demonstrates that HAN value positively correlates with better OS in patients with HCC. HANs trigger antitumour activity by activating tumour-reactive CD39⁺CD8⁺ T cells, and patients in HAN-high group benefited more from anti-PD-1 therapy than HAN-low group. These findings may provide a novel strategy for personalised antitumour therapies for HCC.

Low neoantigen expression and poor T-cell priming underlie early immune escape in colorectal cancer

Immune evasion is a hallmark of cancer, and therapies that restore immune surveillance have proven highly effective in cancers with high tumor mutation burden (TMB) (e.g., those with microsatellite instability (MSI)). Whether low TMB cancers, which are largely refractory to immunotherapy, harbor potentially immunogenic neoantigens remains unclear. Here, we show that tumors from all patients with microsatellite stable (MSS) colorectal cancer (CRC) express clonal predicted neoantigens despite low TMB. Unexpectedly, these neoantigens are broadly expressed at lower levels compared to those in MSI CRC. Using a versatile platform for modulating neoantigen expression in CRC organoids and transplantation into the distal colon of mice, we show that low expression precludes productive cross priming and drives immediate T cell dysfunction. Strikingly, experimental or therapeutic rescue of priming rendered T cells capable of controlling tumors with low neoantigen expression. These findings underscore a critical role of neoantigen expression level in immune evasion and therapy response.

ISOTOPE: ISOform-guided prediction of epiTOPEs in cancer

Immunotherapies provide effective treatments for previously untreatable tumors and identifying tumor-specific epitopes can help elucidate the molecular determinants of therapy response. Here, we describe a pipeline, ISOTOPE (ISOform-guided prediction of epiTOPEs In Cancer), for the comprehensive identification of tumor-specific splicing-derived epitopes. Using RNA sequencing and mass spectrometry for MHC-I associated proteins, ISOTOPE identified neoepitopes from tumor-specific splicing events that are potentially presented by MHC-I complexes. Analysis of multiple samples indicates that splicing alterations may affect the production of self-epitopes and generate more candidate neoepitopes than somatic mutations. Although there was no difference in the number of splicing-derived neoepitopes between responders and non-responders to immune therapy, higher MHC-I binding affinity was associated with a positive response. Our analyses highlight the diversity of the immunogenic impacts of tumor-specific splicing alterations and the importance of studying splicing alterations to fully characterize tumors in the context of immunotherapies. ISOTOPE is available at https://github.com/comprna/ISOTOPE.

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.

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.

Translational oncotargets for immunotherapy: From pet dogs to humans

Preclinical studies in rodent models have been a pivotal role in human clinical research, but many of them fail in the translational process. Spontaneous tumors in pet dogs have the potential to bridge the gap between preclinical models and human clinical trials. Their natural occurrence in an immunocompetent system overcome the limitations of preclinical rodent models. Due to its reasonable cellular, molecular, and genetic homology to humans, the pet dog represents a valuable model to accelerate the translation of preclinical studies to clinical trials in humans, actually with benefits for both species. Moreover, their unique genetic features of breeding and breed-related mutations have contributed to assess and optimize therapeutics in individuals with different genetic backgrounds. This review aims to outline four main immunotherapy approaches - cancer vaccines, adaptive T-cell transfer, antibodies, and cytokines -, under research in veterinary medicine and how they can serve the clinical application crosstalk with humans.

Targeting public neoantigens for cancer immunotherapy

Several current immunotherapy approaches target private neoantigens derived from mutations that are unique to individual patients' tumors. However, immunotherapeutic agents can also be developed against public neoantigens derived from recurrent mutations in cancer driver genes. The latter approaches target proteins that are indispensable for tumor growth, and each therapeutic agent can be applied to numerous patients. Here we review the opportunities and challenges involved in the identification of suitable public neoantigen targets and the development of therapeutic agents targeting them.

Beyond Tumor Mutation Burden: Tumor Neoantigen Burden as a Biomarker for Immunotherapy and Other Types of Therapy

Immunotherapy has significantly improved the clinical outcome of patients with cancer. However, the immune response rate varies greatly, possibly due to lack of effective biomarkers that can be used to distinguish responders from non-responders. Recently, clinical studies have associated high tumor neoantigen burden (TNB) with improved outcomes in patients treated with immunotherapy. Therefore, TNB has emerged as a biomarker for immunotherapy and other types of therapy. In the present review, the potential application of TNB as a biomarker was evaluated. The methods of neoantigen prediction were summarized and the mechanisms involved in TNB were investigated. The impact of high TNB and increased number of infiltrating immune cells on the efficacy of immunotherapy was also addressed. Finally, the future challenges of TNB were discussed.

Optimized polyepitope neoantigen DNA vaccines elicit neoantigen-specific immune responses in preclinical models and in clinical translation

BACKGROUND

Preclinical studies and early clinical trials have shown that targeting cancer neoantigens is a promising approach towards the development of personalized cancer immunotherapies. DNA vaccines can be rapidly and efficiently manufactured and can integrate multiple neoantigens simultaneously. We therefore sought to optimize the design of polyepitope DNA vaccines and test optimized polyepitope neoantigen DNA vaccines in preclinical models and in clinical translation.

METHODS

We developed and optimized a DNA vaccine platform to target multiple neoantigens. The polyepitope DNA vaccine platform was first optimized using model antigens in vitro and in vivo. We then identified neoantigens in preclinical breast cancer models through genome sequencing and in silico neoantigen prediction pipelines. Optimized polyepitope neoantigen DNA vaccines specific for the murine breast tumor E0771 and 4T1 were designed and their immunogenicity was tested in vivo. We also tested an optimized polyepitope neoantigen DNA vaccine in a patient with metastatic pancreatic neuroendocrine tumor.

RESULTS

Our data support an optimized polyepitope neoantigen DNA vaccine design encoding long (≥20-mer) epitopes with a mutant form of ubiquitin (Ubmut) fused to the N-terminus for antigen processing and presentation. Optimized polyepitope neoantigen DNA vaccines were immunogenic and generated robust neoantigen-specific immune responses in mice. The magnitude of immune responses generated by optimized polyepitope neoantigen DNA vaccines was similar to that of synthetic long peptide vaccines specific for the same neoantigens. When combined with immune checkpoint blockade therapy, optimized polyepitope neoantigen DNA vaccines were capable of inducing antitumor immunity in preclinical models. Immune monitoring data suggest that optimized polyepitope neoantigen DNA vaccines are capable of inducing neoantigen-specific T cell responses in a patient with metastatic pancreatic neuroendocrine tumor.

CONCLUSIONS

We have developed and optimized a novel polyepitope neoantigen DNA vaccine platform that can target multiple neoantigens and induce antitumor immune responses in preclinical models and neoantigen-specific responses in clinical translation.

Antitumor T-cell Immunity Contributes to Pancreatic Cancer Immune Resistance

Pancreatic ductal adenocarcinoma (PDAC) is the third leading cause of cancer death in the United States. Pancreatic tumors are minimally infiltrated by T cells and are largely refractory to immunotherapy. Accordingly, the role of T-cell immunity in pancreatic cancer has been somewhat overlooked. Here, we hypothesized that immune resistance in pancreatic cancer was induced in response to antitumor T-cell immune responses and that understanding how pancreatic tumors respond to immune attack may facilitate the development of more effective therapeutic strategies. We now provide evidence that T-cell-dependent host immune responses induce a PDAC-derived myeloid mimicry phenomenon and stimulate immune resistance. Three KPC mouse models of pancreatic cancer were used: the mT3-2D (Kras+/LSL-G12D; Trp53+/LSL-R172H; Pdx1-Cre) subcutaneous and orthotopic models, as well as the KP1 (p48-CRE/LSL-Kras/Trp53 flox/flox ) subcutaneous model. KPC cancer cells were grown in immunocompetent and immunodeficient C57BL/6 mice and analyzed to determine the impact of adaptive immunity on malignant epithelial cells, as well as on whole tumors. We found that induced T-cell antitumor immunity, via signal transducer and activator of transcription 1 (STAT1), stimulated malignant epithelial pancreatic cells to induce the expression of genes typically expressed by myeloid cells and altered intratumoral immunosuppressive myeloid cell profiles. Targeting the Janus Kinase (JAK)/STAT signaling pathway using the FDA-approved drug ruxolitinib overcame these tumor-protective responses and improved anti-PD-1 therapeutic efficacy. These findings provide future directions for treatments that specifically disable this mechanism of resistance in PDAC.

In silico T cell epitope identification for SARS-CoV-2: Progress and perspectives

Growing evidence suggests that T cells may play a critical role in combating severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Hence, COVID-19 vaccines that can elicit a robust T cell response may be particularly important. The design, development and experimental evaluation of such vaccines is aided by an understanding of the landscape of T cell epitopes of SARS-CoV-2, which is largely unknown. Due to the challenges of identifying epitopes experimentally, many studies have proposed the use of in silico methods. Here, we present a review of the in silico methods that have been used for the prediction of SARS-CoV-2 T cell epitopes. These methods employ a diverse set of technical approaches, often rooted in machine learning. A performance comparison is provided based on the ability to identify a specific set of immunogenic epitopes that have been determined experimentally to be targeted by T cells in convalescent COVID-19 patients, shedding light on the relative performance merits of the different approaches adopted by the in silico studies. The review also puts forward perspectives for future research directions.

Neoantigen vaccine platforms in clinical development: understanding the future of personalized immunotherapy

INTRODUCTION

Derived from genetic alterations, cancer neoantigens are proteins with novel amino acid sequences that can be recognized by the immune system. Recent evidence demonstrates that 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. These advances have generated considerable enthusiasm for ?the ?development of neoantigen vaccines. Several neoantigen vaccine platforms are currently being evaluated in early phase clinical trials including the synthetic long peptide (SLP), RNA, dendritic cell (DC), and DNA vaccine platforms.

AREAS COVERED

In this review, we describe, evaluate the mechanism(s) of action, compare the advantages and disadvantages, and summarize early clinical experience with each vaccine platform. We provide perspectives on the future directions of the neoantigen vaccine field. All data are derived from PubMed and ClinicalTrials search updated in October 2020.

EXPERT OPINION

Although the initial clinical experience is promising, significant challenges to the success of neoantigen vaccines include limitations in neoantigen identification and the need to successfully target the immunosuppressive tumor microenvironment.

Clonal architecture in mesothelioma is prognostic and shapes the tumour microenvironment

Malignant Pleural Mesothelioma (MPM) is typically diagnosed 20-50 years after exposure to asbestos and evolves along an unknown evolutionary trajectory. To elucidate this path, we conducted multi-regional exome sequencing of 90 tumour samples from 22 MPMs acquired at surgery. Here we show that exomic intratumour heterogeneity varies widely across the cohort. Phylogenetic tree topology ranges from linear to highly branched, reflecting a steep gradient of genomic instability. Using transfer learning, we detect repeated evolution, resolving 5 clusters that are prognostic, with temporally ordered clonal drivers. BAP1/-3p21 and FBXW7/-chr4 events are always early clonal. In contrast, NF2/-22q events, leading to Hippo pathway inactivation are predominantly late clonal, positively selected, and when subclonal, exhibit parallel evolution indicating an evolutionary constraint. Very late somatic alteration of NF2/22q occurred in one patient 12 years after surgery. Clonal architecture and evolutionary clusters dictate MPM inflammation and immune evasion. These results reveal potentially drugable evolutionary bottlenecking in MPM, and an impact of clonal architecture on shaping the immune landscape, with potential to dictate the clinical response to immune checkpoint inhibition.

Radiotherapy-exposed CD8+ and CD4+ neoantigens enhance tumor control

Neoantigens generated by somatic nonsynonymous mutations are key targets of tumor-specific T cells, but only a small number of mutations predicted to be immunogenic are presented by MHC molecules on cancer cells. Vaccination studies in mice and patients have shown that the majority of neoepitopes that elicit T cell responses fail to induce significant antitumor activity, for incompletely understood reasons. We report that radiotherapy upregulates the expression of genes containing immunogenic mutations in a poorly immunogenic mouse model of triple-negative breast cancer. Vaccination with neoepitopes encoded by these genes elicited CD8+ and CD4+ T cells that, whereas ineffective in preventing tumor growth, improved the therapeutic efficacy of radiotherapy. Mechanistically, neoantigen-specific CD8+ T cells preferentially killed irradiated tumor cells. Neoantigen-specific CD4+ T cells were required for the therapeutic efficacy of vaccination and acted by producing Th1 cytokines, killing irradiated tumor cells, and promoting epitope spread. Such a cytotoxic activity relied on the ability of radiation to upregulate class II MHC molecules as well as the death receptors FAS/CD95 and DR5 on the surface of tumor cells. These results provide proof-of-principle evidence that radiotherapy works in concert with neoantigen vaccination to improve tumor control.

Immunotherapy and Immunotherapy Combinations in Metastatic Castration-Resistant Prostate Cancer

Although most prostate cancers are localized, and the majority are curable, recurrences occur in approximately 35% of men. Among patients with prostate-specific antigen (PSA) recurrence and PSA doubling time (PSADT) less than 15 months after radical prostatectomy, prostate cancer accounted for approximately 90% of the deaths by 15 years after recurrence. An immunosuppressive tumor microenvironment (TME) and impaired cellular immunity are likely largely responsible for the limited utility of checkpoint inhibitors (CPIs) in advanced prostate cancer compared with other tumor types. Thus, for immunologically "cold" malignancies such as prostate cancer, clinical trial development has pivoted towards novel approaches to enhance immune responses. Numerous clinical trials are currently evaluating combination immunomodulatory strategies incorporating vaccine-based therapies, checkpoint inhibitors, and chimeric antigen receptor (CAR) T cells. Other trials evaluate the efficacy and safety of these immunomodulatory agents' combinations with standard approaches such as androgen deprivation therapy (ADT), taxane-based chemotherapy, radiotherapy, and targeted therapies such as tyrosine kinase inhibitors (TKI) and poly ADP ribose polymerase (PARP) inhibitors. Here, we will review promising immunotherapies in development and ongoing trials for metastatic castration-resistant prostate cancer (mCRPC). These novel trials will build on past experiences and promise to usher a new era to treat patients with mCRPC.

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.

Antitumour dendritic cell vaccination in a priming and boosting approach

Mobilizing antitumour immunity through vaccination potentially constitutes a powerful anticancer strategy but has not yet provided robust clinical benefits in large patient populations. Although major hurdles still exist, we believe that currently available strategies for vaccines that target dendritic cells or use them to present antitumour antigens could be integrated into existing clinical practice using prime-boost approaches. In the priming phase, these approaches capitalize on either standard treatment modalities to trigger in situ vaccination and release tumour antigens or vaccination with dendritic cells loaded with tumour lysates or patient-specific neoantigens. In a second boost phase, personalized synthetic vaccines specifically boost T cells that were triggered during the priming phase. This immunotherapy approach has been enabled by the substantial recent improvements in dendritic cell vaccines. In this Perspective, we discuss these improvements, highlight how the prime-boost approach can be translated into clinical practice and provide solutions for various anticipated hurdles.

Neoantigen prediction and computational perspectives towards clinical benefit: recommendations from the ESMO Precision Medicine Working Group

BACKGROUND

The use of next-generation sequencing technologies has enabled the rapid identification of non-synonymous somatic mutations in cancer cells. Neoantigens are mutated peptides derived from somatic mutations not present in normal tissues that may result in the presentation of tumour-specific peptides capable of eliciting antitumour T-cell responses. Personalised neoantigen-based cancer vaccines and adoptive T-cell therapies have been shown to prime host immunity against tumour cells and are under clinical trial development. However, the optimisation and standardisation of neoantigen identification, as well as its delivery as immunotherapy are needed to increase tumour-specific T-cell responses and, thus, the clinical efficacy of current cancer immunotherapies.

METHODS

In this recommendation article, launched by the European Society for Medical Oncology (ESMO), we outline and discuss the available framework for neoantigen prediction and present a systematic review of the current scientific evidence.

RESULTS

A number of computational pipelines for neoantigen prediction are available. Most of them provide peptide major histocompatibility complex (MHC) binding affinity predictions, but more recent approaches incorporate additional features like variant allele fraction, gene expression, and clonality of mutations. Neoantigens can be predicted in all cancer types with high and low tumour mutation burden, in part by exploiting tumour-specific aberrations derived from mutational frameshifts, splice variants, gene fusions, endogenous retroelements and other tumour-specific processes that could yield more potently immunogenic tumour neoantigens. Ongoing clinical trials will highlight those cancer types and combinations of immune therapies that would derive the most benefit from neoantigen-based immunotherapies.

CONCLUSIONS

Improved identification, selection and prioritisation of tumour-specific neoantigens are needed to increase the scope of benefit from cancer vaccines and adoptive T-cell therapies. Novel pipelines are being developed to resolve the challenges posed by high-throughput sequencing and to predict immunogenic neoantigens.

Personalized neoantigen vaccination with synthetic long peptides: recent advances and future perspectives

Therapeutic cancer vaccines are one of the most promising strategies of immunotherapy. Traditional vaccines consisting of tumor-associated antigens have met with limited success. Recently, neoantigens derived from nonsynonymous mutations in tumor cells have emerged as alternatives that can improve tumor-specificity and reduce on-target off-tumor toxicity. Synthetic peptides are a common platform for neoantigen vaccines. It has been suggested that extending short peptides into long peptides can overcome immune tolerance and induce both CD4⁺ and CD8⁺ T cell responses. This review will introduce the history of long peptide-based neoantigen vaccines, discuss their advantages, summarize current preclinical and clinical developments, and propose future perspectives.

New emerging targets in cancer immunotherapy: the role of neoantigens

The success of cancer therapies with immune checkpoint inhibitors is transforming the treatment of patients with cancer and fostering cancer research. Therapies that target immune checkpoint inhibitors have shown unprecedented rates of durable long-lasting responses in patients with various cancer types, but only in a fraction of patients. Thus, novel approaches are needed to make immunotherapy more precise and also less toxic. The advances of next-generation sequencing technologies have allowed fast detection of somatic mutations in genes present in the exome of an individual tumour. Targeting neoantigens, the mutated peptides expressed only by tumour cells, may enable antitumour T-cell responses and tumour destruction without causing harm to healthy tissues. Currently, neoantigens can be identified in tumour clinical samples by using genomic-based computational tools. The two main treatment modalities targeting neoantigens that have been investigated in clinical trials are personalised vaccines and tumour infiltrating lymphocytes-based adoptive T-cell therapy. In this mini review, we discuss the promises and challenges for using neoantigens as emergent targets to personalise and guide cancer immunotherapy in a broader set of cancers.

Mass Spectrometry-Based Identification of MHC-Associated Peptides

Neoantigen-based immunotherapies promise to improve patient outcomes over the current standard of care. However, detecting these cancer-specific antigens is one of the significant challenges in the field of mass spectrometry. Even though the first sequencing of the immunopeptides was done decades ago, today there is still a diversity of the protocols used for neoantigen isolation from the cell surface. This heterogeneity makes it difficult to compare results between the laboratories and the studies. Isolation of the neoantigens from the cell surface is usually done by mild acid elution (MAE) or immunoprecipitation (IP) protocol. However, limited amounts of the neoantigens present on the cell surface impose a challenge and require instrumentation with enough sensitivity and accuracy for their detection. Detecting these neopeptides from small amounts of available patient tissue limits the scope of most of the studies to cell cultures. Here, we summarize protocols for the extraction and identification of the major histocompatibility complex (MHC) class I and II peptides. We aimed to evaluate existing methods in terms of the appropriateness of the isolation procedure, as well as instrumental parameters used for neoantigen detection. We also focus on the amount of the material used in the protocols as the critical factor to consider when analyzing neoantigens. Beyond experimental aspects, there are numerous readily available proteomics suits/tools applicable for neoantigen discovery; however, experimental validation is still necessary for neoantigen characterization.

High-Throughput Prediction of MHC Class I and II Neoantigens with MHCnuggets

Computational prediction of binding between neoantigen peptides and major histocompatibility complex (MHC) proteins can be used to predict patient response to cancer immunotherapy. Current neoantigen predictors focus on in silico estimation of MHC binding affinity and are limited by low predictive value for actual peptide presentation, inadequate support for rare MHC alleles, and poor scalability to high-throughput data sets. To address these limitations, we developed MHCnuggets, a deep neural network method that predicts peptide-MHC binding. MHCnuggets can predict binding for common or rare alleles of MHC class I or II with a single neural network architecture. Using a long short-term memory network (LSTM), MHCnuggets accepts peptides of variable length and is faster than other methods. When compared with methods that integrate binding affinity and MHC-bound peptide (HLAp) data from mass spectrometry, MHCnuggets yields a 4-fold increase in positive predictive value on independent HLAp data. We applied MHCnuggets to 26 cancer types in The Cancer Genome Atlas, processing 26.3 million allele-peptide comparisons in under 2.3 hours, yielding 101,326 unique predicted immunogenic missense mutations (IMM). Predicted IMM hotspots occurred in 38 genes, including 24 driver genes. Predicted IMM load was significantly associated with increased immune cell infiltration (P < 2 × 10^-16), including CD8⁺ T cells. Only 0.16% of predicted IMMs were observed in more than 2 patients, with 61.7% of these derived from driver mutations. Thus, we describe a method for neoantigen prediction and its performance characteristics and demonstrate its utility in data sets representing multiple human cancers.