TruNeo: an integrated pipeline improves personalized true tumor neoantigen identification

A comprehensive proteogenomic pipeline for neoantigen discovery to advance personalized cancer immunotherapy

The accurate identification and prioritization of antigenic peptides is crucial for the development of personalized cancer immunotherapies. Publicly available pipelines to predict clinical neoantigens do not allow direct integration of mass spectrometry immunopeptidomics data, which can uncover antigenic peptides derived from various canonical and noncanonical sources. To address this, we present an end-to-end clinical proteogenomic pipeline, called NeoDisc, that combines state-of-the-art publicly available and in-house software for immunopeptidomics, genomics and transcriptomics with in silico tools for the identification, prediction and prioritization of tumor-specific and immunogenic antigens from multiple sources, including neoantigens, viral antigens, high-confidence tumor-specific antigens and tumor-specific noncanonical antigens. We demonstrate the superiority of NeoDisc in accurately prioritizing immunogenic neoantigens over recent prioritization pipelines. We showcase the various features offered by NeoDisc that enable both rule-based and machine-learning approaches for personalized antigen discovery and neoantigen cancer vaccine design. Additionally, we demonstrate how NeoDisc's multiomics integration identifies defects in the cellular antigen presentation machinery, which influence the heterogeneous tumor antigenic landscape.

Multi-omics analysis unveils immunosuppressive microenvironment in the occurrence and development of multiple pulmonary lung cancers

Multiple pulmonary lung cancers (MPLCs) are frequently encountered on computed tomography (CT) scanning of chest, yet their intrinsic characteristics associated with genomic features and radiological or pathological textures that may lead to distinct clinical outcomes remain largely unexplored. A total of 27 pulmonary nodules covering different radiological or pathological textures as well as matched adjacent normal tissues and blood samples were collected from patients diagnosed with MPLCs. Whole-exome sequencing (WES) and whole-transcriptome sequencing were performed. The molecular and immune features of MPLCs associated with distinct radiological or pathological textures were comprehensively investigated. Genomics analysis unveiled the distinct branches of pulmonary nodules originating independently within the same individual. EGFR and KRAS mutations were found to be prevalent in MPLCs, exhibiting mutual exclusivity. The group with KRAS mutations exhibited stronger immune signatures compared to the group with EGFR mutations. Additionally, MPLCs exhibited a pronounced immunosuppressive microenvironment, which was particularly distinct when compared with normal tissues. The expression of the FDSCP gene was specifically observed in MPLCs. When categorizing MPLCs based on radiological or pathological characteristics, a progressive increase in mutation accumulation was observed, accompanied by heightened chromatin-level instability as ground-glass opacity component declined or invasive progression occurred. A close association with the immunosuppressive microenvironment was also observed during the progression of pulmonary nodules. Notably, the upregulation of B cell and regulatory T cell marker genes occurred progressively. Immune cell abundance analysis further demonstrated a marked increase in exhausted cells and regulatory T cells during the progression of pulmonary nodules. These results were further validated by independent datasets including nCounter RNA profiling, single-cell RNA sequencing, and spatial transcriptomic datasets. Our study provided a comprehensive representation of the diverse landscape of MPLCs originating within the same individual and emphasized the significant influence of the immunosuppressive microenvironment in the occurrence and development of pulmonary nodules. These findings hold great potential for enhancing the clinical diagnosis and treatment strategies for MPLCs.

Transformers meets neoantigen detection: a systematic literature review

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

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

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

Tumor neoantigens derived from RNA editing events show significant clinical relevance in melanoma patients treated with immunotherapy

This study aimed to investigate the clinical significance of RNA editing (RE) and RNA editing derived (RED-) neoantigens in melanoma patients treated with immunotherapy. Vardict and VEP were used to identify the somatic mutations. RE events were identified by Reditools2 and filtered by the custom pipeline. miRTar2GO was implemented to predict the RE whether located in miRNA targets within the 3' UTR region. NetMHCpan and NetCTLpan were used to identify and characterize RED-neoantigens. In total, 7116 RE events were identified, most of which were A-to-I events. Using our custom pipeline, 631 RED-neoantigens were identified that show a significantly greater peptide-MHC affinity, and facilitate epitope processing and presentation than wild-type peptides. The OS of the patients with high RED-neoantigens burden was significantly longer ( P  = 0.035), and a significantly higher RED-neoantigens burden was observed in responders ( P  = 0.048). The area under the curve of the RED-neoantigen was 0.831 of OS. Then, we validated the reliability of RED-neoantigens in predicting the prognosis in an independent cohort and found that patients with high RED-neoantigens exhibited a longer OS ( P  = 0.008). To our knowledge, this is the first study to systematically assess the clinical relevance of RED-neoantigens in melanoma patients treated with immunotherapy.

The potential of mRNA vaccines in cancer nanomedicine and immunotherapy

Owing to their outstanding performance against COVID-19, mRNA vaccines have brought great hope for combating various incurable diseases, including cancer. Differences in the encoded proteins result in different molecular and cellular mechanisms of mRNA vaccines. With the rapid development of nanotechnology and molecular medicine, personalized antigen-encoding mRNA vaccines that enhance antigen presentation can trigger effective immune responses and prevent off-target toxicities. Herein, we review new insights into the influence of encoded antigens, cytokines, and other functional proteins on the mechanisms of mRNA vaccines. We also highlight the importance of delivery systems and chemical modifications for mRNA translation efficiency, stability, and targeting, and we discuss the potential problems and application prospects of mRNA vaccines as versatile tools for combating cancer.

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

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

Effective personalized neoantigen vaccine plus anti-PD-1 in a PD-1 blockade-resistant lung cancer patient

Background: Although significant progress has been made in immune checkpoint inhibitor (ICI) treatment of advanced squamous cell carcinoma (SqCC), most patients still experience acquired drug resistance. Methods: We used a dendritic cell-based neoantigen vaccine combined with ICIs to treat advanced SqCC in a PD-1 blockade-resistant patient. Results: The follow-up of this patient after 12 months revealed significant tumor regression. We also identified a new JAK1 ICI-resistant mutation that could become a potential universal neoantigen target for tumor vaccines. Conclusion: Individualized management of advanced SqCC through a combined neoantigen vaccine and ICI administration could yield beneficial clinical outcomes. Vaccines targeting anti-PD-1-resistant JAK1 mutations might be of particular benefit to a specific group of solid tumor patients.

Neoepitopes prediction strategies: an integration of cancer genomics and immunoinformatics approaches

A major near-term medical impact of the genomic technology revolution will be the elucidation of mechanisms of cancer pathogenesis, leading to improvements in the diagnosis of cancer and the selection of cancer treatment. Next-generation sequencing technologies have accelerated the characterization of a tumor, leading to the comprehensive discovery of all the major alterations in a given cancer genome, followed by the translation of this information using computational and immunoinformatics approaches to cancer diagnostics and therapeutic efforts. In the current article, we review various components of cancer immunoinformatics applied to a series of fields of cancer research, including computational tools for cancer mutation detection, cancer mutation and immunological databases, and computational vaccinology.

Artificial intelligence applied in neoantigen identification facilitates personalized cancer immunotherapy

The field of cancer neoantigen investigation has developed swiftly in the past decade. Predicting novel and true neoantigens derived from large multi-omics data became difficult but critical challenges. The rise of Artificial Intelligence (AI) or Machine Learning (ML) in biomedicine application has brought benefits to strengthen the current computational pipeline for neoantigen prediction. ML algorithms offer powerful tools to recognize the multidimensional nature of the omics data and therefore extract the key neoantigen features enabling a successful discovery of new neoantigens. The present review aims to outline the significant technology progress of machine learning approaches, especially the newly deep learning tools and pipelines, that were recently applied in neoantigen prediction. In this review article, we summarize the current state-of-the-art tools developed to predict neoantigens. The standard workflow includes calling genetic variants in paired tumor and blood samples, and rating the binding affinity between mutated peptide, MHC (I and II) and T cell receptor (TCR), followed by characterizing the immunogenicity of tumor epitopes. More specifically, we highlight the outstanding feature extraction tools and multi-layer neural network architectures in typical ML models. It is noted that more integrated neoantigen-predicting pipelines are constructed with hybrid or combined ML algorithms instead of conventional machine learning models. In addition, the trends and challenges in further optimizing and integrating the existing pipelines are discussed.

Challenges in neoantigen-directed therapeutics

A fundamental prerequisite for the efficacy of cancer immunotherapy is the presence of functional, antigen-specific T cells within the tumor. Neoantigen-directed therapy is a promising strategy that aims at targeting the host's immune response against tumor-specific antigens, thereby eradicating cancer cells. Initial forays have been made in clinical environments utilizing vaccines and adoptive cell therapy; however, many challenges lie ahead. We provide an in-depth overview of the current state of the field with an emphasis on in silico neoantigen discovery and the clinical aspects that need to be addressed to unlock the full potential of this therapy.

Neoadjuvant therapy with immune checkpoint blockade, antiangiogenesis, and chemotherapy for locally advanced gastric cancer

Despite neoadjuvant/conversion chemotherapy, the prognosis of cT4a/bN+ gastric cancer is poor. Immune checkpoint inhibitors (ICIs) and antiangiogenic agents have shown activity in late-stage gastric cancer, but their efficacy in the neoadjuvant/conversion setting is unclear. In this single-armed, phase II, exploratory trial (NCT03878472), we evaluate the efficacy of a combination of ICI (camrelizumab), antiangiogenesis (apatinib), and chemotherapy (S-1 ± oxaliplatin) for neoadjuvant/conversion treatment of cT4a/bN+ gastric cancer. The primary endpoints are pathological responses and their potential biomarkers. Secondary endpoints include safety, objective response, progression-free survival, and overall survival. Complete and major pathological response rates are 15.8% and 26.3%. Pathological responses correlate significantly with microsatellite instability status, PD-L1 expression, and tumor mutational burden. In addition, multi-omics examination reveals several putative biomarkers for pathological responses, including RREB1 and SSPO mutation, immune-related signatures, and a peripheral T cell expansion score. Multi-omics also demonstrates dynamic changes in dominant tumor subclones, immune microenvironments, and T cell receptor repertoires during neoadjuvant immunotherapy. The toxicity and post-surgery complications are limited. These data support further validation of ICI- and antiangiogenesis-based neoadjuvant/conversion therapy in large randomized trials and provide candidate biomarkers.

Promoting the formation of Pi-stacking interaction to improve CTL cells activation between modified peptide and HLA

OBJECTIVE

This study aims to investigate the use of single residue substitution to promote the formation of pi-stacking interactions between peptides and Human leukocyte antigen (HLA)-A*2402 molecules to improve the affinity of peptides and HLA molecules, as well as the level of cytotoxic T lymphocyte (CTL) cells activated by peptides-HLA (p-HLA) complex.

METHODS

Molecular docking and molecular dynamics simulation were used to simulate and analyze the interactions and binding free energies between HLA-A*2402-restricted antigen peptides and HLA molecules, before and after the single residue substitution. HLA-A*2402 restricted antigen peptides before and after the single residue replacement were loaded into dendritic cells (DCs) in vitro, and further Enzyme-Linked ImmunoSpot (ELispot) test was carried out to evaluate the effect of modified antigen peptides on the immune activation of CTL cells.

RESULT

After replacing the antigen peptides with a single residue, some of them could promote the formation of pi-stacking interaction. The binding free energy between the modified antigen peptides and HLA-A*2402, as well as the level of immune activation of CTL cells were mostly higher than before, especially after the replacement of the 9th residue of the polypeptide, such as C9F and C9W. There was a significant negative correlation between the level of activated CTL cells by modified antigen peptides and the total interaction amount of hydrogen bonds and salt bridges.

CONCLUSION

Promoting the formation of pi-stacking interaction between antigen peptides and HLA-A*2402 molecules could increase the total binding free energy of p-HLA complex and the level of CTL cells activation. In addition, the amount of hydrogen bonds and salt bridges between peptides and HLA could reduce the level of immune activation. All the characteristics above can improve the immunogenicities of the weak antigens.

The role of neoantigens in tumor immunotherapy

Tumor neoantigens are aberrant polypeptides produced by tumor cells as a result of genomic mutations. They are also tumor-specific antigens (TSA). Neoantigens are more immunogenic than tumor-related antigens and do not induce autoimmunity. Based on the rapid development of bioinformatics and the continuous update of sequencing technology, cancer immunotherapy with tumor neoantigens has made promising breakthroughs and progress. In this review, the generation, prediction, and identification of novel antigens, as well as the individualized treatments of neoantigens, were first introduced. Secondly, the mechanism of Chimeric Antigen Receptor T-Cell Immunotherapy (CAR-T) therapy and immune checkpoint blockade therapy in the treatment of tumors were outlined, and the three treatment methods were compared. Thirdly, the application of neoantigens in CAR-T therapy and PD-1/PD-L1 blockade therapy was briefly described. The benefits of the neoantigen vaccines over common vaccines were summarized as well. Finally, the prospect of neoantigen therapy was presented.

In silico tools and databases for designing cancer immunotherapy

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

Computational cancer neoantigen prediction: current status and recent advances

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

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Tumors have the ability to acquire immune escape mechanisms.

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

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

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

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.

Durable Response to Immunotherapy With Antiangiogenic Drug in Large-Cell Lung Carcinoma With Multiple Fulminant Postoperative Metastases: A Case Report

Immunotherapy alone or chemo-immunotherapy has recently been recommended for treating advanced lung carcinoma in patients without driver mutations. However, the efficacy of immunotherapy and molecular mechanism in large-cell lung cancer (LCLC) remains unclear. Here, we reported a rare case of multiple fulminant postoperative body and mouth metastases in LCLC treating with combination immunotherapy. Initially, the patient was diagnosed as early stage LCLC and underwent a radical resection of the right lower lobe. Just one month later, multiple fulminant body and mouth lesions appeared in the right upper arm, right elbow, right waist, and tongue root. Meanwhile, serum neuron specific enolase (NSE) concentration dramatically increased from 12.12 to 30.14 ng/ml. Immumohistochemistry findings demonstrated moderate PD-L1 expressions with tumor proportion score (TPS), while next-generation sequencing indicated moderate tumor mutational burden (TMB) levels and gene mutations in PBRM1 L1230P and TP53 L194R of both foci. Besides, loss of heterozygosity (LOH) at human leukocyte antigen (HLA) class I (HLA-A*02:03, HLA-B*55:02 and HLA-C*12:03) were detected in the right upper arm metastasis, which may facilitate malignant postoperative metastases in this case. Notably, this patient received combination therapy with anti-PD-1 antibody sintilimab plus anlotinib, and achieved a partial response for at least 12 months. Using an integrated computational method, the mutant peptide TEIPENDIPL derived from PBRM1 L1230P was predicted to be a specific neoantigen and could still be presented by HLA-B*40:01. This case suggests that immunotherapy plus antiangiogenic drug may provide an alternative therapeutic option for advanced LCLC patients without common gene mutations.