Personalized Cancer Vaccines Directed against Tumor Mutations: Building Evidence from Mice to Humans.

Personalized vaccines directed to tumor mutations have recently gained significant momentum. On the basis of the concept of stimulating T-cell responses against neoantigens encoded by a tumor's host of personal mutations, these vaccines utilize genome or exome sequencing, mutation calling, and epitope prediction followed by manufacturing of a customized vaccine for each patient. In their 2012 Cancer Research publication, Castle and colleagues provided evidence that vaccinating with long peptide vaccines encompassing neoantigens can generate robust immune responses and induce antitumor activity in a mouse B16F10 melanoma. This approach, harnessing the exquisite specificity of mutations to the tumor and thus providing an effective target for cancer vaccines, was subsequently shown to be safe and immunogenic in a series of small first in man trials in patients with melanoma. The field has accelerated and expanded substantially over the last 5 years, propelled by increasing evidence for vaccine-mediated clinical efficacy, leading to ongoing registrational trials using personalized RNA neoantigen vaccines in patients with melanoma and several other malignancies. See related article by Castle and colleagues, Cancer Res 2012;72:1081-91.

Untranslated regions (UTRs) are a potential novel source of neoantigens for personalised immunotherapy.

Background: Neoantigens, mutated tumour-specific antigens, are key targets of anti-tumour immunity during checkpoint inhibitor (CPI) treatment. Their identification is fundamental to designing neoantigen-directed therapy. Non-canonical neoantigens arising from the untranslated regions (UTR) of the genome are an overlooked source of immunogenic neoantigens. Here, we describe the landscape of UTR-derived neoantigens and release a computational tool, PrimeCUTR, to predict UTR neoantigens generated by start-gain and stop-loss mutations. Methods: We applied PrimeCUTR to a whole genome sequencing dataset of pre-treatment tumour samples from CPI-treated patients (n = 341). Cancer immunopeptidomic datasets were interrogated to identify MHC class I presentation of UTR neoantigens. Results: Start-gain neoantigens were predicted in 72.7% of patients, while stop-loss mutations were found in 19.3% of patients. While UTR neoantigens only accounted 2.6% of total predicted neoantigen burden, they contributed 12.4% of neoantigens with high dissimilarity to self-proteome. More start-gain neoantigens were found in CPI responders, but this relationship was not significant when correcting for tumour mutational burden. While most UTR neoantigens are private, we identified two recurrent start-gain mutations in melanoma. Using immunopeptidomic datasets, we identify two distinct MHC class I-presented UTR neoantigens: one from a recurrent start-gain mutation in melanoma, and one private to Jurkat cells. Conclusion: PrimeCUTR is a novel tool which complements existing neoantigen discovery approaches and has potential to increase the detection yield of neoantigens in personalised therapeutics, particularly for neoantigens with high dissimilarity to self. Further studies are warranted to confirm the expression and immunogenicity of UTR neoantigens.

Lack of shared neoantigens in prevalent mutations in cancer.

Tumors are mostly characterized by genetic instability, as result of mutations in surveillance mechanisms, such as DNA damage checkpoint, DNA repair machinery and mitotic checkpoint. Defect in one or more of these mechanisms causes additive accumulation of mutations. Some of these mutations are drivers of transformation and are positively selected during the evolution of the cancer, giving a growth advantage on the cancer cells. If such mutations would result in mutated neoantigens, these could be actionable targets for cancer vaccines and/or adoptive cell therapies. However, the results of the present analysis show, for the first time, that the most prevalent mutations identified in human cancers do not express mutated neoantigens. The hypothesis is that this is the result of the selection operated by the immune system in the very early stages of tumor development. At that stage, the tumor cells characterized by mutations giving rise to highly antigenic non-self-mutated neoantigens would be efficiently targeted and eliminated. Consequently, the outgrowing tumor cells cannot be controlled by the immune system, with an ultimate growth advantage to form large tumors embedded in an immunosuppressive tumor microenvironment (TME). The outcome of such a negative selection operated by the immune system is that the development of off-the-shelf vaccines, based on shared mutated neoantigens, does not seem to be at hand. This finding represents the first demonstration of the key role of the immune system on shaping the tumor antigen presentation and the implication in the development of antitumor immunological strategies.

Multiplex genetic manipulations in Clostridium butyricum and Clostridium sporogenes to secrete recombinant antigen proteins for oral-spore vaccination.

BACKGROUND: Clostridium spp. has demonstrated therapeutic potential in cancer treatment through intravenous or intratumoral administration. This approach has expanded to include non-pathogenic clostridia for the treatment of various diseases, underscoring the innovative concept of oral-spore vaccination using clostridia. Recent advancements in the field of synthetic biology have significantly enhanced the development of Clostridium-based bio-therapeutics. These advancements are particularly notable in the areas of efficient protein overexpression and secretion, which are crucial for the feasibility of oral vaccination strategies. Here, we present two examples of genetically engineered Clostridium candidates: one as an oral cancer vaccine and the other as an antiviral oral vaccine against SARS-CoV-2. RESULTS: Using five validated promoters and a signal peptide derived from Clostridium sporogenes, a series of full-length NY-ESO-1/CTAG1, a promising cancer vaccine candidate, expression vectors were constructed and transformed into C. sporogenes and Clostridium butyricum. Western blotting analysis confirmed efficient expression and secretion of NY-ESO-1 in clostridia, with specific promoters leading to enhanced detection signals. Additionally, the fusion of a reported bacterial adjuvant to NY-ESO-1 for improved immune recognition led to the cloning difficulties in E. coli. The use of an AUU start codon successfully mitigated potential toxicity issues in E. coli, enabling the secretion of recombinant proteins in C. sporogenes and C. butyricum. We further demonstrate the successful replacement of PyrE loci with high-expression cassettes carrying NY-ESO-1 and adjuvant-fused NY-ESO-1, achieving plasmid-free clostridia capable of secreting the antigens. Lastly, the study successfully extends its multiplex genetic manipulations to engineer clostridia for the secretion of SARS-CoV-2-related Spike_S1 antigens. CONCLUSIONS: This study successfully demonstrated that C. butyricum and C. sporogenes can produce the two recombinant antigen proteins (NY-ESO-1 and SARS-CoV-2-related Spike_S1 antigens) through genetic manipulations, utilizing the AUU start codon. This approach overcomes challenges in cloning difficult proteins in E. coli. These findings underscore the feasibility of harnessing commensal clostridia for antigen protein secretion, emphasizing the applicability of non-canonical translation initiation across diverse species with broad implications for medical or industrial biotechnology.

Circular RNA as a source of neoantigens for cancer vaccines.

BACKGROUND: The effectiveness of somatic neoantigen-based immunotherapy is often hindered by the limited number of mutations in tumors with low to moderate mutation burden. Focusing on microsatellite-stable colorectal cancer (CRC), this study investigates the potential of tumor-associated circular RNAs (circRNAs) as an alternative source of neoepitopes in CRC. METHODS: Tumor-associated circRNAs in CRC were identified using the MiOncoCirc database and ribo-depletion RNA sequencing of paired clinical normal and tumor samples. Candidate circRNA expression was validated by quantitative real-time PCR (RT-qPCR) using divergent primers. TransCirc database was used for translation prediction. Human leukocyte antigen binding affinity of open reading frames from potentially translatable circRNA was predicted using pVACtools. Strong binders from messenger RNA-encoded proteins were excluded using BlastP. The immunogenicity of the candidate antigens was functionally validated through stimulation of naïve CD8+ T cells against the predicted neoepitopes and subsequent analysis of the T cells through enzyme-linked immunospot (ELISpot) assay, intracellular cytokine staining (ICS) and granzyme B (GZMB) reporter. The cytotoxicity of T cells trained with antigen peptides was further tested using patient-derived organoids. RESULTS: We identified a neoepitope from circRAPGEF5 that is upregulated in CRC tumor samples from MiOncoCirc database, and two neoepitopes from circMYH9, which is upregulated across various tumor samples from our matched clinical samples. The translation potential of candidate peptides was supported by Clinical Proteomic Tumor Analysis Consortium database using PepQuery. The candidate peptides elicited antigen-specific T cells response and expansion, evidenced by various assays including ELISpot, ICS and GZMB reporter. Furthermore, T cells trained with circMYH9 peptides were able to specifically target and eliminate tumor-derived organoids but not match normal organoids. This observation underscores the potential of circRNAs as a source of immunogenic neoantigens. Lastly, circMYH9 was enriched in the liquid biopsies of patients with CRC, thus enabling a detection-to-vaccination treatment strategy for patients with CRC. CONCLUSIONS: Our findings underscore the feasibility of tumor-associated circRNAs as an alternative source of neoantigens for cancer vaccines targeting tumors with moderate mutation levels.

Methods behind neoantigen prediction for personalized anticancer vaccines.

Next to conventional cancer therapies, immunotherapies such as immune checkpoint inhibitors have broadened the cancer treatment landscape over the past decades. Recent advances in next generation sequencing and bioinformatics technologies have made it possible to identify a patient's own immunogenic neoantigens. These cancer neoantigens serve as important targets for personalized immunotherapy which has the benefit of being more active and effective in targeting cancer cells. This paper is a step-by-step guide discussing the different analyses and challenges encountered during in-silico neoantigen prediction. The protocol describes all the tools and steps required for the identification of immunogenic neoantigens.

Neoantigen identification: Technological advances and challenges.

Neoantigens have emerged as promising targets for cutting-edge immunotherapies, such as cancer vaccines and adoptive cell therapy. These neoantigens are unique to tumors and arise exclusively from somatic mutations or non-genomic aberrations in tumor proteins. They encompass a wide range of alterations, including genomic mutations, post-transcriptomic variants, and viral oncoproteins. With the advancements in technology, the identification of immunogenic neoantigens has seen rapid progress, raising new opportunities for enhancing their clinical significance. Prediction of neoantigens necessitates the acquisition of high-quality samples and sequencing data, followed by mutation calling. Subsequently, the pipeline involves integrating various tools that can predict the expression, processing, binding, and recognition potential of neoantigens. However, the continuous improvement of computational tools is constrained by the availability of datasets which contain validated immunogenic neoantigens. This review article aims to provide a comprehensive summary of the current knowledge as well as limitations in neoantigen prediction and validation. Additionally, it delves into the origin and biological role of neoantigens, offering a deeper understanding of their significance in the field of cancer immunotherapy. This article thus seeks to contribute to the ongoing efforts to harness neoantigens as powerful weapons in the fight against cancer.

Cancer testis antigen MAGEA3 in serum and serum-derived exosomes serves as a promising biomarker in lung adenocarcinoma.

Cancer testis antigen (CTA) Melanoma Antigen Gene A3 (MAGEA3) were overexpressed in multiple tumor types, but the expression pattern of MAGEA3 in the serum of lung adenocarcinoma (LUAD) remains unclear. Clinically derived serum and serum exosome samples were used to assess the mRNA expression of MAGEA3 and MAGEA4 by qRT-PCR, and serum MAGEA3 and MAGEA4 protein expression were evaluated by ELISA in total 133 healthy volunteers' and 289 LUAD patients' serum samples. An analysis of the relationship of the mRNA and protein expression of MAGEA3 and MAGEA4 with clinicopathologic parameters was performed and the diagnostic value of MAGEA3 and MAGEA4 was plotted on an ROC curve. In addition, the correlation of MAGEA3 mRNA with infiltrating immune cells was investigated through TIMER, the CIBERSORT algorithm and the TISIDB database. Expression of serum and serum exosome MAGEA3 and MAGEA4 mRNA were significantly higher in LUAD patients than in healthy donors. MAGEA3 mRNA associated with tumor diameter, TMN stage, and NSE in LUAD serum samples, and MAGEA3 mRNA correlated with N stage in serum-derived exosomes, possessing areas under the curve (AUC) of 0.721 and 0.832, respectively. Besides, serum MAGEA3 protein levels were elevated in LUAD patients, and were closely related to stage and NSE levels, possessing AUC of 0.781. Further analysis signified that the expression of MAGEA3 mRNA was positive correlation with neutrophil, macrophages M2, dendritic cells resting, and eosinophilic, but negatively correlated with B cells, plasma cells, CD8 + T cells, CD4 + T cells, Th17 cells, macrophages and dendritic cells. Collectively, our results suggested that the MAGEA3 expression in mRNA and protein were upregulated in LUAD, and MAGEA3 could be used as a diagnostic biomarker and immunotherapy target for LUAD patients.

TIM-3, LAG-3, or 2B4 gene disruptions increase the anti-tumor response of engineered T cells.

Background: In adoptive T cell therapy, the long term therapeutic benefits in patients treated with engineered tumor specific T cells are limited by the lack of long term persistence of the infused cellular products and by the immunosuppressive mechanisms active in the tumor microenvironment. Exhausted T cells infiltrating the tumor are characterized by loss of effector functions triggered by multiple inhibitory receptors (IRs). In patients, IR blockade reverts T cell exhaustion but has low selectivity, potentially unleashing autoreactive clones and resulting in clinical autoimmune side effects. Furthermore, loss of long term protective immunity in cell therapy has been ascribed to the effector memory phenotype of the infused cells. Methods: We simultaneously redirected T cell specificity towards the NY-ESO-1 antigen via TCR gene editing (TCRED) and permanently disrupted LAG3, TIM-3 or 2B4 genes (IRKO) via CRISPR/Cas9 in a protocol to expand early differentiated long-living memory stem T cells. The effector functions of the TCRED-IRKO and IR competent (TCRED-IRCOMP) cells were tested in short-term co-culture assays and under a chronic stimulation setting in vitro. Finally, the therapeutic efficacy of the developed cellular products were evaluated in multiple myeloma xenograft models. Results: We show that upon chronic stimulation, TCRED-IRKO cells are superior to TCRED-IRCOMP cells in resisting functional exhaustion through different mechanisms and efficiently eliminate cancer cells upon tumor re-challenge in vivo. Our data indicate that TIM-3 and 2B4-disruption preserve T-cell degranulation capacity, while LAG-3 disruption prevents the upregulation of additional inhibitory receptors in T cells. Conclusion: These results highlight that TIM-3, LAG-3, and 2B4 disruptions increase the therapeutic benefit of tumor specific cellular products and suggest distinct, non-redundant roles for IRs in anti-tumor responses.

Challenges in the discovery of tumor-specific alternative splicing-derived cell-surface antigens in glioma.

Despite advancements in cancer immunotherapy, solid tumors remain formidable challenges. In glioma, profound inter- and intra-tumoral heterogeneity of antigen landscape hampers therapeutic development. Therefore, it is critical to consider alternative sources to expand the repertoire of targetable (neo-)antigens and improve therapeutic outcomes. Accumulating evidence suggests that tumor-specific alternative splicing (AS) could be an untapped reservoir of antigens. In this study, we investigated tumor-specific AS events in glioma, focusing on those predicted to generate major histocompatibility complex (MHC)-presentation-independent, cell-surface antigens that could be targeted by antibodies and chimeric antigen receptor-T cells. We systematically analyzed bulk RNA-sequencing datasets comparing 429 tumor samples (from The Cancer Genome Atlas) and 9166 normal tissue samples (from the Genotype-Tissue Expression project), and identified 13 AS events in 7 genes predicted to be expressed in more than 10% of the patients, including PTPRZ1 and BCAN, which were corroborated by an external RNA-sequencing dataset. Subsequently, we validated our predictions and elucidated the complexity of the isoforms using full-length transcript amplicon sequencing on patient-derived glioblastoma cells. However, analyses of the RNA-sequencing datasets of spatially mapped and longitudinally collected clinical tumor samples unveiled remarkable spatiotemporal heterogeneity of the candidate AS events. Furthermore, proteomics analysis did not reveal any peptide spectra matching the putative antigens. Our investigation illustrated the diverse characteristics of the tumor-specific AS events and the challenges of antigen exploration due to their notable spatiotemporal heterogeneity and elusive nature at the protein levels. Redirecting future efforts toward intracellular, MHC-presented antigens could offer a more viable avenue.

Cancer neoepitopes viewed through negative selection and peripheral tolerance: a new path to cancer vaccines.

A proportion of somatic mutations in tumors create neoepitopes that can prime T cell responses that target the MHC I-neoepitope complexes on tumor cells, mediating tumor control or rejection. Despite the compelling centrality of neoepitopes to cancer immunity, we know remarkably little about what constitutes a neoepitope that can mediate tumor control in vivo and what distinguishes such a neoepitope from the vast majority of similar candidate neoepitopes that are inefficacious in vivo. Studies in mice as well as clinical trials have begun to reveal the unexpected paradoxes in this area. Because cancer neoepitopes straddle that ambiguous ground between self and non-self, some rules that are fundamental to immunology of frankly non-self antigens, such as viral or model antigens, do not appear to apply to neoepitopes. Because neoepitopes are so similar to self-epitopes, with only small changes that render them non-self, immune response to them is regulated at least partially the way immune response to self is regulated. Therefore, neoepitopes are viewed and understood here through the clarifying lens of negative thymic selection. Here, the emergent questions in the biology and clinical applications of neoepitopes are discussed critically and a mechanistic and testable framework that explains the complexity and translational potential of these wonderful antigens is proposed.

Dose optimization of an adjuvanted peptide-based personalized neoantigen melanoma vaccine.

The advancements in next-generation sequencing have made it possible to effectively detect somatic mutations, which has led to the development of personalized neoantigen cancer vaccines that are tailored to the unique variants found in a patient's cancer. These vaccines can provide significant clinical benefit by leveraging the patient's immune response to eliminate malignant cells. However, determining the optimal vaccine dose for each patient is a challenge due to the heterogeneity of tumors. To address this challenge, we formulate a mathematical dose optimization problem based on a previous mathematical model that encompasses the immune response cascade produced by the vaccine in a patient. We propose an optimization approach to identify the optimal personalized vaccine doses, considering a fixed vaccination schedule, while simultaneously minimizing the overall number of tumor and activated T cells. To validate our approach, we perform in silico experiments on six real-world clinical trial patients with advanced melanoma. We compare the results of applying an optimal vaccine dose to those of a suboptimal dose (the dose used in the clinical trial and its deviations). Our simulations reveal that an optimal vaccine regimen of higher initial doses and lower final doses may lead to a reduction in tumor size for certain patients. Our mathematical dose optimization offers a promising approach to determining an optimal vaccine dose for each patient and improving clinical outcomes.

Are we getting closer to a successful neoantigen cancer vaccine?

Although significant advances in immunotherapy have revolutionized the treatment of many cancer types over the past decade, the field of vaccine therapy, an important component of cancer immunotherapy, despite decades-long intense efforts, is still transmitting signals of promises and awaiting strong data on efficacy to proceed with regulatory approval. The field of cancer vaccines faces standard challenges, such as tumor-induced immunosuppression, immune response in inhibitory tumor microenvironment (TME), intratumor heterogeneity (ITH), permanently evolving cancer mutational landscape leading to neoantigens, and less known obstacles: neoantigen gain/loss upon immunotherapy, the timing and speed of appearance of neoantigens and responding T cell clonotypes and possible involvement of immune interference/heterologous immunity, in the complex interplay between evolving tumor epitopes and the immune system. In this review, we discuss some key issues related to challenges hampering the development of cancer vaccines, along with the current approaches focusing on neoantigens. We summarize currently well-known ideas/rationales, thus revealing the need for alternative vaccine approaches. Such a discussion should stimulate vaccine researchers to apply out-of-box, unconventional thinking in search of new avenues to deal with critical, often yet unaddressed challenges on the road to a new generation of therapeutics and vaccines.

Antigen/HLA-agnostic strategies for Characterizing Tumor-responsive T cell receptors in PDAC patients via single-cell sequencing and autologous organoid application.

Characterization of tumor-responsive T cell receptors (TCRs) is a critical step in personalized TCR-T cell therapy, and remains challenging for pancreatic ductal adenocarcinoma (PDAC). Here we report a proof-of-concept study to identify and validate antitumor TCRs in two representative PDAC patients using ultradeep single-cell TCR/RNA sequencing and autologous organoids, and reveal the phenotypic dynamics of TCR repertoire in different T cell expansions from the same patient. We first performed comparative sequencing on freshly harvested peripheral blood mononuclear cells (PBMCs) and uncultured tumor infiltrating lymphocytes (TILs), followed by reactivity tests of TIL-enriched TCRs with autologous organoids, in which two tumor-responsive TCRs were successfully characterized and the corresponding TILs were mostly tissue-resident memory-like T cells, and partially expressed both naïve and exhausted T cell markers. For the PDAC patient without high-quality TILs, PBMCs were cultured with neoantigen peptide (KRASG12D), organoids, or anti-CD3 antibody in presence, and experienced extensive clonal expansions within ten days. All derived PBMCs were sequenced in parallel (>82,000 cells), and TCRs enriched in both peptide- and organoid-experienced, but not anti-CD3-treated CD8 T cells, were assessed for their reactivity to antigen-presenting cells (APCs) and organoids, in which three neoantigen-reactive TCRs were identified as tumor-responsive, and the corresponding T cells were characterized by mixed transcriptional signatures including but not limited to typical exhausted T cell markers. Together, our study revealed that the combination of ultradeep single-cell sequencing and organoid techniques enabled rapid characterization of tumor-responsive TCRs for developing practical personalized TCR-T therapy in an antigen/human leukocyte antigen (HLA)-agnostic manner.

STING signalling compensates for low tumour mutation burden to drive anti-tumour immunity.

BACKGROUND: While mutation-derived neoantigens are well recognized in generating anti-tumour T cell response, increasing evidences highlight the complex association between tumour mutation burden (TMB) and tumour infiltrating lymphocytes (TILs). The exploration of non-TMB determinants of active immune response could improve the prognosis prediction and provide guidance for current immunotherapy. METHODS: The transcriptomic and whole exome sequence data in The Cancer Genome Atlas were used to examine the relationship between TMB and exhausted CD8+ T cells (Tex), as an indicator of tumour antigen-specific T cells across nine major cancer types. Computational clustering analysis was performed on 4510 tumours to identify different immune profiles. NanoString gene expression analysis and single cell RNA-seq analysis using fresh human breast cancer were performed for finding validation. FINDINGS: TMB was found to be poorly correlated with active immune response in various cancer types. Patient clustering analysis revealed a group of tumours with abundant Tex but low TMB. In those tumours, we observed significantly higher expression of the stimulator of interferon genes (STING) signalling. Dendritic cells, particularly those of BATF3+ lineage, were also found to be essential for accumulation of Tex within tumours. Mechanistically, loss of genomic and cellular integrity, marked by decreased DNA damage repair, defective replication stress response, and increased apoptosis were shown to drive STING activation. INTERPRETATION: These results highlight that TMB alone does not fully predict tumour immune profiles, with STING signalling compensating for low TMB in non-hypermutated tumours to enhance anti-tumour immunity. Translating these results, STING agonists may benefit patients with non-hypermutated tumours. STING activation may serve as an additional biomarker to predict response to immune checkpoint blockades alongside TMB. Our research also unravelled the interplay between genomic instability and STING activation, informing potential combined chemotherapy targeting the axis of genomic integrity and immunotherapy. FUNDING: City of Hope Christopher Family Endowed Innovation Fund for Alzheimer's Disease and Breast Cancer Research in honor of Vineta Christopher; Breast Cancer Alliance Early Career Investigator Award; National Cancer Institute of the National Institutes of Health under award number R01CA256989 and R01CA240392.

Relevance of mutation-derived neoantigens and non-classical antigens for anticancer therapies.

Efficacy of cancer immunotherapies relies on correct recognition of tumor antigens by lymphocytes, eliciting thus functional responses capable of eliminating tumor cells. Therefore, important efforts have been carried out in antigen identification, with the aim of understanding mechanisms of response to immunotherapy and to design safer and more efficient strategies. In addition to classical tumor-associated antigens identified during the last decades, implementation of next-generation sequencing methodologies is enabling the identification of neoantigens (neoAgs) arising from mutations, leading to the development of new neoAg-directed therapies. Moreover, there are numerous non-classical tumor antigens originated from other sources and identified by new methodologies. Here, we review the relevance of neoAgs in different immunotherapies and the results obtained by applying neoAg-based strategies. In addition, the different types of non-classical tumor antigens and the best approaches for their identification are described. This will help to increase the spectrum of targetable molecules useful in cancer immunotherapies.

ImmuneMirror: A machine learning-based integrative pipeline and web server for neoantigen prediction.

Neoantigens are derived from somatic mutations in the tumors but are absent in normal tissues. Emerging evidence suggests that neoantigens can stimulate tumor-specific T-cell-mediated antitumor immune responses, and therefore are potential immunotherapeutic targets. We developed ImmuneMirror as a stand-alone open-source pipeline and a web server incorporating a balanced random forest model for neoantigen prediction and prioritization. The prediction model was trained and tested using known immunogenic neopeptides collected from 19 published studies. The area under the curve of our trained model was 0.87 based on the testing data. We applied ImmuneMirror to the whole-exome sequencing and RNA sequencing data obtained from gastrointestinal tract cancers including 805 tumors from colorectal cancer (CRC), esophageal squamous cell carcinoma (ESCC) and hepatocellular carcinoma patients. We discovered a subgroup of microsatellite instability-high (MSI-H) CRC patients with a low neoantigen load but a high tumor mutation burden (> 10 mutations per Mbp). Although the efficacy of PD-1 blockade has been demonstrated in advanced MSI-H patients, almost half of such patients do not respond well. Our study identified a subset of MSI-H patients who may not benefit from this treatment with lower neoantigen load for major histocompatibility complex I (P < 0.0001) and II (P = 0.0008) molecules, respectively. Additionally, the neopeptide YMCNSSCMGV-TP53G245V, derived from a hotspot mutation restricted by HLA-A02, was identified as a potential actionable target in ESCC. This is so far the largest study to comprehensively evaluate neoantigen prediction models using experimentally validated neopeptides. Our results demonstrate the reliability and effectiveness of ImmuneMirror for neoantigen prediction.

A Comparison of Preferentially Expressed Antigen in Melanoma Immunohistochemistry and Diagnostic Gene Expression-Profiling Assay in Challenging Melanocytic Proliferations.

ABSTRACT: Most melanocytic tumors are classified as benign or malignant based on clinical morphology, histology, and immunohistochemical (IHC) analysis. A subset of more challenging cases with ambiguous features may require further evaluation with established ancillary diagnostic molecular studies, including fluorescence in situ hybridization and/or single nucleotide polymorphism array, to increase diagnostic certainty. More recently, a diagnostic gene expression-profiling (GEP) assay and an IHC stain for the detection of PRAME (PReferentially expressed Antigen in MElanoma) have been developed. The use of PRAME IHC has been validated in cases of unequivocal and ambiguous melanocytic proliferations via comparing results with fluorescence in situ hybridization and/or single nucleotide polymorphism array. A study comparing performance metrics of PRAME IHC and diagnostic GEP has not been previously published. Herein, we evaluated the use of PRAME IHC in 55 melanocytic tumors with challenging histomorphology by comparing the results with diagnostic GEP and final histomorphologic diagnosis. Intertest agreement occurred in 88% of cases. PRAME IHC supported the final diagnosis in 89% of cases with a sensitivity of 79%, specificity of 95%, and positive predictive value of 88.2%. GEP agreed with the final diagnosis in 88% of cases with a sensitivity of 65%, 97% specificity, and positively predicted melanoma in 91.7% of cases. Because the results of this study align with past publications evaluating the performance metrics of PRAME IHC, showing it to be as sensitive as and more cost effective than all other ancillary molecular tests, we propose the use of PRAME IHC as the optimal first-line diagnostic tool for ambiguous melanocytic proliferations.

Discovery of lung adenocarcinoma tumor antigens and ferroptosis subtypes for developing mRNA vaccines.

mRNA vaccines are becoming a feasible alternative for treating cancer. To develop mRNA vaccines against LUAD, potential antigens were identified and LUAD ferroptosis subtypes distinguished for selecting appropriate patients. The genome expression omnibus, cancer genome atlas (TCGA) and FerrDB were used to collect gene expression profiles, clinical information, and the genes involved in ferroptosis, respectively. cBioPortal was used to visualize and compare genetic alterations, GEPIA2 to calculate prognostic factors of the selected antigens, and TIMER to visualize the relationship between potential antigens and tumor immune cell infiltration. Consensus clustering analysis was utilized to identify ferroptosis subtypes and their prognostic value assessed by Log-rank and cox regression tests. The modules of ferroptosis-related gene screening were conducted by weight gene co-expression network analysis. The LUAD ferroptosis landscape was visualized through dimensionality reduction and graph learning. Six tumor antigens had obvious LUAD-mutations, positively correlated with different antigen-presenting cells, and might induce tumor cell ferroptosis. LUAD patients were stratified into three ferroptosis subtypes (FS1, FS2, and FS3) according to diverse molecular, cellular, and clinical characteristics. FS3 showed the highest tumor mutation burden and the most somatic mutations, deemed potential indicators of mRNA vaccine effectiveness. Moreover, different ferroptosis subtypes expressed distinct immune checkpoints and immunogenic cell death modulators. AGPS, NRAS, MTDH, PANX1, NOX4, and PPARD are potentially suitable for mRNA vaccinations against LUAD, specifically in patients with FS3 tumors. This study defines vaccination candidates and establishes a theoretical basis for LUAD mRNA vaccinations.