Intron retention is a source of neoepitopes in cancer

DIPAN: Detecting personalized intronic polyadenylation derived neoantigens from RNA sequencing data

Intronic polyadenylation (IPA) refers to a particular type of alternative polyadenylation where a gene makes use of a polyadenylation site located within its introns. Aberrant IPA events have been observed in various types of cancer. IPA can produce noncoding transcripts or truncated protein-coding transcripts with altered coding sequences in the resulting protein product. Therefore, IPA events hold the potential to act as a reservoir of tumor neoantigens. Here, we developed a computational method termed DIPAN, which incorporates IPA detection, protein fragmentation, and MHC binding prediction to predict IPA-derived neoantigens. Utilizing RNA-seq from breast cancer cell lines and ovarian cancer clinical samples, we demonstrated the significant contribution of IPA events to the neoantigen repertoire. Through mass spectrometry immunopeptidome analysis, we further illustrated the processing and presentation of IPA-derived neoantigens on the surface of cancer cells. While most IPA-derived neoantigens are sample-specific, shared neoantigens were identified in both cancer cell lines and clinical samples. Furthermore, we demonstrated an association between IPA-derived neoantigen burden and overall survival in cancer patients.

Characterised intron retention profiles in muscle tissue of idiopathic inflammatory myopathy subtypes

OBJECTIVES

Idiopathic inflammatory myopathies (IIMs) are a group of heterogeneous autoimmune diseases. Intron retention (IR) serves as an important post-transcriptional and translational regulatory mechanism. This study aims to identify changes in IR profiles in IIM subtypes, investigating their influence on proteins and their correlations with clinical features.

METHODS

RNA sequencing and liquid chromatography-tandem mass spectrometry were performed on muscle tissues obtained from 174 patients with IIM and 19 controls, following QC procedures. GTFtools and iREAD software were used for IR identification. An analysis of differentially expressed IRs (DEIs), exons and proteins was carried out using edgeR or DEP. Functional analysis was performed with clusterProfiler, and SPIRON was used to assess splicing factors.

RESULTS

A total of 6783 IRs located in 3111 unique genes were identified in all IIM subtypes compared with controls. IIM subtype-specific DEIs were associated with the pathogenesis of respective IIM subtypes. Splicing factors YBX1 and HSPA2 exhibited the most changes in dermatomyositis and immune-mediated necrotising myopathy. Increased IR was associated with reduced protein expression. Some of the IIM-specific DEIs were correlated with clinical parameters (skin rash, MMT-8 scores and muscle enzymes) and muscle histopathological features (myofiber necrosis, regeneration and inflammation). IRs in IFIH1 and TRIM21 were strongly correlated with anti-MDA5+ antibody, while IRs in SRP14 were associated with anti-SRP+ antibody.

CONCLUSION

This study revealed distinct IRs and specific splicing factors associated with IIM subtypes, which might be contributing to the pathogenesis of IIM. We also emphasised the potential impact of IR on protein expression in IIM muscles.

RNA vaccines for cancer: Principles to practice

Vaccines are the most impactful medicines to improve health. Though potent against pathogens, vaccines for cancer remain an unfulfilled promise. However, recent advances in RNA technology coupled with scientific and clinical breakthroughs have spurred rapid discovery and potent delivery of tumor antigens at speed and scale, transforming cancer vaccines into a tantalizing prospect. Yet, despite being at a pivotal juncture, with several randomized clinical trials maturing in upcoming years, several critical questions remain: which antigens, tumors, platforms, and hosts can trigger potent immunity with clinical impact? Here, we address these questions with a principled framework of cancer vaccination from antigen detection to delivery. With this framework, we outline features of emergent RNA technology that enable rapid, robust, real-time vaccination with somatic mutation-derived neoantigens-an emerging "ideal" antigen class-and highlight latent features that have sparked the belief that RNA could realize the enduring vision for vaccines against cancer.

Prediction of tumor-specific splicing from somatic mutations as a source of neoantigen candidates

Motivation

Neoantigens are promising targets for cancer immunotherapies and might arise from alternative splicing. However, detecting tumor-specific splicing is challenging because many non-canonical splice junctions identified in tumors also appear in healthy tissues. To increase tumor-specificity, we focused on splicing caused by somatic mutations as a source for neoantigen candidates in individual patients.

Results

We developed the tool splice2neo with multiple functionalities to integrate predicted splice effects from somatic mutations with splice junctions detected in tumor RNA-seq and to annotate the resulting transcript and peptide sequences. Additionally, we provide the tool EasyQuant for targeted RNA-seq read mapping to candidate splice junctions. Using a stringent detection rule, we predicted 1.7 splice junctions per patient as splice targets with a false discovery rate below 5% in a melanoma cohort. We confirmed tumor-specificity using independent, healthy tissue samples. Furthermore, using tumor-derived RNA, we confirmed individual exon-skipping events experimentally. Most target splice junctions encoded neoepitope candidates with predicted major histocompatibility complex (MHC)-I or MHC-II binding. Compared to neoepitope candidates from non-synonymous point mutations, the splicing-derived MHC-I neoepitope candidates had lower self-similarity to corresponding wild-type peptides. In conclusion, we demonstrate that identifying mutation-derived, tumor-specific splice junctions can lead to additional neoantigen candidates to expand the target repertoire for cancer immunotherapies.

Availability and implementation

The R package splice2neo and the python package EasyQuant are available at https://github.com/TRON-Bioinformatics/splice2neo and https://github.com/TRON-Bioinformatics/easyquant, respectively.

Comprehensive profiling of cancer neoantigens from aberrant RNA splicing

BACKGROUND

Cancer neoantigens arise from protein-altering somatic mutations in tumor and rank among the most promising next-generation immuno-oncology agents when used in combination with immune checkpoint inhibitors. We previously developed a computational framework, REAL-neo, for identification, quality control, and prioritization of both class-I and class-II human leucocyte antigen (HLA)-presented neoantigens resulting from somatic single-nucleotide mutations, small insertions and deletions, and gene fusions. In this study, we developed a new module, SPLICE-neo, to identify neoantigens from aberrant RNA transcripts from two distinct sources: (1) DNA mutations within splice sites and (2) de novo RNA aberrant splicings.

METHODS

First, SPLICE-neo was used to profile all DNA splice-site mutations in 11,892 tumors from The Cancer Genome Atlas (TCGA) and identified 11 profiles of splicing donor or acceptor site gains or losses. Transcript isoforms resulting from the top seven most frequent profiles were computed using novel logic models. Second, SPLICE-neo identified de novo RNA splicing events using RNA sequencing reads mapped to novel exon junctions from either single, double, or multiple exon-skipping events. The aberrant transcripts from both sources were then ranked based on isoform expression levels and z-scores assuming that individual aberrant splicing events are rare. Finally, top-ranked novel isoforms were translated into protein, and the resulting neoepitopes were evaluated for neoantigen potential using REAL-neo. The top splicing neoantigen candidates binding to HLA-A*02:01 were validated using in vitro T2 binding assays.

RESULTS

We identified abundant splicing neoantigens in four representative TCGA cancers: BRCA, LUAD, LUSC, and LIHC. In addition to their substantial contribution to neoantigen load, several splicing neoantigens were potent tumor antigens with stronger bindings to HLA compared with the positive control of antigens from influenza virus.

CONCLUSIONS

SPLICE-neo is the first tool to comprehensively identify and prioritize splicing neoantigens from both DNA splice-site mutations and de novo RNA aberrant splicings. There are two major advances of SPLICE-neo. First, we developed novel logic models that assemble and prioritize full-length aberrant transcripts from DNA splice-site mutations. Second, SPLICE-neo can identify exon-skipping events involving more than two exons, which account for a quarter to one-third of all skipping events.

Expanding the repertoire reveals recurrent, cryptic, and hematopoietic HLA class I minor histocompatibility antigens

ABSTRACT

Allogeneic stem cell transplantation (alloSCT) is a curative treatment for hematological malignancies. After HLA-matched alloSCT, antitumor immunity is caused by donor T cells recognizing polymorphic peptides, designated minor histocompatibility antigens (MiHAs), that are presented by HLA on malignant patient cells. However, T cells often target MiHAs on healthy nonhematopoietic tissues of patients, thereby inducing side effects known as graft-versus-host disease. Here, we aimed to identify the dominant repertoire of HLA-I-restricted MiHAs to enable strategies to predict, monitor or modulate immune responses after alloSCT. To systematically identify novel MiHAs by genome-wide association screening, T-cell clones were isolated from 39 transplanted patients and tested for reactivity against 191 Epstein-Barr virus transformed B cell lines of the 1000 Genomes Project. By discovering 81 new MiHAs, we more than doubled the antigen repertoire to 159 MiHAs and demonstrated that, despite many genetic differences between patients and donors, often the same MiHAs are targeted in multiple patients. Furthermore, we showed that one quarter of the antigens are cryptic, that is translated from unconventional open reading frames, for example long noncoding RNAs, showing that these antigen types are relevant targets in natural immune responses. Finally, using single cell RNA-seq data, we analyzed tissue expression of MiHA-encoding genes to explore their potential role in clinical outcome, and characterized 11 new hematopoietic-restricted MiHAs as potential targets for immunotherapy. In conclusion, we expanded the repertoire of HLA-I-restricted MiHAs and identified recurrent, cryptic and hematopoietic-restricted antigens, which are fundamental to predict, follow or manipulate immune responses to improve clinical outcome after alloSCT.

Tumor Antigens beyond the Human Exome

With the advent of immunotherapeutics, a new era in the combat against cancer has begun. Particularly promising are neo-epitope-targeted therapies as the expression of neo-antigens is tumor-specific. In turn, this allows the selective targeting and killing of cancer cells whilst healthy cells remain largely unaffected. So far, many advances have been made in the development of treatment options which are tailored to the individual neo-epitope repertoire. The next big step is the achievement of efficacious "off-the-shelf" immunotherapies. For this, shared neo-epitopes propose an optimal target. Given the tremendous potential, a thorough understanding of the underlying mechanisms which lead to the formation of neo-antigens is of fundamental importance. Here, we review the various processes which result in the formation of neo-epitopes. Broadly, the origin of neo-epitopes can be categorized into three groups: canonical, noncanonical, and viral neo-epitopes. For the canonical neo-antigens that arise in direct consequence of somatic mutations, we summarize past and recent findings. Beyond that, our main focus is put on the discussion of noncanonical and viral neo-epitopes as we believe that targeting those provides an encouraging perspective to shape the future of cancer immunotherapeutics.

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.

An alternative splicing signature defines the basal-like phenotype and predicts worse clinical outcome in pancreatic cancer

Pancreatic ductal adenocarcinoma (PDAC) is characterized by extremely poor prognosis. PDAC presents with molecularly distinct subtypes, with the basal-like one being associated with enhanced chemoresistance. Splicing dysregulation contributes to PDAC; however, its involvement in subtype specification remains elusive. Herein, we uncover a subtype-specific splicing signature associated with prognosis in PDAC and the splicing factor Quaking (QKI) as a determinant of the basal-like signature. Single-cell sequencing analyses highlight QKI as a marker of the basal-like phenotype. QKI represses splicing events associated with the classical subtype while promoting basal-like events associated with shorter survival. QKI favors a plastic, quasi-mesenchymal phenotype that supports migration and chemoresistance in PDAC organoids and cell lines, and its expression is elevated in high-grade primary tumors and metastatic lesions. These studies identify a splicing signature that defines PDAC subtypes and indicate that QKI promotes an undifferentiated, plastic phenotype, which renders PDAC cells chemoresistant and adaptable to environmental changes.

Splicing neoantigen discovery with SNAF reveals shared targets for cancer immunotherapy

Immunotherapy has emerged as a crucial strategy to combat cancer by "reprogramming" a patient's own immune system. Although immunotherapy is typically reserved for patients with a high mutational burden, neoantigens produced from posttranscriptional regulation may provide an untapped reservoir of common immunogenic targets for new targeted therapies. To comprehensively define tumor-specific and likely immunogenic neoantigens from patient RNA-Seq, we developed Splicing Neo Antigen Finder (SNAF), an easy-to-use and open-source computational workflow to predict splicing-derived immunogenic MHC-bound peptides (T cell antigen) and unannotated transmembrane proteins with altered extracellular epitopes (B cell antigen). This workflow uses a highly accurate deep learning strategy for immunogenicity prediction (DeepImmuno) in conjunction with new algorithms to rank the tumor specificity of neoantigens (BayesTS) and to predict regulators of mis-splicing (RNA-SPRINT). T cell antigens from SNAF were frequently evidenced as HLA-presented peptides from mass spectrometry (MS) and predict response to immunotherapy in melanoma. Splicing neoantigen burden was attributed to coordinated splicing factor dysregulation. Shared splicing neoantigens were found in up to 90% of patients with melanoma, correlated to overall survival in multiple cancer cohorts, induced T cell reactivity, and were characterized by distinct cells of origin and amino acid preferences. In addition to T cell neoantigens, our B cell focused pipeline (SNAF-B) identified a new class of tumor-specific extracellular neoepitopes, which we termed ExNeoEpitopes. ExNeoEpitope full-length mRNA predictions were tumor specific and were validated using long-read isoform sequencing and in vitro transmembrane localization assays. Therefore, our systematic identification of splicing neoantigens revealed potential shared targets for therapy in heterogeneous cancers.

Advancements and hurdles in the development of a vaccine for triple-negative breast cancer: A comprehensive review of multi-omics and immunomics strategies

Triple-Negative Breast Cancer (TNBC) presents a significant challenge in oncology due to its aggressive behavior and limited therapeutic options. This review explores the potential of immunotherapy, particularly vaccine-based approaches, in addressing TNBC. It delves into the role of immunoinformatics in creating effective vaccines against TNBC. The review first underscores the distinct attributes of TNBC and the importance of tumor antigens in vaccine development. It then elaborates on antigen detection techniques such as exome sequencing, HLA typing, and RNA sequencing, which are instrumental in identifying TNBC-specific antigens and selecting vaccine candidates. The discussion then shifts to the in-silico vaccine development process, encompassing antigen selection, epitope prediction, and rational vaccine design. This process merges computational simulations with immunological insights. The role of Artificial Intelligence (AI) in expediting the prediction of antigens and epitopes is also emphasized. The review concludes by encapsulating how Immunoinformatics can augment the design of TNBC vaccines, integrating tumor antigens, advanced detection methods, in-silico strategies, and AI-driven insights to advance TNBC immunotherapy. This could potentially pave the way for more targeted and efficacious treatments.

FLIBase: a comprehensive repository of full-length isoforms across human cancers and tissues

Regulatory processes at the RNA transcript level play a crucial role in generating transcriptome diversity and proteome composition in human cells, impacting both physiological and pathological states. This study introduces FLIBase (www.FLIBase.org), a specialized database that focuses on annotating full-length isoforms using long-read sequencing techniques. We collected and integrated long-read (351 samples) and short-read (12 469 samples) RNA sequencing data from diverse normal and cancerous human tissues and cells. The current version of FLIBase comprises a total of 983 789 full-length spliced isoforms, identified through long-read sequences and verified using short-read exon-exon splice junctions. Of these, 188 248 isoforms have been annotated, while 795 541 isoforms remain unannotated. By overcoming the limitations of short-read RNA sequencing methods, FLIBase provides an accurate and comprehensive representation of full-length transcripts. These comprehensive annotations empower researchers to undertake various downstream analyses and investigations. Importantly, FLIBase exhibits a significant advantage in identifying a substantial number of previously unannotated isoforms and tumor-specific RNA transcripts. These tumor-specific RNA transcripts have the potential to serve as a source of immunogenic recurrent neoantigens. This remarkable discovery holds tremendous promise for advancing the development of tailored RNA-based diagnostic and therapeutic strategies for various types of human cancer.

Computational immunogenomic approaches to predict response to cancer immunotherapies

Cancer immunogenomics is an emerging field that bridges genomics and immunology. The establishment of large-scale genomic collaborative efforts along with the development of new single-cell transcriptomic techniques and multi-omics approaches have enabled characterization of the mutational and transcriptional profiles of many cancer types and helped to identify clinically actionable alterations as well as predictive and prognostic biomarkers. Researchers have developed computational approaches and machine learning algorithms to accurately obtain clinically useful information from genomic and transcriptomic sequencing data from bulk tissue or single cells and explore tumours and their microenvironment. The rapid growth in sequencing and computational approaches has resulted in the unmet need to understand their true potential and limitations in enabling improvements in the management of patients with cancer who are receiving immunotherapies. In this Review, we describe the computational approaches currently available to analyse bulk tissue and single-cell sequencing data from cancer, stromal and immune cells, as well as how best to select the most appropriate tool to address various clinical questions and, ultimately, improve patient outcomes.

Long-Read Sequencing Reveals Alternative Splicing-Driven, Shared Immunogenic Neoepitopes Regardless of SF3B1 Status in Uveal Melanoma

Tumor-specific neoepitopes are promising targets in cancer immunotherapy. However, the identification of functional tumor-specific neoepitopes remains challenging. In addition to the most common source, single-nucleotide variants (SNV), alternative splicing (AS) represents another rich source of neoepitopes and can be utilized in cancers with low SNVs such as uveal melanoma (UM). UM, the most prevalent adult ocular malignancy, has poor clinical outcomes due to a lack of effective therapies. Recent studies have revealed the promise of harnessing tumor neoepitopes to treat UM. Previous studies have focused on neoepitope targets associated with mutations in splicing factor 3b subunit 1 (SF3B1), a key splicing factor; however, little is known about the neoepitopes that are commonly shared by patients independent of SF3B1 status. To identify the AS-derived neoepitopes regardless of SF3B1 status, we herein used a comprehensive nanopore long-read-sequencing approach to elucidate the landscape of AS and novel isoforms in UM. We also performed high-resolution mass spectrometry to further validate the presence of neoepitope candidates and analyzed their structures using the AlphaFold2 algorithm. We experimentally evaluated the antitumor effects of these neoepitopes and found they induced robust immune responses by stimulating interferon (IFN)γ production and activating T cell-based UM tumor killing. These results provide novel insights into UM-specific neoepitopes independent of SF3B1 and lay the foundation for developing therapies by targeting these actionable neoepitopes.

Extensive Alternative Splicing Patterns in Systemic Lupus Erythematosus Highlight Sexual Differences

Substantial evidence highlights divergences in immune responses between men and women. Women are more susceptible to autoimmunity, whereas men suffer from the more severe presentation of autoimmune disorders. The molecular mechanism of this sexual dimorphism remains elusive. Herein, we conducted a comprehensive analysis of sex differences in whole-blood gene expression focusing on alternative splicing (AS) events in systemic lupus erythematosus (SLE), which is a prototype sex-biased disease. This study included 79 SLE patients with active disease and 58 matched healthy controls who underwent whole-blood RNA sequencing. Sex differences in splicing events were widespread, existent in both SLE and a healthy state. However, we observed distinct gene sets and molecular pathways targeted by sex-dependent AS in SLE patients as compared to healthy subjects, as well as a notable sex dissimilarity in intron retention events. Sexually differential spliced genes specific to SLE patients were enriched for dynamic cellular processes including chromatin remodeling, stress and inflammatory responses. Remarkably, the extent of sexual differences in AS in the SLE patients and healthy individuals exceeded those in gene expression. Overall, this study reveals an unprecedent variation in sex-dependent splicing events in SLE and the healthy state, with potential implications for understanding the molecular basis of sexual dimorphism in autoimmunity.

CamoTSS: analysis of alternative transcription start sites for cellular phenotypes and regulatory patterns from 5' scRNA-seq data

Five-prime single-cell RNA-seq (scRNA-seq) has been widely employed to profile cellular transcriptomes, however, its power of analysing transcription start sites (TSS) has not been fully utilised. Here, we present a computational method suite, CamoTSS, to precisely identify TSS and quantify its expression by leveraging the cDNA on read 1, which enables effective detection of alternative TSS usage. With various experimental data sets, we have demonstrated that CamoTSS can accurately identify TSS and the detected alternative TSS usages showed strong specificity in different biological processes, including cell types across human organs, the development of human thymus, and cancer conditions. As evidenced in nasopharyngeal cancer, alternative TSS usage can also reveal regulatory patterns including systematic TSS dysregulations.

Recent advances in immunopeptidomic-based tumor neoantigen discovery

The role of aberrantly expressed proteins in tumors in driving immune-mediated control of cancer has been well documented for more than five decades. Today, we know that both aberrantly expressed normal proteins as well as mutant proteins (neoantigens) can function as tumor antigens in both humans and mice. Next-generation sequencing (NGS) and high-resolution mass spectrometry (MS) technologies have made significant advances since the early 2010s, enabling detection of rare but clinically relevant neoantigens recognized by T cells. MS profiling of tumor-specific immunopeptidomes remains the most direct method to identify mutant peptides bound to cellular MHC. However, the need for use of large numbers of cells or significant amounts of tumor tissue to achieve neoantigen detection has historically limited the application of MS. Newer, more sensitive MS technologies have recently demonstrated the capacities to detect neoantigens from fewer cells. Here, we highlight recent advancements in immunopeptidomics-based characterization of tumor-specific neoantigens. Various tumor antigen categories and neoantigen identification approaches are also discussed. Furthermore, we summarize recent reports that achieved successful tumor neoantigen detection by MS using a variety of starting materials, MS acquisition modes, and novel ion mobility devices.

Immune Surveillance of Acute Myeloid Leukemia Is Mediated by HLA-Presented Antigens on Leukemia Progenitor Cells

Therapy-resistant leukemia stem and progenitor cells (LSC) are a main cause of acute myeloid leukemia (AML) relapse. LSC-targeting therapies may thus improve outcome of patients with AML. Here we demonstrate that LSCs present HLA-restricted antigens that induce T-cell responses allowing for immune surveillance of AML. Using a mass spectrometry-based immunopeptidomics approach, we characterized the antigenic landscape of patient LSCs and identified AML- and AML/LSC-associated HLA-presented antigens absent from normal tissues comprising nonmutated peptides, cryptic neoepitopes, and neoepitopes of common AML driver mutations of NPM1 and IDH2. Functional relevance of shared AML/LSC antigens is illustrated by presence of their cognizant memory T cells in patients. Antigen-specific T-cell recognition and HLA class II immunopeptidome diversity correlated with clinical outcome. Together, these antigens shared among AML and LSCs represent prime targets for T cell-based therapies with potential of eliminating residual LSCs in patients with AML.

SIGNIFICANCE

The elimination of therapy-resistant leukemia stem and progenitor cells (LSC) remains a major challenge in the treatment of AML. This study identifies and functionally validates LSC-associated HLA class I and HLA class II-presented antigens, paving the way to the development of LSC-directed T cell-based immunotherapeutic approaches for patients with AML. See related commentary by Ritz, p. 430 . This article is featured in Selected Articles from This Issue, p. 419.

Tumor-wide RNA splicing aberrations generate immunogenic public neoantigens

T-cell-mediated immunotherapies are limited by the extent to which cancer-specific antigens are homogenously expressed throughout a tumor. We reasoned that recurrent splicing aberrations in cancer represent a potential source of tumor-wide and public neoantigens, and to test this possibility, we developed a novel pipeline for identifying neojunctions expressed uniformly within a tumor across diverse cancer types. Our analyses revealed multiple neojunctions that recur across patients and either exhibited intratumor heterogeneity or, in some cases, were tumor-wide. We identified CD8+ T-cell clones specific for neoantigens derived from tumor-wide and conserved neojunctions in GNAS and RPL22 , respectively. TCR-engineered CD8 + T-cells targeting these mutations conferred neoantigen-specific tumor cell eradication. Furthermore, we revealed that cancer-specific dysregulation in splicing factor expression leads to recurrent neojunction expression. Together, these data reveal that a subset of neojunctions are both intratumorally conserved and public, providing the molecular basis for novel T-cell-based immunotherapies that address intratumoral heterogeneity.

Current perspectives on mass spectrometry-based immunopeptidomics: the computational angle to tumor antigen discovery

Identification of tumor antigens presented by the human leucocyte antigen (HLA) molecules is essential for the design of effective and safe cancer immunotherapies that rely on T cell recognition and killing of tumor cells. Mass spectrometry (MS)-based immunopeptidomics enables high-throughput, direct identification of HLA-bound peptides from a variety of cell lines, tumor tissues, and healthy tissues. It involves immunoaffinity purification of HLA complexes followed by MS profiling of the extracted peptides using data-dependent acquisition, data-independent acquisition, or targeted approaches. By incorporating DNA, RNA, and ribosome sequencing data into immunopeptidomics data analysis, the proteogenomic approach provides a powerful means for identifying tumor antigens encoded within the canonical open reading frames of annotated coding genes and non-canonical tumor antigens derived from presumably non-coding regions of our genome. We discuss emerging computational challenges in immunopeptidomics data analysis and tumor antigen identification, highlighting key considerations in the proteogenomics-based approach, including accurate DNA, RNA and ribosomal sequencing data analysis, careful incorporation of predicted novel protein sequences into reference protein database, special quality control in MS data analysis due to the expanded and heterogeneous search space, cancer-specificity determination, and immunogenicity prediction. The advancements in technology and computation is continually enabling us to identify tumor antigens with higher sensitivity and accuracy, paving the way toward the development of more effective cancer immunotherapies.

RNA splicing alterations in lung cancer pathogenesis and therapy

RNA splicing alterations are widespread and play critical roles in cancer pathogenesis and therapy. Lung cancer is highly heterogeneous and causes the most cancer-related deaths worldwide. Large-scale multi-omics studies have not only characterized the mutational landscapes but also discovered a plethora of transcriptional and post-transcriptional changes in lung cancer. Such resources have greatly facilitated the development of new diagnostic markers and therapeutic options over the past two decades. Intriguingly, altered RNA splicing has emerged as an important molecular feature and therapeutic target of lung cancer. In this review, we provide a brief overview of splicing dysregulation in lung cancer and summarize the recent progress on key splicing events and splicing factors that contribute to lung cancer pathogenesis. Moreover, we describe the general strategies targeting splicing alterations in lung cancer and highlight the potential of combining splicing modulation with currently approved therapies to combat this deadly disease. This review provides new mechanistic and therapeutic insights into splicing dysregulation in cancer.

Intron retention decreases METTL3 expression by inhibiting mRNA export to the cytoplasm

Methyltransferase-like 3 (METTL3), a key component of the m6A methyltransferase complex, regulates the splicing, nuclear transport, stability, and translation of its target genes. However, the mechanism underlying the regulation of METTL3 expression by alternative splicing (AS) remains unknown. We analyzed the expression pattern of METTL3 after AS in human tissues and confirmed the expression of an isoform retaining introns 8 and 9 (METTL3-IR). We confirmed the different intracellular localizations of METTL3-IR and METTL3 proteins using immunofluorescence microscopy. Furthermore, the endogenous expression of METTL3-IR at the protein level was different from that at the mRNA level. We found that 3'-UTR generation by intron retention (IR) inhibited the export of METTL3-IR mRNA to the cytoplasm, which in turn suppressed protein expression. To the best of our knowledge, this is the first study to confirm the regulation of METTL3 gene expression by AS, providing evidence that the suppression of METTL3 protein expression by IR is an integral part of the mechanism by which 3'-UTR generation regulates protein expression via inhibition of RNA export to the cytoplasm. [BMB Reports 2023; 56(9): 514-519].

Intron retention decreases METTL3 expression by inhibiting mRNA export to the cytoplasm

Methyltransferase-like 3 (METTL3), a key component of the m6A methyltransferase complex, regulates the splicing, nuclear transport, stability, and translation of its target genes. However, the mechanism underlying the regulation of METTL3 expression by alternative splicing (AS) remains unknown. We analyzed the expression pattern of METTL3 after AS in human tissues and confirmed the expression of an isoform retaining introns 8 and 9 (METTL3-IR). We confirmed the different intracellular localizations of METTL3-IR and METTL3 proteins using immunofluorescence microscopy. Furthermore, the endogenous expression of METTL3-IR at the protein level was different from that at the mRNA level. We found that 3'-UTR generation by intron retention (IR) inhibited the export of METTL3-IR mRNA to the cytoplasm, which in turn suppressed protein expression. To the best of our knowledge, this is the first study to confirm the regulation of METTL3 gene expression by AS, providing evidence that the suppression of METTL3 protein expression by IR is an integral part of the mechanism by which 3'-UTR generation regulates protein expression via inhibition of RNA export to the cytoplasm. [BMB Reports 2023; 56(9): 514-519].

BamQuery: a proteogenomic tool to explore the immunopeptidome and prioritize actionable tumor antigens

MHC-I-associated peptides deriving from non-coding genomic regions and mutations can generate tumor-specific antigens, including neoantigens. Quantifying tumor-specific antigens' RNA expression in malignant and benign tissues is critical for discriminating actionable targets. We present BamQuery, a tool attributing an exhaustive RNA expression to MHC-I-associated peptides of any origin from bulk and single-cell RNA-sequencing data. We show that many cryptic and mutated tumor-specific antigens can derive from multiple discrete genomic regions, abundantly expressed in normal tissues. BamQuery can also be used to predict MHC-I-associated peptides immunogenicity and identify actionable tumor-specific antigens de novo.

Targeting alternative splicing in cancer immunotherapy

Tumor immunotherapy has made great progress in cancer treatment but still faces several challenges, such as a limited number of targetable antigens and varying responses among patients. Alternative splicing (AS) is an essential process for the maturation of nearly all mammalian mRNAs. Recent studies show that AS contributes to expanding cancer-specific antigens and modulating immunogenicity, making it a promising solution to the above challenges. The organoid technology preserves the individual immune microenvironment and reduces the time/economic costs of the experiment model, facilitating the development of splicing-based immunotherapy. Here, we summarize three critical roles of AS in immunotherapy: resources for generating neoantigens, targets for immune-therapeutic modulation, and biomarkers to guide immunotherapy options. Subsequently, we highlight the benefits of adopting organoids to develop AS-based immunotherapies. Finally, we discuss the current challenges in studying AS-based immunotherapy in terms of existing bioinformatics algorithms and biological technologies.

Proteogenomic analysis reveals RNA as a source for tumor-agnostic neoantigen identification

Systemic pan-tumor analyses may reveal the significance of common features implicated in cancer immunogenicity and patient survival. Here, we provide a comprehensive multi-omics data set for 32 patients across 25 tumor types for proteogenomic-based discovery of neoantigens. By using an optimized computational approach, we discover a large number of tumor-specific and tumor-associated antigens. To create a pipeline for the identification of neoantigens in our cohort, we combine DNA and RNA sequencing with MS-based immunopeptidomics of tumor specimens, followed by the assessment of their immunogenicity and an in-depth validation process. We detect a broad variety of non-canonical HLA-binding peptides in the majority of patients demonstrating partially immunogenicity. Our validation process allows for the selection of 32 potential neoantigen candidates. The majority of neoantigen candidates originates from variants identified in the RNA data set, illustrating the relevance of RNA as a still understudied source of cancer antigens. This study underlines the importance of RNA-centered variant detection for the identification of shared biomarkers and potentially relevant neoantigen candidates.

Comprehensive analysis of neoantigens derived from structural variation across whole genomes from 2528 tumors

BACKGROUND

Neoantigens are critical for anti-tumor immunity and have been long-envisioned as promising therapeutic targets. However, current neoantigen analyses mostly focus on single nucleotide variations (SNVs) and indel mutations and seldom consider structural variations (SVs) that are also prevalent in cancer.

RESULTS

Here, we develop a computational method termed NeoSV, which incorporates SV annotation, protein fragmentation, and MHC binding prediction together, to predict SV-derived neoantigens. Analysis of 2528 whole genomes reveals that SVs significantly contribute to the neoantigen repertoire in both quantity and quality. Whereas most neoantigens are patient-specific, shared neoantigens are identified with high occurrence rates in breast, ovarian, and gastrointestinal cancers. We observe extensive immunoediting on SV-derived neoantigens, especially on clonal events, which suggests their immunogenic potential. We also demonstrate that genomic alteration-related neoantigen burden, which integrates SV-derived neoantigens, depicts the tumor-immune interplay better than tumor neoantigen burden and may improve patient selection for immunotherapy.

CONCLUSIONS

Our study fills the gap in the current neoantigen repertoire and provides a valuable resource for cancer vaccine development.

Targeting CD44 Variant 5 with an Antibody-Drug Conjugate Is an Effective Therapeutic Strategy for Intrahepatic Cholangiocarcinoma

Intrahepatic cholangiocarcinoma (ICC) is the second most frequent type of primary liver cancer. ICC is among the deadliest malignancies, highlighting that novel treatments are urgently needed. Studies have shown that CD44 variant isoforms, rather than the CD44 standard isoform, are selectively expressed in ICC cells, providing an opportunity for the development of an antibody-drug conjugate (ADC)-based targeted therapeutic strategy. In this study, we observed the specific expression of CD44 variant 5 (CD44v5) in ICC tumors. CD44v5 protein was expressed on the surface of most ICC tumors (103 of 155). A CD44v5-targeted ADC, H1D8-DC (H1D8-drug conjugate), was developed that comprises a humanized anti-CD44v5 mAb conjugated to the microtubule inhibitor monomethyl auristatin E (MMAE) via a cleavable valine-citrulline-based linker. H1D8-DC exhibited efficient antigen binding and internalization in cells expressing CD44v5 on the cell surface. Because of the high expression of cathepsin B in ICC cells, the drug was preferentially released in cancer cells but not in normal cells, thus inducing potent cytotoxicity at picomolar concentrations. In vivo studies showed that H1D8-DC was effective against CD44v5-positive ICC cells and induced tumor regression in patient-derived xenograft models, whereas no significant adverse toxicities were observed. These data demonstrate that CD44v5 is a bona fide target in ICC and provide a rationale for the clinical investigation of a CD44v5-targeted ADC-based approach.

SIGNIFICANCE

Elevated expression of CD44 variant 5 in intrahepatic cholangiocarcinoma confers a targetable vulnerability using the newly developed antibody-drug conjugate H1D8-DC, which induces potent growth suppressive effects without significant toxicity.

The Immunopeptidome from a Genomic Perspective: Establishing the Noncanonical Landscape of MHC Class I-Associated Peptides

Tumor antigens can emerge through multiple mechanisms, including translation of noncoding genomic regions. This noncanonical category of tumor antigens has recently gained attention; however, our understanding of how they recur within and between cancer types is still in its infancy. Therefore, we developed a proteogenomic pipeline based on deep learning de novo mass spectrometry (MS) to enable the discovery of noncanonical MHC class I-associated peptides (ncMAP) from noncoding regions. Considering that the emergence of tumor antigens can also involve posttranslational modifications (PTM), we included an open search component in our pipeline. Leveraging the wealth of MS-based immunopeptidomics, we analyzed data from 26 MHC class I immunopeptidomic studies across 11 different cancer types. We validated the de novo identified ncMAPs, along with the most abundant PTMs, using spectral matching and controlled their FDR to 1%. The noncanonical presentation appeared to be 5 times enriched for the A03 HLA supertype, with a projected population coverage of 55%. The data reveal an atlas of 8,601 ncMAPs with varying levels of cancer selectivity and suggest 17 cancer-selective ncMAPs as attractive therapeutic targets according to a stringent cutoff. In summary, the combination of the open-source pipeline and the atlas of ncMAPs reported herein could facilitate the identification and screening of ncMAPs as targets for T-cell therapies or vaccine development.

CrypticProteinDB: an integrated database of proteome and immunopeptidome derived non-canonical cancer proteins

Translated non-canonical proteins derived from noncoding regions or alternative open reading frames (ORFs) can contribute to critical and diverse cellular processes. In the context of cancer, they also represent an under-appreciated source of targets for cancer immunotherapy through their tumor-enriched expression or by harboring somatic mutations that produce neoantigens. Here, we introduce the largest integration and proteogenomic analysis of novel peptides to assess the prevalence of non-canonical ORFs (ncORFs) in more than 900 patient proteomes and 26 immunopeptidome datasets across 14 cancer types. The integrative proteogenomic analysis of whole-cell proteomes and immunopeptidomes revealed peptide support for a nonredundant set of 9760 upstream, downstream, and out-of-frame ncORFs in protein coding genes and 12811 in noncoding RNAs. Notably, 6486 ncORFs were derived from differentially expressed genes and 340 were ubiquitously translated across eight or more cancers. The analysis also led to the discovery of thirty-four epitopes and eight neoantigens from non-canonical proteins in two cohorts as novel cancer immunotargets. Collectively, our analysis integrated both bottom-up proteogenomic and targeted peptide validation to illustrate the prevalence of translated non-canonical proteins in cancer and to provide a resource for the prioritization of novel proteins supported by proteomic, immunopeptidomic, genomic and transcriptomic data, available at https://www.maherlab.com/crypticproteindb.

IRIS: Discovery of cancer immunotherapy targets arising from pre-mRNA alternative splicing

Alternative splicing (AS) is prevalent in cancer, generating an extensive but largely unexplored repertoire of novel immunotherapy targets. We describe Isoform peptides from RNA splicing for Immunotherapy target Screening (IRIS), a computational platform capable of discovering AS-derived tumor antigens (TAs) for T cell receptor (TCR) and chimeric antigen receptor T cell (CAR-T) therapies. IRIS leverages large-scale tumor and normal transcriptome data and incorporates multiple screening approaches to discover AS-derived TAs with tumor-associated or tumor-specific expression. In a proof-of-concept analysis integrating transcriptomics and immunopeptidomics data, we showed that hundreds of IRIS-predicted TCR targets are presented by human leukocyte antigen (HLA) molecules. We applied IRIS to RNA-seq data of neuroendocrine prostate cancer (NEPC). From 2,939 NEPC-associated AS events, IRIS predicted 1,651 epitopes from 808 events as potential TCR targets for two common HLA types (A*02:01 and A*03:01). A more stringent screening test prioritized 48 epitopes from 20 events with "neoantigen-like" NEPC-specific expression. Predicted epitopes are often encoded by microexons of ≤30 nucleotides. To validate the immunogenicity and T cell recognition of IRIS-predicted TCR epitopes, we performed in vitro T cell priming in combination with single-cell TCR sequencing. Seven TCRs transduced into human peripheral blood mononuclear cells (PBMCs) showed high activity against individual IRIS-predicted epitopes, providing strong evidence of isolated TCRs reactive to AS-derived peptides. One selected TCR showed efficient cytotoxicity against target cells expressing the target peptide. Our study illustrates the contribution of AS to the TA repertoire of cancer cells and demonstrates the utility of IRIS for discovering AS-derived TAs and expanding cancer immunotherapies.

In search of the cell biology for self- versus non-self- recognition

Several of today's cancer treatments are based on the immune system's capacity to detect and destroy cells expressing neoantigens on major histocompatibility class-I molecules (MHC-I). Despite this, we still do not know the cell biology behind how antigenic peptide substrates (APSs) for the MHC-I pathway are produced. Indeed, there are few research fields with so many divergent views as the one concerning the source of APSs. This is quite remarkable considering their fundamental role in the immune systems' capacity to detect and destroy virus-infected or transformed cells. A better understanding of the processes generating APSs and how these are regulated will shed light on the evolution of self-recognition and provide new targets for therapeutic intervention. We discuss the search for the elusive source of MHC-I peptides and highlight the cell biology that is still missing to explain how they are synthesised and where they come from.

The biogenesis of the immunopeptidome

The immunopeptidome is the repertoire of peptides bound and presented by the MHC class I, class II, and non-classical molecules. The peptides are produced by the degradation of most cellular proteins, and in some cases, peptides are produced from extracellular proteins taken up by the cells. This review attempts to first describe some of its known and well-accepted concepts, and next, raise some questions about a few of the established dogmas in this field: The production of novel peptides by splicing is questioned, suggesting here that spliced peptides are extremely rare, if existent at all. The degree of the contribution to the immunopeptidome by degradation of cellular protein by the proteasome is doubted, therefore this review attempts to explain why it is likely that this contribution to the immunopeptidome is possibly overstated. The contribution of defective ribosome products (DRiPs) and non-canonical peptides to the immunopeptidome is noted and methods are suggested to quantify them. In addition, the common misconception that the MHC class II peptidome is mostly derived from extracellular proteins is noted, and corrected. It is stressed that the confirmation of sequence assignments of non-canonical and spliced peptides should rely on targeted mass spectrometry using spiking-in of heavy isotope-labeled peptides. Finally, the new methodologies and modern instrumentation currently available for high throughput kinetics and quantitative immunopeptidomics are described. These advanced methods open up new possibilities for utilizing the big data generated and taking a fresh look at the established dogmas and reevaluating them critically.

Sensitive, high-throughput HLA-I and HLA-II immunopeptidomics using parallel accumulation-serial fragmentation mass spectrometry

Comprehensive, in-depth identification of the human leukocyte antigen HLA-I and HLA-II tumor immunopeptidome can inform the development of cancer immunotherapies. Mass spectrometry (MS) is powerful technology for direct identification of HLA peptides from patient derived tumor samples or cell lines. However, achieving sufficient coverage to detect rare, clinically relevant antigens requires highly sensitive MS-based acquisition methods and large amounts of sample. While immunopeptidome depth can be increased by off-line fractionation prior to MS, its use is impractical when analyzing limited amounts of primary tissue biopsies. To address this challenge, we developed and applied a high throughput, sensitive, single-shot MS-based immunopeptidomics workflow that leverages trapped ion mobility time-of-flight mass spectrometry on the Bruker timsTOF SCP. We demonstrate >2-fold improved coverage of HLA immunopeptidomes relative to prior methods with up to 15,000 distinct HLA-I and HLA-II peptides from 4e7 cells. Our optimized single-shot MS acquisition method on the timsTOF SCP maintains high coverage, eliminates the need for off-line fractionation and reduces input requirements to as few as 1e6 A375 cells for > 800 distinct HLA-I peptides. This depth is sufficient to identify HLA-I peptides derived from cancer-testis antigen, and non-canonical proteins. We also apply our optimized single-shot SCP acquisition methods to tumor derived samples, enabling sensitive, high throughput and reproducible immunopeptidome profiling with detection of clinically relevant peptides from less than 4e7 cells or 15 mg wet weight tissue.

Noncoding translation mitigation

Translation is pervasive outside of canonical coding regions, occurring in long noncoding RNAs, canonical untranslated regions and introns1-4, especially in ageing4-6, neurodegeneration5,7 and cancer8-10. Notably, the majority of tumour-specific antigens are results of noncoding translation11-13. Although the resulting polypeptides are often nonfunctional, translation of noncoding regions is nonetheless necessary for the birth of new coding sequences14,15. The mechanisms underlying the surveillance of translation in diverse noncoding regions and how escaped polypeptides evolve new functions remain unclear10,16-19. Functional polypeptides derived from annotated noncoding sequences often localize to membranes20,21. Here we integrate massively parallel analyses of more than 10,000 human genomic sequences and millions of random sequences with genome-wide CRISPR screens, accompanied by in-depth genetic and biochemical characterizations. Our results show that the intrinsic nucleotide bias in the noncoding genome and in the genetic code frequently results in polypeptides with a hydrophobic C-terminal tail, which is captured by the ribosome-associated BAG6 membrane protein triage complex for either proteasomal degradation or membrane targeting. By contrast, canonical proteins have evolved to deplete C-terminal hydrophobic residues. Our results reveal a fail-safe mechanism for the surveillance of unwanted translation from diverse noncoding regions and suggest a possible biochemical route for the preferential membrane localization of newly evolved proteins.

The peptide woods are lovely, dark and deep: Hunting for novel cancer antigens

Harnessing the patient's immune system to control a tumor is a proven avenue for cancer therapy. T cell therapies as well as therapeutic vaccines, which target specific antigens of interest, are being explored as treatments in conjunction with immune checkpoint blockade. For these therapies, selecting the best suited antigens is crucial. Most of the focus has thus far been on neoantigens that arise from tumor-specific somatic mutations. Although there is clear evidence that T-cell responses against mutated neoantigens are protective, the large majority of these mutations are not immunogenic. In addition, most somatic mutations are unique to each individual patient and their targeting requires the development of individualized approaches. Therefore, novel antigen types are needed to broaden the scope of such treatments. We review high throughput approaches for discovering novel tumor antigens and some of the key challenges associated with their detection, and discuss considerations when selecting tumor antigens to target in the clinic.

U1 snRNP increases RNA Pol II elongation rate to enable synthesis of long genes

The expansion of introns within mammalian genomes poses a challenge for the production of full-length messenger RNAs (mRNAs), with increasing evidence that these long AT-rich sequences present obstacles to transcription. Here, we investigate RNA polymerase II (RNAPII) elongation at high resolution in mammalian cells and demonstrate that RNAPII transcribes faster across introns. Moreover, we find that this acceleration requires the association of U1 snRNP (U1) with the elongation complex at 5' splice sites. The role of U1 to stimulate elongation rate through introns reduces the frequency of both premature termination and transcriptional arrest, thereby dramatically increasing RNA production. We further show that changes in RNAPII elongation rate due to AT content and U1 binding explain previous reports of pausing or termination at splice junctions and the edge of CpG islands. We propose that U1-mediated acceleration of elongation has evolved to mitigate the risks that long AT-rich introns pose to transcript completion.

Alternative splicing: a new breakthrough for understanding tumorigenesis and potential clinical applications

BACKGROUND

Alternative splicing (AS) is a post-transcriptional process that produces transcript variants, thus leading to transcriptome complexity. Recently, the scope of AS studies has been greatly expanded toward clinical applications owing to the abundance of RNA sequencing data.

OBJECTIVE

This review consists of two parts. We first summarize bioinformatic resources that are useful for large-scale cancer-related AS studies. We then highlight the research efforts to utilize AS events for predicting clinical outcomes and planning therapeutic strategies.

RESULTS

Computational approaches to interrogate AS events have been reviewed under three categories: (1) databases to provide functional and clinical annotation of AS events, (2) analytical tools to identify cancer-associated AS event, and (3) methods to identify splicing-related DNA variants and splicing-derived neoantigens. We also present the recent progress in exploring the clinical utility of AS under four categories: (1) identification of AS events for cancer prognosis, (2) utilization of AS events in molecular classification of various cancers, (3) regulatory mechanisms of AS underlying drug resistance, and (4) potential use of AS in cancer therapy.

CONCLUSION

This review will be helpful for understanding the biological implications of AS in cancer and facilitate the development of AS markers for cancer prognosis and treatment. We anticipate that future studies will lead to the application of genome-wide AS profiles in cancer precision medicine.

MARVEL: an integrated alternative splicing analysis platform for single-cell RNA sequencing data

Alternative splicing is an important source of heterogeneity underlying gene expression between individual cells but remains an understudied area due to the paucity of computational tools to analyze splicing dynamics at single-cell resolution. Here, we present MARVEL, a comprehensive R package for single-cell splicing analysis applicable to RNA sequencing generated from the plate- and droplet-based methods. We performed extensive benchmarking of MARVEL against available tools and demonstrated its utility by analyzing multiple publicly available datasets in diverse cell types, including in disease. MARVEL enables systematic and integrated splicing and gene expression analysis of single cells to characterize the splicing landscape and reveal biological insights.

Sensitive, high-throughput HLA-I and HLA-II immunopeptidomics using parallel accumulation-serial fragmentation mass spectrometry

Comprehensive, in-depth identification of the human leukocyte antigen HLA-I and HLA-II tumor immunopeptidome can inform the development of cancer immunotherapies. Mass spectrometry (MS) is powerful technology for direct identification of HLA peptides from patient derived tumor samples or cell lines. However, achieving sufficient coverage to detect rare, clinically relevant antigens requires highly sensitive MS-based acquisition methods and large amounts of sample. While immunopeptidome depth can be increased by off-line fractionation prior to MS, its use is impractical when analyzing limited amounts of primary tissue biopsies. To address this challenge, we developed and applied a high throughput, sensitive, single-shot MS-based immunopeptidomics workflow that leverages trapped ion mobility time-of-flight mass spectrometry on the Bruker timsTOF SCP. We demonstrate >2-fold improved coverage of HLA immunopeptidomes relative to prior methods with up to 15,000 distinct HLA-I and HLA-II peptides from 4e7 cells. Our optimized single-shot MS acquisition method on the timsTOF SCP maintains high coverage, eliminates the need for off-line fractionation and reduces input requirements to as few as 1e6 A375 cells for > 800 distinct HLA-I peptides. This depth is sufficient to identify HLA-I peptides derived from cancer-testis antigen, and novel/unannotated open reading frames. We also apply our optimized single-shot SCP acquisition methods to tumor derived samples, enabling sensitive, high throughput and reproducible immunopeptidome profiling with detection of clinically relevant peptides from less than 4e7 cells or 15 mg wet weight tissue.

Noncanonical splicing junctions between exons and transposable elements represent a source of immunogenic recurrent neo-antigens in patients with lung cancer

Although most characterized tumor antigens are encoded by canonical transcripts (such as differentiation or tumor-testis antigens) or mutations (both driver and passenger mutations), recent results have shown that noncanonical transcripts including long noncoding RNAs and transposable elements (TEs) can also encode tumor-specific neo-antigens. Here, we investigate the presentation and immunogenicity of tumor antigens derived from noncanonical mRNA splicing events between coding exons and TEs. Comparing human non-small cell lung cancer (NSCLC) and diverse healthy tissues, we identified a subset of splicing junctions that is both tumor specific and shared across patients. We used HLA-I peptidomics to identify peptides encoded by tumor-specific junctions in primary NSCLC samples and lung tumor cell lines. Recurrent junction-encoded peptides were immunogenic in vitro, and CD8+ T cells specific for junction-encoded epitopes were present in tumors and tumor-draining lymph nodes from patients with NSCLC. We conclude that noncanonical splicing junctions between exons and TEs represent a source of recurrent, immunogenic tumor-specific antigens in patients with NSCLC.

COL3A1: Potential prognostic predictor for head and neck cancer based on immune-microenvironment alternative splicing

We aimed to identify a novel prognostic biomarker for head and neck squamous cell carcinoma (HNSCC) based on tumor immunology-related alternative splicing (AS). Data for 502 HNSCC and 44 normal samples were obtained from the TCGA database and used to establish an AS-related risk model through univariate, least absolute shrinkage, and selection operator Cox regression analyses. Fresh HNSCC and normal oral tissues were surgically obtained from 44 HNSCC patients. Western blotting and quantitative reverse transcription-PCR were used to assess gene expression levels. Kaplan-Meier was performed to evaluate patients' overall survival (OS) rate. The CIBERSORT algorithm, single-sample gene set enrichment analysis, and immune checkpoint analyses were performed to compare immune activities between subgroups. The risk model was established using 10 pivotal AS events first. Collagen Type III Alpha 1 Chain (COL3A1) were screened based on |log2FC| ≥ 1 and FDR < 0.05 criteria. COL3A1 expression levels in HNSCC tissues were elevated relative to normal tissues (p < 0.001). Moreover, COL3A1 was a reliable biomarker for HNSCC patients' prognostic prediction in both cohorts (p < 0.001, p = 0.0085, respectively). COL3A1 protein (p = 0.0054) and mRNA (p < 0.0001) levels were correlated with HNSCC differentiation. Furthermore, the T stage was correlated with COL3A1 expression (p = 0.043), and COL3A1 expression was an independent prognostic predictor for HNSCC patients (p = 0.006). Compared with the risk model, COL3A1 was better at evaluating immune cell infiltrations, immune activities, and immune checkpoint gene expressions of HNSCC lesions.

RNA splicing factors in normal hematopoiesis and hematologic malignancies: novel therapeutic targets and strategies

RNA splicing, a crucial transesterification-based process by which noncoding regions are removed from premature RNA to create mature mRNA, regulates various cellular functions, such as proliferation, survival, and differentiation. Clinical and functional studies over the past 10 y have confirmed that mutations in RNA splicing factors are among the most recurrent genetic abnormalities in hematologic neoplasms, including myeloid malignancies, chronic lymphocytic leukemia, mantle cell lymphoma, and clonal hematopoiesis. These findings indicate an important role for splicing factor mutations in the development of clonal hematopoietic disorders. Mutations in core or accessory components of the RNA spliceosome complex alter splicing sites in a manner of change of function. These changes can result in the dysregulation of cancer-associated gene expression and the generation of novel mRNA transcripts, some of which are not only critical to disease development but may be also serving as potential therapeutic targets. Furthermore, multiple studies have revealed that hematopoietic cells bearing mutations in splicing factors depend on the expression of the residual wild-type allele for survival, and these cells are more sensitive to reduced expression of wild-type splicing factors or chemical perturbations of the splicing machinery. These findings suggest a promising possibility for developing novel therapeutic opportunities in tumor cells based on mutations in splicing factors. Here, we combine current knowledge of the mechanistic and functional effects of frequently mutated splicing factors in normal hematopoiesis and the effects of their mutations in hematologic malignancies. Moreover, we discuss the development of potential therapeutic opportunities based on these mutations.

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.

Circular RNAs as a potential source of neoepitopes in cancer

Neoepitopes have attracted much attention as targets for immunotherapy against cancer. Therefore, efficient neoepitope screening technology is an essential step in the development of personalized vaccines. Circular RNAs (circRNAs) are generated by back-splicing and have a single-stranded continuous circular structure. So far, various circRNAs have been poorly characterized, though new evidence suggests that a few translated circRNAs may play a role in cancer. In the present study, circRNA was used as a source of neoepitope, a novel strategy as circRNA-derived neoepitopes have never been previously explored. The present study reports CIRC_neo (circRNA-derived neoepitope prediction pipeline), which is a comprehensive and automated bioinformatic pipeline for the prediction of circRNA-derived neoepitopes from RNA sequencing data. The computational prediction from sequencing data requires complex computational workflows to identify circRNAs, derive the resulting peptides, infer the types of human leukocyte antigens (HLA I and HLA II) in patients, and predict the neoepitopes binding to these antigens. The present study proposes a novel source of neoepitopes. The study focused on cancer-specific circRNAs, which have greatly expanded the source pool for neoepitope discovery. The statistical analysis of different features of circRNA-derived neoepitopes revealed that circRNAs could produce long proteins or truncated proteins. Because the peptides were completely foreign to the human body, they could be highly immunogenic. Importantly, circRNA-derived neoepitopes capable of binding to HLA were discovered. In the current study, circRNAs were systematically analyzed, revealing potential targets and novel research clues for cancer diagnosis, treatment, and prospective personalized vaccine research.

Oncogenic KRAS alters splicing factor phosphorylation and alternative splicing in lung cancer

BACKGROUND

Alternative RNA splicing is widely dysregulated in cancers including lung adenocarcinoma, where aberrant splicing events are frequently caused by somatic splice site mutations or somatic mutations of splicing factor genes. However, the majority of mis-splicing in cancers is unexplained by these known mechanisms. We hypothesize that the aberrant Ras signaling characteristic of lung cancers plays a role in promoting the alternative splicing observed in tumors.

METHODS

We recently performed transcriptome and proteome profiling of human lung epithelial cells ectopically expressing oncogenic KRAS and another cancer-associated Ras GTPase, RIT1. Unbiased analysis of phosphoproteome data identified altered splicing factor phosphorylation in KRAS-mutant cells, so we performed differential alternative splicing analysis using rMATS to identify significantly altered isoforms in lung epithelial cells. To determine whether these isoforms were uniquely regulated by KRAS, we performed a large-scale splicing screen in which we generated over 300 unique RNA sequencing profiles of isogenic A549 lung adenocarcinoma cells ectopically expressing 75 different wild-type or variant alleles across 28 genes implicated in lung cancer.

RESULTS

Mass spectrometry data showed widespread downregulation of splicing factor phosphorylation in lung epithelial cells expressing mutant KRAS compared to cells expressing wild-type KRAS. We observed alternative splicing in the same cells, with 2196 and 2416 skipped exon events in KRASG12V and KRASQ61H cells, respectively, 997 of which were shared (p < 0.001 by hypergeometric test). In the high-throughput splicing screen, mutant KRAS induced the greatest number of differential alternative splicing events, second only to the RNA binding protein RBM45 and its variant RBM45M126I. We identified ten high confidence cassette exon events across multiple KRAS variants and cell lines. These included differential splicing of the Myc Associated Zinc Finger (MAZ). As MAZ regulates expression of KRAS, this splice variant may be a mechanism for the cell to modulate wild-type KRAS levels in the presence of oncogenic KRAS.

CONCLUSION

Proteomic and transcriptomic profiling of lung epithelial cells uncovered splicing factor phosphorylation and mRNA splicing events regulated by oncogenic KRAS. These data suggest that in addition to widespread transcriptional changes, the Ras signaling pathway can promote post-transcriptional splicing changes that may contribute to oncogenic processes.

What do cancer-specific T cells 'see'?

Complex cellular interactions between the immune system and cancer can impact tumour development, growth, and progression. T cells play a key role in these interactions; however, the challenge for T cells is to recognize tumour antigens whilst minimizing cross-reactivity with antigens associated with healthy tissue. Some tumour cells, including those associated with viral infections, have clear, tumour-specific antigens that can be targeted by T cells. A high mutational burden can lead to increased numbers of mutational neoantigens that allow very specific immune responses to be generated but also allow escape variants to develop. Other cancer indications and those with low mutational burden are less easily distinguished from normal tissue. Recent studies have suggested that cancer-associated alterations in tumour cell biology including changes in post-translational modification (PTM) patterns may also lead to novel antigens that can be directly recognized by T cells. The PTM-derived antigens provide tumour-specific T-cell responses that both escape central tolerance and avoid the necessity for individualized therapies. PTM-specific CD4 T-cell responses have shown tumour therapy in murine models and highlight the importance of CD4 T cells as well as CD8 T cells in reversing the immunosuppressive tumour microenvironment. Understanding which cancer-specific antigens can be recognized by T cells and the way that immune tolerance and the tumour microenvironment shape immune responses to cancer is vital for the future development of cancer therapies.

RNA and RNA Derivatives: Light and Dark Sides in Cancer Immunotherapy

Significance: Immunotherapy, which utilizes the patient's immune system to fight tumor cells, has been approved for the treatment of some types of advanced cancer. Recent Advances: The complexity and diversity of tumor immunity are responsible for the varying response rates toward current immunotherapy strategies and highlight the importance of exploring regulators in tumor immunotherapy. Several genetic factors have proved to be critical regulators of tumor immunotherapy. RNAs, including messenger RNAs and non-coding RNAs, play vital and diverse roles in tumorigenesis, metastasis, drug resistance, and immunotherapy response. RNA modifications, including N6-methyladenosine methylation, are involved in tumor immunity. Critical Issues: A critical issue is the lack of summary of the regulatory RNA molecules and their derivatives in mediating immune activities in human cancers that could provide potential applications for tumor immunotherapeutic strategy. Future Directions: This review summarizes the dual roles (the light and dark sides) of RNA and its derivatives in tumor immunotherapy and discusses the development of RNA-based therapies as novel immunotherapeutic strategies for cancer treatment. Antioxid. Redox Signal. 37, 1266-1290.

Chemical induction of splice-neoantigens attenuates tumor growth in a preclinical model of colorectal cancer

Neoantigen production is a determinant of cancer immunotherapy. However, the expansion of neoantigen abundance for cancer therapeutics is technically challenging. Here, we report that the synthetic compound RECTAS can induce the production of splice-neoantigens that could be used to boost antitumor immune responses. RECTAS suppressed tumor growth in a CD8⁺ T cell- and tumor major histocompatibility complex class I-dependent manner and enhanced immune checkpoint blockade efficacy. Subsequent transcriptome analysis and validation for immunogenicity identified six splice-neoantigen candidates whose expression was induced by RECTAS treatment. Vaccination of the identified neoepitopes elicited T cell responses capable of killing cancer cells in vitro, in addition to suppression of tumor growth in vivo upon sensitization with RECTAS. Collectively, these results provide support for the further development of splice variant-inducing treatments for cancer immunotherapy.